Tootfinder

Solving the bongard-logo problem by modeling a probabilistic model
Ruizhuo Song, Beiming Yuan
https://arxiv.org/abs/2403.03173 https://

Solving the bongard-logo problem by modeling a probabilistic model
Abstract reasoning problems challenge the perceptual and cognitive abilities of AI algorithms, demanding deeper pattern discernment and inductive reasoning beyond explicit image features. This study introduces PMoC, a tailored probability model for the Bongard-Logo problem, achieving high reasoning accuracy by constructing independent probability models. Additionally, we present Pose-Transformer, an enhanced Transformer-Encoder designed for complex abstract reasoning tasks, including Bongard-Lo…

Search for a $\mu^ \mu^-$ resonance in four-muon final states at Belle II
Collaboration, Adachi, Adamczyk, Aggarwal, Ahmed, Aihara, Akopov, Aloisio, Ky, Asner, Atmacan, Aushev, Aversano, Ayad, Babu, Bae, Bahinipati, Bambade, Banerjee, Bansal, Barrett, Baudot, Baur, Beaubien, Becherer, Becker, Bennett, Bernlochner, Bertacchi, Bertemes, Bertholet, Bessner, Bettarini, Bhuyan, Bianchi, Bilka, Bilokin, Biswas, Bobrov, Bodrov, Bolz, Bozek, Bra\v{c}ko, Branchini, Browder, Budano, Bussino, Campajola, Cao, Casarosa, Cecchi, Cerasoli, Chang, Chang, Cheaib, Cheema, Cheon, Chilikin, Chirapatpimol, Cho, Cho, Cho, Choi, Choudhury, Corona, Cremaldi, Das, Dattola, De La Cruz-Burelo, De La Motte, De Nardo, De Nuccio, De Pietro, de Sangro, Destefanis, Dhamija, Di Canto, Di Capua, Dingfelder, Dole\v{z}al, Dong, Dorigo, Dort, Dreyer, Dubey, Dujany, Ecker, Eliachevitch, Epifanov, Feichtinger, Ferber, Ferlewicz, Fillinger, Finck, Finocchiaro, Fodor, Forti, Frey, Fulsom, Gabrielli, Ganiev, Garcia-Hernandez, Garg, Gaudino, Gaur, Gaz, Gellrich, Ghevondyan, Ghosh, Ghumaryan, Giakoustidis, Giordano, Giri, Glazov, Gobbo, Godang, Gogota, Goldenzweig, Gradl, Grammatico, Graziani, Greenwald, Gruberov\'a, Gu, Gudkova, Halder, Han, Hara, Hayashii, Hazra, Hearty, Hedges, Heidelbach, de la Cruz, Villanueva, Higuchi, Hoek, Hohmann, Horak, Hsu, Humair, Iijima, Inguglia, Ipsita, Ishikawa, Itoh, Iwasaki, Jackson, Jacobs, Jang, Ji, Jia, Jin, Joo, Junkerkalefeld, Kalita, Kandra, Kang, Karyan, Kawasaki, Keil, Kiesling, Kim, Kim, Kim, Kim, Kindo, Kinoshita, Kody\v{s}, Koga, Kohani, Kojima, Korobov, Korpar, Kovalenko, Kowalewski, Kraetzschmar, Kri\v{z}an, Krokovny, Kuhr, Kumar, Kumar, Kumar, Kumara, Kunigo, Kuzmin, Kwon, Lacaprara, Lai, Lam, Lanceri, Lange, Laurenza, Lautenbach, Leboucher, Le Diberder, Lee, Levit, Lewis, Li, Li, Li, Li, Libby, Liu, Liu, Liu, Liventsev, Longo, Lueck, Lyu, Ma, Maggiora, Maharana, Maiti, Maity, Mancinelli, Manfredi, Manoni, Mantovano, Marcantonio, Marcello, Marinas, Martel, Martellini, Martini, Martinov, Massaccesi, Masuda, Matsuoka, Matvienko, Maurya, McKenna, Mehta, Meier, Merola, Metzner, Milesi, Miller, Mirra, Miyabayashi, Miyake, Mizuk, Mohanty, Molina-Gonzalez, Mondal, Moneta, Moser, Mrvar, Mussa, Nakamura, Nakao, Nakazawa, Charan, Naruki, Narwal, Natkaniec, Natochii, Nayak, Nayak, Nazaryan, Niebuhr, Nishida, Ogawa, Onishchuk, Ono, Onuki, Oskin, Otani, Pakhlova, Panta, Pardi, Parham, Park, Park, Paschen, Passeri, Patra, Paul, Pedlar, Peschke, Pestotnik, Piccolo, Piilonen, Angioni, Podesta-Lerma, Podobnik, Pokharel, Praz, Prell, Prencipe, Prim, Purwar, Rados, Raeuber, Raiz, Rauls, Reif, Reiter, Remnev, Ripp-Baudot, Rizzo, Robertson, Roehrken, Roney, Rostomyan, Rout, Russo, Sanders, Sandilya, Santelj, Sato, Savinov, Scavino, Schmitt, Schwanda, Schwickardi, Seino, Selce, Senyo, Serrano, Sevior, Sfienti, Shan, Shen, Shi, Shillington, Shimasaki, Shiu, Shtol, Sibidanov, Simon, Singh, Skorupa, Sobie, Sobotzik, Soffer, Sokolov, Solovieva, Spataro, Spruck, Stari\v{c}, Stavroulakis, Stefkova, Stroili, Sumihama, Sumisawa, Sutcliffe, Svidras, Takizawa, Tamponi, Tanaka, Tanida, Tenchini, Tittel, Tiwary, Tonelli, Torassa, Trabelsi, Tsaklidis, Uchida, Ueda, Unger, Unno, Uno, Uno, Urquijo, Ushiroda, Vahsen, van Tonder, Varvell, Veronesi, Vinokurova, Vismaya, Vitale, Vobbilisetti, Volpe, Wach, Wakai, Wallner, Wang, Wang, Wang, Wang, Warburton, Watanuki, Wessel, Won, Xu, Yabsley, Yamada, Yan, Yang, Yelton, Yin, Yoshihara, Yuan, Yusa, Zani, Zhang, Zhilich, Zhou, Zhou, Zhukova
https://arxiv.org/abs/2403.02841 https://arxiv.org/pdf/2403.02841
arXiv:2403.02841v1 Announce Type: new
Abstract: We report on a search for a resonance $X$ decaying to a pair of muons in $e^{ }e^{-}\rightarrow \mu^ \mu^- X$ events in the 0.212-9.000 GeV/$c^{2}$ mass range, using 178 fb$^{-1}$ of data collected by the BelleII experiment at the SuperKEKB collider at a center of mass energy of 10.58 GeV. The analysis probes two different models of $X$ beyond the standard model: a $Z^{\prime}$ vector boson in the $L_{\mu}-L_{\tau}$ model and a muonphilic scalar. We observe no evidence for a signal and set exclusion limits at the 90\% confidence level on the products of cross section and branching fraction for these processes, ranging from 0.046 fb to 0.97 fb for the $L_{\mu}-L_{\tau}$ model and from 0.055 fb to 1.3 fb for the muonphilic scalar model. For masses below 6 GeV/$c^{2}$, the corresponding constraints on the couplings of these processes to the standard model range from 0.0008 to 0.039 for the $L_{\mu}-L_{\tau}$ model and from 0.0018 to 0.040 for the muonphilic scalar model. These are the first constraints on the muonphilic scalar from a dedicated search.

Oh wow, so many interesting things (that I have no idea what they are) to google for in this image. Like Arbitrary Code Execution in the Universal Turing Machine, wtf? https://arxiv.org/abs/2105.02124
From: @…

Intrinsic Propensity for Vulnerability in Computers? Arbitrary Code Execution in the Universal Turing Machine
The universal Turing machine is generally considered to be the simplest, most abstract model of a computer. This paper reports on the discovery of an accidental arbitrary code execution vulnerability in Marvin Minsky's 1967 implementation of the universal Turing machine. By submitting crafted data, the machine may be coerced into executing user-provided code. The article presents the discovered vulnerability in detail and discusses its potential implications. To the best of our knowledge, an ar…

This https://arxiv.org/abs/2403.02046 has been replaced.
link: https://scholar.google.com/scholar?q=a

Array Coupling in Terms of Characteristic Modes and Generalized Scattering Matrices
Modeling of mutual coupling in antenna arrays using generalized scattering matrices in terms of characteristic modes is proposed. Potential applications of this model are diverse. On the one hand, the proposed model can be used as a basis for mutual coupling calculation methods. On the other hand, the parameters introduced by the model provide a new intermediate level to understand coupling phenomena at a higher and more abstract level. After introducing the model, the question of how to descri…

On human-centred security: A new systems model based on modes and mode transitions
Edwin J Beggs, John V Tucker, Victoria Wang
https://arxiv.org/abs/2405.02043

On human-centred security: A new systems model based on modes and mode transitions
We propose an abstract conceptual framework for analysing complex security systems using a new notion of modes and mode transitions. A mode is an independent component of a system with its own objectives, monitoring data, algorithms, and scope and limits. The behaviour of a mode, including its transitions to other modes, is determined by interpretations of the mode's monitoring data in the light of its objectives and capabilities -- these interpretations we call beliefs. We formalise the concep…

Exponential decay of solutions to linear evolution equations with time-dependent time delay
Elisa Continelli, Cristina Pignotti
https://arxiv.org/abs/2404.03467

Exponential decay of solutions to linear evolution equations with time-dependent time delay
In this note, we analyze an abstract evolution equation with time-dependent time delay and time-dependent delay feedback coefficient. We assume that the operator corresponding to the nondelayed part of the model generates an exponentially stable semigroup. Under an appropriate assumption on the delay feedback, we prove the well-posedness and an exponential stability estimate for our model. Applications of our abstract results to concrete models are also illustrated.

The vertical structure of galactic discs: nonlocal gravity versus dark matter
Tahere Kashfi, Mahmood Roshan
https://arxiv.org/abs/2403.02441 https://arxiv.org/pdf/2403.02441
arXiv:2403.02441v1 Announce Type: new
Abstract: Recent isolated galactic simulations show that the morphology of galactic discs in modified gravity differs from that of the standard dark matter model. In this study, we focused on the vertical structure of galactic discs and compared the bending instability in the vertical direction for both paradigms. To achieve this, we utilized high-resolution N-body simulations to construct two models in a specific nonlocal gravity theory (NLG) and the standard dark matter model and compared their stability against the bending perturbations. Our numerical results demonstrate that the outer regions of the disc are more susceptible to the instability in NLG, whereas the disc embedded in the dark matter halo is more unstable in the central regions. We then interpret these results based on the dispersion relation of the bending waves. To do so, we presented an analytical study to derive the dispersion relation in NLG. Our numerical results align with the predictions of our analytical models. Consequently, we conclude that the analysis of bending instability in galactic discs offers an explanation for the distinct vertical structures observed in simulated galactic discs under these two theories. These findings represent a significant step towards distinguishing between the modified gravity and dark matter models.

"If it does turn out to be anything like human understanding, it will probably not be based on LLMs.
After all, LLMs learn in the opposite direction from humans. LLMs start out learning language and attempt to abstract concepts. Human babies learn concepts first, and only later acquire the language to describe them."

Why large language models aren’t headed toward humanlike understanding
Unlike people, today's generative AI isn’t good at learning concepts that it can apply to new situations.

Explaining Grover's algorithm with a colony of ants: a pedagogical model for making quantum technology comprehensible
Merel A Schalkers, Kamiel Dankers, Michael Wimmer, Pieter Vermaas
https://arxiv.org/abs/2405.00014 https://arxiv.org/pdf/2405.00014
arXiv:2405.00014v1 Announce Type: new
Abstract: The rapid growth of quantum technologies requires an increasing number of physicists, computer scientists, and engineers who can work on these technologies. For educating these professionals, quantum mechanics should stop being perceived as incomprehensible. In this paper we contribute to this change by presenting a pedagogical model for explaining Grover's search algorithm, a prominent quantum algorithm. This model visualizes the three main steps of Grover's algorithm and, in addition to explaining the algorithm itself, introduces three key principles of quantum mechanics: superposition, interference, and state collapse at measurement. The pedagogical model, visualized by a video, is called the "Ant Colony Maze model". It represents the search problems as finding the exit of a maze, and visualizes Grover's search algorithm as a strategy by which a colony of ants finds that exit.

Fiducial and differential cross-section measurements of electroweak $W\gamma jj$ production in $pp$ collisions at $\sqrt{s} = 13$ TeV with the ATLAS detector
ATLAS Collaboration
https://arxiv.org/abs/2403.02809 https://arxiv.org/pdf/2403.02809
arXiv:2403.02809v1 Announce Type: new
Abstract: The observation of the electroweak production of a $W$ boson and a photon in association with two jets, using $pp$ collision data at the Large Hadron Collider at a centre of mass energy of $\sqrt{s}=13$~TeV, is reported. The data were recorded by the ATLAS experiment from 2015 to 2018 and correspond to an integrated luminosity of 140 fb$^{-1}$. This process is sensitive to the quartic gauge boson couplings via the vector boson scattering mechanism and provides a stringent test of the electroweak gauge symmetry breaking of the Standard Model. Events are selected if they contain one electron or muon, missing transverse momentum, at least one photon, and two jets. Multivariate techniques are used to distinguish the electroweak $W\gamma jj$ process from irreducible background processes. The observed significance of the electroweak $W\gamma jj$ process is well above six standard deviations, compared to an expected significance of 6.3 standard deviations. Fiducial and differential cross sections are measured in a fiducial phase space close to the detector acceptance, which are in reasonable agreement with leading order Standard Model predictions from MadGraph5 Pythia8 and Sherpa. The results are used to constrain new physics effects in the context of an effective field theory.

