Tootfinder

Development of a Photon-Counting Deadtime Noise Model that Extends Dynamic Range and Resolution in Atmospheric Lidar
Grant J. Kirchhoff, Matthew Hayman, Willem J. Marais, Jeffrey P. Thayer, Rory A. Barton-Grimley
https://arxiv.org/abs/2506.09253

Development of a Photon-Counting Deadtime Noise Model that Extends Dynamic Range and Resolution in Atmospheric Lidar
This work derives and validates a noise model that encapsulates deadtime of non-paralyzable detectors with random photon arrivals to enable advanced processing, like maximum-likelihood estimation, of high resolution atmospheric lidar profiles while accounting for deadtime bias. This estimator was validated across a wide dynamic range at high resolution (4 millimeters in range, 17 milliseconds in time). Experiments demonstrate that the noise model outperforms the current state-of-the-art for ver…

Optimal Evacuation Control in Large Urban Networks With Stochastic Demand
Alexander Hammerl, Wenlong Jin, Ravi Seshadri, Thomas Kj{\ae}r Rasmusssen, Otoo Anker Nielsen
https://arxiv.org/abs/2508.06797 …

Optimal Evacuation Control in Large Urban Networks With Stochastic Demand
We develop a risk-aware Model Predictive Control (MPC) framework for large-scale vehicular evacuations. Traffic dynamics are captured by the Generalized Bathtub Model, which describes the network-wide trip completion rate by tracking the time evolution of the distribution of remaining trip distances. We model evacuation inflow as a stochastic inflow process, and employ origin gating as the control policy, implemented through staged departure orders or adaptive ramp metering. A convex objective …

A Deep Generative Model for the Simulation of Discrete Karst Networks
Dany Lauzon, Julien Straubhaar, Philippe Renard
https://arxiv.org/abs/2506.09832 http…

A Deep Generative Model for the Simulation of Discrete Karst Networks
The simulation of discrete karst networks presents a significant challenge due to the complexity of the physicochemical processes occurring within various geological and hydrogeological contexts over extended periods. This complex interplay leads to a wide variety of karst network patterns, each intricately linked to specific hydrogeological conditions. We explore a novel approach that represents karst networks as graphs and applies graph generative models (deep learning techniques) to capture …

This https://arxiv.org/abs/2505.07263 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Skywork-VL Reward: An Effective Reward Model for Multimodal Understanding and Reasoning
We propose Skywork-VL Reward, a multimodal reward model that provides reward signals for both multimodal understanding and reasoning tasks. Our technical approach comprises two key components: First, we construct a large-scale multimodal preference dataset that covers a wide range of tasks and scenarios, with responses collected from both standard vision-language models (VLMs) and advanced VLM reasoners. Second, we design a reward model architecture based on Qwen2.5-VL-7B-Instruct, integrating …

From Partial to Monadic: Combinatory Algebra with Effects
Liron Cohen (BGU), Ariel Grunfeld (BGU), Dominik Kirst (PICUBE), \'Etienne Miquey (I2M)
https://arxiv.org/abs/2506.09453

From Partial to Monadic: Combinatory Algebra with Effects
Partial Combinatory Algebras (PCAs) provide a foundational model of the untyped $λ$-calculus and serve as the basis for many notions of computability, such as realizability theory. However, PCAs support a very limited notion of computation by only incorporating non-termination as a computational effect. To provide a framework that better internalizes a wide range of computational effects, this paper puts forward the notion of Monadic Combinatory Algebras (MCAs). MCAs generalize the notion of P…

Transaction Categorization with Relational Deep Learning in QuickBooks
Kaiwen Dong, Padmaja Jonnalagedda, Xiang Gao, Ayan Acharya, Maria Kissa, Mauricio Flores, Nitesh V. Chawla, Kamalika Das
https://arxiv.org/abs/2506.09234

Transaction Categorization with Relational Deep Learning in QuickBooks
Automatic transaction categorization is crucial for enhancing the customer experience in QuickBooks by providing accurate accounting and bookkeeping. The distinct challenges in this domain stem from the unique formatting of transaction descriptions, the wide variety of transaction categories, and the vast scale of the data involved. Furthermore, organizing transaction data in a relational database creates difficulties in developing a unified model that covers the entire database. In this work, …

Parametrizing the Hubble function instead of dark energy: Many possibilities
Sivasish Paul, Raj Kumar Das, Supriya Pan
https://arxiv.org/abs/2508.08072 https://

Parametrizing the Hubble function instead of dark energy: Many possibilities
In the present article, we propose a very simple parametrization of the Hubble function without parametrizing the dark components of the Universe. One of the novelties of the parametrization is that it may include a wide variety of the cosmological models, such as dark energy (both noninteracting and interacting fluids), modified gravity, cosmological matter creation and other known scenarios. The model is constrained with the latest astronomical probes from Hubble parameter measurements, three…

Signal Model and Energy Reconstruction for the Radio Detection of Inclined Air Showers in the 50-200 MHz Frequency Band
Lukas G\"ulzow, Ralph Engel, Tim Huege, Markus Roth, Felix Schl\"uter
https://arxiv.org/abs/2507.06698

Signal Model and Energy Reconstruction for the Radio Detection of Inclined Air Showers in the 50-200 MHz Frequency Band
The radio emission of cosmic-ray air-showers changes significantly depending on parameters like signal frequency, magnetic field configuration and observing altitude. We use CoREAS simulations to adapt an existing signal model for the radio emission of inclined showers in the 30-80 MHz frequency band to the wide 50-200 MHz band. Our model uses a parametrisation of the charge excess fraction to isolate the geomagnetic emission component. We reconstruct the geomagnetic radiation energy by fitting…

Designing de novo TIM Barrels: Insights into Stabilization, Diversification, and Functionalization Strategies
Julian Beck, Sergio Romero-Romero
https://arxiv.org/abs/2508.05699 …

Designing de novo TIM Barrels: Insights into Stabilization, Diversification, and Functionalization Strategies
The TIM-barrel fold is one of the most versatile and ubiquitous protein folds in nature, hosting a wide variety of catalytic activities and functions while serving as a model system in protein biochemistry and engineering. This review explores its role as a key fold model in protein design, particularly in addressing challenges in stabilization and functionalization. We discuss historical and recent advances in de novo TIM barrel design from the landmark creation of sTIM11 to the development of…

Enhanced superconductivity via layer differentiation in trilayer Hubbard model
Xun Liu, Mi Jiang
https://arxiv.org/abs/2507.06614 https://

Enhanced superconductivity via layer differentiation in trilayer Hubbard model
Motivated by the highest superconducting transition temperature ($T_c$) in multilayer cuprates,we investigated the trilayer Hubbard model by adopting the large-scale dynamical cluster quantum Monte Carlo simulations. Focusing on the systems with higher hole dopings within the two outer layers (OL) than the inner layer (IL), which is believed to be relevant to the realistic multilayer cuprates, our exploration discovered that the IL and OL manifest strong differentiation in a wide range of hole …

Improving BERT for Symbolic Music Understanding Using Token Denoising and Pianoroll Prediction
Jun-You Wang, Li Su
https://arxiv.org/abs/2507.04776 https:/…

Improving BERT for Symbolic Music Understanding Using Token Denoising and Pianoroll Prediction
We propose a pre-trained BERT-like model for symbolic music understanding that achieves competitive performance across a wide range of downstream tasks. To achieve this target, we design two novel pre-training objectives, namely token correction and pianoroll prediction. First, we sample a portion of note tokens and corrupt them with a limited amount of noise, and then train the model to denoise the corrupted tokens; second, we also train the model to predict bar-level and local pianoroll-deriv…

The Landscape of Memorization in LLMs: Mechanisms, Measurement, and Mitigation
Alexander Xiong, Xuandong Zhao, Aneesh Pappu, Dawn Song
https://arxiv.org/abs/2507.05578

The Landscape of Memorization in LLMs: Mechanisms, Measurement, and Mitigation
Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, yet they also exhibit memorization of their training data. This phenomenon raises critical questions about model behavior, privacy risks, and the boundary between learning and memorization. Addressing these concerns, this paper synthesizes recent studies and investigates the landscape of memorization, the factors influencing it, and methods for its detection and mitigation. We explore key driver…

Fantasy Football WR draft strategy: Ja'Marr Chase, CeeDee Lamb, or Justin Jefferson No. 1?

https://www.cbssports.com/fantasy/football

CLASS_SZ II: Notes and Examples of Fast and Accurate Calculations of Halo Model, Large Scale Structure and Cosmic Microwave Background Observables
Boris Bolliet, Aleksandra Kusiak, Fiona McCarthy, Alina Sabyr, Kristen Surrao, Jens Chluba, Carmen Embil Villagra, Simone Ferraro, Boryana Hadzhiyska, Dongwon Han, J. Colin Hill, Juan Francisco Mac\'ias-P\'erez, Mathew Madhavacheril, Abhishek Maniyar, Yogesh Mehta, Shivam Pandey, Emmanuel Schaan, Blake Sherwin, Alessio Spurio Mancini…

