Tootfinder

Assessing Visually-Continuous Corruption Robustness of Neural Networks Relative to Human Performance
Huakun Shen, Boyue Caroline Hu, Krzysztof Czarnecki, Lina Marsso, Marsha Chechik
https://arxiv.org/abs/2402.19401

Assessing Visually-Continuous Corruption Robustness of Neural Networks Relative to Human Performance
While Neural Networks (NNs) have surpassed human accuracy in image classification on ImageNet, they often lack robustness against image corruption, i.e., corruption robustness. Yet such robustness is seemingly effortless for human perception. In this paper, we propose visually-continuous corruption robustness (VCR) -- an extension of corruption robustness to allow assessing it over the wide and continuous range of changes that correspond to the human perceptive quality (i.e., from the original …

This https://arxiv.org/abs/2312.16729 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Behavioural pseudometrics for continuous-time diffusions
Bisimulation is a concept that captures behavioural equivalence of states in a variety of types of transition systems. It has been widely studied in a discrete-time setting where the notion of a step is fundamental. In our setting we are considering "flow"-processes emphasizing that they evolve in continuous time. In such continuous-time settings, the concepts are not straightforward adaptations of their discrete-time analogues and we restrict our study to diffusions that do not lose mass over …

Generating, Reconstructing, and Representing Discrete and Continuous Data: Generalized Diffusion with Learnable Encoding-Decoding
Guangyi Liu, Yu Wang, Zeyu Feng, Qiyu Wu, Liping Tang, Yuan Gao, Zhen Li, Shuguang Cui, Julian McAuley, Eric P. Xing, Zichao Yang, Zhiting Hu
https://arxiv.org/abs/2402.19009

Generating, Reconstructing, and Representing Discrete and Continuous Data: Generalized Diffusion with Learnable Encoding-Decoding
The vast applications of deep generative models are anchored in three core capabilities -- generating new instances, reconstructing inputs, and learning compact representations -- across various data types, such as discrete text/protein sequences and continuous images. Existing model families, like Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), autoregressive models, and diffusion models, generally excel in specific capabilities and data types but fall short in others.…

A General Framework for Random Effects Models for Binary, Ordinal, Count Type and Continuous Dependent Variables Including Variable Selection
Gerhard Tutz
https://arxiv.org/abs/2404.17792

A General Framework for Random Effects Models for Binary, Ordinal, Count Type and Continuous Dependent Variables Including Variable Selection
A general random effects model is proposed that allows for continuous as well as discrete distributions of the responses. Responses can be unrestricted continuous, bounded continuous, binary, ordered categorical or given in the form of counts. The distribution of the responses is not restricted to exponential families, which is a severe restriction in generalized mixed models. Generalized mixed models use fixed distributions for responses, for example the Poisson distribution in count data, whi…

QOSST: A Highly-Modular Open Source Platform for Experimental Continuous-Variable Quantum Key Distribution
Yoann Pi\'etri, Matteo Schiavon, Valentina Marulanda Acosta, Baptiste Gouraud, Luis Trigo Vidarte, Phillipe Grangier, Amine Rhouni, Eleni Diamanti
https://arxiv.org/abs/2404.18637…

QOSST: A Highly-Modular Open Source Platform for Experimental Continuous-Variable Quantum Key Distribution
Quantum Key Distribution (QKD) enables secret key exchange between two remote parties with information-theoretic security rooted in the laws of quantum physics. Encoding key information in continuous variables (CV), such as the values of quadrature components of coherent states of light, brings implementations much closer to standard optical communication systems, but this comes at the price of significant complexity in the digital signal processing techniques required for operation at low sign…

VulMCI : Code Splicing-based Pixel-row Oversampling for More Continuous Vulnerability Image Generation
Tao Peng, Ling Gui, Yi Sun
https://arxiv.org/abs/2402.18189

VulMCI : Code Splicing-based Pixel-row Oversampling for More Continuous Vulnerability Image Generation
In recent years, the rapid development of deep learning technology has brought new prospects to the field of vulnerability detection. Many vulnerability detection methods involve converting source code into images for detection, yet they often overlook the quality of the generated images. Due to the fact that vulnerability images lack clear and continuous contours, unlike images used in object detection, Convolutional Neural Networks (CNNs) tend to lose semantic information during the convoluti…

Mixed Strategy Constraints in Continuous Games
Mel Krusniak, Forrest Laine
https://arxiv.org/abs/2402.17874 https://arxiv.org/pdf/240…

Mixed Strategy Constraints in Continuous Games
Equilibrium problems representing interaction in physical environments typically require continuous strategies which satisfy opponent-dependent constraints, such as those modeling collision avoidance. However, as with finite games, mixed strategies are often desired, both from an equilibrium existence perspective as well as a competitive perspective. To that end, this work investigates a chance-constraint-based approach to coupled constraints in generalized Nash equilibrium problems which are s…

Specific Wasserstein divergence between continuous martingales
Julio Backhoff-Veraguas, Xin Zhang
https://arxiv.org/abs/2404.19672 https://

Specific Wasserstein divergence between continuous martingales
Defining a divergence between the laws of continuous martingales is a delicate task, owing to the fact that these laws tend to be singular to each other. An important idea, put forward by N. Gantert, is to instead consider a scaling limit of the relative entropy between such continuous martingales sampled over a finite time grid. This gives rise to the concept of specific relative entropy. In order to develop a general theory of divergences between continuous martingales, it is only natural to …

This https://arxiv.org/abs/2312.05706 has been replaced.
link: https://scholar.google.com/scholar?q=a

Bit Blasting Probabilistic Programs
Probabilistic programming languages (PPLs) are expressive means for creating and reasoning about probabilistic models. Unfortunately hybrid probabilistic programs, involving both continuous and discrete structures, are not well supported by today's PPLs. In this paper we develop a new approximate inference algorithm for hybrid probabilistic programs that first discretizes the continuous distributions and then performs discrete inference on the resulting program. The key novelty is a form of dis…

https://graydon2.dreamwidth.org/1597.html - The "not rocket science" rule of continuous integration (and the story of monotone and bors).

Continuous Sign Language Recognition Based on Motor attention mechanism and frame-level Self-distillation
Qidan Zhu, Jing Li, Fei Yuan, Quan Gan
https://arxiv.org/abs/2402.19118 <…

Continuous Sign Language Recognition Based on Motor attention mechanism and frame-level Self-distillation
Changes in facial expression, head movement, body movement and gesture movement are remarkable cues in sign language recognition, and most of the current continuous sign language recognition(CSLR) research methods mainly focus on static images in video sequences at the frame-level feature extraction stage, while ignoring the dynamic changes in the images. In this paper, we propose a novel motor attention mechanism to capture the distorted changes in local motion regions during sign language exp…

Machine learning of continuous and discrete variational ODEs with convergence guarantee and uncertainty quantification
Christian Offen
https://arxiv.org/abs/2404.19626

Machine learning of continuous and discrete variational ODEs with convergence guarantee and uncertainty quantification
The article introduces a method to learn dynamical systems that are governed by Euler--Lagrange equations from data. The method is based on Gaussian process regression and identifies continuous or discrete Lagrangians and is, therefore, structure preserving by design. A rigorous proof of convergence as the distance between observation data points converges to zero is provided. Next to convergence guarantees, the method allows for quantification of model uncertainty, which can provide a basis of…

This https://arxiv.org/abs/2403.14353 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAR_…

DaCapo: Accelerating Continuous Learning in Autonomous Systems for Video Analytics
Deep neural network (DNN) video analytics is crucial for autonomous systems such as self-driving vehicles, unmanned aerial vehicles (UAVs), and security robots. However, real-world deployment faces challenges due to their limited computational resources and battery power. To tackle these challenges, continuous learning exploits a lightweight "student" model at deployment (inference), leverages a larger "teacher" model for labeling sampled data (labeling), and continuously retrains the student m…

