December 2024 arXiv papers — page 81

Haochen Niu, Xingwu Ji, Lantao Zhang, Fei Wen

The capability of autonomous exploration in complex, unknown environments is important in many robotic applications. While recent research on autonomous exploration have achieved much progress, there are still limitations, e.g., existing methods relying on greedy heuristics or optimal path planning are often hindered by repetitive paths and high computationa

Benjamin Warner, Antoine Chaffin, Benjamin Clavié, Orion Weller

Encoder-only transformer models such as BERT offer a great performance-size tradeoff for retrieval and classification tasks with respect to larger decoder-only models. Despite being the workhorse of numerous production pipelines, there have been limited Pareto improvements to BERT since its release. In this paper, we introduce ModernBERT, bringing modern mod

Tongzhou Mu, Zhaoyang Li, Stanisław Wiktor Strzelecki, Xiu Yuan

Learning policies from high-dimensional visual inputs, such as pixels and point clouds, is crucial in various applications. Visual reinforcement learning is a promising approach that directly trains policies from visual observations, although it faces challenges in sample efficiency and computational costs. This study conducts an empirical comparison of Stat

Jiayin Gu, Fan Zhang

The time evolution of Markovian open quantum systems is governed by Lindblad master equations, whose solution can be formally written as the Lindbladian exponential acting on the initial density matrix. By expanding this Lindbladian exponential into the Taylor series, we propose a generic method for integrating Lindblad master equations. In this method, the

Ziqing Wang, Yuetong Fang, Jiahang Cao, Hongwei Ren

Spiking Neural Networks (SNNs) are seen as an energy-efficient alternative to traditional Artificial Neural Networks (ANNs), but the performance gap remains a challenge. While this gap is narrowing through ANN-to-SNN conversion, substantial computational resources are still needed, and the energy efficiency of converted SNNs cannot be ensured. To address thi

Haojie Xie, Yirong Chen, Xiaofen Xing, Jingkai Lin

Currently, large language models (LLMs) have made significant progress in the field of psychological counseling. However, existing mental health LLMs overlook a critical issue where they do not consider the fact that different psychological counselors exhibit different personal styles, including linguistic style and therapy techniques, etc. As a result, thes

Mrigendra Singh Kushwaha

We give Gelfand-Tsetlin crystals for the Kostant-Kumar modules for the finite simple Lie algebra of type A. Kostant-Kumar modules are cyclic submodules of the tensor product of two irreducible highest weight modules of a symmetrizable Kac-Moody Lie algebras. In this case (type A), we also provide a polytopal model for Kostant-Kumar modules in terms of BiKoga

Maximilian Rimbach-Russ, Valentin John, Barnaby van Straaten, Stefano Bosco

All-electrical baseband control of qubits facilitates scaling up quantum processors by removing issues of crosstalk and heat generation. In semiconductor quantum dots, this is enabled by multi-spin qubit encodings, such as the exchange-only qubit, where high-fidelity readout and both single- and two-qubit operations have been demonstrated. However, their per

Shota Garuchava, Bachana Beradze, Tatia Sharia, George I. Japaridze

We consider a half-filled system of spin-1/2 fermions on a triangular ladder with spin-dependent hopping in the presence of spin-dependent flux. Using the Schrieffer-Wolff transformation, we derive an effective spin Hamiltonian describing the infrared properties of the system in the limit of strong on-site repulsion. The expansion is performed up to the thir

Indrajeet Mandal, Jitendra Soni, Mohd Zaki, Morten M. Smedskjaer

Large language models (LLMs) are revolutionizing self driving laboratories (SDLs) for materials research, promising unprecedented acceleration of scientific discovery. However, current SDL implementations rely on rigid protocols that fail to capture the adaptability and intuition of expert scientists in dynamic experimental settings. We introduce Artificiall

Xiaocan Chen, Qilin Yin, Jiarui Liu, Wei Lu

Talking face generation (TFG) allows for producing lifelike talking videos of any character using only facial images and accompanying text. Abuse of this technology could pose significant risks to society, creating the urgent need for research into corresponding detection methods. However, research in this field has been hindered by the lack of public datase

Chen Zhao, Mengyuan Yu, Fan Yang, Peiguang Jing

Images captured in severe low-light circumstances often suffer from significant information absence. Existing singular modality image enhancement methods struggle to restore image regions lacking valid information. By leveraging light-impervious infrared images, visible and infrared image fusion methods have the potential to reveal information hidden in dark

