December 2024 arXiv papers — page 140

Anant Prakash Awasthi, Girdhar Gopal Agarwal, Chandraketu Singh, Rakshit Varma

The growing reliance on artificial intelligence (AI) in customer support has significantly improved operational efficiency and user experience. However, traditional machine learning (ML) approaches, which require extensive local training on sensitive datasets, pose substantial privacy risks and compliance challenges with regulations like the General Data Pro

Jonas Nüßlein, Maximilian Zorn, Fabian Ritz, Jonas Stein

Reinforcement Learning (RL) policies are designed to predict actions based on current observations to maximize cumulative future rewards. In real-world applications (i.e., non-simulated environments), sensors are essential for measuring the current state and providing the observations on which RL policies rely to make decisions. A significant challenge in de

Xuan-Zhao Gao, Yi-Jia Wang, Pan Zhang, Jin-Guo Liu

The branching algorithm is a fundamental technique for designing fast exponential-time algorithms to solve combinatorial optimization problems exactly. It divides the entire solution space into independent search branches using predetermined branching rules, and ignores the search on suboptimal branches to reduce the time complexity. The complexity of a bran

Reza Bayat, Mohammad Pezeshki, Elvis Dohmatob, David Lopez-Paz

Neural networks often learn simple explanations that fit the majority of the data while memorizing exceptions that deviate from these explanations.This behavior leads to poor generalization when the learned explanations rely on spurious correlations. In this work, we formalize the interplay between memorization and generalization, showing that spurious corre

Jiawei Meng, Danail Stoyanov

With the development of science and technology, mobile robots are playing a significant important role in the new round of world revolution. Further, mobile robots might assist or replace human beings in a great number of areas. To increase the degree of automation for mobile robots, advanced motion planners need to be integrated into them to cope with vario

Alfredo Garrachón Ruiz, Tomás de la Rosa, Daniel Borrajo

The high inference cost of Large Language Models (LLMs) poses challenges, especially for tasks requiring lengthy outputs. However, natural language often contains redundancy, which presents an opportunity for optimization. We have observed that LLMs can generate distilled language (i.e., concise outputs that retain essential meaning) when prompted appropriat

Kai Wang, Li Yu, Jianhua Zhang, Yixuan Tian

The stability and reliability of wireless data transmission in vehicular networks face significant challenges due to the high dynamics of path loss caused by the complexity of rapidly changing environments. This paper proposes a multi-modal environmental sensing-based path loss prediction architecture (MES-PLA) for V2I communications. First, we establish a m

Bruno Le Floch, Boris Pioline, Rishi Raj

BPS states in type II string compactified on a Calabi-Yau threefold can typically be decomposed as moduli-dependent bound states of absolutely stable constituents, with a hierarchical structure labelled by attractor flow trees. This decomposition is best understood from the scattering diagram, an arrangement of real codimension-one loci (or rays) in the spac

Greg Heinrich, Mike Ranzinger, Hongxu, Yin

Agglomerative models have recently emerged as a powerful approach to training vision foundation models, leveraging multi-teacher distillation from existing models such as CLIP, DINO, and SAM. This strategy enables the efficient creation of robust models, combining the strengths of individual teachers while significantly reducing computational and resource de

Wang Liang

Multilingual transfer ability, which reflects how well models fine-tuned on one source language can be applied to other languages, has been well studied in multilingual pre-trained models. However, the existence of such capability transfer between natural language and gene sequences/languages remains under explored.This study addresses this gap by drawing in

Bo-Qiang Lu

Domain walls represent two-dimensional topological defects that emerge from the spontaneous breaking of discrete symmetries in various new physics models. In this study, we undertake the first calculation of gravitational waves produced by scalar perturbations generated from the gravitational wave network. Our findings indicate that the gravitational wave sp