Physical Properties and Kinematics of Dense Cores Associated with Regions of Massive Star Formation from the Southern Sky
L. E. Pirogov, P. M. Zemlyanukha, E. M. Dombek, M. A. Voronkov
https://arxiv.org/abs/2403.03074 https://arxiv.org/pdf/2403.03074
arXiv:2403.03074v1 Announce Type: new
Abstract: The results of spectral observations in the $\sim 84-92$ GHz frequency range of six objects in the southern sky containing dense cores and associated with regions of massive stars and star clusters formation are presented. The observations were carried out with the MOPRA-22m radio telescope. Within the framework of the local thermodynamic equilibrium (LTE) approximation, the column densities and abundances of the H$^{13}$CN, H$^{13}$CO$^ $, HN$^{13}$C, HC$_3$N, c-C$_3$H$_2$, SiO, CH$_3$C$_2$H and CH$_3$CN molecules are calculated. Estimates of kinetic temperatures ($\sim 30-50$ K), sizes of emission regions ($\sim 0.2-3.1$ pc) and virial masses ($\sim 70-4600~M_{\odot}$) are obtained. The line widths in the three cores decrease with increasing distance from the center. In four cores, asymmetry in the profiles of the optically thick lines HCO$^ $(1-0) and HCN(1-0) is observed, indicating the presence of systematic motions along the line of sight. In two cases, the asymmetry can be caused by contraction of gas. The model spectral maps of HCO$^ $(1-0) and H$^{13}$CO$^ $(1-0), obtained within the framework of the non-LTE spherically symmetric model, are fitted into the observed ones. The radial profiles of density ($\propto r^{-1.6}$), turbulent velocity ($\propto r^{-0.2}$), and contraction velocity ($\propto r^{0.5}$) in the G268.42--0.85 core have been calculated. The contraction velocity profile differs from that expected both in the case of free fall of gas onto a protostar ($\propto r^{-0.5}$), and in the case of global core collapse (contraction velocity does not depend on distance). A discussion of the obtained results is provided.

Umwelt: Accessible Structured Editing of Multimodal Data Representations
Jonathan ZongKatie, Isabella Pedraza PinerosKatie, MengzhuKatie, Chen, Daniel Hajas, Arvind Satyanarayan
https://arxiv.org/abs/2403.00106

Umwelt: Accessible Structured Editing of Multimodal Data Representations
We present Umwelt, an authoring environment for interactive multimodal data representations. In contrast to prior approaches, which center the visual modality, Umwelt treats visualization, sonification, and textual description as coequal representations: they are all derived from a shared abstract data model, such that no modality is prioritized over the others. To simplify specification, Umwelt evaluates a set of heuristics to generate default multimodal representations that express a dataset'…

Early dark energy and scalarization in a scalar-tensor model
H. Mohseni Sadjadi
https://arxiv.org/abs/2404.19695 https://arxiv.org/pdf/2404.19695
arXiv:2404.19695v1 Announce Type: new
Abstract: We present a model in which the Gauss-Bonnet invariant holds the quintessence at a fixed point, respecting an initial $Z_2$ symmetry in the radiation-dominated era. This results in an early dark energy, which becomes significant around the matter-radiation equality era. However, due to $Z_2$ symmetry breaking, scalarization occurs, leading to a rapid reduction in the early dark energy density. The model then quickly behaves like the $\Lambda$CDM model. This scenario alleviates the Hubble tension and aligns with the assumption that the gravitational wave speed is infinitesimally close to the speed of light.

A coupled fluid-dynamics-heat transfer model for 3D simulations of the aqueous humor flow in the human eye
Thomas Saigre (IRMA), Christophe Prud'Homme (IRMA), Marcela Szopos (MAP5 - UMR 8145), Vincent Chabannes (IRMA)
https://arxiv.org/abs/2404.19353 https://arxiv.org/pdf/2404.19353
arXiv:2404.19353v1 Announce Type: new
Abstract: Understanding human eye behavior involves intricate interactions between physical phenomena such as heat transfer and fluid dynamics. Accurate computational models are vital for comprehending ocular diseases and therapeutic interventions.This work focuses on modeling and simulating aqueous humor flow in the anterior and posterior chambers of the eye, coupled with overall heat transfer.Aqueous humor dynamics regulates intraocular pressure, crucial for understanding conditions like glaucoma.Convective effects from temperature disparities also influence this flow.Extending prior research, this work develops a comprehensive three-dimensional computational model to simulate coupled fluid-dynamic-heat transfer model, thus contributing to the understanding of ocular physiology.

Optimizing Neural Networks for Bermudan Option Pricing: Convergence Acceleration, Future Exposure Evaluation and Interpolation in Counterparty Credit Risk
Vikranth Lokeshwar Dhandapani, Shashi Jain
https://arxiv.org/abs/2402.15936 https://arxiv.org/pdf/2402.15936
arXiv:2402.15936v1 Announce Type: new
Abstract: This paper presents a Monte-Carlo-based artificial neural network framework for pricing Bermudan options, offering several notable advantages. These advantages encompass the efficient static hedging of the target Bermudan option and the effective generation of exposure profiles for risk management. We also introduce a novel optimisation algorithm designed to expedite the convergence of the neural network framework proposed by Lokeshwar et al. (2022) supported by a comprehensive error convergence analysis. We conduct an extensive comparative analysis of the Present Value (PV) distribution under Markovian and no-arbitrage assumptions. We compare the proposed neural network model in conjunction with the approach initially introduced by Longstaff and Schwartz (2001) and benchmark it against the COS model, the pricing model pioneered by Fang and Oosterlee (2009), across all Bermudan exercise time points. Additionally, we evaluate exposure profiles, including Expected Exposure and Potential Future Exposure, generated by our proposed model and the Longstaff-Schwartz model, comparing them against the COS model. We also derive exposure profiles at finer non-standard grid points or risk horizons using the proposed approach, juxtaposed with the Longstaff Schwartz method with linear interpolation and benchmark against the COS method. In addition, we explore the effectiveness of various interpolation schemes within the context of the Longstaff-Schwartz method for generating exposures at finer grid horizons.

This https://arxiv.org/abs/2404.10727 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model
Understanding what makes high-dimensional data learnable is a fundamental question in machine learning. On the one hand, it is believed that the success of deep learning lies in its ability to build a hierarchy of representations that become increasingly more abstract with depth, going from simple features like edges to more complex concepts. On the other hand, learning to be insensitive to invariances of the task, such as smooth transformations for image datasets, has been argued to be importa…

Pre-training on High Definition X-ray Images: An Experimental Study
Xiao Wang, Yuehang Li, Wentao Wu, Jiandong Jin, Yao Rong, Bo Jiang, Chuanfu Li, Jin Tang
https://arxiv.org/abs/2404.17926 https://arxiv.org/pdf/2404.17926
arXiv:2404.17926v1 Announce Type: new
Abstract: Existing X-ray based pre-trained vision models are usually conducted on a relatively small-scale dataset (less than 500k samples) with limited resolution (e.g., 224 $\times$ 224). However, the key to the success of self-supervised pre-training large models lies in massive training data, and maintaining high resolution in the field of X-ray images is the guarantee of effective solutions to difficult miscellaneous diseases. In this paper, we address these issues by proposing the first high-definition (1280 $\times$ 1280) X-ray based pre-trained foundation vision model on our newly collected large-scale dataset which contains more than 1 million X-ray images. Our model follows the masked auto-encoder framework which takes the tokens after mask processing (with a high rate) is used as input, and the masked image patches are reconstructed by the Transformer encoder-decoder network. More importantly, we introduce a novel context-aware masking strategy that utilizes the chest contour as a boundary for adaptive masking operations. We validate the effectiveness of our model on two downstream tasks, including X-ray report generation and disease recognition. Extensive experiments demonstrate that our pre-trained medical foundation vision model achieves comparable or even new state-of-the-art performance on downstream benchmark datasets. The source code and pre-trained models of this paper will be released on https://github.com/Event-AHU/Medical_Image_Analysis.

Better & Faster Large Language Models via Multi-token Prediction
Fabian Gloeckle, Badr Youbi Idrissi, Baptiste Rozi\`ere, David Lopez-Paz, Gabriel Synnaeve
https://arxiv.org/abs/2404.19737 https://arxiv.org/pdf/2404.19737
arXiv:2404.19737v1 Announce Type: new
Abstract: Large language models such as GPT and Llama are trained with a next-token prediction loss. In this work, we suggest that training language models to predict multiple future tokens at once results in higher sample efficiency. More specifically, at each position in the training corpus, we ask the model to predict the following n tokens using n independent output heads, operating on top of a shared model trunk. Considering multi-token prediction as an auxiliary training task, we measure improved downstream capabilities with no overhead in training time for both code and natural language models. The method is increasingly useful for larger model sizes, and keeps its appeal when training for multiple epochs. Gains are especially pronounced on generative benchmarks like coding, where our models consistently outperform strong baselines by several percentage points. Our 13B parameter models solves 12 % more problems on HumanEval and 17 % more on MBPP than comparable next-token models. Experiments on small algorithmic tasks demonstrate that multi-token prediction is favorable for the development of induction heads and algorithmic reasoning capabilities. As an additional benefit, models trained with 4-token prediction are up to 3 times faster at inference, even with large batch sizes.

Magnetic control of magnetotactic bacteria swarms
Mihails Birjukovs, Klaas Bente, Damien Faivre, Guntars Kitenbergs, Andrejs Cebers
https://arxiv.org/abs/2404.18941 https://arxiv.org/pdf/2404.18941
arXiv:2404.18941v1 Announce Type: new
Abstract: Magnetotactic bacteria (MTB) are of significant fundamental and practical interest, especially for applications such as drug delivery and general-purpose object manipulators and payload carriers. While magnetic and other modes of control for individual MTB have been demonstrated, formation, motion and control of MTB swarms are much less studied and understood. Here, we present a torque dipole-based theoretical model for magnetic control of MTB swarms and two methods for swarm formation, and provide experimental validation of the proposed motion model. Model predictions are in good qualitative and quantitative agreement with experiments and literature. Additionally, we were able to determine the torque generated by Magnetospirillium gryphiswaldense (MSR-1) MTB, and the value corresponds to the reported estimates reasonably well.

Real-fluid Transport Property Computations Based on the Boltzmann-weighted Full-dimensional Potential Model
Xin Zhang, Junfeng Bai, Bowen Liu, Tong Zhu, Hao Zhao
https://arxiv.org/abs/2404.18700 https://arxiv.org/pdf/2404.18700
arXiv:2404.18700v1 Announce Type: new
Abstract: The intermolecular potential plays crucial roles in real-fluid interactions away from the ideal-gas equilibrium, such as supercritical fluid, high-enthalpy fluid, plasma interactions, etc. We propose a Boltzmann-weighted Full-dimensional (BWF) potential model for real-fluid computations. It includes diverse intermolecular interactions so as to determine the potential well, molecular diameter, dipole moment, polarizability of species without introducing bath gases, allowing more accurate descriptions of potential surfaces with more potential parameters. The anisotropy and temperature dependence of potential parameters are also considered by applying the Boltzmann weighting on all orientations. Through the high-level Symmetry-Adapted Perturbation Theory calculations, full-dimensional potential energy surface datasets are obtained in 432 orientations for each species. Subsequently, the Boltzmann-weighted Full-dimensional potential parameters are derived by training the dataset exceeding 5*106 data, including nonpolar and polar molecules, radicals, long-chain molecules, and ions. These BWF transport properties calculated by the BWF potential have been compared against the Lennard-Jones transport properties as well as experimental viscosity, mass diffusivity, and thermal conductivity coefficients. It shows discrepancies of viscosity coefficients within 1% and 5% for nonpolar and polar molecules, respectively. Furthermore, this potential model is applied to study radicals, long-chain molecules, and ions, for which the experimental data is rarely accessed in high accuracy. It indicates significant prediction improvements of complex interactions between various particles. The new transport properties are also embedded to predict the laminar flame speeds and the flame extinction limits of methane, dimethyl ether, and n-heptane at elevated pressures, confirming its predictivity and effectiveness.

Simulations of galaxies in an expanding Universe with modified Newtonian dynamics (MOND) and with modified gravitational attractions (MOGA)
S{\o}ren Toxvaerd
https://arxiv.org/abs/2403.02848 https://arxiv.org/pdf/2403.02848
arXiv:2403.02848v1 Announce Type: new
Abstract: The stability of galaxies is either explained by the existence of dark matter or caused by a modification of Newtonian acceleration (MOND). Here we show that the modification of the Newtonian dynamics can equally well be obtained by a modification of Newton's law of universal gravitational attraction (MOGA), by which an inverse square attraction from a distant object is replaced with an inverse attraction. This modification is often proposed in the standard model, and with the modification of the attraction caused by dark matter. The recently derived algorithm, Eur. Phys. J. Plus 137, 99 (2022); Class. Quantum Grav. 39, 225006 (2022), for classical celestial dynamics is used to simulate models of the Milky Way in an expanding universe and with either MOND or MOGA. The simulations show that the galaxies with MOND dynamics are unstable whereas MOGA stabilizes the galaxies. The rotation velocities for objects in galaxies with classical Newtonian dynamics decline inversely proportional to the square root of the distance $r$ to the galaxy's center. However, the rotation velocities are relatively independent of $r$ for MOGA and qualitatively in agreement with experimentally determined rotation curves for galaxies in the Universe. The modification of the attractions may be caused by the masses of the objects in the central part of the galaxy by the lensing of gravitational waves from far-away objects in the galaxy.

Measurement of $CP$ asymmetries in $B^0 \rightarrow K^0_S K^0_S K^0_S$ decays at Belle II
Belle II Collaboration, et al.
https://arxiv.org/abs/2403.02590 https://arxiv.org/pdf/2403.02590
arXiv:2403.02590v1 Announce Type: new
Abstract: We report a measurement of decay-time dependent charge-parity ($CP$) asymmetries in $B^0 \rightarrow K^0_S K^0_S K^0_S$ decays. We use $387 \times 10^6 B\bar{B}$ pairs collected at the $\Upsilon(4S)$ resonance with the Belle II detector at the SuperKEKB asymmetric-energy electron-positron collider. We reconstruct 220 signal events and extract the $CP$-violating parameters $S$ and $C$ from a fit to the distribution of the decay-time difference between the two $B$ mesons. The resulting confidence region is consistent with previous measurements in $B^0 \rightarrow K^0_S K^0_S K^0_S$ and $B^0 \rightarrow (c\bar{c})K^0$ decays, and with predictions based on the standard model.