Radio Resource Management Design for RSMA: Optimization of Beamforming, User Admission, and Discrete/Continuous Rates with Imperfect SIC
L. F. Abanto-Leon, A. Krishnamoorthy, A. Garcia-Saavedra, G. H. Sim, R. Schober, M. Hollick
https://arxiv.org/abs/2404.19611

Radio Resource Management Design for RSMA: Optimization of Beamforming, User Admission, and Discrete/Continuous Rates with Imperfect SIC
This paper investigates the radio resource management (RRM) design for multiuser rate-splitting multiple access (RSMA), accounting for various characteristics of practical wireless systems, such as the use of discrete rates, the inability to serve all users, and the imperfect successive interference cancellation (SIC). Specifically, failure to consider these characteristics in RRM design may lead to inefficient use of radio resources. Therefore, we formulate the RRM of RSMA as optimization prob…

This https://arxiv.org/abs/2305.00424 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Policy Iteration Reinforcement Learning Method for Continuous-time Mean-Field Linear-Quadratic Optimal Problem
This paper employs a policy iteration reinforcement learning (RL) method to study continuous-time linear quadratic mean-field control problems in the infinite horizon. The drift and diffusion terms in the dynamics involve the state as well as the control. We investigate the stability and convergence of the RL algorithm using a Lyapunov Recursion. Instead of solving a pair of coupled Riccati equations, the RL technique focuses on strengthening an auxiliary function and the cost functional as the…

Gravitational wave radiation from magnetar-driven supernovae
Lang Xie, Hong-Yu Gong, Long LI, Da-Ming Wei, J. L. Han
https://arxiv.org/abs/2404.18076 https…

Gravitational wave radiation from magnetar-driven supernovae
Rapidly spinning magnetars are potential candidates for the energy source of supernovae (SNe) and gamma-ray bursts and the most promising sources for continuous gravitational waves (GWs) detected by ground-based GW detectors. Continuous GWs can be radiated from magnetars due to magnetic-induced deformation or fluid oscillations, compatible with magnetic dipole (MD) radiation for spin-down energy. In this paper, we investigate the diverse light curves of magnetar-driven SNe in the scenario that …

Some hard facts for the eco-techno-optimists from the always well researched Low Tech magazine.
"Unfortunately, the planned switch to low-carbon energy sources and the electrification of heating and transport technologies will not decrease our dependency on the steel industry – on the contrary."
htt…

How to Escape From the Iron Age?
We cannot lower carbon emissions if we keep producing steel with fossil fuels.

This https://arxiv.org/abs/2402.15167 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

$\widetilde{O}(N^2)$ Representation of General Continuous Anti-symmetric Function
In quantum mechanics, the wave function of fermion systems such as many-body electron systems are anti-symmetric (AS) and continuous, and it is crucial yet challenging to find an ansatz to represent them. This paper addresses this challenge by presenting an ${\widetilde O}(N^2)$ ansatz based on permutation-equivariant functions. We prove that our ansatz can represent any AS continuous functions, and can accommodate the determinant-based structure proposed by Hutter [14], solving the proposed op…

This https://arxiv.org/abs/2404.12727 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Characterizations of open and semi-open maps of compact Hausdorff spaces by induced maps
Let $f\colon X\rightarrow Y$ be a continuous surjection of compact Hausdorff spaces. By $$f_*\colon\mathfrak{M}(X)\rightarrow\mathfrak{M}(Y),\ μ\mapsto μ\circ f^{-1} \quad{\rm and}\quad 2^f\colon2^X\rightarrow2^Y,\ A\mapsto f[A]$$ we denote the induced continuous surjections on the probability measure spaces and hyperspaces, respectively. In this paper we mainly show the following facts: (1) If $f_*$ is semi-open, then $f$ is semi-open. (2) If $f$ is semi-open densely open, then $f_*$ is …

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.

This https://arxiv.org/abs/2404.03735 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Homology and homotopy for arbitrary categories
One of the prime motivation for topology was Homotopy theory, which captures the general idea of a continuous transformation between two entities, which may be spaces or maps. In later decades, an algebraic formulation of topology was discovered with the development of Homology theory. Some of the deepest results in topology are about the connections between Homotopy and Homology. These results are proved using intricate constructions. This paper takes an axiomatic approach that provides a comm…

Classical origins of Landau-incompatible transitions
Abhishodh Prakash, Nick G. Jones
https://arxiv.org/abs/2404.19009 https://arxiv.…

Classical origins of Landau-incompatible transitions
Continuous phase transitions where symmetry is spontaneously broken are ubiquitous in physics and often found between `Landau-compatible' phases where residual symmetries of one phase are a subset of the other. However, continuous `deconfined quantum critical' transitions between Landau-incompatible symmetry-breaking phases are known to exist in certain quantum systems, often with anomalous microscopic symmetries. In this paper, we investigate the need for such special conditions. We show that …

Aluminum nuclear demagnetization refrigerator for powerful continuous cooling
Matthias Raba, S\'ebastien Triqueneaux, James Butterworth, David Schmoranzer, Emilio Barria, J\'er\^ome Debray, Guillaume Donnier-Valentin, Thibaut Gandit, Anne Gerardin, Johannes Goupy, Olivier Tissot, Eddy Collin, Andrew Fefferman
https://arx…

Aluminum nuclear demagnetization refrigerator for powerful continuous cooling
Many laboratories routinely cool samples to 10 mK, but relatively few can cool condensed matter below 1 mK. Easy access to the microkelvin range would propel fields such as quantum sensors and quantum materials. Such temperatures are achieved with adiabatic nuclear demagnetization. Existing nuclear demagnetization refrigerators (NDR) are "single-shot", and the recycling time is incompatible with proposed sub-mK experiments. Furthermore, a high cooling power is required to overcome the excess he…

Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generation
Ian Dunn, David Ryan Koes
https://arxiv.org/abs/2404.19739 https://

Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generation
Deep generative models that produce novel molecular structures have the potential to facilitate chemical discovery. Diffusion models currently achieve state of the art performance for 3D molecule generation. In this work, we explore the use of flow matching, a recently proposed generative modeling framework that generalizes diffusion models, for the task of de novo molecule generation. Flow matching provides flexibility in model design; however, the framework is predicated on the assumption of …

Converse Barrier Certificates for Finite-time Safety Verification of Continuous-time Perturbed Deterministic Systems
Yonghan Li, Chenyu Wu, Taoran Wu, Shijie Wang, Bai Xue
https://arxiv.org/abs/2402.17167

Converse Barrier Certificates for Finite-time Safety Verification of Continuous-time Perturbed Deterministic Systems
In this paper, we investigate the problem of verifying the finite-time safety of continuous-time perturbed deterministic systems represented by ordinary differential equations in the presence of measurable disturbances. Given a finite time horizon, if the system is safe, it, starting from a compact initial set, will remain within an open and bounded safe region throughout the specified time horizon, regardless of the disturbances. The main contribution of this work is to uncover that there exis…

The Dirichlet problem for second-order elliptic equations in non-divergence form with continuous coefficients
Hongjie Dong, Dong-ha Kim, Seick Kim
https://arxiv.org/abs/2402.17948

The Dirichlet problem for second-order elliptic equations in non-divergence form with continuous coefficients
This paper investigates the Dirichlet problem for a non-divergence form elliptic operator $L$ in a bounded domain of $\mathbb{R}^d$. Under certain conditions on the coefficients of $L$, we first establish the existence of a unique Green's function in a ball and derive two-sided pointwise estimates for it. Utilizing these results, we demonstrate the equivalence of regular points for $L$ and those for the Laplace operator, characterized via the Wiener test. This equivalence facilitates the unique…

Finite Ehrenfeucht-Fra\"iss\'e Games for Continuous First-Order Logic
\r{A}sa Hirvonen, Joni Puljuj\"arvi
https://arxiv.org/abs/2402.16662 ht…

Finite Ehrenfeucht-Fraïssé Games for Continuous First-Order Logic
We define a version of the Ehrenfeucht-Fraïssé game in the setting of metric model theory and continuous first-order logic and show that the second player having a winning strategy in a game of length $n$ exactly corresponds to being elementarily equivalent up to quantifier rank $n$. We then demonstrate the usefulness of the game with some examples.