Tyler J. Kovach, Daniel Schug, M. A. Wolfe, E. R. MacQuarrie

Semiconductor quantum dot (QD) devices have become central to advancements in spin-based quantum computing. However, the increasing complexity of modern QD devices makes calibration and control -- particularly at elevated temperatures -- a bottleneck to progress, highlighting the need for robust and scalable autonomous solutions. A major hurdle arises from t

Shuo Yang, Bardh Prenkaj, Gjergji Kasneci

Despite the widespread use of LLMs due to their superior performance in various tasks, their high computational costs often lead potential users to opt for the pretraining-finetuning pipeline. However, biases prevalent in manually constructed datasets can introduce spurious correlations between tokens and labels, creating so-called shortcuts and hindering th

Tong Wu, Yinghao Xu, Ryan Po, Mengchen Zhang

Recent advances in text-to-image generation have enabled the creation of high-quality images with diverse applications. However, accurately describing desired visual attributes can be challenging, especially for non-experts in art and photography. An intuitive solution involves adopting favorable attributes from the source images. Current methods attempt to

Shuyu Shen, Sirong Lu, Leixian Shen, Yuyu Luo

Visualization authoring is an iterative process requiring users to adjust parameters to achieve desired aesthetics. Due to its complexity, users often create defective visualizations and struggle to fix them. Many seek help on forums (e.g., Stack Overflow), while others turn to AI, yet little is known about the strengths and limitations of these approaches,

Xiaoqun Liu, Weiming Qi, Qiben Yan

Large language models (LLMs) remain vulnerable to jailbreak attacks in which adversarial prompts induce harmful outputs. Existing defenses often require access to the model internals or additional training, limiting their applicability for service providers deployed through black-box APIs. In this paper, we propose DYNASHIELD, a moving target defense framewo

Yunkai Bai, Jungmin Park, Domenic Forte

Side-channel based instruction disassembly has been proposed as a low-cost and non-invasive approach for security applications such as IP infringement detection, code flow analysis, malware detection, and reconstructing unknown code from obsolete systems. However, existing approaches to side-channel based disassembly rely on setups to collect and process sid

Matt. J. Bedalov, Matt Blakely, Peter. D. Buttler, Caitlin Carnahan

We report on the fault-tolerant operation of logical qubits on a neutral atom quantum computer, with logical performance surpassing physical performance for multiple circuits including Bell states (12x error reduction), random circuits (15x), and a prototype Anderson Impurity Model ground state solver for materials science applications (up to 6x, non-fault-t

Massimiliano Semeraro, Giuseppe Negro, Antonio Suma, Federico Corberi

Due to its inherent intertwinement with irreversibility, entropy production is a prime observable to monitor in systems of active particles. In this numerical study, entropy production in the liquid, hexatic and solid phases of a two-dimensional system of active Brownian particles is examined at both average and fluctuation level. The trends of averages as f

Nimrod Busany, Ethan Hadar, Hananel Hadad, Gil Rosenblum

Eliciting requirements for Business Intelligence (BI) systems remains a significant challenge, particularly in changing business environments. This paper introduces a novel AI-driven system, called AutoBIR, that leverages semantic search and Large Language Models (LLMs) to automate and accelerate the specification of BI requirements. The system facilitates i

Lucy Martinez

The power of symbolic computation, as opposed to mere numerical computation, is illustrated with efficient algorithms for studying the generalized gambler's ruin problem in one and two dimensions. We also consider a new generalization of the classical gambler's ruin where we add a third step which we call the mirror step. In this scenario, we provide closed

Greta Lupi, Jose L. Lado

Impurities in quantum materials have provided successful strategies for learning properties of complex states, ranging from unconventional superconductors to topological insulators. In quantum magnetism, inferring the Hamiltonian of an engineered system becomes a challenging open problem in the presence of complex interactions. Here we show how a supervised