Representing Knowledge and Querying Data using Double-Functorial Semantics
Michael Lambert, Evan Patterson
https://arxiv.org/abs/2403.19884 https://…

Representing Knowledge and Querying Data using Double-Functorial Semantics
Category theory offers a mathematical foundation for knowledge representation and database systems. Popular existing approaches model a database instance as a functor into the category of sets and functions, or as a 2-functor into the 2-category of sets, relations, and implications. The functional and relational models are unified by double functors into the double category of sets, functions, relations, and implications. In an accessible, example-driven style, we show that the abstract structu…

Efficient Online Learning for Networks of Two-Compartment Spiking Neurons
Yujia Yin, Xinyi Chen, Chenxiang Ma, Jibin Wu, Kay Chen Tan
https://arxiv.org/abs/2402.15969 https://arxiv.org/pdf/2402.15969
arXiv:2402.15969v1 Announce Type: new
Abstract: The brain-inspired Spiking Neural Networks (SNNs) have garnered considerable research interest due to their superior performance and energy efficiency in processing temporal signals. Recently, a novel multi-compartment spiking neuron model, namely the Two-Compartment LIF (TC-LIF) model, has been proposed and exhibited a remarkable capacity for sequential modelling. However, training the TC-LIF model presents challenges stemming from the large memory consumption and the issue of gradient vanishing associated with the Backpropagation Through Time (BPTT) algorithm. To address these challenges, online learning methodologies emerge as a promising solution. Yet, to date, the application of online learning methods in SNNs has been predominantly confined to simplified Leaky Integrate-and-Fire (LIF) neuron models. In this paper, we present a novel online learning method specifically tailored for networks of TC-LIF neurons. Additionally, we propose a refined TC-LIF neuron model called Adaptive TC-LIF, which is carefully designed to enhance temporal information integration in online learning scenarios. Extensive experiments, conducted on various sequential benchmarks, demonstrate that our approach successfully preserves the superior sequential modeling capabilities of the TC-LIF neuron while incorporating the training efficiency and hardware friendliness of online learning. As a result, it offers a multitude of opportunities to leverage neuromorphic solutions for processing temporal signals.

Hybrid Physics-Based and Data-Driven Modeling of Vascular Bifurcation Pressure Differences
Natalia L. Rubio, Luca Pegolotti, Martin R. Pfaller, Eric F. Darve, Alison L. Marsden
https://arxiv.org/abs/2402.15651 https://arxiv.org/pdf/2402.15651
arXiv:2402.15651v1 Announce Type: new
Abstract: Reduced-order models (ROMs) allow for the simulation of blood flow in patient-specific vasculatures without the high computational cost and wait time associated with traditional computational fluid dynamics (CFD) models. Unfortunately, due to the simplifications made in their formulations, ROMs can suffer from significantly reduced accuracy. One common simplifying assumption is the continuity of static or total pressure over vascular junctions. In many cases, this assumption has been shown to introduce significant error. We propose a model to account for this pressure difference, with the ultimate goal of increasing the accuracy of cardiovascular ROMs. Our model successfully uses a structure common in existing ROMs in conjunction with machine-learning techniques to predict the pressure difference over a vascular bifurcation. We analyze the performance of our model on steady and transient flows, testing it on three bifurcation cohorts representing three different bifurcation geometric types. We also compare the efficacy of different machine-learning techniques and two different model modalities.

Die #COVID-19-Lockdowns im Frühjahr 2020 haben zu einer signifikanten #Reduzierung der #Luftverschmutzung in der

Air pollution reductions caused by the COVID-19 lockdown open up a way to preserve the Himalayan glaciers
Abstract. The rapid melting of glaciers in the Hindu Kush Himalayas (HKH) during recent decades poses an alarming threat to water security for larger parts of Asia. If this melting persists, the entirety of the Himalayan glaciers are estimated to disappear by end of the 21st century. Here, we assess the influence of the spring 2020 COVID-19 lockdown on the HKH, demonstrating the potential benefits of a strict emission reduction roadmap. Chemistry–climate model simulations, supported by satell…

The true number density of massive galaxies in the early Universe revealed by JWST/MIRI
Tao Wang, Hanwen Sun, Luwenjia Zhou, Ke Xu, Cheng Cheng, Zhaozhou Li, Yangyao Chen, H. J. Mo, Avishai Dekel, Xianzhong Zheng, Zheng Cai, Tiacheng Yang, Y. -S. Dai, David Elbaz, J. -S. Huang
https://arxiv.org/abs/2403.02399 https://arxiv.org/pdf/2403.02399
arXiv:2403.02399v1 Announce Type: new
Abstract: One of the main challenges in galaxy formation that has emerged recently is the early assembly of massive galaxies. The observed number density and the maximum stellar mass ($M_{\star}$) of massive galaxies in the early Universe appear to be higher than model predictions, which may pose a serious problem to the LCDM cosmology. A major limitation in many previous studies is the large uncertainty in estimating $M_{\star}$ due to the lack of constraints in the rest-frame near-infrared part of the spectral energy distribution, which is critical to determining $M_{\star}$ accurately. Here we use data from a large JWST/MIRI survey in the PRIMER program to carry out a systematic analysis of massive galaxies at $z \sim 3-8$, leveraging photometric constraints at rest-frame $\gtrsim 1 \mu$m. We find a significant reduction in the number and mass densities of massive galaxies at $z > 5$ compared to earlier results that did not use the MIRI photometry. Within the standard $\Lambda$CDM cosmology, our results require a moderate increase in the baryon-to-star conversion efficiency ($\epsilon$) towards higher redshifts and higher $M_{\star}$. For the most massive galaxies at $z\sim 8$, the required $\epsilon$ is $\sim 0.3$, in comparison to $\epsilon \sim 0.14$ for typical low-redshift galaxies. Our findings are consistent with models assuming suppressed stellar feedback due to the high gas density and the associated short free-fall time expected for massive halos at high redshift.

Differential cross-sections for events with missing transverse momentum and jets measured with the ATLAS detector in 13 TeV proton-proton collisions
ATLAS Collaboration
https://arxiv.org/abs/2403.02793 https://arxiv.org/pdf/2403.02793
arXiv:2403.02793v1 Announce Type: new
Abstract: Measurements of inclusive, differential cross-sections for the production of events with missing transverse momentum in association with jets in proton-proton collisions at $\sqrt{s}=13$~TeV are presented. The measurements are made with the ATLAS detector using an integrated luminosity of 140~fb$^{-1}$ and include measurements of dijet distributions in a region in which vector-boson fusion processes are enhanced. They are unfolded to correct for detector resolution and efficiency within the fiducial acceptance, and are designed to allow robust comparisons with a wide range of theoretical predictions. A measurement of differential cross sections for the $Z~\to \nu\nu$ process is made. The measurements are generally well-described by Standard Model predictions except for the dijet invariant mass distribution. Auxiliary measurements of the hadronic system recoiling against isolated leptons, and photons, are also made in the same phase space. Ratios between the measured distributions are then derived, to take advantage of cancellations in modelling effects and some of the major systematic uncertainties. These measurements are sensitive to new phenomena, and provide a mechanism to easily set constraints on phenomenological models. To illustrate the robustness of the approach, these ratios are compared with two common Dark Matter models, where the constraints derived from the measurement are comparable to those set by dedicated detector-level searches.

Catalyzing Social Interactions in Mixed Reality using ML Recommendation Systems
Sparsh Srivastava, Rohan Arora
https://arxiv.org/abs/2404.19095 https://arxiv.org/pdf/2404.19095
arXiv:2404.19095v1 Announce Type: new
Abstract: We create an innovative mixed reality-first social recommendation model, utilizing features uniquely collected through mixed reality (MR) systems to promote social interaction, such as gaze recognition, proximity, noise level, congestion level, and conversational intensity. We further extend these models to include right-time features to deliver timely notifications. We measure performance metrics across various models by creating a new intersection of user features, MR features, and right-time features. We create four model types trained on different combinations of the feature classes, where we compare the baseline model trained on the class of user features against the models trained on MR features, right-time features, and a combination of all of the feature classes. Due to limitations in data collection and cost, we observe performance degradation in the right-time, mixed reality, and combination models. Despite these challenges, we introduce optimizations to improve accuracy across all models by over 14 percentage points, where the best performing model achieved 24% greater accuracy.

This https://arxiv.org/abs/2402.14096 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

EyeTrans: Merging Human and Machine Attention for Neural Code Summarization
Neural code summarization leverages deep learning models to automatically generate brief natural language summaries of code snippets. The development of Transformer models has led to extensive use of attention during model design. While existing work has primarily and almost exclusively focused on static properties of source code and related structural representations like the Abstract Syntax Tree (AST), few studies have considered human attention, that is, where programmers focus while examini…

AQM: A Refresh of the Abstract Qubit Model for Quantum Co-design
Chenxu Liu, Samuel A. Stein, Muqing Zheng, James Ang, Ang Li
https://arxiv.org/abs/2403.11329

AQM: A Refresh of the Abstract Qubit Model for Quantum Co-design
Qubits are the fundamental building blocks of quantum information science and applications, whose concept is widely utilized in both quantum physics and quantum computation. While the significance of qubits and their implementation in physical devices have been extensively examined, we posit that now is the right time to revisit this understanding. In this paper, we introduce an abstract qubit model (AQM), offering a mathematical framework for higher-level algorithms and applications, and setti…

Self-Assembly of Patterns in the abstract Tile Assembly Model
Phillip Drake, Matthew J. Patitz, Scott M. Summers, Tyler Tracy
https://arxiv.org/abs/2402.16284

Self-Assembly of Patterns in the abstract Tile Assembly Model
In the abstract Tile Assembly Model, self-assembling systems consisting of tiles of different colors can form structures on which colored patterns are ``painted.'' We explore the complexity, in terms of the unique tile types required, of assembling various patterns, proving several upper and lower bounds.

This https://arxiv.org/abs/2108.06777 has been replaced.
link: https://scholar.google.com/scholar?q=a

Stochastic quantization of the $Φ^3_3$-model
(Due to the limit on the number of characters for an abstract set by arXiv, the full abstract can not be displayed here. See the abstract in the paper.) We study the construction of the $Φ^3_3$-measure and complete the program on the (non-)construction of the focusing Gibbs measures, initiated by Lebowitz, Rose, and Speer (1988). This problem turns out to be critical, exhibiting the following phase transition. In the weakly nonlinear regime, we prove normalizability of the $Φ^3_3$-measure and…

Interplay between Vector-like Lepton and Seesaw Mechanism:Oblique Corrections
Shuyang Han, Zhaofeng Kang, Jiang Zhu
https://arxiv.org/abs/2404.19502 https://arxiv.org/pdf/2404.19502
arXiv:2404.19502v1 Announce Type: new
Abstract: The non-vanishing neutrino mass strongly hints the existence of right-handed neutrinos (RHNs), singlets of the standard model (SM). However, they are highly decoupled from the SM and difficult to probe. In this work, we consider the Majorana RHNs from the type-I seesaw mechanism may well mix with the heavy neutral lepton dwelling in certain vector-like lepton (VLL), thus acquiring a sizable electroweak charge. Such a simple scenario yields many interesting consequences, and the imprint on oblique corrections, well expected from the mass splitting between components of VLL by virtue of VLL-RHN mixing, is our focus here. We analytically calculate the Peskin-Takeuchi parameters S, T and U with full details, carefully treating the Majorana loop to obtain the self consistent expressions free of divergence. Then, we constrain on the VLL-RHN system which only gives a sizable $T$ parameter using the PDG-2021 data and CDF-II data, separately, by imposing $T\lesssim{\cal O}(0.1)$. It is found that for the RHN and VLL below the TeV scale, with a properly large mixing, stands in the frontier of the electroweak precision test such as W-boson mass.

Benchmarking the IRDC G351.77-0.53: Gaia DR3 distance, mass distribution, and star formation content
S. D. Reyes-Reyes, A. M. Stutz, S. T. Megeath, Fengwei Xu, R. H. \'Alvarez-Guti\'errez, N. Sandoval-Garrido, H. -L. Liu
https://arxiv.org/abs/2403.02456 https://arxiv.org/pdf/2403.02456
arXiv:2403.02456v1 Announce Type: new
Abstract: While intensively studied, it remains unclear how the star formation (SF) in Infrared Dark Clouds (IRDCs) compares to that of nearby clouds. We study G351.77-0.53 (henceforth G351), a cluster-forming filamentary IRDC. We begin by characterizing its young stellar object (YSO) content. Based on the average parallax of likely members, we obtain a Gaia distance of $\sim\,2.0\pm0.14$ kpc, resolving the literature distance ambiguity. Using our Herschel-derived N(H$_2$) map, we measure a total gas mass of 10200 M$_{\odot}$ (within 11 pc$^2$) and the average line-mass profile of the entire filament, which we model as $\lambda =~1660 (w/\rm pc )^{0.62}\,\,M_{\odot}\,\rm{pc}^{-1}$. At $w < 0.63$ pc, our $\lambda$ profile is higher and has a steeper power-law index than $\lambda$ profiles extracted in Orion A and most of its substructures. Based on the YSOs inside the filament area, we estimate the SF efficiency (SFE) and SF rate (SFR). We calculate a factor of 5 incompleteness correction for our YSO catalog relative to Spitzer surveys of Orion A. The G351 SFE is $\sim 1.8$ times lower than that of Orion A and lower than the median value for local clouds. We measure SFR and gas masses to estimate the efficiency per free-fall time, $\epsilon _{\rm ff}$. We find that $\epsilon_{\rm ff}$ is $\sim$ 1.1 dex below the previously proposed mean local relation, and $\sim\,4.7\times$ below Orion A. These observations indicate that local SF-relations do not capture variations present in the Galaxy. We speculate that cloud youth and/or magnetic fields might account for the G351 inefficiency.

Context-Aware Machine Translation with Source Coreference Explanation
Huy Hien Vu, Hidetaka Kamigaito, Taro Watanabe
https://arxiv.org/abs/2404.19505 https://arxiv.org/pdf/2404.19505
arXiv:2404.19505v1 Announce Type: new
Abstract: Despite significant improvements in enhancing the quality of translation, context-aware machine translation (MT) models underperform in many cases. One of the main reasons is that they fail to utilize the correct features from context when the context is too long or their models are overly complex. This can lead to the explain-away effect, wherein the models only consider features easier to explain predictions, resulting in inaccurate translations. To address this issue, we propose a model that explains the decisions made for translation by predicting coreference features in the input. We construct a model for input coreference by exploiting contextual features from both the input and translation output representations on top of an existing MT model. We evaluate and analyze our method in the WMT document-level translation task of English-German dataset, the English-Russian dataset, and the multilingual TED talk dataset, demonstrating an improvement of over 1.0 BLEU score when compared with other context-aware models.