Yuning Peng, Haiping Wang, Yuan Liu, Chenglu Wen

3D open-vocabulary scene understanding, which accurately perceives complex semantic properties of objects in space, has gained significant attention in recent years. In this paper, we propose GAGS, a framework that distills 2D CLIP features into 3D Gaussian splatting, enabling open-vocabulary queries for renderings on arbitrary viewpoints. The main challenge

Alva Kosasih, Ozlem Tugfe Demir, Emil Bjornson

A reconfigurable intelligent surface (RIS) alters the reflection of incoming signals based on the phase-shift configuration assigned to its elements. This feature can be used to improve the signal strength for user equipments (UEs), expand coverage, and enhance spectral efficiency in wideband communication systems. Having accurate channel state information (

Sebastian Koch, Johanna Wald, Mirco Colosi, Narunas Vaskevicius

Neural radiance fields are an emerging 3D scene representation and recently even been extended to learn features for scene understanding by distilling open-vocabulary features from vision-language models. However, current method primarily focus on object-centric representations, supporting object segmentation or detection, while understanding semantic relati

W. Shi, J. C. Liu

The even Gaussian dual Minkowski problem studied by Feng, Hu and Xu, In this paper, we consider the even $L_p$ dual-Gaussian Minkowski problem for $p>1$. The existence of $o$-symmetric solution in the case $p>1$ is obtained.

Priyanka Grover, Veer Singh Panwar

Let $\pi(A)$, $\xi(A)$ and $\nu(A)$, respectively, denote the number of positive, zero and negative eigenvalues of the matrix $A$. Then the triplet $(\pi(A), \xi(A), \nu(A))$ is called the \emph{inertia} of $A$ and is denoted by $\textup{Inertia(A)}$. Let $\beta$ be the beta function. The inertia of the matrix $\left[\beta(i,j )\right]$ is shown to be $\left

Jialong Wu, Zhenglin Wang, Linhai Zhang, Yilong Lai

Key-Value (KV) cache has become a bottleneck of LLMs for long-context generation. Despite the numerous efforts in this area, the optimization for the decoding phase is generally ignored. However, we believe such optimization is crucial, especially for long-output generation tasks based on the following two observations: (i) Excessive compression during the p

C. Ciampi, J. D. Frankland, D. Gruyer, N. Le Neindre

In this work we provide a model-independent experimental evaluation of the degree of isospin equilibration taking place in $^{58,64}$Ni+$^{58,64}$Ni collisions at 32 MeV/nucleon across varying reaction centralities. This result has been obtained by combining the complementary information provided by two different datasets, sharing common characteristics. The

Tony Cheng Tong, Sirui He, Zhiwen Shao, Dit-Yan Yeung

Evaluation metric of visual captioning is important yet not thoroughly explored. Traditional metrics like BLEU, METEOR, CIDEr, and ROUGE often miss semantic depth, while trained metrics such as CLIP-Score, PAC-S, and Polos are limited in zero-shot scenarios. Advanced Language Model-based metrics also struggle with aligning to nuanced human preferences. To ad

Jeonghun Park, Sung Whan Yoon

Recently, semantic communications have drawn great attention as the groundbreaking concept surpasses the limited capacity of Shannon's theory. Specifically, semantic communications probably become crucial in realizing visual tasks that demand massive network traffic. Although highly distinctive forms of visual semantics exist for computer vision tasks, a tho

Zhuo Liu, Ding Yu, Hangfeng He

Large Language Models (LLMs) show impressive inductive reasoning capabilities, enabling them to generate hypotheses that could generalize effectively to new instances when guided by in-context demonstrations. However, in real-world applications, LLMs' hypothesis generation is not solely determined by these demonstrations but is significantly shaped by task-s

Øystein Sørensen, Anja Stein, Waldir Leoncio Netto, David S. Leslie

The Bayesian Mallows model is a flexible tool for analyzing data in the form of complete or partial rankings, and transitive or intransitive pairwise preferences. In many potential applications of preference learning, data arrive sequentially and it is of practical interest to update posterior beliefs and predictions efficiently, based on the currently avail

R. Dey, A. P. Misra

We study the Rayleigh-Taylor instability (RTI) of electrostatic plane wave perturbations in compressible relativistic magnetoplasma fluids with thermal ions under gravity in three different cases of when (i) electrons are in isothermal equilibrium, i.e., classical or nondegenerate, (ii) electrons are fully degenerate (with $T_e=0$), and (iii) electrons are p