Patrick Concha, Lucrezia Ravera

In this paper, we present and classify the supersymmetric extensions of extended kinematical algebras, at the basis of non-Lorentzian physics theories. The diverse kinematical superalgebras are here derived by applying non- and ultra-relativistic expansion procedures involving different semigroups. We then build three-dimensional Chern-Simons non-Lorentzian

Soroush Vahidi, Ioannis Koutis

The Minimum Weighted Feedback Arc Set (MWFAS) problem is closely related to the task of deriving a global ranking from pairwise comparisons. Recent work by He et al. (ICML 2022) advanced the state of the art on ranking benchmarks using learning based methods, but did not examine the underlying connection to MWFAS. In this paper, we investigate this relations

Jiawei Meng, Yuanchang Liu, Danail Stoyanov

During recent years, unmanned surface vehicles are extensively utilised in a variety of maritime applications such as the exploration of unknown areas, autonomous transportation, offshore patrol and others. In such maritime applications, unmanned surface vehicles executing relevant missions that might collide with potential static obstacles such as islands a

S. A. Khrapak, A. G. Khrapak

We calculate the shear relaxation times in four important simple monatomic model fluids: Lennard-Jones, Yukawa, soft-sphere and hard-sphere fluids. It is observed that in properly reduced units, the shear relaxation times exhibit quasi-universal behaviour when the density increases from the gas-like low values to the high-density regime near crystallization.

Eugenio Paris, Wenliang Zhang, Yi Tseng, Anna Efimenko

We probe the spectrum of elementary excitations in SrIrO$_3$ by using heterostructured [(SrIrO$_3$)$_m$/(SrTiO$_3$)$_l$] samples to approach the bulk limit. Our resonant inelastic x-ray scattering (RIXS) measurements at the Ir $L_3$-edge reveal a robust low-lying collective magnetic mode with an antiferromagnetic (AF) dispersion similar to the insulators Sr$

Miquel Saucedo, Sergey Tikhonov

We characterize the parameters $(\alpha,\beta,p,q)$ for which the condition $f|x|^\alpha\in L^p$ and $\widehat{f}|\xi|^\beta\in L^q$ implies the validity of the Poisson summation formula, thus completing the study of Kahane and Lemari\'e-Rieusset.

Yixuan Li, Xingjian Ran, Linning Xu, Tao Lu

Buildings are primary components of cities, often featuring repeated elements such as windows and doors. Traditional 3D building asset creation is labor-intensive and requires specialized skills to develop design rules. Recent generative models for building creation often overlook these patterns, leading to low visual fidelity and limited scalability. Drawin

Axel Martinez, Emilio Hernandez, Matthieu Olague, Gustavo Olague

Low-light image enhancement remains an open problem, and the new wave of artificial intelligence is at the center of this problem. This work describes the use of genetic algorithms for optimizing analytical models that can improve the visualization of images with poor light. Genetic algorithms are part of metaheuristic approaches, which proved helpful in sol

Anubhav Jain, Yuya Kobayashi, Takashi Shibuya, Yuhta Takida

Recent advancements in text-to-image diffusion models have brought them to the public spotlight, becoming widely accessible and embraced by everyday users. However, these models have been shown to generate harmful content such as not-safe-for-work (NSFW) images. While approaches have been proposed to erase such abstract concepts from the models, jail-breakin

Kieran Richards, Kelly Fleetwood, Regina Prigge, Paolo Missier

The co-occurrence of multiple long-term conditions (MLTC), or multimorbidity, in an individual can reduce their lifespan and severely impact their quality of life. Exploring the longitudinal patterns, e.g. clusters, of disease accrual can help better understand the genetic and environmental drivers of multimorbidity, and potentially identify individuals who

Pablo Valgañón, Antonio Brotons, David Soriano-Paños, Jesús Gómez-Gardeñes

Human interactions and mobility shape epidemic dynamics by facilitating disease outbreaks and their spatial spread across regions. Traditional models often isolate commuting and random mobility as separate behaviors, focusing either on short, recurrent trips or on random, exploratory movements. Here, we propose a unified formalism that allows a smooth transi