CLASS_SZ II: Notes and Examples of Fast and Accurate Calculations of Halo Model, Large Scale Structure and Cosmic Microwave Background Observables
These notes are very much work-in-progress and simply intended to showcase, in various degrees of details (and rigour), some of the cosmology calculations that class_sz can do. We describe the class_sz code in C, Python and Jax. Based on the Boltzmann code class, it can compute a wide range of observables relevant to current and forthcoming CMB and Large Scale Structure surveys. This includes galaxy shear and clustering, CMB lensing, thermal and kinetic Sunyaev and Zeldovich observables, Cosmic…

I bought #HamRadio. However, the insert style that came with them is only for RG58 and RG59. So I pulled out

Deep Learning and Model Independence
Martin King
https://arxiv.org/abs/2507.03438 https://arxiv.org/pdf/2507.03438

Deep Learning and Model Independence
The lack of evidence in favor of any new physics models means that the search for new physics beyond the Standard Model (BSM) is wide open, with no direction clearly more promising than any other. This marks a turn towards what can be called `model-independent' methods-strategies that reduce the influence of modelling assumptions by performing minimally-biased precision measurements, using effective field theories, or using Deep Learning methods (DL). In this paper, I present the novel and prom…

Does HESS J1731-347 have a thick crust ?
Sebastian Kubis, W{\l}odzimierz W\'ojcik
https://arxiv.org/abs/2507.06837 https://arxiv.…

Does HESS J1731-347 have a thick crust ?
A relativistic mean-field model with a crossing term of isovector-scalar and isoscalar mesons, the Cracow crossing terms (CCT) model, has previously been shown to be capable of explaining the light and compact object HESS J1731-347. Here, the model is supplemented with a correct treatment of the phase transition. Applying the thermodynamically consistent Gibbs conditions shows that a mixed-phase system occurs in the core of the neutron star over a wide range of densities, extending up to the ne…

"Hi AirStar, Guide Me to the Badminton Court."
Ziqin Wang, Jinyu Chen, Xiangyi Zheng, Qinan Liao, Linjiang Huang, Si Liu
https://arxiv.org/abs/2507.04430

"Hi AirStar, Guide Me to the Badminton Court."
Unmanned Aerial Vehicles, operating in environments with relatively few obstacles, offer high maneuverability and full three-dimensional mobility. This allows them to rapidly approach objects and perform a wide range of tasks often challenging for ground robots, making them ideal for exploration, inspection, aerial imaging, and everyday assistance. In this paper, we introduce AirStar, a UAV-centric embodied platform that turns a UAV into an intelligent aerial assistant: a large language model a…

IGSM: Improved Geometric and Sensitivity Matching for Finetuning Pruned Diffusion Models
Caleb Zheng, Eli Shlizerman
https://arxiv.org/abs/2506.05398 https…

IGSM: Improved Geometric and Sensitivity Matching for Finetuning Pruned Diffusion Models
Diffusion models achieve realistic outcomes across a wide range of generative tasks, but their high computational cost remains a major barrier to deployment. Model pruning has emerged as a promising strategy to reduce inference cost and enable lightweight diffusion models. While effective, pruned diffusion models are proned to quality reduction due to limited capacity. A key limitation of current pruning approaches is that pruned models are finetuned using the same objective as the dense model,…

CTA: Cross-Task Alignment for Better Test Time Training
Samuel Barbeau, Pedram Fekri, David Osowiechi, Ali Bahri, Moslem YazdanpanahMasih Aminbeidokhti, Christian Desrosiers
https://arxiv.org/abs/2507.05221

CTA: Cross-Task Alignment for Better Test Time Training
Deep learning models have demonstrated exceptional performance across a wide range of computer vision tasks. However, their performance often degrades significantly when faced with distribution shifts, such as domain or dataset changes. Test-Time Training (TTT) has emerged as an effective method to enhance model robustness by incorporating an auxiliary unsupervised task during training and leveraging it for model updates at test time. In this work, we introduce CTA (Cross-Task Alignment), a nov…

Vision Language Model-based Testing of Industrial Autonomous Mobile Robots
Jiahui Wu, Chengjie Lu, Aitor Arrieta, Shaukat Ali, Thomas Peyrucain
https://arxiv.org/abs/2508.02338 …

Vision Language Model-based Testing of Industrial Autonomous Mobile Robots
Autonomous Mobile Robots (AMRs) are deployed in diverse environments (e.g., warehouses, retail spaces, and offices), where they work alongside humans. Given that human behavior can be unpredictable and that AMRs may not have been trained to handle all possible unknown and uncertain behaviors, it is important to test AMRs under a wide range of human interactions to ensure their safe behavior. Moreover, testing in real environments with actual AMRs and humans is often costly, impractical, and pot…

Monte Carlo studies of the emergent spacetime in the polarized IKKT model
Chien-Yu Chou, Jun Nishimura, Cheng-Tsung Wang
https://arxiv.org/abs/2507.18472 https://

Monte Carlo studies of the emergent spacetime in the polarized IKKT model
The IKKT matrix model has been investigated as a promising nonperturbative formulation of superstring theory. One of the recent developments concerning this model is the discovery of the dual supergravity solution corresponding to the model obtained after supersymmetry-preserving mass deformation, which is dubbed the polarized IKKT model. Here we perform Monte Carlo simulations of this model in the case of matrix size N = 2 for a wide range of the deformation parameter Omega. While we reproduce…

Realizing Scaling Laws in Recommender Systems: A Foundation-Expert Paradigm for Hyperscale Model Deployment
Dai Li, Kevin Course, Wei Li, Hongwei Li, Jie Hua, Yiqi Chen, Zhao Zhu, Rui Jian, Xuan Cao, Bi Xue, Yu Shi, Jing Qian, Kai Ren, Matt Ma, Qunshu Zhang, Rui Li
https://arxiv.org/abs/2508.02929 …

Realizing Scaling Laws in Recommender Systems: A Foundation-Expert Paradigm for Hyperscale Model Deployment
While scaling laws promise significant performance gains for recommender systems, efficiently deploying hyperscale models remains a major unsolved challenge. In contrast to fields where FMs are already widely adopted such as natural language processing and computer vision, progress in recommender systems is hindered by unique challenges including the need to learn from online streaming data under shifting data distributions, the need to adapt to different recommendation surfaces with a wide div…

A Hot Jupiter with a Retrograde Orbit around a Sun-like Star and a Toy Model of Hot Jupiters in Wide Binary Star Systems
Steven Giacalone, Andrew W. Howard, Ryan A. Rubenzahl, Fei Dai, Luke B. Handley, Howard Isaacson, Samuel Halverson, Max Brodheim, Matt Brown, Theron W. Carmichael, William Deich, Benjamin J. Fulton, Steven R. Gibson, Grant M. Hill, Bradford Holden, Aaron Householder, Russ R. Laher, Kyle Lanclos, Joel Payne, Erik A. Petigura, Arpita Roy, Christian Schwab, Martin M. Si…

A Hot Jupiter with a Retrograde Orbit around a Sun-like Star and a Toy Model of Hot Jupiters in Wide Binary Star Systems
We report an observation of a transit of the hot Jupiter (HJ) KELT-23A b with the Keck Planet Finder spectrograph and a measurement of the sky-projected obliquity ($λ$) of its Sun-like ($T_{\rm eff} \approx 5900$ K) host star. We measured a projected stellar obliquity of $λ\approx 180^\circ$, indicating that the orbit of the HJ is retrograde relative to the direction of the stellar spin. Due to the slow sky-projected rotational velocity of the host star ($v \sin{i_\star} \approx 0.5$ km s$^{-…

Model for the Thermodynamics of Iron at High Pressures Near Melting
Rann Shikler, Noya Dimanstein Firman, Yinon Ashkenazy
https://arxiv.org/abs/2506.03386 …

Model for the Thermodynamics of Iron at High Pressures Near Melting
The Fe pressure-temperature phase diagram and its melting line have a wide range of applications, including providing constraints for iron-core planetary models. We propose an equation of state (EOS) model based on the interstitial theory of simple condensed matter (ITCM), as suggested by A.V. Granato. When applied to Fe, this model enables the extrapolation of measured melting lines to the conditions of the Earth's inner core boundary (ICB). The ITCM describes the solid-liquid phase transition…

A near-exact linear mixed model for genome-wide association studies
Zhibin Pu, Shufei Ge, Shijia Wang
https://arxiv.org/abs/2508.05278 https://arxiv.org/pd…

A near-exact linear mixed model for genome-wide association studies
Linear mixed models (LMM) are widely adopted in genome-wide association studies (GWAS) to account for population stratification and cryptic relatedness. However, the parameter estimation of LMMs imposes substantial computational burdens due to large-scale operations on genetic similarity matrices (GSM). We introduced the near-exact linear mixed model (NExt-LMM), a novel LMM framework that overcomes critical computational bottlenecks in GWAS through the following key innovations. Firstly, we exp…

I'm definitely going to miss Mona's column customizability, as I was set up with notifications in one column of the left and home timeline twice as wide on the right. @… has Model-T style column support: any layout you want as long as the columns are all the same size and the home timeline is the leftmost.