This https://arxiv.org/abs/2307.05370 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Origami Single-end Capacitive Sensing for Continuous Shape Estimation of Morphing Structures
In this work, we propose a novel single-end morphing capacitive sensing method for shape tracking, FxC, by combining Folding origami structures and Capacitive sensing to detect the morphing structural motions using state-of-the-art sensing circuits and deep learning. It was observed through embedding areas of origami structures with conductive materials as single-end capacitive sensing patches, that the sensor signals change coherently with the motion of the structure. Different from other orig…

This https://arxiv.org/abs/2312.02731 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

D-LGP: Dynamic Logic-Geometric Program for Reactive Task and Motion Planning
Many real-world sequential manipulation tasks involve a combination of discrete symbolic search and continuous motion planning, collectively known as combined task and motion planning (TAMP). However, prevailing methods often struggle with the computational burden and intricate combinatorial challenges, limiting their applications for online replanning in the real world. To address this, we propose Dynamic Logic-Geometric Program (D-LGP), a novel approach integrating Dynamic Tree Search and glo…

This https://arxiv.org/abs/2404.14926 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csPF_…

Towards self-optimization of publish/subscribe IoT systems using continuous performance monitoring
Today, more and more embedded devices are being connected through a network, generally Internet, offering users different services. This concept refers to Internet of Things (IoT), bringing information and control capabilities in many fields like medicine, smart homes, home security, etc. Main drawbacks of IoT environment are its dependency on Internet connectivity and need continuous devices power. These dependencies may affect system performances, namely request processing response times. In …

Looking for Complexity at Phase Boundaries in Continuous Cellular Automata
Vassilis Papadopoulos, Guilhem Doat, Arthur Renard, Cl\'ement Hongler
https://arxiv.org/abs/2402.17848

Looking for Complexity at Phase Boundaries in Continuous Cellular Automata
One key challenge in Artificial Life is designing systems that display an emergence of complex behaviors. Many such systems depend on a high-dimensional parameter space, only a small subset of which displays interesting dynamics. Focusing on the case of continuous systems, we introduce the 'Phase Transition Finder'(PTF) algorithm, which can be used to efficiently generate parameters lying at the border between two phases. We argue that such points are more likely to display complex behaviors, a…

This https://arxiv.org/abs/2309.12683 has been replaced.
initial toot: https://mastoxiv.page/@a…

Accelerating the laser-induced phase transition in nanostructured FeRh via plasmonic absorption
By ultrafast x-ray diffraction we show that the laser-induced magnetostructural phase transition in FeRh nanoislands proceeds faster and more complete than in continuous films. We observe an intrinsic 8 ps timescale for nucleation of ferromagnetic (FM) domains in both types of samples. For the continuous film, the substrate-near regions, which are not directly exposed to light, are only slowly transformed to the FM state by domain wall motion following heat transport. In contrast, numerical mod…

This https://arxiv.org/abs/2202.03960 has been replaced.
link: https://scholar.google.com/scholar?q=a

Continuous permanent unobserved heterogeneity in dynamic discrete choice models
In dynamic discrete choice (DDC) analysis, it is common to use mixture models to control for unobserved heterogeneity. However, consistent estimation typically requires both restrictions on the support of unobserved heterogeneity and a high-level injectivity condition that is difficult to verify. This paper provides primitive conditions for point identification of a broad class of DDC models with multivariate continuous permanent unobserved heterogeneity. The results apply to both finite- and i…

Extracting quantum-critical properties from directly evaluated enhanced perturbative continuous unitary transformations
L. Schamri\ss, M. R. Walther, K. P. Schmidt
https://arxiv.org/abs/2402.18989

Extracting quantum-critical properties from directly evaluated enhanced perturbative continuous unitary transformations
Directly evaluated enhanced perturbative continuous unitary transformations (deepCUTs) are used to calculate non-perturbatively extrapolated numerical data for the ground-state energy and the energy gap. The data coincides with the perturbative series up to the order with respect to which the deepCUT is truncated. We develop a general scheme to extract quantum-critical properties from the deepCUT data based on critical scaling and a strict correspondence between the truncation used for deepCUT …

Parameter choice strategies for regularized least squares approximation of noisy continuous functions on the unit circle
Congpei An, Mou Cai
https://arxiv.org/abs/2403.19927

Parameter choice strategies for regularized least squares approximation of noisy continuous functions on the unit circle
In this paper, we consider a trigonometric polynomial reconstruction of continuous periodic functions from their noisy values at equidistant nodes of the unit circle by a regularized least squares method. We indicate that the constructed trigonometric polynomial can be determined in explicit due to the exactness of trapezoidal rule. Then a concrete error bound is derived based on the estimation of the Lebesgue constant. In particular, we analyze three regularization parameter choice strategies:…

Edwards Smiltens on Latvian continuous support of Ukraine: https://benborges.xyz/2024/04/24/edwards-smiltens-on.html

#Requiescat For #MarianZazeela. https://www.artnews.com/art-news/news/mari

Marian Zazeela, Minimalist Artist Behind 'Dream House,' Dies at 83
Marian Zazeela, a Minimalist artist known for the 'Dream House' installation, has died at 83.

Advancing Continuous Distribution Generation: An Exponentiated Odds Ratio Generator Approach
Xinyu Chen, Yuanqi Xie, Achraf Cohen, Shusen Pu
https://arxiv.org/abs/2402.17294

Advancing Continuous Distribution Generation: An Exponentiated Odds Ratio Generator Approach
This paper presents a new methodology for generating continuous statistical distributions, integrating the exponentiated odds ratio within the framework of survival analysis. This new method enhances the flexibility and adaptability of distribution models to effectively address the complexities inherent in contemporary datasets. The core of this advancement is illustrated by introducing a particular subfamily, the "Type-2 Gumbel Weibull-G Family of Distributions." We provide a comprehensive ana…

Active Dendrites Enable Efficient Continual Learning in Time-To-First-Spike Neural Networks
Lorenzo Pes, Rick Luiken, Federico Corradi, Charlotte Frenkel
https://arxiv.org/abs/2404.19419

Active Dendrites Enable Efficient Continual Learning in Time-To-First-Spike Neural Networks
While the human brain efficiently adapts to new tasks from a continuous stream of information, neural network models struggle to learn from sequential information without catastrophically forgetting previously learned tasks. This limitation presents a significant hurdle in deploying edge devices in real-world scenarios where information is presented in an inherently sequential manner. Active dendrites of pyramidal neurons play an important role in the brain ability to learn new tasks incrementa…

Higher-group global symmetry and the bosonic M5 brane
Jay Armas, Giorgos Batzios, Akash Jain
https://arxiv.org/abs/2402.19458 https://

Higher-group global symmetry and the bosonic M5 brane
Higher-group symmetries are combinations of higher-form symmetries which appear in various field theories. In this paper, we explain how higher-group symmetries arise in 10d and 11d supergravities when the latter are coupled to brane sources. Motivated by this observation, we study field theories at zero and finite temperature invariant under a class of continuous Abelian higher-group symmetries. We restrict the analysis to the low-energy regime where the dynamical field content exclusively con…

Ensemble Methodology:Innovations in Credit Default Prediction Using LightGBM, XGBoost, and LocalEnsemble
Mengran Zhu, Ye Zhang, Yulu Gong, Kaijuan Xing, Xu Yan, Jintong Song
https://arxiv.org/abs/2402.17979

Ensemble Methodology:Innovations in Credit Default Prediction Using LightGBM, XGBoost, and LocalEnsemble
In the realm of consumer lending, accurate credit default prediction stands as a critical element in risk mitigation and lending decision optimization. Extensive research has sought continuous improvement in existing models to enhance customer experiences and ensure the sound economic functioning of lending institutions. This study responds to the evolving landscape of credit default prediction, challenging conventional models and introducing innovative approaches. By building upon foundational…