Alexandre Arias Junior, Patrik Wahlberg

We study continuity of the multiplier operator $e^{i q}$ acting on Gelfand--Shilov spaces, where $q$ is a polynomial on $\mathbf R^d$ of degree at least two with real coefficients. In the parameter quadrant for the spaces we identify a wedge that depends on the polynomial degree for which the operator is continuous. We also show that in a large part of the c

Marcel Heisler, Christian Becker-Asano

The ability to display rich facial expressions is crucial for human-like robotic heads. While manually defining such expressions is intricate, there already exist approaches to automatically learn them. In this work one such approach is applied to evaluate and control a robot head different from the one in the original study. To improve the mapping of facial

Jianqing Liang, Xinkai Wei, Min Chen, Zhiqiang Wang

Graph contrastive learning (GCL) has become a hot topic in the field of graph representation learning. In contrast to traditional supervised learning relying on a large number of labels, GCL exploits augmentation strategies to generate multiple views and positive/negative pairs, both of which greatly influence the performance. Unfortunately, commonly used ra

Giulio Magli

On April 1, 2471 bC an impressive, unpredictable phenomenon occurred over the Delta of the Nile: a total solar eclipse, with totality band almost centered on the sacred city of Buto, and with the capital Memphis very close (>95%) to totality. This date is compatible with existing chronologies for the reign of Pharaoh Shepseskaf, who adopted a clamorous symbo

Fernando Amodeo, Luis Merino, Fernando Caballero

4D millimeter-wave (mmWave) radars are sensors that provide robustness against adverse weather conditions (rain, snow, fog, etc.), and as such they are increasingly used for odometry and SLAM (Simultaneous Location and Mapping). However, the noisy and sparse nature of the returned scan data proves to be a challenging obstacle for existing registration algori

Andreas Mueller

Parallel kinematic manipulators (PKM) are characterized by closed kinematic loops, due to the parallel arrangement of limbs but also due to the existence of kinematic loops within the limbs. Moreover, many PKM are built with limbs constructed by serially combining kinematic loops. Such limbs are called hybrid, which form a particular class of complex limbs.

Christopher Joel Russo, Kabir Husain, Arvind Murugan

All biological systems are subject to perturbations: due to thermal fluctuations, external environments, or mutations. Yet, while biological systems are composed of thousands of interacting components, recent high-throughput experiments show that their response to perturbations is surprisingly low-dimensional: confined to only a few stereotyped changes out o

Chuanhao Li, Zhen Li, Chenchen Jing, Xiaomeng Fan

Compositional generalization is the capability of a model to understand novel compositions composed of seen concepts. There are multiple levels of novel compositions including phrase-phrase level, phrase-word level, and word-word level. Existing methods achieve promising compositional generalization, but the consistency of compositional generalization across

Qiaoying Qu, Shiyu Shen

Autoregressive conditional image generation algorithms are capable of generating photorealistic images that are consistent with given textual or image conditions, and have great potential for a wide range of applications. Nevertheless, the majority of popular autoregressive image generation methods rely heavily on vector quantization, and the inherent discre

Ivailo Hartarsky, Cristina Toninelli

The goal of this book is to provide an introduction to the mathematical theory of Kinetically constrained models developed in the last twenty years, intended for both mathematicians and physicists.

Yiyan Xu

We would like to study new Ricci flow invariant curvature conditions. Specifically, we provide quantitative evidence for an unpublished conjecture of B\"ohm and Wilking. As an application, we study the topology of manifolds with pinched curvature.

Lars Bengel, Giovanni Buraglio, Jan Maly, Kenneth Skiba

In this paper, we introduce a new family of argument-ranking semantics which can be seen as a refinement of the classification of arguments into skeptically accepted, credulously accepted and rejected. To this end we use so-called social ranking functions which have been developed recently to rank individuals based on their performance in groups. We provide

Eitan Wagner, Nitay Alon, Joseph M. Barnby, Omri Abend

Theory of Mind (ToM) capabilities in LLMs have recently become a central object of investigation. Cognitive science distinguishes between two steps required for ToM tasks: 1) determine whether to invoke ToM, which includes the appropriate Depth of Mentalizing (DoM), or level of recursion required to complete a task; and 2) applying the correct inference give

Xiu Yuan, Tongzhou Mu, Stone Tao, Yunhao Fang

Recent advancements in robot learning have used imitation learning with large models and extensive demonstrations to develop effective policies. However, these models are often limited by the quantity, quality, and diversity of demonstrations. This paper explores improving offline-trained imitation learning models through online interactions with the environ