TokenSmith: Streamlining Data Editing, Search, and Inspection for Large-Scale Language Model Training and Interpretability
Mohammad Aflah Khan, Ameya Godbole, Johnny Tian-Zheng Wei, Ryan Wang, James Flemings, Krishna Gummadi, Willie Neiswanger, Robin Jia
https://arxiv.org/abs/2507.19419

TokenSmith: Streamlining Data Editing, Search, and Inspection for Large-Scale Language Model Training and Interpretability
Understanding the relationship between training data and model behavior during pretraining is crucial, but existing workflows make this process cumbersome, fragmented, and often inaccessible to researchers. We present TokenSmith, an open-source library for interactive editing, inspection, and analysis of datasets used in Megatron-style pretraining frameworks such as GPT-NeoX, Megatron, and NVIDIA NeMo. TokenSmith supports a wide range of operations including searching, viewing, ingesting, expor…

ENMA: Tokenwise Autoregression for Generative Neural PDE Operators
Armand Kassa\"i Koupa\"i, Lise Le Boudec, Louis Serrano, Patrick Gallinari
https://arxiv.org/abs/2506.06158

ENMA: Tokenwise Autoregression for Generative Neural PDE Operators
Solving time-dependent parametric partial differential equations (PDEs) remains a fundamental challenge for neural solvers, particularly when generalizing across a wide range of physical parameters and dynamics. When data is uncertain or incomplete-as is often the case-a natural approach is to turn to generative models. We introduce ENMA, a generative neural operator designed to model spatio-temporal dynamics arising from physical phenomena. ENMA predicts future dynamics in a compressed latent …

This https://arxiv.org/abs/2405.02696 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

DiffuseTrace: A Transparent and Flexible Watermarking Scheme for Latent Diffusion Model
Latent Diffusion Models (LDMs) enable a wide range of applications but raise ethical concerns regarding illegal utilization. Adding watermarks to generative model outputs is a vital technique employed for copyright tracking and mitigating potential risks associated with Artificial Intelligence (AI)-generated contents. However, post-processed watermarking methods are unable to withstand generative watermark attacks and there exists a trade-off between image fidelity and watermark strength. There…

This https://arxiv.org/abs/2409.18040 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_nuc…

Temperature dependent ultracold neutron transmission in D$_2$ gas $-$ a test of the Young-Koppel model
The Young-Koppel model (YK) describes comprehensively the interaction of slow neutrons with diatomic gases such as H$_2$ and D$_2$. This paper reports on the first experimental results of ultracold neutron (UCN) scattering over a wide temperature range vindicating the YK model for gaseous D$_2$ and showing an important difference in the temperature dependence to a low-energy low-temperature approximation (LETA). LETA is confirmed, however, to be valid for monoatomic gases such as Ne. Calculated…

This https://arxiv.org/abs/2409.13825 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

A personalized time-resolved 3D mesh generative model for unveiling normal heart dynamics
Understanding the structure and motion of the heart is crucial for diagnosing and managing cardiovascular diseases, the leading cause of global death. There is wide variation in cardiac shape and motion patterns, influenced by demographic, anthropometric and disease factors. Unravelling normal patterns of shape and motion, and understanding how each individual deviates from the norm, would facilitate accurate diagnosis and personalised treatment strategies. To this end, we developed a condition…

A robust intermittency equation formulation for transition modeling in Spalart-Allmaras RANS simulations of airfoil flows across a wide range of Reynolds numbers
Valerio D'Alessandro, Matteo Falone, Luca Giammichele, Renato Ricci
https://arxiv.org/abs/2508.02547

A robust intermittency equation formulation for transition modeling in Spalart-Allmaras RANS simulations of airfoil flows across a wide range of Reynolds numbers
This paper introduces a new robust formulation for local correlation-based laminar-to-turbulent transition models. This mechanism is incorporated into Reynolds-Averaged Navier-Stokes (RANS) equations, coupled with the Spalart-Allmaras (SA) turbulence model, considering both $γ$ and $γ$-${\widetilde{\mathrm{Re}}_{θ,t}}$ transition frameworks. In this context, special attention is placed on numerical stabilization of the $γ$ transport equation, which is identified as the root cause of instabi…

Partial Floquet Transformation and Model Order Reduction of Linear Time-Periodic Systems
Sam Bender, Christopher Beattie
https://arxiv.org/abs/2508.00221 https://

Partial Floquet Transformation and Model Order Reduction of Linear Time-Periodic Systems
Time-periodic dynamical systems occur commonly both in nature and as engineered systems. Large-scale linear time-periodic dynamical systems, for example, may arise through linearization of a nonlinear system about a given periodic solution (possibly as a consequence of a baseline periodic forcing) with subsequent spatial discretization. The potential need to simulate responses to a wide variety of input profiles (viewed as perturbations off a baseline periodic forcing) creates a potent incentiv…

A Lightweight Deep Learning-based Model for Ranking Influential Nodes in Complex Networks
Mohammed A. Ramadhan, Abdulhakeem O. Mohammed
https://arxiv.org/abs/2507.19702 https://…

A Lightweight Deep Learning-based Model for Ranking Influential Nodes in Complex Networks
Identifying influential nodes in complex networks is a critical task with a wide range of applications across different domains. However, existing approaches often face trade-offs between accuracy and computational efficiency. To address these challenges, we propose 1D-CGS, a lightweight and effective hybrid model that integrates the speed of one-dimensional convolutional neural networks (1D-CNN) with the topological representation power of GraphSAGE for efficient node ranking. The model uses a…

Spatio-Temporal Information Freshness for Remote Source Monitoring in IoT Systems
Andrea Munari, Federico Chiariotti, Leonardo Badia, Petar Popovski
https://arxiv.org/abs/2506.04804

Spatio-Temporal Information Freshness for Remote Source Monitoring in IoT Systems
The widespread adoption of age of information (AoI) as a meaningful and analytically tractable information freshness metric has led to a wide body of work on the timing performance of Internet of things (IoT) systems. However, the spatial correlation inherent to environmental monitoring has been mostly neglected in the recent literature, due to the significant modeling complexity it introduces. In this work, we address this gap by presenting a model of spatio-temporal information freshness, con…

This https://arxiv.org/abs/2410.21527 has been replaced.
link: https://scholar.google.com/scholar?q=a

VISTA-SSM: Varying and Irregular Sampling Time-series Analysis via State Space Models
We introduce VISTA, a clustering approach for multivariate and irregularly sampled time series based on a parametric state space mixture model. VISTA is specifically designed for the unsupervised identification of groups in datasets originating from healthcare and psychology where such sampling issues are commonplace. Our approach adapts linear Gaussian state space models (LGSSMs) to provide a flexible parametric framework for fitting a wide range of time series dynamics. The clustering approac…

The Evolution of Pop III.1 Protostars Powered by Dark Matter Annihilation. I. Fiducial model and first results
Devesh Nandal, Konstantinos Topalakis, Jonathan C. Tan, Vasilisa Sergienko, Ana\"is Pauchett, Maya Petkova
https://arxiv.org/abs/2507.00870

The Evolution of Pop III.1 Protostars Powered by Dark Matter Annihilation. I. Fiducial model and first results
The existence of billion-solar-mass quasars at redshifts $z \gtrsim 7$ poses a formidable challenge to theories of black hole formation, requiring pathways for the rapid growth of massive seeds. Population III.1 stars, forming in pristine, dense dark matter (DM) minihalos, are compelling progenitors. This study presents a suite of stellar evolution models for accreting Pop III.1 protostars, calculated with the \textsc{GENEC} code. We systematically explore a wide parameter space, spanning ambie…

Asymptotic convexity of wide and shallow neural networks
Vivek Borkar, Parthe Pandit
https://arxiv.org/abs/2507.01044 https://arxiv.o…

Asymptotic convexity of wide and shallow neural networks
For a simple model of shallow and wide neural networks, we show that the epigraph of its input-output map as a function of the network parameters approximates epigraph of a. convex function in a precise sense. This leads to a plausible explanation of their observed good performance.