Ashik E Rasul, Humaira Tasnim, Hyung-Jin Yoon, Ayoosh Bansal

Learning-based solutions have enabled incredible capabilities for autonomous systems. Autonomous vehicles, both aerial and ground, rely on DNN for various integral tasks, including perception. The efficacy of supervised learning solutions hinges on the quality of the training data. Discrepancies between training data and operating conditions result in faults

Axel Guérin, Pierre Chauvet, Frédéric Saubion

Self-organising maps are a powerful tool for cluster analysis in a wide range of data contexts. From the pioneer work of Kohonen, many variants and improvements have been proposed. This review focuses on the last decade, in order to provide an overview of the main evolution of the seminal SOM algorithm as well as of the methodological developments that have

Daniel Papa, Christophe Josserand, Caroline Cohen

The growth of ice stalagmites obtained by the solidification of impacting droplets on a cooled substrate ($-50^{\circ}$C to $-140^{\circ}$C) is investigated experimentally. It is shown that for any combination of substrate temperature and drop discharge, there is a critical height above which unfrozen water accumulates at the stalagmite's tip, drips and deve

Hanyu Xue

In this paper, we develop a novel theory that generalizes the concept of anyon statistics to Abelian topological excitations of any dimension. We axiomatize excitations as a selected collection of states and operators satisfying the configuration axiom and the locality axiom, purely based on many-body quantum mechanics. Upon these axioms, we define a rigorou

Trystyn A. M. Berg, Andrea Afruni, Cédric Ledoux, Sebastian Lopez

HI-rich absorbers seen within quasar spectra contain the bulk of neutral gas in the Universe. However, the spatial extent of these reservoirs are not extensively studied due to the pencil beam nature of quasar sightlines. Using two giant gravitational arc fields (at redshifts 1.17 and 2.06) as 2D background sources with known strong MgII absorption observed

Niko Hauzenberger, Florian Huber, Karin Klieber, Massimiliano Marcellino

We propose a method to learn the nonlinear impulse responses to structural shocks using neural networks, and apply it to uncover the effects of US financial shocks. The results reveal substantial asymmetries with respect to the sign of the shock. Adverse financial shocks have powerful effects on the US economy, while benign shocks trigger much smaller reacti

Rithvik Prakki

This paper introduces a novel approach to creating adaptive language agents by integrating active inference with large language models (LLMs). While LLMs demonstrate remarkable capabilities, their reliance on static prompts limits adaptation to new information and changing environments. We address this by implementing an active inference framework that acts

Claudia Montero-Ramírez, Esther Rituerto-González, Carmen Peláez-Moreno

In the field of acoustic scene analysis, this paper presents a novel approach to find spatio-temporal latent representations from in-the-wild audio data. By using WE-LIVE, an in-house collected dataset that includes audio recordings in diverse real-world environments together with sparse GPS coordinates, self-annotated emotional and situational labels, we ta

Laura K. Rogers, Christopher J. Manser, Amy Bonsor, Erik Dennihy

There is increasing evidence for the presence and variability of circumstellar dust and gas around white dwarfs that are polluted with exoplanetary material, although the origin of this dust and gas remains debated. This paper presents the first near-simultaneous observations of both circumstellar dust (via broadband emission) and gas (via emission lines) ar

Tom Kouwenhoven, Max Peeperkorn, Tessa Verhoef

Human languages have evolved to be structured through repeated language learning and use. These processes introduce biases that operate during language acquisition and shape linguistic systems toward communicative efficiency. In this paper, we investigate whether the same happens if artificial languages are optimised for implicit biases of Large Language Mod

Marcos Da Silveira, Louis Deladiennee, Kheira Acem, Oona Freudenthal

Human health is increasingly threatened by exposure to hazardous substances, particularly persistent and toxic chemicals. The link between these substances, often encountered in complex mixtures, and various diseases are demonstrated in scientific studies. However, this information is scattered across several sources and hardly accessible by humans and machi