Advancing low-field MRI with a universal denoising imaging transformer: Towards fast and high-quality imaging
Zheren Zhu, Azaan Rehman, Xiaozhi Cao, Congyu Liao, Yoo Jin Lee, Michael Ohliger, Hui Xue, Yang Yang
https://arxiv.org/abs/2404.19167 https://arxiv.org/pdf/2404.19167
arXiv:2404.19167v1 Announce Type: new
Abstract: Recent developments in low-field (LF) magnetic resonance imaging (MRI) systems present remarkable opportunities for affordable and widespread MRI access. A robust denoising method to overcome the intrinsic low signal-noise-ratio (SNR) barrier is critical to the success of LF MRI. However, current data-driven MRI denoising methods predominantly handle magnitude images and rely on customized models with constrained data diversity and quantity, which exhibit limited generalizability in clinical applications across diverse MRI systems, pulse sequences, and organs. In this study, we present ImT-MRD: a complex-valued imaging transformer trained on a vast number of clinical MRI scans aiming at universal MR denoising at LF systems. Compared with averaging multiple-repeated scans for higher image SNR, the model obtains better image quality from fewer repetitions, demonstrating its capability for accelerating scans under various clinical settings. Moreover, with its complex-valued image input, the model can denoise intermediate results before advanced post-processing and prepare high-quality data for further MRI research. By delivering universal and accurate denoising across clinical and research tasks, our model holds great promise to expedite the evolution of LF MRI for accessible and equal biomedical applications.

TIL about the Littorina transgression, and what could well be a feature of sea level rise we should know about…
Rapid sea-level rise during the first phase of the Littorina transgression in the western Baltic Sea - NASA/ADS
https://ui.adsabs.harvard.edu/abs/2023

This https://arxiv.org/abs/2307.04270 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

A Complete Finite Axiomatisation of the Equational Theory of Common Meadows
We analyse abstract data types that model numerical structures with a concept of error. Specifically, we focus on arithmetic data types that contain an error value $\bot$ whose main purpose is to always return a value for division. To rings and fields, we add a division operator $x/y$ and study a class of algebras called common meadows wherein $x/0 = \bot$. The set of equations true in all common meadows is named the equational theory of common meadows. We give a finite equational axiomatisatio…

Semantically consistent Video-to-Audio Generation using Multimodal Language Large Model
Gehui Chen, Guan'an Wang, Xiaowen Huang, Jitao Sang
https://arxiv.org/abs/2404.16305 https://arxiv.org/pdf/2404.16305
arXiv:2404.16305v1 Announce Type: new
Abstract: Existing works have made strides in video generation, but the lack of sound effects (SFX) and background music (BGM) hinders a complete and immersive viewer experience. We introduce a novel semantically consistent v ideo-to-audio generation framework, namely SVA, which automatically generates audio semantically consistent with the given video content. The framework harnesses the power of multimodal large language model (MLLM) to understand video semantics from a key frame and generate creative audio schemes, which are then utilized as prompts for text-to-audio models, resulting in video-to-audio generation with natural language as an interface. We show the satisfactory performance of SVA through case study and discuss the limitations along with the future research direction. The project page is available at https://huiz-a.github.io/audio4video.github.io/.

Radial and azimuthal gradients of the moving groups in Gaia DR3: The slow/fast bar degeneracy problem
Marcel Bernet, Pau Ramos, Teresa Antoja, Giacomo Monari, Benoit Famaey
https://arxiv.org/abs/2403.02393 https://arxiv.org/pdf/2403.02393
arXiv:2403.02393v1 Announce Type: new
Abstract: The structure and dynamics of the central bar of the Milky Way are still under debate whilst being fundamental ingredients for the evolution of our Galaxy. The recent Gaia DR3 offers an unprecedented detailed view of the 6D phase-space of the MW. We aim to characterise the dynamical moving groups across the MW disc, and use their large-scale distribution to help constrain the properties of the Galactic bar. We used wavelet transforms of the azimuthal velocity ($V_\phi$) distribution in bins of radial velocity to robustly detect the kinematic substructure in the Gaia DR3 catalogue. We then connected these structures across the disc to measure the azimuthal ($\phi$) and radial ($R$) gradients of the moving groups. We simulated thousands of perturbed distribution functions using Backwards Integration of feasible Galaxy models that include a bar, to compare them with the data and to explore and quantify the degeneracies. The radial gradient of the Hercules moving group ($\partial V_\phi/\partial R$ = 28.1$\pm$2.8 km$\,$s$^{-1}\,$kpc$^{-1}$) cannot be reproduced by our simple models of the Galaxy which show much larger slopes both for a fast and a slow bar. This suggests the need for more complex dynamics (e.g. spiral arms, a slowing bar, external perturbations, etc.). We measure an azimuthal gradient for Hercules of $\partial V_\phi/\partial \phi$ = -0.63$\pm$0.13$\,$km$\,$s$^{-1}$deg$^{-1}$ and find that it is compatible with both the slow and fast bar models. Our analysis points out that using this type of analysis at least two moving groups are needed to start breaking the degeneracies. We conclude that it is not sufficient for a model to replicate the local velocity distribution; it must also capture its larger-scale variations. The accurate quantification of the gradients, especially in the azimuthal direction, will be key for the understanding of the dynamics governing the disc. (ABR)

Representing Knowledge and Querying Data using Double-Functorial Semantics
Michael Lambert, Evan Patterson
https://arxiv.org/abs/2403.19884 https://…

Representing Knowledge and Querying Data using Double-Functorial Semantics
Category theory offers a mathematical foundation for knowledge representation and database systems. Popular existing approaches model a database instance as a functor into the category of sets and functions, or as a 2-functor into the 2-category of sets, relations, and implications. The functional and relational models are unified by double functors into the double category of sets, functions, relations, and implications. In an accessible, example-driven style, we show that the abstract structu…

Cycling on rough roads: A model for resistance and vibration
Miles M. Turner
https://arxiv.org/abs/2405.00019 https://arxiv.org/pdf/2405.00019
arXiv:2405.00019v1 Announce Type: new
Abstract: Minimising opposing forces is a matter of interest to most cyclists. These forces arise from passage through air ("drag") and interaction with the road surface ("resistance"). Recent work recognises that resistance forces arise not only from the deformation of the tyre ("rolling resistance") but also from irregularities in the road surface ("roughness resistance"), which lead to power dissipation in the body of the rider through vibration. The latter effect may also have an adverse impact on human health. In this work we offer a quantitative theory of roughness resistance and vibration that links these effects to a surface characterisation in terms of the International Roughness Index (IRI). We show that the roughness resistance and the Vibration Dose Value (or VDV, the usual vibration dosage metric) can be expressed in terms of elementary formulae. The roughness resistance depends only on the vertical stiffness of the bicycle and the roughness index. Surprisingly, other apparently relevant parameters, such as physiological characteristics of the bicycle rider and other features of the bicycle, do not enter. For roads of moderate roughness, roughness resistance is larger than rolling resistance. For very rough roads, roughness resistance is larger than aerodynamic drag. So only on roads of high quality (in most jurisdictions, accounting for less than 10~\% of the total) can roughness resistance be ignored. Roughness resistance can be mitigated by reducing the vertical stiffness of the bicycle. In common with other recent reports, we find that almost any cycling activity will breach public health guidelines relating to Vibration Dose Value.

Metrology of microwave fields based on trap-loss spectroscopy with cold Rydberg atoms
Romain Duverger, Alexis Bonnin, Romain Granier, Quentin Marolleau, C\'edric Blanchard, Nassim Zahzam, Yannick Bidel, Malo Cadoret, Alexandre Bresson, Sylvain Schwartz
https://arxiv.org/abs/2404.17445 https://arxiv.org/pdf/2404.17445
arXiv:2404.17445v1 Announce Type: new
Abstract: We demonstrate a new approach for the metrology of microwave fields based on the trap-loss-spectroscopy of cold Rydberg atoms in a magneto-optical trap. Compared to state-of-the-art sensors using room-temperature vapors, cold atoms allow longer interaction times, better isolation from the environment and a reduced Doppler effect. Our approach is particularly simple as the detection relies on fluorescence measurements only. Moreover, our signal is well described by a two-level model across a broad measurement range, allowing in principle to reconstruct the amplitude and the frequency of the microwave field simultaneously without the need for an external reference field. We report on a scale factor linearity at the percent level and no noticeable drifts over two hours, paving the way for new applications of cold Rydberg atoms in metrology such as calibrating blackbody shifts in state-of-the-art optical clocks, monitoring the Earth cryosphere from space, measuring the cosmic microwave background or searching for dark matter.

Deterministic Molecular Assembly with a Finite Set of Building Blocks: Universal Assembly Kits for Backbone-Assisted and Sequence-Directed Abstract Tile Assembly Models
Jeremy Guntoro, Thomas Ouldridge
https://arxiv.org/abs/2402.19225

Deterministic Molecular Assembly with a Finite Set of Building Blocks: Universal Assembly Kits for Backbone-Assisted and Sequence-Directed Abstract Tile Assembly Models
Backbone-assisted assembly processes -- such as protein folding -- allow the assembly of a large number of structures with high accuracy from only a small handful of fundamental building blocks. We aim to explore general principles underlying this phenomenon by studying variants of the temperature-1 abstract tile assembly model (aTAM). We consider the existence of finite sets of tile types that can deterministically assemble any shape producible by a given assembly model; we call such tile type…

Upward L\"owenheim-Skolem-Tarski Numbers for Abstract Logics
Victoria Gitman, Jonathan Osinski
https://arxiv.org/abs/2404.12269 https://

Upward Löwenheim-Skolem-Tarski Numbers for Abstract Logics
Galeotti, Khomskii and Väänäanen recently introduced the notion of the upward Löwenheim-Skolem-Tarski number for a logic, strengthening the classical notion of a Hanf number. A cardinal $κ$ is the \emph{upward Löwenheim-Skolem-Tarski number} (ULST) of a logic $\mathcal L$ if it is the least cardinal with the property that whenever $M$ is a model of size at least $κ$ satisfying a sentence $φ$ in $\mathcal L$, then there are arbitrarily large models satisfying $φ$ and having $M$ as a sub…

Longitudinal Mammogram Risk Prediction
Batuhan K. Karaman, Katerina Dodelzon, Gozde B. Akar, Mert R. Sabuncu
https://arxiv.org/abs/2404.19083 https://arxiv.org/pdf/2404.19083
arXiv:2404.19083v1 Announce Type: new
Abstract: Breast cancer is one of the leading causes of mortality among women worldwide. Early detection and risk assessment play a crucial role in improving survival rates. Therefore, annual or biennial mammograms are often recommended for screening in high-risk groups. Mammograms are typically interpreted by expert radiologists based on the Breast Imaging Reporting and Data System (BI-RADS), which provides a uniform way to describe findings and categorizes them to indicate the level of concern for breast cancer. Recently, machine learning (ML) and computational approaches have been developed to automate and improve the interpretation of mammograms. However, both BI-RADS and the ML-based methods focus on the analysis of data from the present and sometimes the most recent prior visit. While it is clear that temporal changes in image features of the longitudinal scans should carry value for quantifying breast cancer risk, no prior work has conducted a systematic study of this. In this paper, we extend a state-of-the-art ML model to ingest an arbitrary number of longitudinal mammograms and predict future breast cancer risk. On a large-scale dataset, we demonstrate that our model, LoMaR, achieves state-of-the-art performance when presented with only the present mammogram. Furthermore, we use LoMaR to characterize the predictive value of prior visits. Our results show that longer histories (e.g., up to four prior annual mammograms) can significantly boost the accuracy of predicting future breast cancer risk, particularly beyond the short-term. Our code and model weights are available at https://github.com/batuhankmkaraman/LoMaR.

Credential Control Balance: A Universal Blockchain Account Model Abstract From Bank to Bitcoin, Ethereum External Owned Account and Account Abstraction
Huifeng Jiao, Dr. Nathapon Udomlertsakul, Dr. Anukul Tamprasirt
https://arxiv.org/abs/2402.10616

Credential Control Balance: A Universal Blockchain Account Model Abstract From Bank to Bitcoin, Ethereum External Owned Account and Account Abstraction
Blockchain market value peaked at $3 trillion, fell to $1 trillion, then recovered to $1.5 trillion and is rising again. Blockchain accounts secure most on-chain assets in this huge market (Web-12). This paper initiates a universal blockchain account model from a comprehensive review of blockchain account development, encompassing both academic and industry perspectives. This paper uses a model analysis method to analysis the account progress and create high level new account model. And it uses…

Optimizing Portfolio Management and Risk Assessment in Digital Assets Using Deep Learning for Predictive Analysis
Qishuo Cheng, Le Yang, Jiajian Zheng, Miao Tian, Duan Xin
https://arxiv.org/abs/2402.15994 https://arxiv.org/pdf/2402.15994
arXiv:2402.15994v1 Announce Type: new
Abstract: Portfolio management issues have been extensively studied in the field of artificial intelligence in recent years, but existing deep learning-based quantitative trading methods have some areas where they could be improved. First of all, the prediction mode of stocks is singular; often, only one trading expert is trained by a model, and the trading decision is solely based on the prediction results of the model. Secondly, the data source used by the model is relatively simple, and only considers the data of the stock itself, ignoring the impact of the whole market risk on the stock. In this paper, the DQN algorithm is introduced into asset management portfolios in a novel and straightforward way, and the performance greatly exceeds the benchmark, which fully proves the effectiveness of the DRL algorithm in portfolio management. This also inspires us to consider the complexity of financial problems, and the use of algorithms should be fully combined with the problems to adapt. Finally, in this paper, the strategy is implemented by selecting the assets and actions with the largest Q value. Since different assets are trained separately as environments, there may be a phenomenon of Q value drift among different assets (different assets have different Q value distribution areas), which may easily lead to incorrect asset selection. Consider adding constraints so that the Q values of different assets share a Q value distribution to improve results.