Salvatore Capozziello, Maurizio Capriolo, Amodio Carleo, Gaetano Lambiase

General Relativity suffers for two main problems which have not yet been overcome: it predicts spacetime singularities and cannot be formulated as a perturbative renormalizable theory. In particular, many attempts have been made for avoiding singularities, such as considering higher order or infinite derivative theories. The price to pay in both cases is to

H. -W. Glock, J. Knobloch, J. -M. Köszegi, A. Velez

Under-vacuum low- and mid-temperature baking revealed beneficial effects on the performance of niobium-made cavities for superconducting radio-frequency (SRF) applications, primarily seen in particle accelerators. Such a baking process is typically performed in a dedicated oven. In this paper the experimental investigation is described, whether an appropriat

Rahul Sundar, Yucong Hu, Nishant Parashar, Antoine Blanchard

Deterministic regression-based downscaling models for climate variables often suffer from spectral bias, which can be mitigated by generative models like diffusion models. To enable efficient and reliable simulation of extreme weather events, it is crucial to achieve rapid turnaround, dynamical consistency, and accurate spatio-temporal spectral recovery. We

Yansheng Mao, Jiaqi Li, Fanxu Meng, Jing Xiong

Long context understanding remains challenging for large language models due to their limited context windows. This paper introduces Long Input Fine-Tuning (LIFT) for long context modeling, a novel framework that enhances LLM performance on long-context tasks by adapting model parameters to the context at test time. LIFT enables efficient processing of lengt

B. Sania, Z. Iqbal, Ch. Rozina, Hafeez ur Rehman

Separate spin evolution-quantum hydrodynamic (SSE-QHD) model is employed to address the nonlinear propagation of ion-acoustic wave (IAW) and spin electron-acoustic wave (SEAW) in a spin polarized electron-ion plasma. The analysis has been made under the self-consistent field approximation and with exchange correlation effects. The reductive perturbation meth

Arne Gevaert, Yvan Saeys

The black box problem in machine learning has led to the introduction of an ever-increasing set of explanation methods for complex models. These explanations have different properties, which in turn has led to the problem of method selection: which explanation method is most suitable for a given use case? In this work, we propose a unifying framework of attr

Zhaohong Sun, Makoto Yokoo

Student placements under diversity constraints are a common practice globally. This paper addresses the selection of students by a single school under a \emph{one-to-one convention}, where students can belong to multiple types but are counted only once based on one type. While existing algorithms in economics and computer science aim to help schools meet div

Jean-Michel Pipeau

In this text I study quantum dynamics of quantized linear automorphisms of the torus after the Ehrenfest time. I show that, in the wave packet basis, the 'matrix' of the associated propagator is well approximated by Birkhoff sums of nilrotations on the torus. In the second part, I conduct a thorough study of these sums and relate the equidistribution of evol

Jing Guo, Ziwei Wang, Weibang Bai

Various pipes are extensively used in both industrial settings and daily life, but the pipe inspection especially those with narrow sizes are still very challenging with tremendous time and manufacturing consumed. Quadrupedal robots, inspired from patrol dogs, can be a substitution of traditional solutions but always suffer from navigation and locomotion dif

Eran Assaf, Chan Ieong Kuan, David Lowry-Duda, Alexander Walker

We introduce a family of Dirichlet series associated to real quadratic number fields that generalize the ordinary Fibonacci zeta function $\sum F(n)^{-s}$, where $F(n)$ denotes the $n$th Fibonacci number. We then give three different methods of meromorphic continuation to $\mathbb{C}$. Two are purely analytic and classical, while the third uses shifted convo

Elnur Gasanov, Peter Richtárik

Stochastic proximal point methods have recently garnered renewed attention within the optimization community, primarily due to their desirable theoretical properties. Notably, these methods exhibit a convergence rate that is independent of the Lipschitz smoothness constants of the loss function, a feature often missing in the loss functions of modern ML appl

Jiaping Ren, Jiahao Xiang, Hongfei Gao, Jinchuan Zhang

Fuel efficiency is a crucial aspect of long-distance cargo transportation by oil-powered trucks that economize on costs and decrease carbon emissions. Current predictive control methods depend on an accurate model of vehicle dynamics and engine, including weight, drag coefficient, and the Brake-specific Fuel Consumption (BSFC) map of the engine. We propose a