Goran Radunović

We study fractal properties of unbounded domains with infinite Lebesgue measure via their complex fractal dimensions. These complex dimensions are defined as poles of a suitable defined Lapidus fractal zeta function at infinity and are a generalization of the Minkowski dimension for a special kind of a degenerated relative fractal drum at infinity. It is a n

Alan Chávez, Jolbyn Castañeda, Alexis R. Carranza, Kamal Khalil

Let \(\mathcal{G}\) be a non-empty subset of the Euclidean space \(\mathbb{R}^m\) (\(m \geq 1\)). This work is dedicated to further exploring the properties of \(\mathcal{G}\)-multi-almost automorphic functions defined on \(\mathbb{R}^m\) with values in a Banach space \(\mathbb{X}\). Using the theory of \(\mathcal{G}\)-multi-almost automorphic functions, we

Nawaf Bou-Rabee, Andreas Eberle, Stefan Oberdörster

Hit-and-Run is a coordinate-free Gibbs sampler, yet the quantitative advantages of its coordinate-free property remain largely unexplored beyond empirical studies. In this paper, we prove sharp estimates for the Wasserstein contraction of Hit-and-Run in Gaussian target measures via coupling methods and conclude mixing time bounds. Our results uncover ballist

Hong Jie Fan, Fei Wang, Ying Kai Zhang

We propose a solution to the SUSY $\mu$-problem within the framework of modular flavor symmetry. The explicit $\mu$-term is prohibited by modular symmetry, and an effective $\mu$-term is regenerated following the stabilization of the modulus field. We examine the stabilization mechanism of a single modulus field with the presence of SUSY breaking contributio

Y. Monceaux

We present an overview of a novel approach to the QCD Light-Cone Sum Rule method, employing $B$-meson Light-Cone Distribution Amplitudes. This method circumvents the semi-global quark-hadron duality (QHD) approximation, which can introduce an unknown and potentially significant systematic error in form factor predictions. Instead, our approach is more depend

Ding Xu, Zhi Hao Peng, Chiara Trovatello, Shan-Wen Cheng

Van der Waals (vdW) semiconductors have emerged as promising platforms for efficient nonlinear optical conversion, including harmonic and entangled photon generation. Although major efforts are devoted to integrating vdW materials in nanoscale waveguides for miniaturization, the realization of efficient, phase-matched conversion in these platforms remains ch

Zongkai Liu, Qian Lin, Chao Yu, Xiawei Wu

Offline Multi-Agent Reinforcement Learning (MARL) is an emerging field that aims to learn optimal multi-agent policies from pre-collected datasets. Compared to single-agent case, multi-agent setting involves a large joint state-action space and coupled behaviors of multiple agents, which bring extra complexity to offline policy optimization. In this work, we

Lev V. Utkin, Semen P. Khomets, Vlada A. Efremenko, Andrei V. Konstantinov

Many ensemble-based models have been proposed to solve machine learning problems in the survival analysis framework, including random survival forests, the gradient boosting machine with weak survival models, ensembles of the Cox models. To extend the set of models, a new ensemble-based model called SurvBETA (the Survival Beran estimator Ensemble using Three

Paul Hagemann, Janina Schütte, David Sommer, Martin Eigel

Our method proposes the efficient generation of samples from an unnormalized Boltzmann density by solving the underlying continuity equation in the low-rank tensor train (TT) format. It is based on the annealing path commonly used in MCMC literature, which is given by the linear interpolation in the space of energies. Inspired by Sequential Monte Carlo, we a

Md Omar Faruque, Peter Jamieson, Ahmad Patooghy, Abdel-Hameed A. Badawy

Existing Hardware Trojans (HT) detection methods face several critical limitations: logic testing struggles with scalability and coverage for large designs, side-channel analysis requires golden reference chips, and formal verification methods suffer from state-space explosion. The emergence of Large Language Models (LLMs) offers a promising new direction fo