X-ray polarization of Z-type neutron star low-mass X-ray binaries -- I. Model-independent, time-resolved X-ray polarimetry
Andrea Gnarini, Francesco Ursini, Giorgio Matt, Stefano Bianchi, Fiamma Capitanio, Massimo Cocchi, Sergio Fabiani, Ruben Farinelli, Antonella Tarana
https://arxiv.org/abs/2507.00302

X-ray polarization of Z-type neutron star low-mass X-ray binaries -- I. Model-independent, time-resolved X-ray polarimetry
Z-sources are a particular class of neutron star low-mass X-ray binaries characterized by a wide Z-like track in their hard colorsoft color (or hardness-intensity) diagrams, with three branches: the horizontal (HB), the normal (NB), and the flaring branch (FB). Spectropolarimetric observations with the Imaging X-ray Polarimetry Explorer (IXPE) show that the polarization in these sources varies along the Z-track, reaching unexpectedly high values in the HB. In this work, we collected all the pol…

RoboBrain 2.0 Technical Report
BAAI RoboBrain Team, Mingyu Cao, Huajie Tan, Yuheng Ji, Minglan Lin, Zhiyu Li, Zhou Cao, Pengwei Wang, Enshen Zhou, Yi Han, Yingbo Tang, Xiangqi Xu, Wei Guo, Yaoxu Lyu, Yijie Xu, Jiayu Shi, Cheng Chi, Mengdi Zhao, Xiaoshuai Hao, Shanyu Rong, Zhengliang Cai, Bolun Zhang, Shuyi Zhang, Huaihai Lyu, Mengfei Du, Lingfeng Zhang, Xi Feng, Xiaodan Liu, Yance Jiao, Chenrui He, Mengsi Lyu, Zhuo Chen, Yulong Ao, Xue Sun, Zheqi He, Jingshu Zheng, Xi Yang, Donghai Shi…

RoboBrain 2.0 Technical Report
We introduce RoboBrain 2.0, our latest generation of embodied vision-language foundation models, designed to unify perception, reasoning, and planning for complex embodied tasks in physical environments. It comes in two variants: a lightweight 7B model and a full-scale 32B model, featuring a heterogeneous architecture with a vision encoder and a language model. Despite its compact size, RoboBrain 2.0 achieves strong performance across a wide spectrum of embodied reasoning tasks. On both spatial…

Quantum master equation for nanoelectromechanical systems beyond the wide-band limit
Sofia Sevitz, Federico Cerisola, Janet Anders
https://arxiv.org/abs/2506.20593

Quantum master equation for nanoelectromechanical systems beyond the wide-band limit
Coupling the vibrations of an oscillator to electronic transport is a key building block for nanoelectromechanical systems. They describe many nanoscale electrical components such as molecular junctions. Inspired by recent experimental developments, we derive a quantum master equation that describes nanoelectromechanical systems in a generally overlooked situation: when the electronic transport is slower than the natural frequency of the oscillator. Here, a semi-classical model is no longer val…

Microscopic analysis of the giant monopole resonance excitation energy
M. K. Gaidarov, M. V. Ivanov, Y. I. Katsarov, A. N. Antonov, I. C. Danchev
https://arxiv.org/abs/2506.08736 …

Microscopic analysis of the giant monopole resonance excitation energy
A systematic study of the isoscalar giant monopole resonance (ISGMR) in a wide range of nuclei from various isotopic chains is performed within the microscopic self-consistent Skyrme HF+BCS method and coherent density fluctuation model (CDFM). The calculations for the nuclear incompressibility are based on the Brueckner and Barcelona-Catania-Paris-Madrid (BCPM) energy density functionals for nuclear matter using the capability of the CDFM to make a transition to the corresponding incompressibil…

aLLoyM: A large language model for alloy phase diagram prediction
Yuna Oikawa, Guillaume Deffrennes, Taichi Abe, Ryo Tamura, Koji Tsuda
https://arxiv.org/abs/2507.22558 https://…

aLLoyM: A large language model for alloy phase diagram prediction
Large Language Models (LLMs) are general-purpose tools with wide-ranging applications, including in materials science. In this work, we introduce aLLoyM, a fine-tuned LLM specifically trained on alloy compositions, temperatures, and their corresponding phase information. To develop aLLoyM, we curated question-and-answer (Q&A) pairs for binary and ternary phase diagrams using the open-source Computational Phase Diagram Database (CPDDB) and assessments based on CALPHAD (CALculation of PHAse Diagr…

Vanilla-Converter: A Tool for Converting Camunda 7 BPMN Models into Camunda 8 Models
Dragana Sunaric, Charlotte Verbruggen, Dominik Bork
https://arxiv.org/abs/2508.04352 https:/…

Vanilla-Converter: A Tool for Converting Camunda 7 BPMN Models into Camunda 8 Models
As organizations prepare for the end-of-life of Camunda 7, manual migration remains complex due to fundamental differences between the two platforms. We present Vanilla-Converter, a command-line tool that facilitates the migration of BPMN models from Camunda 7 to Camunda 8. Vanilla-Converter automates the transformation process, supports a wide range of BPMN elements, and produces a transformed model and a detailed transformation log indicating automatic changes and remaining manual conversion …

This https://arxiv.org/abs/2505.05532 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Design and FPGA Implementation of WOMBAT: A Deep Neural Network Level-1 Trigger System for Jet Substructure Identification and Boosted $H\rightarrow b\bar{b}$ Tagging at the CMS Experiment
This thesis investigates the physics performance, trigger efficiency, and Field Programmable Gate Array (FPGA) implementation of machine learning (ML)-based algorithms for Lorentz-boosted $H\rightarrow b\bar{b}$ tagging within the CMS Level-1 Trigger (L1T) under Phase-1 conditions. The proposed algorithm, WOMBAT (Wide Object ML Boosted Algorithm Trigger), comprises a high-performance Master Model (W-MM) and a quantized, FPGA-synthesizable Apprentice Model (W-AM), benchmarked against the standar…

Performance Measurements in the AI-Centric Computing Continuum Systems
Praveen Kumar Donta, Qiyang Zhang, Schahram Dustdar
https://arxiv.org/abs/2506.22884

Performance Measurements in the AI-Centric Computing Continuum Systems
Over the Eight decades, computing paradigms have shifted from large, centralized systems to compact, distributed architectures, leading to the rise of the Distributed Computing Continuum (DCC). In this model, multiple layers such as cloud, edge, Internet of Things (IoT), and mobile platforms work together to support a wide range of applications. Recently, the emergence of Generative AI and large language models has further intensified the demand for computational resources across this continuum…

This https://arxiv.org/abs/2411.16699 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_nli…

Bistability and chaos in the discrete two-gene Andrecut-Kauffman model
We conduct numerical analysis of the 2-dimensional discrete-time gene expression model originally introduced by Andrecut and Kauffman (Phys. Lett. A 367: 281-287, 2007). In contrast to the previous studies, we analyze the dynamics with different reaction rates $α_1$ and $α_2$ for each of the two genes under consideration. We explore bifurcation diagrams for the model with $α_1$ varying in a wide range and $α_2$ fixed. We detect chaotic dynamics by means of the positive maximum Lyapunov expo…

Model Editing as a Double-Edged Sword: Steering Agent Ethical Behavior Toward Beneficence or Harm
Baixiang Huang, Zhen Tan, Haoran Wang, Zijie Liu, Dawei Li, Ali Payani, Huan Liu, Tianlong Chen, Kai Shu
https://arxiv.org/abs/2506.20606

Model Editing as a Double-Edged Sword: Steering Agent Ethical Behavior Toward Beneficence or Harm
Agents based on Large Language Models (LLMs) have demonstrated strong capabilities across a wide range of tasks. However, deploying LLM-based agents in high-stakes domains comes with significant safety and ethical risks. Unethical behavior by these agents can directly result in serious real-world consequences, including physical harm and financial loss. To efficiently steer the ethical behavior of agents, we frame agent behavior steering as a model editing task, which we term Behavior Editing. …

Ultrastable jammed sphere packings with a wide range of particle dispersities
Robert S. Hoy
https://arxiv.org/abs/2506.16521 https://…

Ultrastable jammed sphere packings with a wide range of particle dispersities
We show that for a standard continuously-polydisperse model with particle-diameter distribution $P(σ) \propto σ^{-3}$ and polydispersity index $Δ$, employing a combination of standard SWAP moves and transient degree of freedom (TDOF) moves during a Lubachevsky-Stillinger-like particle-growth process dramatically increases the generated packings' jamming densities $ϕ_{\rm J}(Δ)$ and coordination numbers $Z_{\rm J}(Δ)$, for a wide range of $Δ$. We find that the fractional increase in $ϕ_{…

Large Bayesian VARs for Binary and Censored Variables
Joshua C. C. Chan, Michael Pfarrhofer
https://arxiv.org/abs/2506.01422 https://…

Large Bayesian VARs for Binary and Censored Variables
We extend the standard VAR to jointly model the dynamics of binary, censored and continuous variables, and develop an efficient estimation approach that scales well to high-dimensional settings. In an out-of-sample forecasting exercise, we show that the proposed VARs forecast recessions and short-term interest rates well. We demonstrate the utility of the proposed framework using a wide rage of empirical applications, including conditional forecasting and a structural analysis that examines the…

Method-Based Reasoning for Large Language Models: Extraction, Reuse, and Continuous Improvement
Hong Su
https://arxiv.org/abs/2508.04289 https://arxiv.org/…

Method-Based Reasoning for Large Language Models: Extraction, Reuse, and Continuous Improvement
Large language models (LLMs) have shown impressive capabilities across a wide range of language tasks. However, their reasoning process is primarily guided by statistical patterns in training data, which limits their ability to handle novel problems and perform consistent logical reasoning. In this paper, we propose a method-based model that enhances LLMs with explicit, reusable procedures extracted from training content, generated responses, and user interactions. Each method is represented as…