Márton Karsai, Hang-Hyun Jo

Bursty dynamics characterizes systems that evolve through short active periods of several events, which are separated by long periods of inactivity. Systems with such temporal heterogeneities are not only found in nature but also include examples from most aspects of human dynamics. In this Chapter, we briefly introduce such bursty phenomena by first walking

Xiaohai Li, Bineng Zhong, Qihua Liang, Guorong Li

Effectively constructing context information with long-term dependencies from video sequences is crucial for object tracking. However, the context length constructed by existing work is limited, only considering object information from adjacent frames or video clips, leading to insufficient utilization of contextual information. To address this issue, we pro

Carole Chatel, Ludovic Mathieu, Mourad Aïche, Abdel Rebii

The accuracy of neutronics simulations of actual or future reactor cores is nowadays driven by the precision of the nuclear data used as input. Among the most important neutron-induced fission cross sections to understand well are the actinides. It is, indeed, of primary importance to know accurately these cross sections around 1 MeV for the safety of Genera

Zhengfei Xu, Sijia Zhao, Yanchao Hao, Xiaolong Liu

Visual Entity Linking (VEL) is a crucial task for achieving fine-grained visual understanding, matching objects within images (visual mentions) to entities in a knowledge base. Previous VEL tasks rely on textual inputs, but writing queries for complex scenes can be challenging. Visual inputs like clicks or bounding boxes offer a more convenient alternative.

Ziyi Ni, Yifan Li, Daxiang Dong

The exceptional capabilities of large language models (LLMs) have substantially accelerated the rapid rise and widespread adoption of agents. Recent studies have demonstrated that generating Python code to consolidate LLM-based agents' actions into a unified action space (CodeAct) is a promising approach for developing real-world LLM agents. However, this st

Xuemei Tang, Xufeng Duan, Zhenguang G. Cai

Large language models (LLMs) have emerged as a potential solution to automate the complex processes involved in writing literature reviews, such as literature collection, organization, and summarization. However, it is yet unclear how good LLMs are at automating comprehensive and reliable literature reviews. This study introduces a framework to automatically

Evariste Njomgue Fotso, Buntheng Ly, Hubert Cochet, Maxime Sermesant

Cardiac disease evaluation depends on multiple diagnostic modalities: electrocardiogram (ECG) to diagnose abnormal heart rhythms, and imaging modalities such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and echocardiography to detect signs of structural abnormalities. Each of these modalities brings complementary information for a better dia

Jinxia Xie, Bineng Zhong, Qihua Liang, Ning Li

How to make a good trade-off between performance and computational cost is crucial for a tracker. However, current famous methods typically focus on complicated and time-consuming learning that combining temporal and appearance information by input more and more images (or features). Consequently, these methods not only increase the model's computational sou

Zecheng Hao, Qichao Ma, Kang Chen, Yi Zhang

Spiking Neural Network (SNN), as a brain-inspired and energy-efficient network, is currently facing the pivotal challenge of exploring a suitable and efficient learning framework. The predominant training methodologies, namely Spatial-Temporal Back-propagation (STBP) and ANN-SNN Conversion, are encumbered by substantial training overhead or pronounced infere

Shengeng Tang, Jiayi He, Dan Guo, Yanyan Wei

Sign Language Production (SLP) aims to generate semantically consistent sign videos from textual statements, where the conversion from textual glosses to sign poses (G2P) is a crucial step. Existing G2P methods typically treat sign poses as discrete three-dimensional coordinates and directly fit them, which overlooks the relative positional relationships amo

Alessandro Viani, Boris A Gutman, Emile d'Angremont, Marco Lorenzi

Modelling the progression of Degenerative Diseases (DD) is essential for detection, prevention, and treatment, yet it remains challenging due to the heterogeneity in disease trajectories among individuals. Factors such as demographics, genetic conditions, and lifestyle contribute to diverse phenotypical manifestations, necessitating patient stratification ba

Tongtong Zhang, Zhiyong Cui, Bingzhang Wang, Yilong Ren

In urban computing, precise and swift forecasting of multivariate time series data from traffic networks is crucial. This data incorporates additional spatial contexts such as sensor placements and road network layouts, and exhibits complex temporal patterns that amplify challenges for predictive learning in traffic management, smart mobility demand, and urb

Ignace Bleukx, Hélène Verhaeghe, Bart Bogaerts, Tias Guns

In eXplainable Constraint Solving (XCS), it is common to extract a Minimal Unsatisfiable Subset (MUS) from a set of unsatisfiable constraints. This helps explain to a user why a constraint specification does not admit a solution. Finding MUSes can be computationally expensive for highly symmetric problems, as many combinations of constraints need to be consi