Manipulating a Continuous Instrumental Variable in an Observational Study of Premature Babies: Algorithm, Partial Identification Bounds, and Inference under Randomization and Biased Randomization Assumptions
Zhe Chen, Min Haeng Cho, Bo Zhang
https://arxiv.org/abs/2404.17734

Manipulating a Continuous Instrumental Variable in an Observational Study of Premature Babies: Algorithm, Partial Identification Bounds, and Inference under Randomization and Biased Randomization Assumptions
Regionalization of intensive care for premature babies refers to a triage system of mothers with high-risk pregnancies to hospitals of varied capabilities based on risks faced by infants. Due to the limited capacity of high-level hospitals, which are equipped with advanced expertise to provide critical care, understanding the effect of delivering premature babies at such hospitals on infant mortality for different subgroups of high-risk mothers could facilitate the design of an efficient perina…

Decomposition Model Assisted Energy-Saving Design in Radio Access Network
Xiaoxue Zhao, Yijun Yu, Yexing Li, Dong Li, Yao Wang, Chungang Yang
https://arxiv.org/abs/2404.18418

Decomposition Model Assisted Energy-Saving Design in Radio Access Network
The continuous emergence of novel services and massive connections involve huge energy consumption towards ultra-dense radio access networks. Moreover, there exist much more number of controllable parameters that can be adjusted to reduce the energy consumption from a network-wide perspective. However, a network-level energy-saving intent usually contains multiple network objectives and constraints. Therefore, it is critical to decompose a network-level energy-saving intent into multiple levels…

Integrable semi-discretization for a modified Camassa-Holm equation with cubic nonlinearity
Bao-Feng Feng, Heng-Chun Hu, Han-Han Sheng, Wei Yin, Guo-Fu Yu
https://arxiv.org/abs/2404.18372 https://arxiv.org/pdf/2404.18372
arXiv:2404.18372v1 Announce Type: new
Abstract: In the present paper, an integrable semi-discretization of the modified Camassa-Holm (mCH) equation with cubic nonlinearity is presented. The key points of the construction are based on the discrete Kadomtsev-Petviashvili (KP) equation and appropriate definition of discrete reciprocal transformations. First, we demonstrate that these bilinear equations and their determinant solutions can be derived from the discrete KP equation through Miwa transformation and some reductions. Then, by scrutinizing the reduction process, we obtain a set of semi-discrete bilinear equations and their general soliton solutions in the Gram-type determinant form. Finally, we obtain an integrable semi-discrete analog of the mCH equation by introducing dependent variables and discrete reciprocal transformation. It is also shown that the semi-discrete mCH equation converges to the continuous one in the continuum limit.

Continuous family of surfaces translating by powers of Gauss curvature
Beomjun Choi, Kyeongsu Choi, Soojung Kim
https://arxiv.org/abs/2402.17075 https://…

Continuous family of surfaces translating by powers of Gauss curvature
This paper shows the existence of convex translating surfaces under the flow by the $α$-th power of Gauss curvature for the sub-affine-critical regime $ 0 < α< 1/4$. The key aspect of our study is that our ansatz at infinity is the graph of homogeneous functions whose level sets are closed curves shrinking under the flow by the $\fracα{1-α}$-th power of curvature. For each ansatz, we construct a family of translating surfaces generated by the Jacobi fields with effective growth rates. Moreo…

Polymer Collapse and Liquid-Liquid Phase-Separation are Coupled in a Generalized Prewetting Transition
Mason N. Rouches, Benjamin B. Machta
https://arxiv.org/abs/2404.19158 https://arxiv.org/pdf/2404.19158
arXiv:2404.19158v1 Announce Type: new
Abstract: The three-dimensional organization of chromatin is thought to play an important role in controlling gene expression. Specificity in expression is achieved through the interaction of transcription factors and other nuclear proteins with particular sequences of DNA. At unphysiological concentrations many of these nuclear proteins can phase-separate in the absence of DNA, and it has been hypothesized that, in vivo, the thermodynamic forces driving these phases help determine chromosomal organization. However it is unclear how DNA, itself a long polymer subject to configurational transitions, interacts with three-dimensional protein phases. Here we show that a long compressible polymer can be coupled to interacting protein mixtures, leading to a generalized prewetting transition where polymer collapse is coincident with a locally stabilized liquid droplet. We use lattice Monte-Carlo simulations and a mean-field theory to show that these phases can be stable even in regimes where both polymer collapse and coexisting liquid phases are unstable in isolation, and that these new transitions can be either abrupt or continuous. For polymers with internal linear structure we further show that changes in the concentration of bulk components can lead to changes in three-dimensional polymer structure. In the nucleus there are many distinct proteins that interact with many different regions of chromatin, potentially giving rise to many different Prewet phases. The simple systems we consider here highlight chromatin's role as a lower-dimensional surface whose interactions with proteins are required for these novel phases.

This https://arxiv.org/abs/2310.13219 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

HierCas: Hierarchical Temporal Graph Attention Networks for Popularity Prediction in Information Cascades
Information cascade popularity prediction is critical for many applications, including but not limited to identifying fake news and accurate recommendations. Traditional feature-based methods heavily rely on handcrafted features, which are domain-specific and lack generalizability to new domains. To address this problem, researchers have turned to neural network-based approaches. However, most existing methods follow a sampling-based modeling approach, potentially losing continuous dynamic info…

Dual-Wavelength SLA 3D Printing: Fast Continuous Printing With ROMP And RFP Resins
https://poliverso.org/display/0477a01e-62f4a760-a4c8b9d9866091a4
Dual-Wavelength SLA 3D Printing: Fast Continuous Printing With ROMP And RFP Resins As widespread as 3…

Solving Time-Continuous Stochastic Optimal Control Problems: Algorithm Design and Convergence Analysis of Actor-Critic Flow
Mo Zhou, Jianfeng Lu
https://arxiv.org/abs/2402.17208 <…

Solving Time-Continuous Stochastic Optimal Control Problems: Algorithm Design and Convergence Analysis of Actor-Critic Flow
We propose an actor-critic framework to solve the time-continuous stochastic optimal control problem. A least square temporal difference method is applied to compute the value function for the critic. The policy gradient method is implemented as policy improvement for the actor. Our key contribution lies in establishing the global convergence property of our proposed actor-critic flow, demonstrating a linear rate of convergence. Theoretical findings are further validated through numerical examp…

The Jive Verification System and its Transformative Impact on Weather Forecasting Operations
Nicholas Loveday, Deryn Griffiths, Tennessee Leeuwenburg, Robert Taggart, Thomas C. Pagano, George Cheng, Kevin Plastow, Elizabeth Ebert, Cassandra Templeton, Maree Carroll, Mohammadreza Khanarmuei, Isha Nagpal
https://arxiv.org/abs/2404…

The Jive Verification System and its Transformative Impact on Weather Forecasting Operations
Forecast verification is critical for continuous improvement in meteorological organizations. The Jive verification system was originally developed to assess the accuracy of public weather forecasts issued by the Australian Bureau of Meteorology. It started as a research project in 2015 and gradually evolved into the operational verification system that went live in 2022. The system includes daily verification dashboards for forecasters to visualize recent forecast performance and "Evidence Tar…

Locally approximable CR functions, a sharp maximum modulus principle and holomorphic extension
Mauro Nacinovich, Egmont Porten
https://arxiv.org/abs/2402.19057

Locally approximable CR functions, a sharp maximum modulus principle and holomorphic extension
We introduce a notion of locally approximable continuous CR functions on locally closed subsets of reduced complex spaces, generalizing both holomorphic functions and CR functions on CR submanifolds. Under additional assumptions of set-theoretical weak pseudoconcavity we prove optimal maximum modulus principles for these functions. Restricting to real submanifolds (possibly with CR singularities) of complexmanifolds, we generalize results on holomorphic extension known for CR submanifolds.