This https://arxiv.org/abs/2502.05206 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Safety at Scale: A Comprehensive Survey of Large Model Safety
The rapid advancement of large models, driven by their exceptional abilities in learning and generalization through large-scale pre-training, has reshaped the landscape of Artificial Intelligence (AI). These models are now foundational to a wide range of applications, including conversational AI, recommendation systems, autonomous driving, content generation, medical diagnostics, and scientific discovery. However, their widespread deployment also exposes them to significant safety risks, raisin…

MRI-CORE: A Foundation Model for Magnetic Resonance Imaging
Haoyu Dong, Yuwen Chen, Hanxue Gu, Nicholas Konz, Yaqian Chen, Qihang Li, Maciej A. Mazurowski
https://arxiv.org/abs/2506.12186

MRI-CORE: A Foundation Model for Magnetic Resonance Imaging
The widespread use of Magnetic Resonance Imaging (MRI) and the rise of deep learning have enabled the development of powerful predictive models for a wide range of diagnostic tasks in MRI, such as image classification or object segmentation. However, training models for specific new tasks often requires large amounts of labeled data, which is difficult to obtain due to high annotation costs and data privacy concerns. To circumvent this issue, we introduce MRI-CORE (MRI COmprehensive Representat…

Efficient LLM Inference: Bandwidth, Compute, Synchronization, and Capacity are all you need
Michael Davies, Neal Crago, Karthikeyan Sankaralingam, Christos Kozyrakis
https://arxiv.org/abs/2507.14397

Efficient LLM Inference: Bandwidth, Compute, Synchronization, and Capacity are all you need
This paper presents a limit study of transformer-based large language model (LLM) inference, focusing on the fundamental performance bottlenecks imposed by memory bandwidth, memory capacity, and synchronization overhead in distributed inference systems. We develop a hardware-agnostic performance model that abstracts away implementation details, enabling the analysis of a wide range of current and near-future hardware technologies. Our analysis spans from current HBM3 memory technology used in A…

Testing Homogeneity in a heteroscedastic contaminated normal mixture
Xiaoqing Niu, Pengfei Li, Yuejiao Fu
https://arxiv.org/abs/2507.15630 https://

Testing Homogeneity in a heteroscedastic contaminated normal mixture
Large-scale simultaneous hypothesis testing appears in many areas such as microarray studies, genome-wide association studies, brain imaging, disease mapping and astronomical surveys. A well-known inference method is to control the false discovery rate. One popular approach is to model the $z$-scores derived from the individual $t$-tests and then use this model to control the false discovery rate. We propose a new class of contaminated normal mixtures for modelling $z$-scores. We further design…

Analytical Modeling of Batteryless IoT Sensors Powered by Ambient Energy Harvesting
Jimmy Fernandez Landivar, Andrea Zanella, Ihsane Gryech, Sofie Pollin, Hazem Sallouha
https://arxiv.org/abs/2507.20952

Analytical Modeling of Batteryless IoT Sensors Powered by Ambient Energy Harvesting
This paper presents a comprehensive mathematical model to characterize the energy dynamics of batteryless IoT sensor nodes powered entirely by ambient energy harvesting. The model captures both the energy harvesting and consumption phases, explicitly incorporating power management tasks to enable precise estimation of device behavior across diverse environmental conditions. The proposed model is applicable to a wide range of IoT devices and supports intelligent power management units designed t…

Prospects for the Detection of High-Redshift Gamma-Ray Bursts in the Era of EP and SVOM
Jun-Jie Wei, Xue-Feng Wu
https://arxiv.org/abs/2506.04709 https://

Prospects for the Detection of High-Redshift Gamma-Ray Bursts in the Era of EP and SVOM
Gamma-ray bursts (GRBs) are a promising probe of the high-redshift Universe, but their detection remains observationally challenging. In this work, we explore the detectability of high-$z$ GRBs by the Wide-field X-ray Telescope (WXT) aboard the Einstein Probe (\emph{EP}) and the coded-mask gamma-ray imager (ECLAIRs) aboard the Space-based multi-band astronomical Variable Objects Monitor (\emph{SVOM}). Using a population synthesis model calibrated to $Swift$ GRB observations, we develop a tool t…

Computing phylogenetic invariants for time-reversible models: from TN93 to its submodels
Marta Casanellas, Jennifer Garbett, Roser Homs, Annachiara Korchmaros, Niharika Chakrabarty Paul
https://arxiv.org/abs/2505.20526

Computing phylogenetic invariants for time-reversible models: from TN93 to its submodels
Phylogenetic invariants are equations that vanish on algebraic varieties associated with Markov processes that model molecular substitutions on phylogenetic trees. For practical applications, it is essential to understand these equations across a wide range of substitution models. Recent work has shown that, for equivariant models, phylogenetic invariants can be derived from those of the general Markov model by restricting to the linear space defined by the model (namely, the space of mixtures …

Constraining the Stellar-to-Halo Mass Relation with Galaxy Clustering and Weak Lensing from DES Year 3 Data
G. Zacharegkas, C. Chang, J. Prat, W. Hartley, S. Mucesh, A. Alarcon, O. Alves, A. Amon, K. Bechtol, M. R. Becker, G. Bernstein, J. Blazek, A. Campos, A. Carnero Rosell, M. Carrasco Kind, R. Cawthon, R. Chen, A. Choi, J. Cordero, C. Davis, J. Derose, H. Diehl, S. Dodelson, C. Doux, A. Drlica-Wagner, K. Eckert, T. F. Eifler, J. Elvin-Poole, S. Everett, X. Fang, A. Ferte, M. Gatti,…

Constraining the Stellar-to-Halo Mass Relation with Galaxy Clustering and Weak Lensing from DES Year 3 Data
We develop a framework to study the relation between the stellar mass of a galaxy and the total mass of its host dark matter halo using galaxy clustering and galaxy-galaxy lensing measurements. We model a wide range of scales, roughly from $\sim 100 \; {\rm kpc}$ to $\sim 100 \; {\rm Mpc}$, using a theoretical framework based on the Halo Occupation Distribution and data from Year 3 of the Dark Energy Survey (DES) dataset. The new advances of this work include: 1) the generation and validation o…

This https://arxiv.org/abs/2505.14613 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Virtual Cells: Predict, Explain, Discover
Drug discovery is fundamentally a process of inferring the effects of treatments on patients, and would therefore benefit immensely from computational models that can reliably simulate patient responses, enabling researchers to generate and test large numbers of therapeutic hypotheses safely and economically before initiating costly clinical trials. Even a more specific model that predicts the functional response of cells to a wide range of perturbations would be tremendously valuable for disco…

New Physics Opportunities at Neutrino Facilities: BSM Physics at Accelerator, Atmospheric, and Reactor Neutrino Experiments
Koun Choi, Doojin Kim, Jong-Chul Park, Seodong Shin, Pouya Bakhti, Ki-Young Choi, Chang Hyon Ha, Kazumi Hata, Wooyoung Jang, Yu Seon Jeong, Young Ju Ko, Hyun Su Lee, Weijun Li, Yu-Feng Li, Mehedi Masud, Kenny C. Y. Ng, Jungsic Park, Min-Gwa Park, Komninos-John Plows, Meshkat Rajaee, Eunil Won, Byeongsu Yang, Seong Moon Yoo, Jaehoon Yu, Seokhoon Yun

New Physics Opportunities at Neutrino Facilities: BSM Physics at Accelerator, Atmospheric, and Reactor Neutrino Experiments
Since the discovery of the Higgs boson, the long-standing task at hand in particle physics is the search for new physics beyond the Standard Model, which accounts for only about 5\% of the Universe. In light of this situation, the neutrino sector has drawn significant attention due to neutrino oscillations, which require physics beyond the Standard Model and have prompted a wide array of active and planned experimental programs. Notably, neutrino facilities offer substantial potential to se…

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials
Dhananjeyan Jeyaraj, Hamidreza Eivazi, Jendrik-Alexander Tr\"oger, Stefan Wittek, Stefan Hartmann, Andreas Rausch
https://arxiv.org/abs/2506.16918

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials
The behavior of materials is influenced by a wide range of phenomena occurring across various time and length scales. To better understand the impact of microstructure on macroscopic response, multiscale modeling strategies are essential. Numerical methods, such as the $\text{FE}^2$ approach, account for micro-macro interactions to predict the global response in a concurrent manner. However, these methods are computationally intensive due to the repeated evaluations of the microscale. This chal…

Cross-Attention Message-Passing Transformers for Code-Agnostic Decoding in 6G Networks
Seong-Joon Park, Hee-Youl Kwak, Sang-Hyo Kim, Yongjune Kim, Jong-Seon No
https://arxiv.org/abs/2507.01038