Mamoru Aihara

In this paper, we obtain a stochastic approximation that converges to the viscosity solution of the weighted $p$-Laplace equation. We consider a stochastic two-player zero-sum game controlled by a random walk, two player's choices, and the gradient of the weight function. The proof is based on the boundary conditions in the viscosity sense and the comparison

Kecheng Liu, Takeshi Suzuki, Yigui Zhong, Teruto Kanai

Non-equilibrium states generated by ultrafast laser pulses are characterized by specific phenomena that are not accessible in static measurements. Previous time- and angle-resolved photoemission spectroscopy (TARPES) studies on rare-earth tritelluride materials have revealed the laser-driven melting of the charge density wave order as well as its collective

Bastien Grosse

In this paper, we prove a quantitative approximation result by orthonormal polynomials associated to an exponential weight of the form e -$\Phi$ , where $\Phi$ is an even polynomial with positive leading coefficient. This result is a consequence of a recursion relation for the orthonormal polynomials and of the strong Poincar{\'e} inequality. Simulations are

Wenye Lin, Jonathan Roberts, Yunhan Yang, Samuel Albanie

Large Language Models (LLMs) are increasingly deployed in real-world applications that demand complex reasoning. To track progress, robust benchmarks are required to evaluate their capabilities beyond superficial pattern recognition. However, current LLM reasoning benchmarks often face challenges such as insufficient interpretability, performance saturation

Muhammad Emad-ud-din

The paper presents a novel Wi-Fi fingerprinting system that uses Channel State Information (CSI) data for fine-grained pedestrian localization. The proposed system exploits the frequency diversity and spatial diversity of the features extracted from CSI data to generate a 2D+channel image termed as a CSI Fingerprint Map. We then use this CSI Fingerprint Map

Silvano Cortesi, Michele Crabolu, Prodromos-Vasileios Mekikis, Giovanni Bellusci

Asset tracking solutions have proven their significance in industrial contexts, as evidenced by their successful commercialization (e.g., Hilti On!Track). However, a seamless solution for matching assets with their users, such as operators of construction power tools, is still missing. By enabling assetuser matching, organizations gain valuable insights that

Jinghan Sun, Dong Wei, Zhe Xu, Donghuan Lu

Anatomical abnormality detection and report generation of chest X-ray (CXR) are two essential tasks in clinical practice. The former aims at localizing and characterizing cardiopulmonary radiological findings in CXRs, while the latter summarizes the findings in a detailed report for further diagnosis and treatment. Existing methods often focused on either ta

Valentina Giovacchini, Eric Favre, Francesco A. Volpe

Renaissance Fusion proposes thick liquid metal walls as plasma-facing components for future commercial fusion reactors. It designs and operates proof-of-concept experiments aiming at actively suspending and stabilizing a flowing free-surface liquid metal layer against gravity using Lorentz forces. The first operating prototype consists of a 1 m-diameter cham

van Duong Dinh, Nicolas Rougerie

We study the mean-field limit of the 1D bosonic canonical ensemble in a superharmonic trap. This is the regime with temperature proportional to particle number, both diverging to infinity, and correspondingly scaled interactions. We prove that the limit model is a classical field theory based on a non-linear Schr{\"o}dinger-Gibbs measure conditioned on the L

Tobias Dornheim, Panagiotis Tolias, Zhandos Moldabekov, Jan Vorberger

We explore the recently introduced $\eta$-ensemble approach to compute the free energy directly from \emph{ab initio} path integral Monte Carlo (PIMC) simulations [T.~Dornheim \emph{et al.}, arXiv:2407.01044] and apply it to the archetypal uniform electron gas model both in the warm dense matter and strongly coupled regimes. Specifically, we present an in-de

Elif Köksal-Ersöz, Pascal Chossat, Frédéric Lavigne

Adaptation of behavior requires the brain to change goals in a changing environment. Synaptic learning has demonstrated its effectiveness in changing the probability of selecting actions based on their outcome. In the extreme case, it is vital not to repeat an action to a given goal that led to harmful punishment. The present model proposes a simple neural m

Shuang-Shuang Chen, Hsiang-Yi Karen Yang, Hsi-Yu Schive, Chun-Yen Chen

The origin of cool-core (CC) and non-cool-core (NCC) dichotomy of galaxy clusters remains uncertain. Previous simulations have found that cluster mergers are effective in destroying CCs but fail to prevent overcooling in cluster cores when radiative cooling is included. Feedback from active galactic nuclei (AGN) is a promising mechanism for balancing cooling