This https://arxiv.org/abs/2307.11630 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Multiparameter Water Quality Monitoring System for Continuous Monitoring of Fresh Waters
Due to the global water crisis there is a strong need for real-time water quality monitoring with high temporal and spatial resolution. This paper presents an economical multiparameter water quality monitoring system for continuous monitoring of fresh waters. It is based on a sensor node that integrates turbidity, temperature, and conductivity sensors, a miniature eighteen-channel spectrophotometer, and a sensor for the detection of thermotolerant coliforms, which is a major novelty of the syst…

This https://arxiv.org/abs/2403.10063 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Unified Projection-Free Algorithms for Adversarial DR-Submodular Optimization
This paper introduces unified projection-free Frank-Wolfe type algorithms for adversarial continuous DR-submodular optimization, spanning scenarios such as full information and (semi-)bandit feedback, monotone and non-monotone functions, different constraints, and types of stochastic queries. For every problem considered in the non-monotone setting, the proposed algorithms are either the first with proven sub-linear $α$-regret bounds or have better $α$-regret bounds than the state of the art,…

This https://arxiv.org/abs/2210.16311 has been replaced.
link: https://scholar.google.com/scholar?q=a

Simultaneous off-the-grid learning of mixtures issued from a continuous dictionary
In this paper we observe a set, possibly a continuum, of signals corrupted by noise. Each signal is a finite mixture of an unknown number of features belonging to a continuous dictionary. The continuous dictionary is parametrized by a real non-linear parameter. We shall assume that the signals share an underlying structure by assuming that each signal has its active features included in a finite and sparse set. We formulate regularized optimization problem to estimate simultaneously the linear …

Continuous K-biframes in Hilbert spaces
Abdelilah Karara, Mohamed Rossafi
https://arxiv.org/abs/2402.16573 https://arxiv.org/pdf/2402…

Continuous K-biframes in Hilbert spaces
In this paper, we will introduce the concept of a continuous K-biframe for Hilbert spaces and we present various examples of continuous K-biframes. Furthermore, we investigate their characteristics from the perspective of operator theory by establishing various properties.

This https://arxiv.org/abs/2203.06693 has been replaced.
link: https://scholar.google.com/scholar?q=a

Continuum damping of topologically-protected edge modes at the boundary of a magnetized plasma
Recent extension of the topological ideas to continuous systems with broken time-reversal symmetry, such as magnetized plasmas, provides new insights into the nature of scattering-free topologically-protected surface plasma waves (TSPWs). We demonstrate a unique characteristic of TSPWs propagating above the electron cyclotron frequency: their collisionless damping via coupling to the continuum of resonant modes localized inside a smooth plasma-vacuum interface. Damped TSPWs retain their unidire…

The Dirichlet problem for second-order elliptic equations in non-divergence form with continuous coefficients
Hongjie Dong, Dong-ha Kim, Seick Kim
https://arxiv.org/abs/2402.17948

The Dirichlet problem for second-order elliptic equations in non-divergence form with continuous coefficients
This paper investigates the Dirichlet problem for a non-divergence form elliptic operator $L$ in a bounded domain of $\mathbb{R}^d$. Under certain conditions on the coefficients of $L$, we first establish the existence of a unique Green's function in a ball and derive two-sided pointwise estimates for it. Utilizing these results, we demonstrate the equivalence of regular points for $L$ and those for the Laplace operator, characterized via the Wiener test. This equivalence facilitates the unique…

Finite Ehrenfeucht-Fra\"iss\'e Games for Continuous First-Order Logic
\r{A}sa Hirvonen, Joni Puljuj\"arvi
https://arxiv.org/abs/2402.16662 ht…

Finite Ehrenfeucht-Fraïssé Games for Continuous First-Order Logic
We define a version of the Ehrenfeucht-Fraïssé game in the setting of metric model theory and continuous first-order logic and show that the second player having a winning strategy in a game of length $n$ exactly corresponds to being elementarily equivalent up to quantifier rank $n$. We then demonstrate the usefulness of the game with some examples.

This https://arxiv.org/abs/2404.14926 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csPF_…

Towards self-optimization of publish/subscribe IoT systems using continuous performance monitoring
Today, more and more embedded devices are being connected through a network, generally Internet, offering users different services. This concept refers to Internet of Things (IoT), bringing information and control capabilities in many fields like medicine, smart homes, home security, etc. Main drawbacks of IoT environment are its dependency on Internet connectivity and need continuous devices power. These dependencies may affect system performances, namely request processing response times. In …

This https://arxiv.org/abs/2308.00984 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

On the Metric Temporal Logic for Continuous Stochastic Processes
In this paper, we prove measurability of event for which a general continuous-time stochastic process satisfies continuous-time Metric Temporal Logic (MTL) formula. Continuous-time MTL can define temporal constrains for physical system in natural way. Then there are several researches that deal with probability of continuous MTL semantics for stochastic processes. However, proving measurability for such events is by no means an obvious task, even though it is essential. The difficulty comes fro…

This https://arxiv.org/abs/2310.19620 has been replaced.
link: https://scholar.google.com/scholar?q=a

Large Trajectory Models are Scalable Motion Predictors and Planners
Motion prediction and planning are vital tasks in autonomous driving, and recent efforts have shifted to machine learning-based approaches. The challenges include understanding diverse road topologies, reasoning traffic dynamics over a long time horizon, interpreting heterogeneous behaviors, and generating policies in a large continuous state space. Inspired by the success of large language models in addressing similar complexities through model scaling, we introduce a scalable trajectory model…

JEL ratio test for independence between a continuous and a categorical random variable
Saparya Suresh, Sudheesh K. Kattumannil
https://arxiv.org/abs/2402.18105

JEL ratio test for independence between a continuous and a categorical random variable
The categorical Gini covariance is a dependence measure between a numerical variable and a categorical variable. The Gini covariance measures dependence by quantifying the difference between the conditional and unconditional distributional functions. A value of zero for the categorical Gini covariance implies independence of the numerical variable and the categorical variable. We propose a non-parametric test for testing the independence between a numerical and categorical variable using the ca…

This https://arxiv.org/abs/2310.07333 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

Computing approximate roots of monotone functions
Given a function f: [a,b] -> R, if f(a) < 0 and f(b)> 0 and f is continuous, the Intermediate Value Theorem implies that f has a root in [a,b]. Moreover, given a value-oracle for f, an approximate root of f can be computed using the bisection method, and the number of required evaluations is polynomial in the number of accuracy digits. The goal of this note is to identify conditions under which this polynomiality result extends to a multi-dimensional function that satisfies the conditions of Mi…

This https://arxiv.org/abs/2208.01989 has been replaced.
link: https://scholar.google.com/scholar?q=a

Thermodynamic Formalism on the Skorokhod space: the continuous time Ruelle operator, entropy, pressure, entropy production and expansiveness
Consider the semi-flow given by the continuous time shift $Θ_t:\mathcal{D} \to \mathcal{D} $, $t \geq 0$, acting on the $\mathcal{D} $ of \textit{càdlàg} paths $w: [0,\infty) \to S^1$, where $S^1$ is the unitary circle. We equip the space $\mathcal{D} $ with the Skorokhod metric, and we show that the semi-flow is expanding. We also introduce a stochastic semi-group $e^{t\, L}$, $t \geq 0,$ where $L$ acts linearly on continuous functions $f:S^1\to\mathbb{R}$. This stochastic semigroup and a…

Impact of Computation in Integral Reinforcement Learning for Continuous-Time Control
Wenhan Cao, Wei Pan
https://arxiv.org/abs/2402.17375 https://

Impact of Computation in Integral Reinforcement Learning for Continuous-Time Control
Integral reinforcement learning (IntRL) demands the precise computation of the utility function's integral at its policy evaluation (PEV) stage. This is achieved through quadrature rules, which are weighted sums of utility functions evaluated from state samples obtained in discrete time. Our research reveals a critical yet underexplored phenomenon: the choice of the computational method -- in this case, the quadrature rule -- can significantly impact control performance. This impact is traced b…