Memory Traces: Are Transformers Tulving Machines?
Jean-Marie Chauvet
https://arxiv.org/abs/2404.08543 https://arxiv.org/pdf/2404.0854…

Memory Traces: Are Transformers Tulving Machines?
Memory traces--changes in the memory system that result from the perception and encoding of an event--were measured in pioneering studies by Endel Tulving and Michael J. Watkins in 1975. These and further experiments informed the maturation of Tulving's memory model, from the GAPS (General Abstract Processing System} to the SPI (Serial-Parallel Independent) model. Having current top of the line LLMs revisit the original Tulving-Watkins tests may help in assessing whether foundation models compl…

RAG and RAU: A Survey on Retrieval-Augmented Language Model in Natural Language Processing
Yucheng Hu, Yuxing Lu
https://arxiv.org/abs/2404.19543 https://arxiv.org/pdf/2404.19543
arXiv:2404.19543v1 Announce Type: new
Abstract: Large Language Models (LLMs) have catalyzed significant advancements in Natural Language Processing (NLP), yet they encounter challenges such as hallucination and the need for domain-specific knowledge. To mitigate these, recent methodologies have integrated information retrieved from external resources with LLMs, substantially enhancing their performance across NLP tasks. This survey paper addresses the absence of a comprehensive overview on Retrieval-Augmented Language Models (RALMs), both Retrieval-Augmented Generation (RAG) and Retrieval-Augmented Understanding (RAU), providing an in-depth examination of their paradigm, evolution, taxonomy, and applications. The paper discusses the essential components of RALMs, including Retrievers, Language Models, and Augmentations, and how their interactions lead to diverse model structures and applications. RALMs demonstrate utility in a spectrum of tasks, from translation and dialogue systems to knowledge-intensive applications. The survey includes several evaluation methods of RALMs, emphasizing the importance of robustness, accuracy, and relevance in their assessment. It also acknowledges the limitations of RALMs, particularly in retrieval quality and computational efficiency, offering directions for future research. In conclusion, this survey aims to offer a structured insight into RALMs, their potential, and the avenues for their future development in NLP. The paper is supplemented with a Github Repository containing the surveyed works and resources for further study: https://github.com/2471023025/RALM_Survey.

Regularity and long-time behavior of global weak solutions to a coupled Cahn-Hilliard system: the off-critical case
Bohan Ouyang
https://arxiv.org/abs/2404.19271 https://arxiv.org/pdf/2404.19271
arXiv:2404.19271v1 Announce Type: new
Abstract: We consider a diffuse interface model that describes the macro- and micro-phase separation processes of a polymer mixture. The resulting system consists of a Cahn-Hilliard equation and a Cahn-Hilliard-Oono type equation endowed with the singular Flory-Huggins potential. For the initial boundary value problem in a bounded smooth domain of $\mathbb{R}^d$ ($d\in\{2,3\}$) with homogeneous Neumann boundary conditions for the phase functions as well as chemical potentials, we study the regularity and long-time behavior of global weak solutions in the off-critical case, i.e., the mass is not conserved during the micro-phase separation of diblock copolymers. By investigating an auxiliary system with viscous regularizations, we show that every global weak solution regularizes instantaneously for $t>0$. In two dimensions, we obtain the instantaneous strict separation property under a mild growth condition on the first derivative of potential functions near pure phases $\pm 1$, while in three dimensions, we establish the eventual strict separation property for sufficiently large time. Finally, we prove that every global weak solution converges to a single equilibrium as $t\to \infty$.

ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processing
Liuzhenghao Lv, Zongying Lin, Hao Li, Yuyang Liu, Jiaxi Cui, Calvin Yu-Chian Chen, Li Yuan, Yonghong Tian
https://arxiv.org/abs/2402.16445 https://arxiv.org/pdf/2402.16445
arXiv:2402.16445v1 Announce Type: new
Abstract: Large Language Models (LLMs), including GPT-x and LLaMA2, have achieved remarkable performance in multiple Natural Language Processing (NLP) tasks. Under the premise that protein sequences constitute the protein language, Protein Large Language Models (ProLLMs) trained on protein corpora excel at de novo protein sequence generation. However, as of now, unlike LLMs in NLP, no ProLLM is capable of multiple tasks in the Protein Language Processing (PLP) field. This prompts us to delineate the inherent limitations in current ProLLMs: (i) the lack of natural language capabilities, (ii) insufficient instruction understanding, and (iii) high training resource demands. To address these challenges, we introduce a training framework to transform any general LLM into a ProLLM capable of handling multiple PLP tasks. Specifically, our framework utilizes low-rank adaptation and employs a two-stage training approach, and it is distinguished by its universality, low overhead, and scalability. Through training under this framework, we propose the ProLLaMA model, the first known ProLLM to handle multiple PLP tasks simultaneously. Experiments show that ProLLaMA achieves state-of-the-art results in the unconditional protein sequence generation task. In the controllable protein sequence generation task, ProLLaMA can design novel proteins with desired functionalities. In the protein property prediction task, ProLLaMA achieves nearly 100\% accuracy across many categories. The latter two tasks are beyond the reach of other ProLLMs. Code is available at \url{https://github.com/Lyu6PosHao/ProLLaMA}.

This https://arxiv.org/abs/2403.11329 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

AQM: A Refresh of the Abstract Qubit Model for Quantum Computing Co-design
Qubits are the fundamental building blocks of quantum information science and applications, whose concept is widely utilized in both quantum physics and quantum computation. While the significance of qubits and their implementation in physical devices have been extensively examined, now is the right time to revisit this understanding. In this paper, we introduce an abstract qubit model (AQM), offering a mathematical framework for higher-level algorithms and applications, and setting forth crite…

Dynamic Human Trust Modeling of Autonomous Agents With Varying Capability and Strategy
Jason Dekarske (University of California, Davis), Zhaodan Kong (University of California, Davis), Sanjay Joshi (University of California, Davis)
https://arxiv.org/abs/2404.19291 https://arxiv.org/pdf/2404.19291
arXiv:2404.19291v1 Announce Type: new
Abstract: Objective We model the dynamic trust of human subjects in a human-autonomy-teaming screen-based task.
Background Trust is an emerging area of study in human-robot collaboration. Many studies have looked at the issue of robot performance as a sole predictor of human trust, but this could underestimate the complexity of the interaction.
Method Subjects were paired with autonomous agents to search an on-screen grid to determine the number of outlier objects. In each trial, a different autonomous agent with a preassigned capability used one of three search strategies and then reported the number of outliers it found as a fraction of its capability. Then, the subject reported their total outlier estimate. Human subjects then evaluated statements about the agent's behavior, reliability, and their trust in the agent.
Results 80 subjects were recruited. Self-reported trust was modeled using Ordinary Least Squares, but the group that interacted with varying capability agents on a short time order produced a better performing ARIMAX model. Models were cross-validated between groups and found a moderate improvement in the next trial trust prediction.
Conclusion A time series modeling approach reveals the effects of temporal ordering of agent performance on estimated trust. Recency bias may affect how subjects weigh the contribution of strategy or capability to trust. Understanding the connections between agent behavior, agent performance, and human trust is crucial to improving human-robot collaborative tasks.
Application The modeling approach in this study demonstrates the need to represent autonomous agent characteristics over time to capture changes in human trust.

Credential Control Balance: A Universal Blockchain Account Model Abstract From Bank to Bitcoin, Ethereum External Owned Account and Account Abstraction
Huifeng Jiao, Dr. Nathapon Udomlertsakul, Dr. Anukul Tamprasirt
https://arxiv.org/abs/2402.10616

Credential Control Balance: A Universal Blockchain Account Model Abstract From Bank to Bitcoin, Ethereum External Owned Account and Account Abstraction
Blockchain market value peaked at $3 trillion, fell to $1 trillion, then recovered to $1.5 trillion and is rising again. Blockchain accounts secure most on-chain assets in this huge market (Web-12). This paper initiates a universal blockchain account model from a comprehensive review of blockchain account development, encompassing both academic and industry perspectives. This paper uses a model analysis method to analysis the account progress and create high level new account model. And it uses…

An image-computable model of speeded decision-making
Paul I. Jaffe, Gustavo X. Santiago-Reyes, Robert J. Schafer, Patrick G. Bissett, Russell A. Poldrack
https://arxiv.org/abs/2403.16382

An image-computable model of speeded decision-making
Evidence accumulation models (EAMs) are the dominant framework for modeling response time (RT) data from speeded decision-making tasks. While providing a good quantitative description of RT data in terms of abstract perceptual representations, EAMs do not explain how the visual system extracts these representations in the first place. To address this limitation, we introduce the visual accumulator model (VAM), in which convolutional neural network models of visual processing and traditional EAM…

Search for heavy neutral Higgs bosons decaying into a top quark pair in 140 fb$^{-1}$ of proton-proton collision data at $\sqrt{s}=13$ TeV with the ATLAS detector
ATLAS Collaboration
https://arxiv.org/abs/2404.18986 https://arxiv.org/pdf/2404.18986
arXiv:2404.18986v1 Announce Type: new
Abstract: A search for heavy pseudo-scalar ($A$) and scalar ($H$) Higgs bosons decaying into a top-quark pair ($t\bar{t}$) has been performed with 140 fb$^{-1}$ of proton-proton collision data collected by the ATLAS experiment at the Large Hadron Collider at a centre-of-mass energy of $\sqrt{s}=13$ TeV. Interference effects between the signal process and Standard Model (SM) $t\bar{t}$ production are taken into account. Final states with exactly one or exactly two electrons or muons are considered. No significant deviation from the SM prediction is observed. The results of the search are interpreted in the context of a two-Higgs-doublet model (2HDM) of type II in the alignment limit with mass-degenerate pseudo-scalar and scalar Higgs bosons ($m_A = m_H$) and the hMSSM parameterisation of the minimal supersymmetric extension of the Standard Model. Ratios of the two vacuum expectation values, $\tan\beta$, smaller than 3.49 (3.16) are excluded at 95% confidence level for $m_A = m_H = 400$ GeV in the 2HDM (hMSSM). Masses up to 1240 GeV are excluded for the lowest tested $\tan\beta$ value of 0.4 in the 2HDM. In the hMSSM, masses up to 950 GeV are excluded for $\tan\beta=1.0$. In addition, generic exclusion limits are derived separately for single scalar and pseudo-scalar states for different choices of their mass and total width.

This https://arxiv.org/abs/2301.10550 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Structural insulators and promotors in networks under generic problem-solving dynamics
The collective coordination of distributed tasks in a complex system can be represented as decision dynamics on a graph. This abstract representation allows studying the performance of local decision heuristics as a function of task complexity and network architecture. Here we identify hard-to-solve and easy-to-solve networks in a social differentiation task within the basic model of small-world graphs. We show that, depending on the details of the decision heuristic as well as the length of th…

Extending Llama-3's Context Ten-Fold Overnight
Peitian Zhang, Ninglu Shao, Zheng Liu, Shitao Xiao, Hongjin Qian, Qiwei Ye, Zhicheng Dou
https://arxiv.org/abs/2404.19553 https://arxiv.org/pdf/2404.19553
arXiv:2404.19553v1 Announce Type: new
Abstract: We extend the context length of Llama-3-8B-Instruct from 8K to 80K via QLoRA fine-tuning. The entire training cycle is super efficient, which takes 8 hours on one 8xA800 (80G) GPU machine. The resulted model exhibits superior performances across a broad range of evaluation tasks, such as NIHS, topic retrieval, and long-context language understanding; meanwhile, it also well preserves the original capability over short contexts. The dramatic context extension is mainly attributed to merely 3.5K synthetic training samples generated by GPT-4 , which indicates the LLMs' inherent (yet largely underestimated) potential to extend its original context length. In fact, the context length could be extended far beyond 80K with more computation resources. Therefore, the team will publicly release the entire resources (including data, model, data generation pipeline, training code) so as to facilitate the future research from the community: \url{https://github.com/FlagOpen/FlagEmbedding}.

X-Diffusion: Generating Detailed 3D MRI Volumes From a Single Image Using Cross-Sectional Diffusion Models
Emmanuelle Bourigault, Abdullah Hamdi, Amir Jamaludin
https://arxiv.org/abs/2404.19604 https://arxiv.org/pdf/2404.19604
arXiv:2404.19604v1 Announce Type: new
Abstract: In this work, we present X-Diffusion, a cross-sectional diffusion model tailored for Magnetic Resonance Imaging (MRI) data. X-Diffusion is capable of generating the entire MRI volume from just a single MRI slice or optionally from few multiple slices, setting new benchmarks in the precision of synthesized MRIs from extremely sparse observations. The uniqueness lies in the novel view-conditional training and inference of X-Diffusion on MRI volumes, allowing for generalized MRI learning. Our evaluations span both brain tumour MRIs from the BRATS dataset and full-body MRIs from the UK Biobank dataset. Utilizing the paired pre-registered Dual-energy X-ray Absorptiometry (DXA) and MRI modalities in the UK Biobank dataset, X-Diffusion is able to generate detailed 3D MRI volume from a single full-body DXA. Remarkably, the resultant MRIs not only stand out in precision on unseen examples (surpassing state-of-the-art results by large margins) but also flawlessly retain essential features of the original MRI, including tumour profiles, spine curvature, brain volume, and beyond. Furthermore, the trained X-Diffusion model on the MRI datasets attains a generalization capacity out-of-domain (e.g. generating knee MRIs even though it is trained on brains). The code is available on the project website https://emmanuelleb985.github.io/XDiffusion/ .

Truth Factors
Robert E. Kent
https://arxiv.org/abs/2404.14470 https://arxiv.org/pdf/2404.14470<…

Truth Factors
Truth refers to the satisfaction relation used to define the semantics of model-theoretic languages. The satisfaction relation for first order languages (truth classification), and the preservation of truth by first order interpretations (truth infomorphism), is a motivating example in the theory of Information Flow (IF) (Barwise and Seligman 1997). The abstract theory of satisfaction is the basis for the theory of institutions (Goguen and Burstall 1992). Factoring refers to categorical factori…

Fake it to make it: Using synthetic data to remedy the data shortage in joint multimodal speech-and-gesture synthesis
Shivam Mehta, Anna Deichler, Jim O'Regan, Birger Mo\"ell, Jonas Beskow, Gustav Eje Henter, Simon Alexanderson
https://arxiv.org/abs/2404.19622 https://arxiv.org/pdf/2404.19622
arXiv:2404.19622v1 Announce Type: new
Abstract: Although humans engaged in face-to-face conversation simultaneously communicate both verbally and non-verbally, methods for joint and unified synthesis of speech audio and co-speech 3D gesture motion from text are a new and emerging field. These technologies hold great promise for more human-like, efficient, expressive, and robust synthetic communication, but are currently held back by the lack of suitably large datasets, as existing methods are trained on parallel data from all constituent modalities. Inspired by student-teacher methods, we propose a straightforward solution to the data shortage, by simply synthesising additional training material. Specifically, we use unimodal synthesis models trained on large datasets to create multimodal (but synthetic) parallel training data, and then pre-train a joint synthesis model on that material. In addition, we propose a new synthesis architecture that adds better and more controllable prosody modelling to the state-of-the-art method in the field. Our results confirm that pre-training on large amounts of synthetic data improves the quality of both the speech and the motion synthesised by the multimodal model, with the proposed architecture yielding further benefits when pre-trained on the synthetic data. See https://shivammehta25.github.io/MAGI/ for example output.