Xiaojun Zhu

This paper introduces a new Phase I design aimed at enhancing the performance of existing methods, including algorithm-based, model-based, and model-assisted designs. The design, developed by integrating the concept of Fisher information, is easily operationalized. The new design addresses the issue of the classical designs'slow dosage escalation. Simulation

Lucka Barbeau, Marc-Étienne Lamarche-Gagnon, Florin Ilinca

The Projected Gradient Descent (PGD) algorithm is a widely used and efficient first-order method for solving constrained optimization problems due to its simplicity and scalability in large design spaces. Building on recent advancements in the PGD algorithm where an inertial step component has been introduced to improve efficiency in solving constrained opti

Matthieu Meeus, Anthony Rathé, François Remy, Pieter Delobelle

While Large Language Models (LLMs) have shown remarkable capabilities in natural language understanding and generation, their performance often lags in lower-resource, non-English languages due to biases in the training data. In this work, we explore strategies for adapting the primarily English LLMs (Llama-2 and Llama-3) to Dutch, a language spoken by 30 mi

Bartosz Kasza, Sebastian Borówka, Wojciech Wasilewski, Michał Parniak

The development of novel radio frequency atomic receivers brings attention to the theoretical description of atom-light interactions in sophisticated, multilevel schemes. Of special interest, are the schemes where several interaction paths interfere with each other, bringing about the phase-sensitive measurement of detected radio fields. In the theoretical m

M. Chillarón, G. Quintana-Ortí, V. Vidal, G. Verdú

Algebraic methods applied to the reconstruction of Sparse-view Computed Tomography (CT) can provide both a high image quality and a decrease in the dose received by patients, although with an increased reconstruction time since their computational costs are higher. In our work, we present a new algebraic implementation that obtains an exact solution to the s

Jaswanth Uppalapati, Paul A McClarty, Masudul Haque, Shovan Dutta

Common intuition tells us that if one part of a connected system is cooled continuously, the other parts should also cool down. This intuition can be given a microscopic foundation for the case of a generic quantum system coupled to a "lead" that is maintained at a given temperature. We show that by suppressing resonant energy exchange between the two parts,

Wonjin Lee, Kyumin Kim, Sungjae Lee, Jihun Lee

Applying language models (LMs) to tables is challenging due to the inherent structural differences between two-dimensional tables and one-dimensional text for which the LMs were originally designed. Furthermore, when applying linearized tables to LMs, the maximum token lengths often imposed in self-attention calculations make it difficult to comprehensively

Wen-Xuan Ma, Chun-E Fu

In brane-world theory, through Kaluza-Klein (KK) reduction, a higher-dimensional U(1) gauge field manifests on the brane as a series of vector and scalar KK modes, while a bulk fermion field manifests as left- and right-handed components. However, these conclusions rely on the common assumption that there is no interaction between different levels of KK mode

Di Che, Stefano Grillanda, Yang Liu, Zheru Qiu

Coherent technologies have revolutionized optical communications, driving the capacity per fiber to multi-terabit per second (Tb/s) in combination with wavelength division multiplexing (WDM). With an ever-increasing deployment density of coherent systems, the demand for highly integrated WDM coherent transceivers has been rising. While tremendous progress ha

Linke Ouyang, Yuan Qu, Hongbin Zhou, Jiawei Zhu

Document content extraction is a critical task in computer vision, underpinning the data needs of large language models (LLMs) and retrieval-augmented generation (RAG) systems. Despite recent progress, current document parsing methods have not been fairly and comprehensively evaluated due to the narrow coverage of document types and the simplified, unrealist