This https://arxiv.org/abs/2402.16783 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Conditional optimal sets and the quantization coefficients for some uniform distributions
Bucklew and Wise (1982) showed that the quantization dimension of an absolutely continuous probability measure on a given Euclidean space is constant and equals the Euclidean dimension of the space, and the quantization coefficient exists as a finite positive number. By giving different examples, in this paper, we have shown that the quantization coefficients for absolutely continuous probability measures defined on the same Euclidean space can be different. We have taken uniform distribution a…

Spatial, Temporal, and Geometric Fusion for Remote Sensing Images
Hessah Albanwan
https://arxiv.org/abs/2404.17851 https://arxiv.org/pdf/2404.17851
arXiv:2404.17851v1 Announce Type: new
Abstract: Remote sensing (RS) images are important to monitor and survey earth at varying spatial scales. Continuous observations from various RS sources complement single observations to improve applications. Fusion into single or multiple images provides more informative, accurate, complete, and coherent data. Studies intensively investigated spatial-temporal fusion for specific applications like pan-sharpening and spatial-temporal fusion for time-series analysis. Fusion methods can process different images, modalities, and tasks and are expected to be robust and adaptive to various types of images (e.g., spectral images, classification maps, and elevation maps) and scene complexities. This work presents solutions to improve existing fusion methods that process gridded data and consider their type-specific uncertainties. The contributions include: 1) A spatial-temporal filter that addresses spectral heterogeneity of multitemporal images. 2) 3D iterative spatiotemporal filter that enhances spatiotemporal inconsistencies of classification maps. 3) Adaptive semantic-guided fusion that enhances the accuracy of DSMs and compares them with traditional fusion approaches to show the significance of adaptive methods. 4) A comprehensive analysis of DL stereo matching methods against traditional Census-SGM to obtain detailed knowledge on the accuracy of the DSMs at the stereo matching level. We analyze the overall performance, robustness, and generalization capability, which helps identify the limitations of current DSM generation methods. 5) Based on previous analysis, we develop a novel finetuning strategy to enhance transferability of DL stereo matching methods, hence, the accuracy of DSMs. Our work shows the importance of spatial, temporal, and geometric fusion in enhancing RS applications. It shows that the fusion problem is case-specific and depends on the image type, scene content, and application.

AdapTics: A Toolkit for Creative Design and Integration of Real-Time Adaptive Mid-Air Ultrasound Tactons
Kevin John, Yinan Li, Hasti Seifi
https://arxiv.org/abs/2404.19275 https://arxiv.org/pdf/2404.19275
arXiv:2404.19275v1 Announce Type: new
Abstract: Mid-air ultrasound haptic technology can enhance user interaction and immersion in extended reality (XR) applications through contactless touch feedback. Yet, existing design tools for mid-air haptics primarily support creating tactile sensations (i.e., tactons) which cannot change at runtime. These tactons lack expressiveness in interactive scenarios where a continuous closed-loop response to user movement or environmental states is desirable. This paper introduces AdapTics, a toolkit featuring a graphical interface for rapid prototyping of adaptive tactons-dynamic sensations that can adjust at runtime based on user interactions, environmental changes, or other inputs. A software library and a Unity package accompany the graphical interface to enable integration of adaptive tactons in existing applications. We present the design space offered by AdapTics for creating adaptive mid-air ultrasound tactons and show the design tool can improve Creativity Support Index ratings for Exploration and Expressiveness in a user study with 12 XR and haptic designers.

A diving heuristic for mixed-integer problems with unbounded semi-continuous variables
Katrin Halbig, Alexander Hoen, Ambros Gleixner, Jakob Witzig, Dieter Weninger
https://arxiv.org/abs/2403.19411

A diving heuristic for mixed-integer problems with unbounded semi-continuous variables
Semi-continuous decision variables arise naturally in many real-world applications. They are defined to take either value zero or any value within a specified range, and occur mainly to prevent small nonzero values in the solution. One particular challenge that can come with semi-continuous variables in practical models is that their upper bound may be large or even infinite. In this article, we briefly discuss these challenges, and present a new diving heuristic tailored for mixed-integer opti…

Analysis of Processing Pipelines for Indoor Human Tracking using FMCW radar
Dingyang Wang, Francesco Fioranelli, Alexander Yarovoy
https://arxiv.org/abs/2402.19124

Analysis of Processing Pipelines for Indoor Human Tracking using FMCW radar
In this paper, the problem of formulating effective processing pipelines for indoor human tracking is investigated, with the usage of a Multiple Input Multiple Output (MIMO) Frequency Modulated Continuous Wave (FMCW) radar. Specifically, two processing pipelines starting with detections on the Range-Azimuth (RA) maps and the Range-Doppler (RD) maps are formulated and compared, together with subsequent clustering and tracking algorithms and their relevant parameters. Experimental results are pre…

How to surpass no-go limits in Gaussian quantum error correction and entangled Gaussian state distillation?
En-Jui Chang, Ching-Yi Lai
https://arxiv.org/abs/2404.18586

How to surpass no-go limits in Gaussian quantum error correction and entangled Gaussian state distillation?
Gaussian quantum information processing with continuous-variable (CV) quantum information carriers holds significant promise for applications in quantum communication and quantum internet. However, applying Gaussian state distillation and quantum error correction (QEC) faces limitations imposed by no-go results concerning local Gaussian unitary operations and classical communications. This paper introduces a Gaussian QEC protocol that relies solely on local Gaussian resources. A pivotal compone…

This https://arxiv.org/abs/2310.20426 has been replaced.
link: https://scholar.google.com/scholar?q=a

Evolutionary Pareto Set Learning with Structure Constraints
The multiobjective evolutionary optimization algorithm (MOEA) is a powerful approach for tackling multiobjective optimization problems (MOPs), which can find a finite set of approximate Pareto solutions in a single run. However, under mild regularity conditions, the Pareto optimal set of a continuous MOP could be a low dimensional continuous manifold that contains infinite solutions. In addition, structure constraints on the whole optimal solution set, which characterize the patterns shared amo…

Quarantining Malicious IoT Devices in Intelligent Sliced Mobile Networks
David Candal-Ventureira, Pablo Fondo-Ferreiro, Felipe Gil-Casti\~neira, Francisco Javier Gonz\'alez-Casta\~no
https://arxiv.org/abs/2403.19731

Quarantining Malicious IoT Devices in Intelligent Sliced Mobile Networks
The unstoppable adoption of the Internet of Things (IoT) is driven by the deployment of new services that require continuous capture of information from huge populations of sensors, or actuating over a myriad of "smart" objects. Accordingly, next generation networks are being designed to support such massive numbers of devices and connections. For example, the 3rd Generation Partnership Project (3GPP) is designing the different 5G releases specifically with IoT in mind. Nevertheless, from a sec…

Best Subset Solution Path for Linear Dimension Reduction Models using Continuous Optimization
Benoit Liquet, Sarat Moka, Samuel Muller
https://arxiv.org/abs/2403.20007

Best Subset Solution Path for Linear Dimension Reduction Models using Continuous Optimization
The selection of best variables is a challenging problem in supervised and unsupervised learning, especially in high dimensional contexts where the number of variables is usually much larger than the number of observations. In this paper, we focus on two multivariate statistical methods: principal components analysis and partial least squares. Both approaches are popular linear dimension-reduction methods with numerous applications in several fields including in genomics, biology, environmental…

Deep generative modelling of canonical ensemble with differentiable thermal properties
Shuo-Hui Li, Yao-Wen Zhang, Ding Pan
https://arxiv.org/abs/2404.18404

Deep generative modelling of canonical ensemble with differentiable thermal properties
We propose a variational modelling method with differentiable temperature for canonical ensembles. Using a deep generative model, the free energy is estimated and minimized simultaneously in a continuous temperature range. At optimal, this generative model is a Boltzmann distribution with temperature dependence. The training process requires no dataset, and works with arbitrary explicit density generative models. We applied our method to study the phase transitions (PT) in the Ising and XY mode…