Cross-Attention Message-Passing Transformers for Code-Agnostic Decoding in 6G Networks
Channel coding for 6G networks is expected to support a wide range of requirements arising from heterogeneous communication scenarios. These demands challenge traditional code-specific decoders, which lack the flexibility and scalability required for next-generation systems. To tackle this problem, we propose an AI-native foundation model for unified and code-agnostic decoding based on the transformer architecture. We first introduce a cross-attention message-passing transformer (CrossMPT). Cro…

UniWorld: High-Resolution Semantic Encoders for Unified Visual Understanding and Generation
Bin Lin, Zongjian Li, Xinhua Cheng, Yuwei Niu, Yang Ye, Xianyi He, Shenghai Yuan, Wangbo Yu, Shaodong Wang, Yunyang Ge, Yatian Pang, Li Yuan
https://arxiv.org/abs/2506.03147

UniWorld: High-Resolution Semantic Encoders for Unified Visual Understanding and Generation
Although existing unified models deliver strong performance on vision-language understanding and text-to-image generation, their models are limited in exploring image perception and manipulation tasks, which are urgently desired by users for wide applications. Recently, OpenAI released their powerful GPT-4o-Image model for comprehensive image perception and manipulation, achieving expressive capability and attracting community interests. By observing the performance of GPT-4o-Image in our caref…

Electron-Transfer and Exchange-Interaction Model of the Ligand Hyperfine Structure of Alkylated Iron-Sulfur Clusters
William C. Robinson, Victoria Pascutti, David A. Hall, Martin A. Mosquera
https://arxiv.org/abs/2507.15838

Electron-Transfer and Exchange-Interaction Model of the Ligand Hyperfine Structure of Alkylated Iron-Sulfur Clusters
Iron-sulfur clusters (ISCs) play a wide variety of roles in biochemical processes that are common to all domains of life on earth. The hyperfine structure of reactive ligands of these clusters can be studied experimentally and theoretically by means of hyperfine spectroscopy, which can reveal catalytic intermediates in these biochemical processes. Their theoretical prediction, however, requires either advanced methods that describe strongly correlated systems, or Hamiltonian modeling based on s…

Placing the Near-Earth Object Impact Probability in Context
C. R. Nugent, K. P. Andersen, James M. Bauer, C. T. Jensen, L. K. Kristiansen, C. P. Hansen, M. M. Nielsen, C. F. Vesterg{\aa}rd
https://arxiv.org/abs/2508.02418

Placing the Near-Earth Object Impact Probability in Context
Near-Earth objects (NEOs) have the potential to cause extensive damage and loss of life on Earth. Advancements in NEO discovery, trajectory prediction, and deflection technology indicate that an impact could be prevented, with sufficient warning time. We derive an impact frequency of NEOs 140m and larger, using the NEOMOD2 NEO population model and JPL Horizons. We then place that frequency in context with other preventable causes of death; allowing for comparison between a planet-wide event and…

AudioGen-Omni: A Unified Multimodal Diffusion Transformer for Video-Synchronized Audio, Speech, and Song Generation
Le Wang, Jun Wang, Feng Deng, Chen Zhang, Kun Gai, Di Zhang
https://arxiv.org/abs/2508.00733

AudioGen-Omni: A Unified Multimodal Diffusion Transformer for Video-Synchronized Audio, Speech, and Song Generation
We present AudioGen-Omni - a unified approach based on multimodal diffusion transformers (MMDit), capable of generating high-fidelity audio, speech, and songs coherently synchronized with the input video. AudioGen-Omni introduces a novel joint training paradigm that seamlessly integrates large-scale video-text-audio corpora, enabling a model capable of generating semantically rich, acoustically diverse audio conditioned on multimodal inputs and adaptable to a wide range of audio generation task…

Gas of thin tubes of massless scalar field as a possible model of unified dark fluid
Alexander Lelyakov, Stepan Lelyakov
https://arxiv.org/abs/2507.08054 h…

Gas of thin tubes of massless scalar field as a possible model of unified dark fluid
In this work, we investigated the influence of constants that characterize the motion of thin tubes of a massless scalar field (TToMSF) in a gas on the global properties of such a gas, in particular, on its ability to accelerated expansion. The peculiarity of the TToMSF gas is related to the fact that the parameter of its equation of state $w$ depends on the degree of rarefaction of the gas and can take values in a very wide range of values $-1< w <1$. In this case, fluctuations in the density …

StacKAT: Infinite State Network Verification
Jules Jacobs, Nate Foster, Tobias Kapp\'e, Dexter Kozen, Lily Saada, Alexandra Silva, Jana Wagemaker
https://arxiv.org/abs/2506.13383

StacKAT: Infinite State Network Verification
We develop StacKAT, a network verification language featuring loops, finite state variables, nondeterminism, and - most importantly - access to a stack with accompanying push and pop operations. By viewing the variables and stack as the (parsed) headers and (to-be-parsed) contents of a network packet, StacKAT can express a wide range of network behaviors including parsing, source routing, and telemetry. These behaviors are difficult or impossible to model using existing languages like NetKAT. W…

A Coordination-Based Model for the Prediction of Surface Energies and the Shape of Metal Particles
Shyama Charan Mandal, Frank Abild-Pedersen
https://arxiv.org/abs/2506.15144

A Coordination-Based Model for the Prediction of Surface Energies and the Shape of Metal Particles
Surface energies of metal-based systems are important for determining the Wulff-constructed shapes of metal nanoparticles and understanding the stability. We have developed a coordination number-based model to predict the total energy of metal-based systems across a wide range of configurations. Our model has been tested against Density Functional Theory (DFT) calculations for late transition metals. This method enables on-the-fly surface energy predictions and allows for the Wulff construction…

WebChoreArena: Evaluating Web Browsing Agents on Realistic Tedious Web Tasks
Atsuyuki Miyai, Zaiying Zhao, Kazuki Egashira, Atsuki Sato, Tatsumi Sunada, Shota Onohara, Hiromasa Yamanishi, Mashiro Toyooka, Kunato Nishina, Ryoma Maeda, Kiyoharu Aizawa, Toshihiko Yamasaki
https://arxiv.org/abs/2506.01952

WebChoreArena: Evaluating Web Browsing Agents on Realistic Tedious Web Tasks
Powered by a large language model (LLM), a web browsing agent operates web browsers in a human-like manner and offers a highly transparent path toward automating a wide range of everyday tasks. As web agents become increasingly capable and demonstrate proficiency in general browsing tasks, a critical question emerges: Can they go beyond general browsing to robustly handle tasks that are tedious and complex, or chores that humans often avoid doing themselves? In this paper, we introduce WebChore…

A modal approach towards substitutions
Yaxin Tu, Sujata Ghosh, Fenrong Liu, Dazhu Li
https://arxiv.org/abs/2507.12320 https://arxiv.org/pdf/2507.12320

A modal approach towards substitutions
Substitutions play a crucial role in a wide range of contexts, from analyzing the dynamics of social opinions and conducting mathematical computations to engaging in game-theoretical analysis. For many situations, considering one-step substitutions is often adequate. Yet, for more complex cases, iterative substitutions become indispensable. In this article, our primary focus is to study logical frameworks that model both single-step and iterative substitutions. We explore a number of properties…

Blockchain Powered Edge Intelligence for U-Healthcare in Privacy Critical and Time Sensitive Environment
Anum Nawaz, Hafiz Humza Mahmood Ramzan, Xianjia Yu, Zhuo Zou, Tomi Westerlund
https://arxiv.org/abs/2506.02038

Blockchain Powered Edge Intelligence for U-Healthcare in Privacy Critical and Time Sensitive Environment
Edge Intelligence (EI) serves as a critical enabler for privacy-preserving systems by providing AI-empowered computation and distributed caching services at the edge, thereby minimizing latency and enhancing data privacy. The integration of blockchain technology further augments EI frameworks by ensuring transactional transparency, auditability, and system-wide reliability through a decentralized network model. However, the operational architecture of such systems introduces inherent vulnerabil…

An approach to measuring the performance of Automatic Speech Recognition (ASR) models in the context of Large Language Model (LLM) powered applications
Sujith Pulikodan, Sahapthan K, Prasanta Kumar Ghosh, Visruth Sanka, Nihar Desai
https://arxiv.org/abs/2507.16456

An approach to measuring the performance of Automatic Speech Recognition (ASR) models in the context of Large Language Model (LLM) powered applications
Automatic Speech Recognition (ASR) plays a crucial role in human-machine interaction and serves as an interface for a wide range of applications. Traditionally, ASR performance has been evaluated using Word Error Rate (WER), a metric that quantifies the number of insertions, deletions, and substitutions in the generated transcriptions. However, with the increasing adoption of large and powerful Large Language Models (LLMs) as the core processing component in various applications, the significan…

Rules, agents and order
Amalia Puente, Diego Radillo-Ochoa, C. A. Terrero-Escalante
https://arxiv.org/abs/2505.23985 https://arxiv.or…