Mingyue Chen, Jun Tian, Yu Pan, Tonghua Liu

In the gravity quantum theory, the quantization of spacetime may lead to the modification of the dispersion relation between the energy and the momentum and the Lorentz invariance violation (LIV). High energy and long-distance gamma-ray bursts (GRBs) observations in the universe provide a unique opportunity to test the possibility of LIV. In this work, we us

Andrea Cavaglià, Nikolay Gromov, Julius Julius, Michelangelo Preti

We continue our study of the defect CFT on a Maldacena-Wilson line in N=4 Super-Yang-Mills theory using Bootstrability -- the conformal bootstrap supplemented with exact integrability data. In this paper, we extend this program to charged sectors of the theory, considering a mixed-correlator setup first introduced by Liendo, Meneghelli, and Mitev. The exact

Christopher Vairogs, Bin Yan

Verifying the proper preparation of quantum states is essential in modern quantum information science. Various protocols have been developed to estimate the fidelity of quantum states produced by different parties. Direct fidelity estimation is a leading approach, as it typically requires a number of measurements that scale linearly with the Hilbert space di

Saeed Mansourlakouraj, Hadi Barati, Mehdi Fardmanesh

In this study, we presented two innovative methods, which are Threshold-Based Derivative (TBD) and Adaptive Derivative Peak Detection(ADPD), that enhance the accuracy of Learning models for blood glucose estimation using Mid-Infrared (MIR) spectroscopy. In these presented methods, we have enhanced the model's accuracy by integrating absorbance data and its d

F. S. Gama, J. R. Nascimento, A. Yu. Petrov, P. J. Porfirio

We investigate the $\mathcal{N} = 2$ supersymmetric generalization of the dual equivalence between the Maxwell-Chern-Simons model and two massive self-dual models. One of the self-dual models is described by a real scalar superfield, while the other is described by a chiral spinor superfield. The equivalence is analyzed in the presence of dynamical matter ch

Ningyue Fan, James F. Steiner, Cosimo Bambi, Erin Kara

We present a spectral and timing analysis of NICER observations of the black hole X-ray binary 4U 1630-47 from 2018 to 2024. We find relativistic reflection features in the hard and soft intermediate states, and disk wind absorption features in the soft intermediate state and soft state. We fit the reflection features with relxillCP and find a stable and unt

Matheus de Morais Leça, Mariana Bento, Ronnie de Souza Santos

Responsible AI principles provide ethical guidelines for developing AI systems, yet their practical implementation in software engineering lacks thorough investigation. Therefore, this study explores the practices and challenges faced by software practitioners in aligning with these principles. Through semi-structured interviews with 25 practitioners, we inv

Ellen Yi-Ge, Jiechao Gao, Wei Han, Wei Zhu

Recently, large language models (LLMs) have demonstrated impressive capabilities in dealing with new tasks with the help of in-context learning (ICL). In the study of Large Vision-Language Models (LVLMs), when implementing ICL, researchers usually adopts the naive strategies like fixed demonstrations across different samples, or selecting demonstrations dire

Xiaqiang Tang, Jian Li, Nan Du, Sihong Xie

Despite the superior performance of Large language models on many NLP tasks, they still face significant limitations in memorizing extensive world knowledge. Recent studies have demonstrated that leveraging the Retrieval-Augmented Generation (RAG) framework, combined with Knowledge Graphs that encapsulate extensive factual data in a structured format, robust

Jonas Nüßlein, Maximilian Zorn, Philipp Altmann, Claudia Linnhoff-Popien

In sequential decision-making environments, the primary approaches for training agents are Reinforcement Learning (RL) and Imitation Learning (IL). Unlike RL, which relies on modeling a reward function, IL leverages expert demonstrations, where an expert policy $\pi_e$ (e.g., a human) provides the desired behavior. Formally, a dataset $D$ of state-action pai