Metrology of microwave fields based on trap-loss spectroscopy with cold Rydberg atoms
Romain Duverger, Alexis Bonnin, Romain Granier, Quentin Marolleau, C\'edric Blanchard, Nassim Zahzam, Yannick Bidel, Malo Cadoret, Alexandre Bresson, Sylvain Schwartz
https://arxiv.org/abs/2404.17445 https://arxiv.org/pdf/2404.17445
arXiv:2404.17445v1 Announce Type: new
Abstract: We demonstrate a new approach for the metrology of microwave fields based on the trap-loss-spectroscopy of cold Rydberg atoms in a magneto-optical trap. Compared to state-of-the-art sensors using room-temperature vapors, cold atoms allow longer interaction times, better isolation from the environment and a reduced Doppler effect. Our approach is particularly simple as the detection relies on fluorescence measurements only. Moreover, our signal is well described by a two-level model across a broad measurement range, allowing in principle to reconstruct the amplitude and the frequency of the microwave field simultaneously without the need for an external reference field. We report on a scale factor linearity at the percent level and no noticeable drifts over two hours, paving the way for new applications of cold Rydberg atoms in metrology such as calibrating blackbody shifts in state-of-the-art optical clocks, monitoring the Earth cryosphere from space, measuring the cosmic microwave background or searching for dark matter.

Morphodynamics of chloroplast network control light-avoidance response in the non-motile dinoflagellate Pyrocystis lunula
Nico Schramma, Gloria Casas Canales, Maziyar Jalaal
https://arxiv.org/abs/2404.19570 https://arxiv.org/pdf/2404.19570
arXiv:2404.19570v1 Announce Type: new
Abstract: Photosynthetic algae play a significant role in oceanic carbon capture. Their performance, however, is constantly challenged by fluctuations in environmental light conditions. Here, we show that the non-motile single-celled marine dinoflagellate Pyrocystis lunula can internally contract its chloroplast network in response to light. By exposing the cell to various physiological light conditions and applying temporal illumination sequences, we find that network morphodynamics follows simple rules, as established in a mathematical model. Our analysis of the chloroplast structure reveals that its unusual reticulated morphology constitutes properties similar to auxetic metamaterials, facilitating drastic deformations for light-avoidance, while confined by the cell wall. Our study shows how the topologically complex network of chloroplasts is crucial in supporting the dinoflagellate's adaptation to varying light conditions, thereby facilitating essential life-sustaining processes.

This https://arxiv.org/abs/2402.14096 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

EyeTrans: Merging Human and Machine Attention for Neural Code Summarization
Neural code summarization leverages deep learning models to automatically generate brief natural language summaries of code snippets. The development of Transformer models has led to extensive use of attention during model design. While existing work has primarily and almost exclusively focused on static properties of source code and related structural representations like the Abstract Syntax Tree (AST), few studies have considered human attention, that is, where programmers focus while examini…

Countering Reward Over-optimization in LLM with Demonstration-Guided Reinforcement Learning
Mathieu Rita, Florian Strub, Rahma Chaabouni, Paul Michel, Emmanuel Dupoux, Olivier Pietquin
https://arxiv.org/abs/2404.19409 https://arxiv.org/pdf/2404.19409
arXiv:2404.19409v1 Announce Type: new
Abstract: While Reinforcement Learning (RL) has been proven essential for tuning large language models (LLMs), it can lead to reward over-optimization (ROO). Existing approaches address ROO by adding KL regularization, requiring computationally expensive hyperparameter tuning. Additionally, KL regularization focuses solely on regularizing the language policy, neglecting a potential source of regularization: the reward function itself. Inspired by demonstration-guided RL, we here introduce the Reward Calibration from Demonstration (RCfD), which leverages human demonstrations and a reward model to recalibrate the reward objective. Formally, given a prompt, the RCfD objective minimizes the distance between the demonstrations' and LLM's rewards rather than directly maximizing the reward function. This objective shift avoids incentivizing the LLM to exploit the reward model and promotes more natural and diverse language generation. We show the effectiveness of RCfD on three language tasks, which achieves comparable performance to carefully tuned baselines while mitigating ROO.

SpecstatOR: Speckle statistics-based iOCT Segmentation Network for Ophthalmic Surgery
Kristina Mach, Hessam Roodaki, Michael Sommersperger, Nassir Navab
https://arxiv.org/abs/2404.19481 https://arxiv.org/pdf/2404.19481
arXiv:2404.19481v1 Announce Type: new
Abstract: This paper presents an innovative approach to intraoperative Optical Coherence Tomography (iOCT) image segmentation in ophthalmic surgery, leveraging statistical analysis of speckle patterns to incorporate statistical pathology-specific prior knowledge. Our findings indicate statistically different speckle patterns within the retina and between retinal layers and surgical tools, facilitating the segmentation of previously unseen data without the necessity for manual labeling. The research involves fitting various statistical distributions to iOCT data, enabling the differentiation of different ocular structures and surgical tools. The proposed segmentation model aims to refine the statistical findings based on prior tissue understanding to leverage statistical and biological knowledge. Incorporating statistical parameters, physical analysis of light-tissue interaction, and deep learning informed by biological structures enhance segmentation accuracy, offering potential benefits to real-time applications in ophthalmic surgical procedures. The study demonstrates the adaptability and precision of using Gamma distribution parameters and the derived binary maps as sole inputs for segmentation, notably enhancing the model's inference performance on unseen data.

Did the Big Bang and cosmic inflation really happen? (A tale of alternative cosmological models)
Marcin Postolak
https://arxiv.org/abs/2404.18503 https://arxiv.org/pdf/2404.18503
arXiv:2404.18503v1 Announce Type: new
Abstract: A popular science article designed to introduce people familiar with basic cosmological nomenclature with models alternative to cosmological inflation. The paper briefly discusses the modern view of the Big Bang model, inflation (both its advantages and potential deficiencies). This is followed by a discussion of historical alternative models and modern approaches such as matter bounce, ekpyrotic Universe, Conformal Cyclic Cosmology, Hartle-Hawking state and loop quantum cosmology. The final aspect of the paper is to present the advantages and potential problems associated with alternative models and to present the conceptual challenges associated with the uniqueness of cosmology as a specific domain of physics.

Memory Traces: Are Transformers Tulving Machines?
Jean-Marie Chauvet
https://arxiv.org/abs/2404.08543 https://arxiv.org/pdf/2404.0854…

Memory Traces: Are Transformers Tulving Machines?
Memory traces--changes in the memory system that result from the perception and encoding of an event--were measured in pioneering studies by Endel Tulving and Michael J. Watkins in 1975. These and further experiments informed the maturation of Tulving's memory model, from the GAPS (General Abstract Processing System} to the SPI (Serial-Parallel Independent) model. Having current top of the line LLMs revisit the original Tulving-Watkins tests may help in assessing whether foundation models compl…

PrivComp-KG : Leveraging Knowledge Graph and Large Language Models for Privacy Policy Compliance Verification
Leon Garza, Lavanya Elluri, Anantaa Kotal, Aritran Piplai, Deepti Gupta, Anupam Joshi
https://arxiv.org/abs/2404.19744 https://arxiv.org/pdf/2404.19744
arXiv:2404.19744v1 Announce Type: new
Abstract: Data protection and privacy is becoming increasingly crucial in the digital era. Numerous companies depend on third-party vendors and service providers to carry out critical functions within their operations, encompassing tasks such as data handling and storage. However, this reliance introduces potential vulnerabilities, as these vendors' security measures and practices may not always align with the standards expected by regulatory bodies. Businesses are required, often under the penalty of law, to ensure compliance with the evolving regulatory rules. Interpreting and implementing these regulations pose challenges due to their complexity. Regulatory documents are extensive, demanding significant effort for interpretation, while vendor-drafted privacy policies often lack the detail required for full legal compliance, leading to ambiguity. To ensure a concise interpretation of the regulatory requirements and compliance of organizational privacy policy with said regulations, we propose a Large Language Model (LLM) and Semantic Web based approach for privacy compliance. In this paper, we develop the novel Privacy Policy Compliance Verification Knowledge Graph, PrivComp-KG. It is designed to efficiently store and retrieve comprehensive information concerning privacy policies, regulatory frameworks, and domain-specific knowledge pertaining to the legal landscape of privacy. Using Retrieval Augmented Generation, we identify the relevant sections in a privacy policy with corresponding regulatory rules. This information about individual privacy policies is populated into the PrivComp-KG. Combining this with the domain context and rules, the PrivComp-KG can be queried to check for compliance with privacy policies by each vendor against relevant policy regulations. We demonstrate the relevance of the PrivComp-KG, by verifying compliance of privacy policy documents for various organizations.

How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model
Umberto Tomasini, Matthieu Wyart
https://arxiv.org/abs/2404.10727 h…

How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model
Understanding what makes high-dimensional data learnable is a fundamental question in machine learning. On the one hand, it is believed that the success of deep learning lies in its ability to build a hierarchy of representations that become increasingly more abstract with depth, going from simple features like edges to more complex concepts. On the other hand, learning to be insensitive to invariances of the task, such as smooth transformations for image datasets, has been argued to be importa…

Distributed Stochastic Optimization of a Neural Representation Network for Time-Space Tomography Reconstruction
K. Aditya Mohan, Massimiliano Ferrucci, Chuck Divin, Garrett A. Stevenson, Hyojin Kim
https://arxiv.org/abs/2404.19075 https://arxiv.org/pdf/2404.19075
arXiv:2404.19075v1 Announce Type: new
Abstract: 4D time-space reconstruction of dynamic events or deforming objects using X-ray computed tomography (CT) is an extremely ill-posed inverse problem. Existing approaches assume that the object remains static for the duration of several tens or hundreds of X-ray projection measurement images (reconstruction of consecutive limited-angle CT scans). However, this is an unrealistic assumption for many in-situ experiments that causes spurious artifacts and inaccurate morphological reconstructions of the object. To solve this problem, we propose to perform a 4D time-space reconstruction using a distributed implicit neural representation (DINR) network that is trained using a novel distributed stochastic training algorithm. Our DINR network learns to reconstruct the object at its output by iterative optimization of its network parameters such that the measured projection images best match the output of the CT forward measurement model. We use a continuous time and space forward measurement model that is a function of the DINR outputs at a sparsely sampled set of continuous valued object coordinates. Unlike existing state-of-the-art neural representation architectures that forward and back propagate through dense voxel grids that sample the object's entire time-space coordinates, we only propagate through the DINR at a small subset of object coordinates in each iteration resulting in an order-of-magnitude reduction in memory and compute for training. DINR leverages distributed computation across several compute nodes and GPUs to produce high-fidelity 4D time-space reconstructions even for extremely large CT data sizes. We use both simulated parallel-beam and experimental cone-beam X-ray CT datasets to demonstrate the superior performance of our approach.

Balanced Resonate-and-Fire Neurons
Saya Higuchi, Sebastian Kairat, Sander M. Bohte. Sebastian Otte
https://arxiv.org/abs/2402.14603 https://arxiv.org/pdf/2402.14603
arXiv:2402.14603v1 Announce Type: new
Abstract: The resonate-and-fire (RF) neuron, introduced over two decades ago, is a simple, efficient, yet biologically plausible spiking neuron model, which can extract frequency patterns within the time domain due to its resonating membrane dynamics. However, previous RF formulations suffer from intrinsic shortcomings that limit effective learning and prevent exploiting the principled advantage of RF neurons. Here, we introduce the balanced RF (BRF) neuron, which alleviates some of the intrinsic limitations of vanilla RF neurons and demonstrates its effectiveness within recurrent spiking neural networks (RSNNs) on various sequence learning tasks. We show that networks of BRF neurons achieve overall higher task performance, produce only a fraction of the spikes, and require significantly fewer parameters as compared to modern RSNNs. Moreover, BRF-RSNN consistently provide much faster and more stable training convergence, even when bridging many hundreds of time steps during backpropagation through time (BPTT). These results underscore that our BRF-RSNN is a strong candidate for future large-scale RSNN architectures, further lines of research in SNN methodology, and more efficient hardware implementations.

An image-computable model of speeded decision-making
Paul I. Jaffe, Gustavo X. Santiago-Reyes, Robert J. Schafer, Patrick G. Bissett, Russell A. Poldrack
https://arxiv.org/abs/2403.16382

An image-computable model of speeded decision-making
Evidence accumulation models (EAMs) are the dominant framework for modeling response time (RT) data from speeded decision-making tasks. While providing a good quantitative description of RT data in terms of abstract perceptual representations, EAMs do not explain how the visual system extracts these representations in the first place. To address this limitation, we introduce the visual accumulator model (VAM), in which convolutional neural network models of visual processing and traditional EAM…

PSTAIC regularization for 2D spatiotemporal image reconstruction
Deepak G Skariah, Muthuvel Arigovindan
https://arxiv.org/abs/2404.18294 https://arxiv.org/pdf/2404.18294
arXiv:2404.18294v1 Announce Type: new
Abstract: We propose a model for restoration of spatio-temporal TIRF images based on infimal decomposition regularization model named STAIC proposed earlier. We propose to strengthen the STAIC algorithm by enabling it to estimate the relative weights in the regularization term by incorporating it as part of the optimization problem. We also design an iterative scheme which alternatively minimizes the weight and image sub-problems. We demonstrate the restoration quality of this regularization scheme against other restoration models enabled by similar weight estimation schemes.