Rules, agents and order
Complex systems often exhibit highly structured network topologies that reflect functional constraints. In this work, we investigate how, under varying combinations of system-wide selection rules and special agents, different classes of random processes give rise to global order, with a focus restricted to finite-size networks. Using the large-$N$ Erdos-Renyi model as a null baseline, we contrast purely random link-adding processes with goal-directed dynamics, including variants of the chip-fir…

VQ-VLA: Improving Vision-Language-Action Models via Scaling Vector-Quantized Action Tokenizers
Yating Wang, Haoyi Zhu, Mingyu Liu, Jiange Yang, Hao-Shu Fang, Tong He
https://arxiv.org/abs/2507.01016

VQ-VLA: Improving Vision-Language-Action Models via Scaling Vector-Quantized Action Tokenizers
In this paper, we introduce an innovative vector quantization based action tokenizer built upon the largest-scale action trajectory dataset to date, leveraging over 100 times more data than previous approaches. This extensive dataset enables our tokenizer to capture rich spatiotemporal dynamics, resulting in a model that not only accelerates inference but also generates smoother and more coherent action outputs. Once trained, the tokenizer can be seamlessly adapted to a wide range of downstream…

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models
Yifan Hao, Chenlu Ye, Chi Han, Tong Zhang
https://arxiv.org/abs/2506.01919

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models
Transformer based models have shown remarkable capabilities in sequence learning across a wide range of tasks, often performing well on specific task by leveraging input-output examples. Despite their empirical success, a comprehensive theoretical understanding of this phenomenon remains limited. In this work, we investigate the layerwise behavior of Transformers to uncover the mechanisms underlying their multi-task generalization ability. Taking explorations on a typical sequence model, i.e, H…

Effects of strong turbulence for water waves
E. A. Kochurin, E. A. Kuznetsov
https://arxiv.org/abs/2507.22557 https://arxiv.org/pdf/2507.22557

Effects of strong turbulence for water waves
The results of direct numerical simulation of plane-symmetric turbulence of water waves for potential flows within the framework of conformal variables taking into account low-frequency pumping and high-frequency viscous dissipation are presented. In this model, for a wide range of pumping amplitudes, the weak turbulence regime was not detected. It is shown that for typical turbulence parameters, the main effects are the processes of wave breaking, the formation of cusps on wave crests, which m…

Rydberg Atomic Receivers for Multi-Band Communications and Sensing
Mingyao Cui, Qunsong Zeng, Zhanwei Wang, Kaibin Huang
https://arxiv.org/abs/2505.24168 h…

Rydberg Atomic Receivers for Multi-Band Communications and Sensing
Harnessing multi-level electron transitions, Rydberg Atomic Receivers (RAREs) can detect wireless signals across a wide range of frequency bands, from Megahertz to Terahertz, enabling multi-band communications and sensing (C&S). Current research on multi-band RAREs primarily focuses on experimental demonstrations, lacking an interpretable model to mathematically characterize their mechanisms. This issue leaves the multi-band RARE as a black box, posing challenges in its practical C&S applicatio…

Sorcha: A Solar System Survey Simulator for the Legacy Survey of Space and Time
Stephanie R. Merritt, Grigori Fedorets, Megan E. Schwamb, Samuel Cornwall, Pedro H. Bernardinelli, Mario Juric, Matthew J. Holman, Jacob A. Kurlander, Siegfried Eggl, Drew Oldag, Maxine West, Jeremy Kubica, Joseph Murtagh, R. Lynne Jones, Peter Yoachim, Ryan R. Lyttle, Michael S. P. Kelley, Joachim Moeyens, Kathleen Kiker, Shantanu P. Naidu, Colin Snodgrass, Shannon M. Matthews, Colin Orion Chandler

Sorcha: A Solar System Survey Simulator for the Legacy Survey of Space and Time
The upcoming Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory is expected to revolutionize solar system astronomy. Unprecedented in scale, this ten-year wide-field survey will collect billions of observations and discover a predicted $\sim$5 million new solar system objects. Like all astronomical surveys, its results will be affected by a complex system of intertwined detection biases. Survey simulators have long been used to forward-model the effects of these biases on a…

Network Cross-Validation for Nested Models by Edge-Sampling: Selection Consistency
Bokai Yang
https://arxiv.org/abs/2506.14244 https://

Network Cross-Validation for Nested Models by Edge-Sampling: Selection Consistency
In the network literature, a wide range of statistical models have been proposed to exploit structural patterns in the data. Therefore, model selection between different models is a fundamental problem. Cross-validation is a powerful candidate to solve this problem, and Li et al. have already proposed an edge-sampling procedure to choose the number of communities in the block model framework. In this paper, we propose a penalized edge-sampling cross-validation framework for nested network model…

Test-Time Consistency in Vision Language Models
Shih-Han Chou, Shivam Chandhok, James J. Little, Leonid Sigal
https://arxiv.org/abs/2506.22395 https://

Test-Time Consistency in Vision Language Models
Vision-Language Models (VLMs) have achieved impressive performance across a wide range of multimodal tasks, yet they often exhibit inconsistent behavior when faced with semantically equivalent inputs, undermining their reliability and robustness. Recent benchmarks, such as MM-R3, highlight that even state-of-the-art VLMs can produce divergent predictions across semantically equivalent inputs, despite maintaining high average accuracy. Prior work addresses this issue by modifying model architect…

TCDiff : An End-to-end Trajectory-Controllable Diffusion Model for Harmonious Music-Driven Group Choreography
Yuqin Dai, Wanlu Zhu, Ronghui Li, Xiu Li, Zhenyu Zhang, Jun Li, Jian Yang
https://arxiv.org/abs/2506.18671

TCDiff++: An End-to-end Trajectory-Controllable Diffusion Model for Harmonious Music-Driven Group Choreography
Music-driven dance generation has garnered significant attention due to its wide range of industrial applications, particularly in the creation of group choreography. During the group dance generation process, however, most existing methods still face three primary issues: multi-dancer collisions, single-dancer foot sliding and abrupt swapping in the generation of long group dance. In this paper, we propose TCDiff++, a music-driven end-to-end framework designed to generate harmonious group danc…

Impact of Geant4's Electromagnetic Physics Constructors on Accuracy and Performance of Simulations for Rare Event Searches
H. Kluck (Institut f\"ur Hochenergiephysik der \"Osterreichischen Akademie der Wissenschaften, Wien, Austria, Atominstitut, Technische Universit\"at Wien, Wien, Austria), R. Breier (Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovakia), A. Fu{\ss} (Institut f\"ur Hochenergiephysik der \"Osterreichis…

Impact of Geant4's Electromagnetic Physics Constructors on Accuracy and Performance of Simulations for Rare Event Searches
A primary objective in contemporary low background physics is the search for rare and novel phenomena beyond the Standard Model of particle physics, e.g. the scattering off of a potential Dark Matter particle or the neutrinoless double beta decay. The success of such searches depends on a reliable background prediction via Monte Carlo simulations. A widely used toolkit to construct these simulations is Geant4, which offers the user a wide choice of how to model the physics of particle interacti…

mimic-one: a Scalable Model Recipe for General Purpose Robot Dexterity
Elvis Nava, Victoriano Montesinos, Erik Bauer, Benedek Forrai, Jonas Pai, Stefan Weirich, Stephan-Daniel Gravert, Philipp Wand, Stephan Polinski, Benjamin F. Grewe, Robert K. Katzschmann
https://arxiv.org/abs/2506.11916…

mimic-one: a Scalable Model Recipe for General Purpose Robot Dexterity
We present a diffusion-based model recipe for real-world control of a highly dexterous humanoid robotic hand, designed for sample-efficient learning and smooth fine-motor action inference. Our system features a newly designed 16-DoF tendon-driven hand, equipped with wide angle wrist cameras and mounted on a Franka Emika Panda arm. We develop a versatile teleoperation pipeline and data collection protocol using both glove-based and VR interfaces, enabling high-quality data collection across dive…

Ironman: Accelerating Oblivious Transfer Extension for Privacy-Preserving AI with Near-Memory Processing
Chenqi Lin, Kang Yang, Tianshi Xu, Ling Liang, Yufei Wang, Zhaohui Chen, Runsheng Wang, Mingyu Gao, Meng Li
https://arxiv.org/abs/2507.16391

Ironman: Accelerating Oblivious Transfer Extension for Privacy-Preserving AI with Near-Memory Processing
With the wide application of machine learning (ML), privacy concerns arise with user data as they may contain sensitive information. Privacy-preserving ML (PPML) based on cryptographic primitives has emerged as a promising solution in which an ML model is directly computed on the encrypted data to provide a formal privacy guarantee. However, PPML frameworks heavily rely on the oblivious transfer (OT) primitive to compute nonlinear functions. OT mainly involves the computation of single-point co…

Roles of SN and AGN Feedback in Shaping the Baryonic Content in a Wide Range of Dark Matter Halo Mass
Emanuele Contini, Chanjo Seo, Jinsu Rhee, Jeon Seyoung, Sukyoung K. Yi
https://arxiv.org/abs/2507.10673