Di Xiong, Shuoyuan Wang, Lei Zhang, Wenbo Huang

Human Activity Recognition (HAR) aims to recognize activities by training models on massive sensor data. In real-world deployment, a crucial aspect of HAR that has been largely overlooked is that the test sets may have different distributions from training sets due to inter-subject variability including age, gender, behavioral habits, etc., which leads to po

Etienne Lasalle, Rémi Vaudaine, Titouan Vayer, Pierre Borgnat

Clustering the nodes of a graph is a cornerstone of graph analysis and has been extensively studied. However, some popular methods are not suitable for very large graphs: e.g., spectral clustering requires the computation of the spectral decomposition of the Laplacian matrix, which is not applicable for large graphs with a large number of communities. This w

José Camacho, Michael Sorochan Armstrong, Luz García-Martínez, Caridad Díaz

Over the past few years, technological advances have allowed for measurement of omics data at the cell level, creating a new type of data generally referred to as single-cell (sc) omics. On the other hand, the so-called spatial omics are a family of techniques that generate biological information in a spatial domain, for instance, in the volume of a tissue.

Hao Xiao

This paper characterizes sharp solute-solvent interfaces in hydrophobic environments, and there are three major ingredients. The first is the variational implicit solvent model (VISM) which establishes the free energy functional of arbitrary solvation states. The minimization of this functional yields a PDE which characterizes both the stable and saddle solu

Xinyang Liu, Pengchao Han, Xuan Li, Bo Liu

Decentralized federated learning (DFL) realizes cooperative model training among connected clients without relying on a central server, thereby mitigating communication bottlenecks and eliminating the single-point failure issue present in centralized federated learning (CFL). Most existing work on DFL focuses on supervised learning, assuming each client poss

Chang-Chun He, Shao-Gang Xu, Jiarui Zeng, Weijie Huang

Energy degeneracy in physical systems may be induced by symmetries of the Hamiltonian, and the resonance of degeneracy states in carbon nanostructures can effectively enhance the stability of the system. Combining the octet rule, we introduce a parameter-free statistical model to determine the physical properties by lifting the energy degeneracy in carbon na

E. Eftekhari, A. Vazdekis, R. Riffel, L. G. Dahmer-Hahn

Many recent studies have pointed out significant discrepancies between observations and models of stellar populations in the near-infrared (NIR). With current and future observing facilities being focused in this wavelength range, properly assessing and solving these issues is of utmost importance. Here, we present the first application of the extragalactic

Peng Shu, Meng Zhao, Zhen-Yi Li, Wei Sun

The increasing population of objects in geostationary orbit has raised concerns about the potential risks posed by debris clouds resulting from fragmentation. The short-term evolution and associated hazards of debris generated by collisions in the geostationary region is investigated in this study. The initial distribution of two debris clouds is modeled usi

L. G. Dahmer-Hahn, A. L. Chies-Santos, E. Eftekhari, E. Zanatta

One way to constrain the evolutionary histories of galaxies is to analyse their stellar populations. In the local Universe, our understanding of the stellar population properties of galaxies has traditionally relied on the study of optical absorption and emission-line features. In order to overcome limitations intrinsic to this wavelength range, such as the

Sittinon Jirattikansakul, Inbar Oren, Assaf Rinot

In a classical paper by Ben-David and Magidor, a model of set theory was exhibited in which $\aleph_{\omega+1}$ carries a uniform ultrafilter that is $\theta$-indecomposable for every uncountable cardinal $\theta<\aleph_\omega$. In this paper, we give a global version of this result, as follows: Assuming the consistency of a supercompact cardinal, we produce

Laura Shou, Wei Wang, Shiwen Zhang

In this work, we study the Anderson model on the Sierpinski gasket graph. We first identify the almost sure spectrum of the Anderson model when the support of the random potential has no gaps. We then prove the existence of the integrated density states of the Anderson model and show that it has Lifshitz tails with Lifshitz exponent determined by the ratio o

Wei Tang, Yixin Cao, Yang Deng, Jiahao Ying

Knowledge utilization is a critical aspect of LLMs, and understanding how they adapt to evolving knowledge is essential for their effective deployment. However, existing benchmarks are predominantly static, failing to capture the evolving nature of LLMs and knowledge, leading to inaccuracies and vulnerabilities such as contamination. In this paper, we introd