This https://arxiv.org/abs/2312.07041 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Probabilistic Lookahead Strong Branching via a Stochastic Abstract Branching Model
Strong Branching (SB) is a cornerstone of all modern branching rules used in the Branch-and-Bound (BnB) algorithm, which is at the center of Mixed-Integer Programming solvers. In its full form, SB evaluates all variables to branch on and then selects the one producing the best relaxation, leading to small trees, but high runtimes. State-of-the-art branching rules therefore use SB with working limits to achieve both small enough trees and short run times. So far, these working limits have been e…

On groups and fields interpretable in differentially closed valued fields and in various $\mathrm{NTP_2}$ fields
Paul Z. Wang
https://arxiv.org/abs/2402.09143

On groups and fields interpretable in differentially closed valued fields and in various $\mathrm{NTP_2}$ fields
This paper aims at developing model-theoretic tools to study interpretable fields and definably amenable groups, mainly in $\mathrm{NIP}$ or $\mathrm{NTP_2}$ settings. An abstract theorem constructing definable group homomorphisms from generic data is proved. It relies heavily on a stabilizer theorem of Montenegro, Onshuus and Simon. The main application is a structure theorem for definably amenable groups that are interpretable in algebraically bounded perfect $\mathrm{NTP_2}$ fields with boun…

Safe Training with Sensitive In-domain Data: Leveraging Data Fragmentation To Mitigate Linkage Attacks
Mariia Ignashina, Julia Ive
https://arxiv.org/abs/2404.19486 https://arxiv.org/pdf/2404.19486
arXiv:2404.19486v1 Announce Type: new
Abstract: Current text generation models are trained using real data which can potentially contain sensitive information, such as confidential patient information and the like. Under certain conditions output of the training data which they have memorised can be triggered, exposing sensitive data. To mitigate against this risk we propose a safer alternative which sees fragmented data in the form of domain-specific short phrases randomly grouped together shared instead of full texts. Thus, text fragments that could re-identify an individual cannot be reproduced by the model in one sequence, giving significant protection against linkage attacks. We fine-tune several state-of-the-art LLMs using meaningful syntactic chunks to explore their utility. In particular, we fine-tune BERT-based models to predict two cardiovascular diagnoses. Our results demonstrate the capacity of LLMs to benefit from the pre-trained knowledge and deliver classification results when fine-tuned with fragmented data comparable to fine-tuning with full training data.

EyeTrans: Merging Human and Machine Attention for Neural Code Summarization
Yifan Zhang, Jiliang Li, Zachary Karas, Aakash Bansal, Toby Jia-Jun Li, Collin McMillan, Kevin Leach, Yu Huang
https://arxiv.org/abs/2402.14096

EyeTrans: Merging Human and Machine Attention for Neural Code Summarization
Neural code summarization leverages deep learning models to automatically generate brief natural language summaries of code snippets. The development of Transformer models has led to extensive use of attention during model design. While existing work has primarily and almost exclusively focused on static properties of source code and related structural representations like the Abstract Syntax Tree (AST), few studies have considered human attention, that is, where programmers focus while examini…

#Wissenschaftler vom #AWI haben eine globale Zusammenstellung von #Sauerstoffisotope​n aus #Kieselalgen

A global compilation of diatom silica oxygen isotope records from lake sediment – trends and implications for climate reconstruction
Abstract. Oxygen isotopes in biogenic silica (δ18OBSi) from lake sediments allow for quantitative reconstruction of past hydroclimate and proxy-model comparison in terrestrial environments. The signals of individual records have been attributed to different factors, such as air temperature (Tair), atmospheric circulation patterns, hydrological changes, and lake evaporation. While every lake has its own local set of drivers of δ18O variability, here we explore the extent to which regional or e…

Federated Learning for Blind Image Super-Resolution
Brian B. Moser, Ahmed Anwar, Federico Raue, Stanislav Frolov, Andreas Dengel
https://arxiv.org/abs/2404.17670 https://arxiv.org/pdf/2404.17670
arXiv:2404.17670v1 Announce Type: new
Abstract: Traditional blind image SR methods need to model real-world degradations precisely. Consequently, current research struggles with this dilemma by assuming idealized degradations, which leads to limited applicability to actual user data. Moreover, the ideal scenario - training models on data from the targeted user base - presents significant privacy concerns. To address both challenges, we propose to fuse image SR with federated learning, allowing real-world degradations to be directly learned from users without invading their privacy. Furthermore, it enables optimization across many devices without data centralization. As this fusion is underexplored, we introduce new benchmarks specifically designed to evaluate new SR methods in this federated setting. By doing so, we employ known degradation modeling techniques from SR research. However, rather than aiming to mirror real degradations, our benchmarks use these degradation models to simulate the variety of degradations found across clients within a distributed user base. This distinction is crucial as it circumvents the need to precisely model real-world degradations, which limits contemporary blind image SR research. Our proposed benchmarks investigate blind image SR under new aspects, namely differently distributed degradation types among users and varying user numbers. We believe new methods tested within these benchmarks will perform more similarly in an application, as the simulated scenario addresses the variety while federated learning enables the training on actual degradations.

Balanced Resonate-and-Fire Neurons
Saya Higuchi, Sebastian Kairat, Sander M. Bohte. Sebastian Otte
https://arxiv.org/abs/2402.14603 https://arxiv.org/pdf/2402.14603
arXiv:2402.14603v1 Announce Type: new
Abstract: The resonate-and-fire (RF) neuron, introduced over two decades ago, is a simple, efficient, yet biologically plausible spiking neuron model, which can extract frequency patterns within the time domain due to its resonating membrane dynamics. However, previous RF formulations suffer from intrinsic shortcomings that limit effective learning and prevent exploiting the principled advantage of RF neurons. Here, we introduce the balanced RF (BRF) neuron, which alleviates some of the intrinsic limitations of vanilla RF neurons and demonstrates its effectiveness within recurrent spiking neural networks (RSNNs) on various sequence learning tasks. We show that networks of BRF neurons achieve overall higher task performance, produce only a fraction of the spikes, and require significantly fewer parameters as compared to modern RSNNs. Moreover, BRF-RSNN consistently provide much faster and more stable training convergence, even when bridging many hundreds of time steps during backpropagation through time (BPTT). These results underscore that our BRF-RSNN is a strong candidate for future large-scale RSNN architectures, further lines of research in SNN methodology, and more efficient hardware implementations.

S\~onajaht: Definition Embeddings and Semantic Search for Reverse Dictionary Creation
Aleksei Dorkin, Kairit Sirts
https://arxiv.org/abs/2404.19430 https://arxiv.org/pdf/2404.19430
arXiv:2404.19430v1 Announce Type: new
Abstract: We present an information retrieval based reverse dictionary system using modern pre-trained language models and approximate nearest neighbors search algorithms. The proposed approach is applied to an existing Estonian language lexicon resource, S\~onaveeb (word web), with the purpose of enhancing and enriching it by introducing cross-lingual reverse dictionary functionality powered by semantic search.
The performance of the system is evaluated using both an existing labeled English dataset of words and definitions that is extended to contain also Estonian and Russian translations, and a novel unlabeled evaluation approach that extracts the evaluation data from the lexicon resource itself using synonymy relations.
Evaluation results indicate that the information retrieval based semantic search approach without any model training is feasible, producing median rank of 1 in the monolingual setting and median rank of 2 in the cross-lingual setting using the unlabeled evaluation approach, with models trained for cross-lingual retrieval and including Estonian in their training data showing superior performance in our particular task.

Snake with Shifted Window: Learning to Adapt Vessel Pattern for OCTA Segmentation
Xinrun Chen, Mei Shen, Haojian Ning, Mengzhan Zhang, Chengliang Wang, Shiying Li
https://arxiv.org/abs/2404.18096 https://arxiv.org/pdf/2404.18096
arXiv:2404.18096v1 Announce Type: new
Abstract: Segmenting specific targets or structures in optical coherence tomography angiography (OCTA) images is fundamental for conducting further pathological studies. The retinal vascular layers are rich and intricate, and such vascular with complex shapes can be captured by the widely-studied OCTA images. In this paper, we thus study how to use OCTA images with projection vascular layers to segment retinal structures. To this end, we propose the SSW-OCTA model, which integrates the advantages of deformable convolutions suited for tubular structures and the swin-transformer for global feature extraction, adapting to the characteristics of OCTA modality images. Our model underwent testing and comparison on the OCTA-500 dataset, achieving state-of-the-art performance. The code is available at: https://github.com/ShellRedia/Snake-SWin-OCTA.

Multielectron effect in strong-field ionization of CO
Mahmoud Abu-samha, L. B. Madsen, N. I. Shvetsov-Shilovski
https://arxiv.org/abs/2404.14254 https://arxiv.org/pdf/2404.14254
arXiv:2404.14254v1 Announce Type: new
Abstract: We investigate the effects of the multielectron polarization of the ion described by the induced dipole potential in photoelectron momentum distributions produced in ionization of the CO molecule by a strong laser field. We present results of the numerical solution of the time-dependent Schr\"{o}dinger equation in three spatial dimensions and semiclassical simulations accounting for quantum interference. We predict the change of the asymmetry and interference patterns in two-dimensional photoelectron momentum distributions as well as longitudinal momentum distributions. By using a semiclassical model we identify the mechanism responsible for the observed effects. It is shown that the modifications of electron momentum distributions are caused by a combined effect of the force acting on photoelectrons due to the induced dipole potential and the linear Stark-shift of the ionization potential.

Navigating Brain Language Representations: A Comparative Analysis of Neural Language Models and Psychologically Plausible Models
Yunhao Zhang, Shaonan Wang, Xinyi Dong, Jiajun Yu, Chengqing Zong
https://arxiv.org/abs/2404.19364 https://arxiv.org/pdf/2404.19364
arXiv:2404.19364v1 Announce Type: new
Abstract: Neural language models, particularly large-scale ones, have been consistently proven to be most effective in predicting brain neural activity across a range of studies. However, previous research overlooked the comparison of these models with psychologically plausible ones. Moreover, evaluations were reliant on limited, single-modality, and English cognitive datasets. To address these questions, we conducted an analysis comparing encoding performance of various neural language models and psychologically plausible models. Our study utilized extensive multi-modal cognitive datasets, examining bilingual word and discourse levels. Surprisingly, our findings revealed that psychologically plausible models outperformed neural language models across diverse contexts, encompassing different modalities such as fMRI and eye-tracking, and spanning languages from English to Chinese. Among psychologically plausible models, the one incorporating embodied information emerged as particularly exceptional. This model demonstrated superior performance at both word and discourse levels, exhibiting robust prediction of brain activation across numerous regions in both English and Chinese.

LpQcM: Adaptable Lesion-Quantification-Consistent Modulation for Deep Learning Low-Count PET Image Denoising
Menghua Xia, Huidong Xie, Qiong Liu, Bo Zhou, Hanzhong Wang, Biao Li, Axel Rominger, Kuangyu Shi, Georges EI Fakhri, Chi Liu
https://arxiv.org/abs/2404.17994 https://arxiv.org/pdf/2404.17994
arXiv:2404.17994v1 Announce Type: new
Abstract: Deep learning-based positron emission tomography (PET) image denoising offers the potential to reduce radiation exposure and scanning time by transforming low-count images into high-count equivalents. However, existing methods typically blur crucial details, leading to inaccurate lesion quantification. This paper proposes a lesion-perceived and quantification-consistent modulation (LpQcM) strategy for enhanced PET image denoising, via employing downstream lesion quantification analysis as auxiliary tools. The LpQcM is a plug-and-play design adaptable to a wide range of model architectures, modulating the sampling and optimization procedures of model training without adding any computational burden to the inference phase. Specifically, the LpQcM consists of two components, the lesion-perceived modulation (LpM) and the multiscale quantification-consistent modulation (QcM). The LpM enhances lesion contrast and visibility by allocating higher sampling weights and stricter loss criteria to lesion-present samples determined by an auxiliary segmentation network than lesion-absent ones. The QcM further emphasizes accuracy of quantification for both the mean and maximum standardized uptake value (SUVmean and SUVmax) across multiscale sub-regions throughout the entire image, thereby enhancing the overall image quality. Experiments conducted on large PET datasets from multiple centers and vendors, and varying noise levels demonstrated the LpQcM efficacy across various denoising frameworks. Compared to frameworks without LpQcM, the integration of LpQcM reduces the lesion SUVmean bias by 2.92% on average and increases the peak signal-to-noise ratio (PSNR) by 0.34 on average, for denoising images of extremely low-count levels below 10%.

DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstruction
Chenhe Du, Xiyue Lin, Qing Wu, Xuanyu Tian, Ying Su, Zhe Luo, Hongjiang Wei, S. Kevin Zhou, Jingyi Yu, Yuyao Zhang
https://arxiv.org/abs/2404.17890 https://arxiv.org/pdf/2404.17890
arXiv:2404.17890v1 Announce Type: new
Abstract: Limited-angle and sparse-view computed tomography (LACT and SVCT) are crucial for expanding the scope of X-ray CT applications. However, they face challenges due to incomplete data acquisition, resulting in diverse artifacts in the reconstructed CT images. Emerging implicit neural representation (INR) techniques, such as NeRF, NeAT, and NeRP, have shown promise in under-determined CT imaging reconstruction tasks. However, the unsupervised nature of INR architecture imposes limited constraints on the solution space, particularly for the highly ill-posed reconstruction task posed by LACT and ultra-SVCT. In this study, we introduce the Diffusion Prior Driven Neural Representation (DPER), an advanced unsupervised framework designed to address the exceptionally ill-posed CT reconstruction inverse problems. DPER adopts the Half Quadratic Splitting (HQS) algorithm to decompose the inverse problem into data fidelity and distribution prior sub-problems. The two sub-problems are respectively addressed by INR reconstruction scheme and pre-trained score-based diffusion model. This combination initially preserves the implicit image local consistency prior from INR. Additionally, it effectively augments the feasibility of the solution space for the inverse problem through the generative diffusion model, resulting in increased stability and precision in the solutions. We conduct comprehensive experiments to evaluate the performance of DPER on LACT and ultra-SVCT reconstruction with two public datasets (AAPM and LIDC). The results show that our method outperforms the state-of-the-art reconstruction methods on in-domain datasets, while achieving significant performance improvements on out-of-domain datasets.

ThangDLU at #SMM4H 2024: Encoder-decoder models for classifying text data on social disorders in children and adolescents
Hoang-Thang Ta, Abu Bakar Siddiqur Rahman, Lotfollah Najjar, Alexander Gelbukh
#SMM4H (Social Media Mining for Health) 2024 Workshop, explicitly targeting the classification challenges within tweet data. Task 3 is a multi-class classification task centered on tweets discussing the impact of outdoor environments on symptoms of social anxiety. Task 5 involves a binary classification task focusing on tweets reporting medical disorders in children. We applied transfer learning from pre-trained encoder-decoder models such as BART-base and T5-small to identify the labels of a set of given tweets. We also presented some data augmentation methods to see their impact on the model performance. Finally, the systems obtained the best F1 score of 0.627 in Task 3 and the best F1 score of 0.841 in Task 5.