Roles of SN and AGN Feedback in Shaping the Baryonic Content in a Wide Range of Dark Matter Halo Mass
We build upon FEGA25 (Contini et al 2025), a previously introduced semi-analytic model for galaxy formation and evolution, focusing on its enhanced treatment of supernova and active galactic nucleus feedback mechanisms. In addition to the traditional AGN feedback modes, negative (suppressing cooling), and the new positive mode (triggering star formation), we introduce two implementations of a third mode: the ejection of hot gas beyond the virial radius, AGNeject1 and AGNeject2. This component a…

Retention analysis of edited knowledge after fine-tuning
Fufang Wen, Shichang Zhang
https://arxiv.org/abs/2507.14198 https://arxiv.or…

Retention analysis of edited knowledge after fine-tuning
Large language models (LLMs) store vast amounts of knowledge, which often requires updates to correct factual errors, incorporate newly acquired information, or adapt model behavior. Model editing methods have emerged as efficient solutions for such updates, offering localized and precise knowledge modification at significantly lower computational cost than continual training. In parallel, LLMs are frequently fine-tuned for a wide range of downstream tasks. However, the effect of fine-tuning on…

This https://arxiv.org/abs/2505.00288 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Nyström Type Exponential Integrators for Strongly Magnetized Charged Particle Dynamics
Calculating the dynamics of charged particles in electromagnetic fields (i.e. the particle pushing problem) is one of the most computationally intensive components of particle-in-cell (PIC) methods for plasma physics simulations. This task is especially challenging when the plasma is strongly magnetized, since in this case the particle motion consists of a wide range of temporal scales from highly oscillatory fast gyromotion to slow macroscopic behavior and the resulting numerical model is very…

Material Loss Model Calibration for Tantalum Superconducting Resonators
Guy Moshel, Sergei Masis, Moshe Schechter, Shay Hacohen-Gourgy
https://arxiv.org/abs/2507.12141

Material Loss Model Calibration for Tantalum Superconducting Resonators
Material research is a key frontier in advancing superconducting qubit and circuit performance. In this work, we develop a simple and broadly applicable framework for accurately characterizing two-level system (TLS) loss using internal quality factor measurements of superconducting transmission line resonators over a range of temperatures and readout powers. We applied this method to a series of $α$-Ta resonators that span a wide frequency range, thus providing a methodology for probing the lo…

Heterogeneous Ensemble Enables a Universal Uncertainty Metric for Atomistic Foundation Models
Kai Liu, Zixiong Wei, Wei Gao, Poulumi Dey, Marcel H. F. Sluiter, Fei Shuang
https://arxiv.org/abs/2507.21297

Heterogeneous Ensemble Enables a Universal Uncertainty Metric for Atomistic Foundation Models
Universal machine learning interatomic potentials (uMLIPs) are reshaping atomistic simulation as foundation models, delivering near \textit{ab initio} accuracy at a fraction of the cost. Yet the lack of reliable, general uncertainty quantification limits their safe, wide-scale use. Here we introduce a unified, scalable uncertainty metric \(U\) based on a heterogeneous model ensemble with reuse of pretrained uMLIPs. Across chemically and structurally diverse datasets, \(U\) shows a strong correl…

Matter-Antimatter Asymmetry in a Rotating Universe I: Conceptual Foundations and the Dirac Field in Bianchi IX Geometry
Tatevik Vardanyan
https://arxiv.org/abs/2507.12164

Matter-Antimatter Asymmetry in a Rotating Universe I: Conceptual Foundations and the Dirac Field in Bianchi IX Geometry
The standard $Λ$CDM model, based on the highly symmetric FLRW geometry, has successfully explained a wide range of cosmological observations. However, it faces unresolved issues, including CMB anomalies, the nature of dark matter, and the matter-antimatter asymmetry. These challenges highlight the limitations of the standard framework and raise questions about the validity of the assumed isotropic background geometry. A natural direction is to explore more general cosmological models that rela…

BEV-LLM: Leveraging Multimodal BEV Maps for Scene Captioning in Autonomous Driving
Felix Brandstaetter, Erik Schuetz, Katharina Winter, Fabian Flohr
https://arxiv.org/abs/2507.19370

BEV-LLM: Leveraging Multimodal BEV Maps for Scene Captioning in Autonomous Driving
Autonomous driving technology has the potential to transform transportation, but its wide adoption depends on the development of interpretable and transparent decision-making systems. Scene captioning, which generates natural language descriptions of the driving environment, plays a crucial role in enhancing transparency, safety, and human-AI interaction. We introduce BEV-LLM, a lightweight model for 3D captioning of autonomous driving scenes. BEV-LLM leverages BEVFusion to combine 3D LiDAR poi…

Energy-consistent dynamic fracture phase field models: unilateral constraints and finite element simulations
Md Mamun Miah, Ryuhei Wakida, Masato Kimura
https://arxiv.org/abs/2506.14788

Energy-consistent dynamic fracture phase field models: unilateral constraints and finite element simulations
Phase field models have emerged as a powerful and flexible framework for simulating complex interface-driven phenomena across a wide range of scientific and engineering applications. In fracture mechanics, the phase field approach--formulated as a gradient flow of the Griffith fracture energy with Ambrosio-Tortorelli regularization--has gained significant attention for its ability to capture complex crack topologies. In this study, we propose a dynamic fracture phase field model (DF-PFM) based …

A continuity equation compatible basis for Effective Field Theory of Dark Energy
Federico Armato, Edoardo Martinelli, Marco Raveri
https://arxiv.org/abs/2507.18526 https://

A continuity equation compatible basis for Effective Field Theory of Dark Energy
The Effective Field Theory (EFT) of Dark Energy (DE) is a model independent framework that allows for the description of a wide class of dark energy and modified gravity models. This is achieved by extending the Hilbert Einstein action through the introduction of time dependent functions. However, the choice of these functions and thus the operator basis is not unique. In this paper, we propose a physically motivated constraint based on the continuity equation, leading to a continuity equation …

Theoretical Analysis of Near-Field MIMO Channel Capacity and Mid-Band Experimental Validation
Haiyang Miao, Jianhua Zhang, Pan Tang, Heng Wang, Lei Tian, Guangyi Liu
https://arxiv.org/abs/2506.15972

Theoretical Analysis of Near-Field MIMO Channel Capacity and Mid-Band Experimental Validation
With the increase of multiple-input-multiple-output (MIMO) array size and carrier frequency, near-field MIMO communications will become crucial in 6G wireless networks. Due to the increase of MIMO near-field range, the research of near-field MIMO capacity has aroused wide interest. In this paper, we focus on the theoretical analysis and empirical study of near-field MIMO capacity. First, the near-field channel model is characterized from the electromagnetic information perspective. Second, with…

Dis-GEN: Disordered crystal structure generation
Martin Hoffmann Petersen, Ruiming Zhu, Haiwen Dai, Savyasanchi Aggarwal, Nong Wei, Andy Paul Chen, Arghya Bhowmik, Juan Maria Garcia Lastra, Kedar Hippalgaonkar
https://arxiv.org/abs/2507.18275

Dis-GEN: Disordered crystal structure generation
A wide range of synthesized crystalline inorganic materials exhibit compositional disorder, where multiple atomic species partially occupy the same crystallographic site. As a result, the physical and chemical properties of such materials are dependent on how the atomic species are distributed among the corresponding symmetrical sites, making them exceptionally challenging to model using computational methods. For this reason, existing generative models cannot handle the complexities of disorde…

Gender Inclusivity Fairness Index (GIFI): A Multilevel Framework for Evaluating Gender Diversity in Large Language Models
Zhengyang Shan, Emily Ruth Diana, Jiawei Zhou
https://arxiv.org/abs/2506.15568

Gender Inclusivity Fairness Index (GIFI): A Multilevel Framework for Evaluating Gender Diversity in Large Language Models
We present a comprehensive evaluation of gender fairness in large language models (LLMs), focusing on their ability to handle both binary and non-binary genders. While previous studies primarily focus on binary gender distinctions, we introduce the Gender Inclusivity Fairness Index (GIFI), a novel and comprehensive metric that quantifies the diverse gender inclusivity of LLMs. GIFI consists of a wide range of evaluations at different levels, from simply probing the model with respect to provide…

Improving Speech Enhancement with Multi-Metric Supervision from Learned Quality Assessment
Wei Wang, Wangyou Zhang, Chenda Li, Jiatong Shi, Shinji Watanabe, Yanmin Qian
https://arxiv.org/abs/2506.12260

Improving Speech Enhancement with Multi-Metric Supervision from Learned Quality Assessment
Speech quality assessment (SQA) aims to predict the perceived quality of speech signals under a wide range of distortions. It is inherently connected to speech enhancement (SE), which seeks to improve speech quality by removing unwanted signal components. While SQA models are widely used to evaluate SE performance, their potential to guide SE training remains underexplored. In this work, we investigate a training framework that leverages a SQA model, trained to predict multiple evaluation metri…