Samuel Chassot, Viktor Kunčak

In this project, we explore the concept of invertibility applied to serialisation and lexing frameworks. Recall that, on one hand, serialisation is the process of taking a data structure and writing it to a bit array while parsing is the reverse operation, i.e., reading the bit array and constructing the data structure back. While lexing, on the other hand,

Imre Kondor, Gábor Papp

We study the $\pm J$ SK model for small $N$'s up to $N=9$. We sort the $2^{N(N-1)/2}$ possible realizations of the coupling matrix into equivalence classes according to the gauge symmetry and permutation symmetry and determine the energy spectra for each of these classes. We also study the energy landscape in these small systems and find that the elements of

Bhawana Chhaglani, Alan Seefeldt

Tech neck is a modern epidemic caused by prolonged device usage and it can lead to significant neck strain and discomfort. This paper addresses the challenge of detecting and preventing tech neck syndrome using non-invasive ubiquitous sensing techniques. We present NeckCare, a novel system leveraging hearable sensors, including IMUs and microphones, to monit

Shanu Kumar, Gauri Kholkar, Saish Mendke, Anubhav Sadana

With the growth of social media and large language models, content moderation has become crucial. Many existing datasets lack adequate representation of different groups, resulting in unreliable assessments. To tackle this, we propose a socio-culturally aware evaluation framework for LLM-driven content moderation and introduce a scalable method for creating

Jiankun Zhu, Sicheng Zhao, Jing Jiang, Wenbo Tang

Visual emotion recognition (VER), which aims at understanding humans' emotional reactions toward different visual stimuli, has attracted increasing attention. Given the subjective and ambiguous characteristics of emotion, annotating a reliable large-scale dataset is hard. For reducing reliance on data labeling, domain adaptation offers an alternative solutio

Wenbo Liu, Akang Wang, Wenguo Yang

The augmentation scheme provides a nontraditional approach to nonlinear integer programming by iteratively refining incumbent solutions along objective-improving directions from the Graver basis. Its main computational bottleneck, however, lies in the practical difficulty of accessing such directions. To address this challenge, we develop a massively paralle

Qianyue Wang, Jinwu Hu, Zhengping Li, Yufeng Wang

Long-form story generation task aims to produce coherent and sufficiently lengthy text, essential for applications such as novel writingand interactive storytelling. However, existing methods, including LLMs, rely on rigid outlines or lack macro-level planning, making it difficult to achieve both contextual consistency and coherent plot development in long-f

Jiyao Wang, Ange Wang, Song Yan, Dengbo He

In real-world driving scenarios, multiple states occur simultaneously due to individual differences and environmental factors, complicating the analysis and estimation of driver states. Previous studies, limited by experimental design and analytical methods, may not be able to disentangle the relationships among multiple driver states and environmental facto

Jiawei Zhang

This research applies Harold Demsetz's concept of the nirvana approach to the realm of AI governance and debunks three common fallacies in various AI policy proposals--"the grass is always greener on the other side," "free lunch," and "the people could be different." Through this, I expose fundamental flaws in the current AI regulatory proposal. First, some

Aodi Li, Liansheng Zhuang, Xiao Long, Minghong Yao

Domain generalization aims to learn a model from multiple training domains and generalize it to unseen test domains. Recent theory has shown that seeking the deep models, whose parameters lie in the flat minima of the loss landscape, can significantly reduce the out-of-domain generalization error. However, existing methods often neglect the consistency of lo

Luca Scrucca

The clustering of bounded data presents unique challenges in statistical analysis due to the constraints imposed on the data values. This paper introduces a novel method for model-based clustering specifically designed for bounded data. Building on the transformation-based approach to Gaussian mixture density estimation introduced by Scrucca (2019), we exten

Ruichen Qiu, Yibo Miao, Shiwen Wang, Lijia Yu

The Kolmogorov-Arnold Network (KAN) is a new network architecture known for its high accuracy in several tasks such as function fitting and PDE solving. The superior expressive capability of KAN arises from the Kolmogorov-Arnold representation theorem and learnable spline functions. However, the computation of spline functions involves multiple iterations, w

Jakob Schwerter, Andrés Romero, Florian Dumpert, Markus Pauly

Tree-based learning methods such as Random Forest and XGBoost are still the gold-standard prediction methods for tabular data. Feature importance measures are usually considered for feature selection as well as to assess the effect of features on the outcome variables in the model. This also applies to survey data, which are frequently encountered in the soc