This https://arxiv.org/abs/2404.16821 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites
In this report, we introduce InternVL 1.5, an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. We introduce three simple improvements: (1) Strong Vision Encoder: we explored a continuous learning strategy for the large-scale vision foundation model -- InternViT-6B, boosting its visual understanding capabilities, and making it can be transferred and reused in different LLMs. (2) Dyna…

Facility Location Games with Scaling Effects
Yu He, Alexander Lam, Minming Li
https://arxiv.org/abs/2402.18908 https://arxiv.org/pdf/…

Facility Location Games with Scaling Effects
We take the classic facility location problem and consider a variation, in which each agent's individual cost function is equal to their distance from the facility multiplied by a scaling factor which is determined by the facility placement. In addition to the general class of continuous scaling functions, we also provide results for piecewise linear scaling functions which can effectively approximate or model the scaling of many real world scenarios. We focus on the objectives of total and max…

This https://arxiv.org/abs/2402.09922 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Interpreting symplectic linear transformations in a two-qubit phase space
For the continuous Wigner function and for certain discrete Wigner functions, permuting the values of the Wigner function in accordance with a symplectic linear transformation is equivalent to performing a certain unitary transformation on the state. That is, performing this unitary transformation is simply a matter of moving Wigner-function values around in phase space. This result holds in particular for the simplest discrete Wigner function defined on a $d \times d$ phase space when the Hilb…

Timer-Based Coverage Control for Mobile Sensors
Federico M. Zegers, Sean Phillips, Gregory P. Hicks
https://arxiv.org/abs/2402.18744 https://

Timer-Based Coverage Control for Mobile Sensors
This work studies the coverage control problem over a static, bounded, and convex workspace and develops a hybrid extension of the continuous-time Lloyd algorithm. Each agent in a multi-agent system (MAS) is equipped with a timer that generates intermittent sampling events, which may occur asynchronously between agents. At each sampling event, the corresponding agents update their controllers, which are otherwise held constant. These controllers are shown to drive the MAS into a neighborhood of…

H\"older regularity for degenerate parabolic double-phase equations
Wontae Kim, Kristian Moring, Lauri S\"arki\"o
https://arxiv.org/abs/2404.19111 https://arxiv.org/pdf/2404.19111
arXiv:2404.19111v1 Announce Type: new
Abstract: We prove that bounded weak solutions to degenerate parabolic double-phase equations of $p$-Laplace type are locally H\"older continuous. The proof is based on phase analysis and methods for the $p$-Laplace equation. In particular, the phase analysis determines whether the double-phase equation is locally similar to the $p$-Laplace or the $q$-Laplace equation.

NERV : An Enhanced Implicit Neural Video Representation
Ahmed Ghorbel, Wassim Hamidouche, Luce Morin
https://arxiv.org/abs/2402.18305 https://

NERV++: An Enhanced Implicit Neural Video Representation
Neural fields, also known as implicit neural representations (INRs), have shown a remarkable capability of representing, generating, and manipulating various data types, allowing for continuous data reconstruction at a low memory footprint. Though promising, INRs applied to video compression still need to improve their rate-distortion performance by a large margin, and require a huge number of parameters and long training iterations to capture high-frequency details, limiting their wider applic…

Constraint Latent Space Matters: An Anti-anomalous Waveform Transformation Solution from Photoplethysmography to Arterial Blood Pressure
Cheng Bian, Xiaoyu Li, Qi Bi, Guangpu Zhu, Jiegeng Lyu, Weile Zhang, Yelei Li, Zijing Zeng
https://arxiv.org/abs/2402.17780

Constraint Latent Space Matters: An Anti-anomalous Waveform Transformation Solution from Photoplethysmography to Arterial Blood Pressure
Arterial blood pressure (ABP) holds substantial promise for proactive cardiovascular health management. Notwithstanding its potential, the invasive nature of ABP measurements confines their utility primarily to clinical environments, limiting their applicability for continuous monitoring beyond medical facilities. The conversion of photoplethysmography (PPG) signals into ABP equivalents has garnered significant attention due to its potential in revolutionizing cardiovascular disease management.…

This https://arxiv.org/abs/2310.14283 has been replaced.
link: https://scholar.google.com/scholar?q=a

Bandwidth Efficient Livestreaming in Mobile Wireless Networks: A Peer-to-Peer ACIDE Solution
In this paper, a media distribution model, Active Control in an Intelligent and Distributed Environment (ACIDE), is proposed for bandwidth efficient livestreaming in mobile wireless networks. Two optimization problems are addressed. The first problem is how to minimize the bandwidth allocated to a cluster of n peers such that a continuous media play for all peers is guaranteed. The second problem is how to find the maximum number of peers n, chosen from a group of N users, that can be admitted …

One-dimensional Continuous-Time Quantum Markov Chains: qubit probabilities and measures
Manuel D. De la Iglesia, Carlos F. Lardizabal
https://arxiv.org/abs/2402.15878

One-dimensional Continuous-Time Quantum Markov Chains: qubit probabilities and measures
Quantum Markov chains (QMCs) are positive maps on a trace-class space describing open quantum dynamics on graphs. Such objects have a statistical resemblance with classical random walks, while at the same time it allows for internal (quantum) degrees of freedom. In this work we study continuous-time QMCs on the integer line, half-line and finite segments, so that we are able to obtain exact probability calculations in terms of the associated matrix-valued orthogonal polynomials and measures. Th…

Deep Reinforcement Learning: A Convex Optimization Approach
Ather Gattami
https://arxiv.org/abs/2402.19212 https://arxiv.org/pdf/2402…

Deep Reinforcement Learning: A Convex Optimization Approach
In this paper, we consider reinforcement learning of nonlinear systems with continuous state and action spaces. We present an episodic learning algorithm, where we for each episode use convex optimization to find a two-layer neural network approximation of the optimal $Q$-function. The convex optimization approach guarantees that the weights calculated at each episode are optimal, with respect to the given sampled states and actions of the current episode. For stable nonlinear systems, we show …

This https://arxiv.org/abs/2304.07146 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Energy cascade for the Klein-Gordon lattice
We study analytically the dynamics of a $d$-dimensional Klein-Gordon lattice with periodic boundary conditions, for $d \leq 3$. We consider initial data supported on one low-frequency Fourier mode. We show that, in the continuous approximation, the resonant normal form of the system is given by a small-dispersion nonlinear Schrödinger (NLS) equation. By exploiting a result about the growth of Sobolev norms for solutions of small-dispersion NLS, we are able to describe an energy cascade phenome…

Decoding Radiologists' Intentions: A Novel System for Accurate Region Identification in Chest X-ray Image Analysis
Akash Awasthi, Safwan Ahmad, Bryant Le, Hien Van Nguyen
https://arxiv.org/abs/2404.18981 https://arxiv.org/pdf/2404.18981
arXiv:2404.18981v1 Announce Type: new
Abstract: In the realm of chest X-ray (CXR) image analysis, radiologists meticulously examine various regions, documenting their observations in reports. The prevalence of errors in CXR diagnoses, particularly among inexperienced radiologists and hospital residents, underscores the importance of understanding radiologists' intentions and the corresponding regions of interest. This understanding is crucial for correcting mistakes by guiding radiologists to the accurate regions of interest, especially in the diagnosis of chest radiograph abnormalities. In response to this imperative, we propose a novel system designed to identify the primary intentions articulated by radiologists in their reports and the corresponding regions of interest in CXR images. This system seeks to elucidate the visual context underlying radiologists' textual findings, with the potential to rectify errors made by less experienced practitioners and direct them to precise regions of interest. Importantly, the proposed system can be instrumental in providing constructive feedback to inexperienced radiologists or junior residents in the hospital, bridging the gap in face-to-face communication. The system represents a valuable tool for enhancing diagnostic accuracy and fostering continuous learning within the medical community.