Learnable Community-Aware Transformer for Brain Connectome Analysis with Token Clustering
Yanting Yang, Beidi Zhao, Zhuohao Ni, Yize Zhao, Xiaoxiao Li
https://arxiv.org/abs/2403.08203 https://arxiv.org/pdf/2403.08203
arXiv:2403.08203v1 Announce Type: new
Abstract: Neuroscientific research has revealed that the complex brain network can be organized into distinct functional communities, each characterized by a cohesive group of regions of interest (ROIs) with strong interconnections. These communities play a crucial role in comprehending the functional organization of the brain and its implications for neurological conditions, including Autism Spectrum Disorder (ASD) and biological differences, such as in gender. Traditional models have been constrained by the necessity of predefined community clusters, limiting their flexibility and adaptability in deciphering the brain's functional organization. Furthermore, these models were restricted by a fixed number of communities, hindering their ability to accurately represent the brain's dynamic nature. In this study, we present a token clustering brain transformer-based model ($\texttt{TC-BrainTF}$) for joint community clustering and classification. Our approach proposes a novel token clustering (TC) module based on the transformer architecture, which utilizes learnable prompt tokens with orthogonal loss where each ROI embedding is projected onto the prompt embedding space, effectively clustering ROIs into communities and reducing the dimensions of the node representation via merging with communities. Our results demonstrate that our learnable community-aware model $\texttt{TC-BrainTF}$ offers improved accuracy in identifying ASD and classifying genders through rigorous testing on ABIDE and HCP datasets. Additionally, the qualitative analysis on $\texttt{TC-BrainTF}$ has demonstrated the effectiveness of the designed TC module and its relevance to neuroscience interpretations.

An Effective Networks Intrusion Detection Approach Based on Hybrid Harris Hawks and Multi-Layer Perceptron
Moutaz Alazab, Ruba Abu Khurma, Pedro A. Castillo, Bilal Abu-Salih, Alejandro Martin, David Camacho
https://arxiv.org/abs/2402.14037 https://arxiv.org/pdf/2402.14037
arXiv:2402.14037v1 Announce Type: new
Abstract: This paper proposes an Intrusion Detection System (IDS) employing the Harris Hawks Optimization algorithm (HHO) to optimize Multilayer Perceptron learning by optimizing bias and weight parameters. HHO-MLP aims to select optimal parameters in its learning process to minimize intrusion detection errors in networks. HHO-MLP has been implemented using EvoloPy NN framework, an open-source Python tool specialized for training MLPs using evolutionary algorithms. For purposes of comparing the HHO model against other evolutionary methodologies currently available, specificity and sensitivity measures, accuracy measures, and mse and rmse measures have been calculated using KDD datasets. Experiments have demonstrated the HHO MLP method is effective at identifying malicious patterns. HHO-MLP has been tested against evolutionary algorithms like Butterfly Optimization Algorithm (BOA), Grasshopper Optimization Algorithms (GOA), and Black Widow Optimizations (BOW), with validation by Random Forest (RF), XG-Boost. HHO-MLP showed superior performance by attaining top scores with accuracy rate of 93.17%, sensitivity level of 89.25%, and specificity percentage of 95.41%.

Explainable agency: human preferences for simple or complex explanations
Michelle Blom, Ronal Singh, Tim Miller, Liz Sonenberg, Kerry Trentelman, Adam Saulwick
https://arxiv.org/abs/2403.12321

Explainable agency: human preferences for simple or complex explanations
Research in cognitive psychology has established that whether people prefer simpler explanations to complex ones is context dependent, but the question of `simple vs. complex' becomes critical when an artificial agent seeks to explain its decisions or predictions to humans. We present a model for abstracting causal reasoning chains for the purpose of explanation. This model uses a set of rules to progressively abstract different types of causal information in causal proof traces. We perform onl…

Enhancing Deep Learning Model Explainability in Brain Tumor Datasets using Post-Heuristic Approaches
Konstantinos Pasvantis, Eftychios Protopapadakis
https://arxiv.org/abs/2404.19568 https://arxiv.org/pdf/2404.19568
arXiv:2404.19568v1 Announce Type: new
Abstract: The application of deep learning models in medical diagnosis has showcased considerable efficacy in recent years. Nevertheless, a notable limitation involves the inherent lack of explainability during decision-making processes. This study addresses such a constraint, by enhancing the interpretability robustness. The primary focus is directed towards refining the explanations generated by the LIME Library and LIME image explainer. This is achieved throuhg post-processing mechanisms, based on scenario-specific rules. Multiple experiments have been conducted using publicly accessible datasets related to brain tumor detection. Our proposed post-heuristic approach demonstrates significant advancements, yielding more robust and concrete results, in the context of medical diagnosis.

Multielectron effect in strong-field ionization of CO
Mahmoud Abu-samha, L. B. Madsen, N. I. Shvetsov-Shilovski
https://arxiv.org/abs/2404.14254 https://arxiv.org/pdf/2404.14254
arXiv:2404.14254v1 Announce Type: new
Abstract: We investigate the effects of the multielectron polarization of the ion described by the induced dipole potential in photoelectron momentum distributions produced in ionization of the CO molecule by a strong laser field. We present results of the numerical solution of the time-dependent Schr\"{o}dinger equation in three spatial dimensions and semiclassical simulations accounting for quantum interference. We predict the change of the asymmetry and interference patterns in two-dimensional photoelectron momentum distributions as well as longitudinal momentum distributions. By using a semiclassical model we identify the mechanism responsible for the observed effects. It is shown that the modifications of electron momentum distributions are caused by a combined effect of the force acting on photoelectrons due to the induced dipole potential and the linear Stark-shift of the ionization potential.

An Effective Networks Intrusion Detection Approach Based on Hybrid Harris Hawks and Multi-Layer Perceptron
Moutaz Alazab, Ruba Abu Khurma, Pedro A. Castillo, Bilal Abu-Salih, Alejandro Martin, David Camacho
https://arxiv.org/abs/2402.14037 https://arxiv.org/pdf/2402.14037
arXiv:2402.14037v1 Announce Type: new
Abstract: This paper proposes an Intrusion Detection System (IDS) employing the Harris Hawks Optimization algorithm (HHO) to optimize Multilayer Perceptron learning by optimizing bias and weight parameters. HHO-MLP aims to select optimal parameters in its learning process to minimize intrusion detection errors in networks. HHO-MLP has been implemented using EvoloPy NN framework, an open-source Python tool specialized for training MLPs using evolutionary algorithms. For purposes of comparing the HHO model against other evolutionary methodologies currently available, specificity and sensitivity measures, accuracy measures, and mse and rmse measures have been calculated using KDD datasets. Experiments have demonstrated the HHO MLP method is effective at identifying malicious patterns. HHO-MLP has been tested against evolutionary algorithms like Butterfly Optimization Algorithm (BOA), Grasshopper Optimization Algorithms (GOA), and Black Widow Optimizations (BOW), with validation by Random Forest (RF), XG-Boost. HHO-MLP showed superior performance by attaining top scores with accuracy rate of 93.17%, sensitivity level of 89.25%, and specificity percentage of 95.41%.

Global Search Optics: Automatically Exploring Optimal Solutions to Compact Computational Imaging Systems
Yao Gao, Qi Jiang, Shaohua Gao, Lei Sun, Kailun Yang, Kaiwei Wang
https://arxiv.org/abs/2404.19201 https://arxiv.org/pdf/2404.19201
arXiv:2404.19201v1 Announce Type: new
Abstract: The popularity of mobile vision creates a demand for advanced compact computational imaging systems, which call for the development of both a lightweight optical system and an effective image reconstruction model. Recently, joint design pipelines come to the research forefront, where the two significant components are simultaneously optimized via data-driven learning to realize the optimal system design. However, the effectiveness of these designs largely depends on the initial setup of the optical system, complicated by a non-convex solution space that impedes reaching a globally optimal solution. In this work, we present Global Search Optics (GSO) to automatically design compact computational imaging systems through two parts: (i) Fused Optimization Method for Automatic Optical Design (OptiFusion), which searches for diverse initial optical systems under certain design specifications; and (ii) Efficient Physic-aware Joint Optimization (EPJO), which conducts parallel joint optimization of initial optical systems and image reconstruction networks with the consideration of physical constraints, culminating in the selection of the optimal solution. Extensive experimental results on the design of three-piece (3P) sphere computational imaging systems illustrate that the GSO serves as a transformative end-to-end lens design paradigm for superior global optimal structure searching ability, which provides compact computational imaging systems with higher imaging quality compared to traditional methods. The source code will be made publicly available at https://github.com/wumengshenyou/GSO.

Isotope shift factors with quantum electrodynamics effects for many-electron systems: A study of the nuclear charge radius of $^{26m}$Al
Leonid V. Skripnikov, Sergey D. Prosnyak, Aleksei V. Malyshev, Michail Athanasakis-Kaklamanakis, Alex Jose Brinson, Kei Minamisono, Fabian C. Pastrana Cruz, Jordan Ray Reilly, Brooke J. Rickey, Ronald. F. Garcia Ruiz
https://arxiv.org/abs/2404.13369 https://arxiv.org/pdf/2404.13369
arXiv:2404.13369v1 Announce Type: new
Abstract: A method for calculating the field shift contribution to isotope shifts in many-electron atoms, incorporating quantum electrodynamics (QED) effects, is introduced. We also implement the model QED approach to incorporate QED contribution to the nuclear recoil effect at the high-order correlation effects treatment level. The proposed computational scheme is used to revise the value of the root-mean-square (rms) nuclear charge radius of the isomer of aluminium-26, $^{26m}$Al. This radius is important for the global analysis of the $V_{ud}$ element of the Cabibbo-Kobayashi-Maskawa matrix. The difference in mean-square nuclear charge radii of $^{27}$Al and $^{26m}$Al, obtained by combining the calculated atomic factors with recently measured isotope shift (IS) of the $3s^23p~^2P_{3/2} \to 3s^24s~^2S_{1/2}$ transition in Al, is $0.443(44)(19)~{\rm fm}^2$, where the first and second uncertainties are experimental and theoretical ones, respectively. The latter is reduced by a factor of 4 with respect to the previous study. Using this value and the known value of the rms charge radius of $^{27}$Al, the resultant value $R_c(^{26m}$Al) = 3.132(10)~fm is obtained. With the improved accuracy of the calculated IS factors the error in $R_c(^{26m}$Al) is now dominated by the experimental uncertainty. Similar revision of rms charge radii is made for the $^{28}$Al, $^{29}$Al, $^{30}$Al, $^{31}$Al and $^{32}$Al isotopes using existing IS measurements. Additionally, atomic factors are computed for the {$3s^23p~^2P_{3/2} \to 3s^24s~^2S_{1/2}$}, {$3s^23p~^2P_{1/2} \to 3s^25s~^2S_{1/2}$} and {$3s^23p~^2P_{3/2} \to 3s^25s~^2S_{1/2}$} transitions in Al, which can be used in future experimental studies.

Isotope shift factors with quantum electrodynamics effects for many-electron systems: A study of the nuclear charge radius of $^{26m}$Al
Leonid V. Skripnikov, Sergey D. Prosnyak, Aleksei V. Malyshev, Michail Athanasakis-Kaklamanakis, Alex Jose Brinson, Kei Minamisono, Fabian C. Pastrana Cruz, Jordan Ray Reilly, Brooke J. Rickey, Ronald. F. Garcia Ruiz
https://arxiv.org/abs/2404.13369 https://arxiv.org/pdf/2404.13369
arXiv:2404.13369v1 Announce Type: new
Abstract: A method for calculating the field shift contribution to isotope shifts in many-electron atoms, incorporating quantum electrodynamics (QED) effects, is introduced. We also implement the model QED approach to incorporate QED contribution to the nuclear recoil effect at the high-order correlation effects treatment level. The proposed computational scheme is used to revise the value of the root-mean-square (rms) nuclear charge radius of the isomer of aluminium-26, $^{26m}$Al. This radius is important for the global analysis of the $V_{ud}$ element of the Cabibbo-Kobayashi-Maskawa matrix. The difference in mean-square nuclear charge radii of $^{27}$Al and $^{26m}$Al, obtained by combining the calculated atomic factors with recently measured isotope shift (IS) of the $3s^23p~^2P_{3/2} \to 3s^24s~^2S_{1/2}$ transition in Al, is $0.443(44)(19)~{\rm fm}^2$, where the first and second uncertainties are experimental and theoretical ones, respectively. The latter is reduced by a factor of 4 with respect to the previous study. Using this value and the known value of the rms charge radius of $^{27}$Al, the resultant value $R_c(^{26m}$Al) = 3.132(10)~fm is obtained. With the improved accuracy of the calculated IS factors the error in $R_c(^{26m}$Al) is now dominated by the experimental uncertainty. Similar revision of rms charge radii is made for the $^{28}$Al, $^{29}$Al, $^{30}$Al, $^{31}$Al and $^{32}$Al isotopes using existing IS measurements. Additionally, atomic factors are computed for the {$3s^23p~^2P_{3/2} \to 3s^24s~^2S_{1/2}$}, {$3s^23p~^2P_{1/2} \to 3s^25s~^2S_{1/2}$} and {$3s^23p~^2P_{3/2} \to 3s^25s~^2S_{1/2}$} transitions in Al, which can be used in future experimental studies.

InternLM-Math: Open Math Large Language Models Toward Verifiable Reasoning
Huaiyuan Ying, Shuo Zhang, Linyang Li, Zhejian Zhou, Yunfan Shao, Zhaoye Fei, Yichuan Ma, Jiawei Hong, Kuikun Liu, Ziyi Wang, Yudong Wang, Zijian Wu, Shuaibin Li, Fengzhe Zhou, Hongwei Liu, Songyang Zhang, Wenwei Zhang, Hang Yan, Xipeng Qiu, Jiayu Wang, Kai Chen, Dahua Lin
htt…

InternLM-Math: Open Math Large Language Models Toward Verifiable Reasoning
The math abilities of large language models can represent their abstract reasoning ability. In this paper, we introduce and open-source our math reasoning LLMs InternLM-Math which is continue pre-trained from InternLM2. We unify chain-of-thought reasoning, reward modeling, formal reasoning, data augmentation, and code interpreter in a unified seq2seq format and supervise our model to be a versatile math reasoner, verifier, prover, and augmenter. These abilities can be used to develop the next m…