One-dimensional Continuous-Time Quantum Markov Chains: qubit probabilities and measures
Manuel D. De la Iglesia, Carlos F. Lardizabal
https://arxiv.org/abs/2402.15878

One-dimensional Continuous-Time Quantum Markov Chains: qubit probabilities and measures
Quantum Markov chains (QMCs) are positive maps on a trace-class space describing open quantum dynamics on graphs. Such objects have a statistical resemblance with classical random walks, while at the same time it allows for internal (quantum) degrees of freedom. In this work we study continuous-time QMCs on the integer line, half-line and finite segments, so that we are able to obtain exact probability calculations in terms of the associated matrix-valued orthogonal polynomials and measures. Th…

A Transformer Model for Boundary Detection in Continuous Sign Language
Razieh Rastgoo, Kourosh Kiani, Sergio Escalera
https://arxiv.org/abs/2402.14720 http…

A Transformer Model for Boundary Detection in Continuous Sign Language
Sign Language Recognition (SLR) has garnered significant attention from researchers in recent years, particularly the intricate domain of Continuous Sign Language Recognition (CSLR), which presents heightened complexity compared to Isolated Sign Language Recognition (ISLR). One of the prominent challenges in CSLR pertains to accurately detecting the boundaries of isolated signs within a continuous video stream. Additionally, the reliance on handcrafted features in existing models poses a challe…

Generative AI for Low-Carbon Artificial Intelligence of Things
Jinbo Wen, Ruichen Zhang, Dusit Niyato, Jiawen Kang, Hongyang Du, Yang Zhang, Zhu Han
https://arxiv.org/abs/2404.18077

Generative AI for Low-Carbon Artificial Intelligence of Things
By integrating Artificial Intelligence (AI) with the Internet of Things (IoT), Artificial Intelligence of Things (AIoT) has revolutionized many fields. However, AIoT is facing the challenges of energy consumption and carbon emissions due to the continuous advancement of mobile technology. Fortunately, Generative AI (GAI) holds immense potential to reduce carbon emissions of AIoT due to its excellent reasoning and generation capabilities. In this article, we explore the potential of GAI for carb…

Pathwise Relaxed Optimal Control of Rough Differential Equations
Prakash Chakraborty, Harsha Honnappa, Samy Tindel
https://arxiv.org/abs/2402.17900 https:/…

Pathwise Relaxed Optimal Control of Rough Differential Equations
This note lays part of the theoretical ground for a definition of differential systems modeling reinforcement learning in continuous time non-Markovian rough environments. Specifically we focus on optimal relaxed control of rough equations (the term relaxed referring to the fact that controls have to be considered as measure valued objects). With reinforcement learning in view, our reward functions encompass forms that involve an entropy-type term favoring exploration. In this context, our cont…

Calculating the Capacity Region of a Quantum Switch
Ian Tillman, Thirupathaiah Vasantam, Don Towsley, Kaushik P. Seshadreesan
https://arxiv.org/abs/2404.18818

Calculating the Capacity Region of a Quantum Switch
Quantum repeaters are necessary to fully realize the capabilities of the emerging quantum internet, especially applications involving distributing entanglement across long distances. A more general notion of this can be called a quantum switch, which connects to many users and can act as a repeater to create end-to-end entanglement between different subsets of these users. Here we present a method of calculating the capacity region of both discrete- and continuous-variable quantum switches that…

This https://arxiv.org/abs/2404.05635 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Semi-Infinite Programs for Robust Control and Optimization: Efficient Solutions and Extensions to Existence Constraints
Discrete-time robust optimal control problems generally take a min-max structure over continuous variable spaces, which can be difficult to solve in practice. In this paper, we extend the class of such problems that can be solved through a previously proposed local reduction method to consider those with existence constraints on the uncountable variables. We also consider the possibility of non-unique trajectories that satisfy equality and inequality constraints. Crucially, we show that the pro…

Data-adaptive structural change-point detection via isolation
Andreas Anastasiou, Sophia Loizidou
https://arxiv.org/abs/2404.19344 https://

Data-adaptive structural change-point detection via isolation
In this paper, a new data-adaptive method, called DAIS (Data Adaptive ISolation), is introduced for the estimation of the number and the location of change-points in a given data sequence. The proposed method can detect changes in various different signal structures; we focus on the examples of piecewise-constant and continuous, piecewise-linear signals. We highlight, however, that our algorithm can be extended to other frameworks, such as piecewise-quadratic signals. The data-adaptivity of our…

This https://arxiv.org/abs/2208.12006 has been replaced.
link: https://scholar.google.com/scholar?q=a

Lie Algebraic Quantum Phase Reduction
We introduce a general framework of phase reduction theory for quantum nonlinear oscillators. By employing the quantum trajectory theory, we define the limit-cycle trajectory and the phase according to a stochastic Schrödinger equation. Because a perturbation is represented by unitary transformation in quantum dynamics, we calculate phase response curves with respect to generators of a Lie algebra. Our method shows that the continuous measurement yields phase clusters and alters the phase resp…

Mapping between measurement scales in meta-analysis, with application to measures of body mass index in children
Annabel L Davies, A E Ades, Julian PT Higgins
https://arxiv.org/abs/2402.18298

Mapping between measurement scales in meta-analysis, with application to measures of body mass index in children
Quantitative evidence synthesis methods aim to combine data from multiple medical trials to infer relative effects of different interventions. A challenge arises when trials report continuous outcomes on different measurement scales. To include all evidence in one coherent analysis, we require methods to `map' the outcomes onto a single scale. This is particularly challenging when trials report aggregate rather than individual data. We are motivated by a meta-analysis of interventions to preven…

Continuous-variable quantum passive optical network
Adnan A. E. Hajomer, Ivan Derkach, Radim Filip, Ulrik L. Andersen, Vladyslav C. Usenko, Tobias Gehring
https://arxiv.org/abs/2402.16044

Continuous-variable quantum passive optical network
Building scalable and secure quantum networks with many users has a high application potential but also holds many practical challenges. A significant stride in this pursuit involves extending quantum key distribution, an information-theoretically secure method for establishing cryptographic keys between two distant users, from a point-to-point protocol implemented on direct optical connections to a quantum access network. Yet, realizations of quantum access networks have, so far, relied on pro…

Continuous-variable quantum passive optical network
Adnan A. E. Hajomer, Ivan Derkach, Radim Filip, Ulrik L. Andersen, Vladyslav C. Usenko, Tobias Gehring
https://arxiv.org/abs/2402.16044

Continuous-variable quantum passive optical network
Building scalable and secure quantum networks with many users has a high application potential but also holds many practical challenges. A significant stride in this pursuit involves extending quantum key distribution, an information-theoretically secure method for establishing cryptographic keys between two distant users, from a point-to-point protocol implemented on direct optical connections to a quantum access network. Yet, realizations of quantum access networks have, so far, relied on pro…

Tests of Macrorealism in Discrete and Continuous Variable Systems
Clement Mawby
https://arxiv.org/abs/2402.16537 https://arxiv.org/pd…

Tests of Macrorealism in Discrete and Continuous Variable Systems
I study several aspects of tests of macrorealism (MR), which for a given data set serves to give a quantitative signal of the presence of a specific notion of non-classical behaviour. The insufficiency of classical understanding underpins both the paradoxes of quantum mechanics, its future technological promise, and so these tests are of interest both foundationally and pragmatically. I derive generalisations of the Leggett-Garg (LG) inequalities and Fine's theorem, which together establish the…

Tests of Macrorealism in Discrete and Continuous Variable Systems
Clement Mawby
https://arxiv.org/abs/2402.16537 https://arxiv.org/pd…

Tests of Macrorealism in Discrete and Continuous Variable Systems
I study several aspects of tests of macrorealism (MR), which for a given data set serves to give a quantitative signal of the presence of a specific notion of non-classical behaviour. The insufficiency of classical understanding underpins both the paradoxes of quantum mechanics, its future technological promise, and so these tests are of interest both foundationally and pragmatically. I derive generalisations of the Leggett-Garg (LG) inequalities and Fine's theorem, which together establish the…