Yujing Xue, Jiaxiang Liu, Jiawei Du, Joey Tianyi Zhou

Recently, polar coordinate-based representations have shown promise for 3D perceptual tasks. Compared to Cartesian methods, polar grids provide a viable alternative, offering better detail preservation in nearby spaces while covering larger areas. However, they face feature distortion due to non-uniform division. To address these issues, we introduce the Pol

Xuan Tao, Yan Wang, Soumya D. Mohanty

Gravitational wave (GW) searches using pulsar timing arrays (PTAs) are commonly assumed to be limited to a GW frequency of $\lesssim 4\times 10^{-7}$Hz given by the Nyquist rate associated with the average observational cadence of $2$ weeks for a single pulsar. However, by taking advantage of asynchronous observations of multiple pulsars, a PTA can detect GW

Yanlong Hao

This paper aims to characterize rank-one arithmetic and locally symmetric metrics in the coarsely geometric setting using coarse-geometric commensurators. We provide a positive answer in general under the Hilbert-Smith conjecture and unconditionally for finite volume negatively curved manifolds with finitely many cusps.

Dominic Breit, Thamsanqa Castern Moyo, Philipp Öffner

In recent years, stochastic effects have become increasingly relevant for describing fluid behaviour, particularly in the context of turbulence. The most important model for inviscid fluids in computational fluid dynamics are the Euler equations of gas dynamics which we focus on in this paper. To take stochastic effects into account, we incorporate a stochas

Subin Varghese, Joshua Gao, Vedhus Hoskere

We introduce a generalized framework for Scene Change Detection (SCD) that addresses the core ambiguity of distinguishing "relevant" from "nuisance" changes, enabling effective joint training of a single model across diverse domains and applications. Existing methods struggle to generalize due to differences in dataset labeling, where changes such as vegetat

Junkai Yin, Yue Zhang, Zhangsheng Yu

Although the Cox proportional hazards model is well established and extensively used in the analysis of survival data, the proportional hazards (PH) assumption may not always hold in practical scenarios. The class of semiparametric transformation models extends the Cox model and also includes many other survival models as special cases. This paper introduces

Arash Dezhang Fard, Marek Kopciuch, Yujie Sun, Przemysław Włodarczyk

Nonlinear magnetic interactions provide access to complex quantum spin dynamics and thus enable the study of intriguing physical phenomena. However, these interactions are often dominated by the linear Zeeman effect, which can complicate system dynamics and make their analysis more challenging. In this article, we theoretically and experimentally introduce a

T. Obukhova, M. Semenenko, M. Dusheiko, S. Davidenko

This study investigates optical and electrical properties of thin melanin films self-organized grown from water solutions of eumelanin extracted from edible plants. The melanin films exhibit the characteristics of a transparent low conductive polymer with stable in time electrical parameters. The films demonstrate indirect allowed optical transitions, an opt

Chengbo Wang, Guozheng Ma, Yifei Xue, Yizhen Lao

3D Gaussian Splatting (3DGS) has emerged as a powerful technique for novel view synthesis, demonstrating remarkable capability in high-fidelity scene reconstruction through its Gaussian primitive representations. However, the computational overhead induced by the massive number of primitives poses a significant bottleneck to training efficiency. To overcome

Pavlo Golub, Chao Yang, Vojtěch Vlček, Libor Veis

Accurate electronic structure calculations are essential in modern materials science, but strongly correlated systems pose a significant challenge due to their computational cost. Traditional methods, such as complete active space self-consistent field (CASSCF), scale exponentially with system size, while alternative methods like the density matrix renormali

L. Paolozzi, M. Milanesio, T. Moretti, R. Cardella

A monolithic silicon pixel ASIC prototype, produced in 2024 as part of the Horizon 2020 MONOLITH ERC Advanced project, was tested with a 120 GeV/c pion beam. The ASIC features a matrix of hexagonal pixels with a 100 \mu m pitch, read by low-noise, high-speed front-end electronics built using 130 nm SiGe BiCMOS technology. It includes the PicoAD sensor, which

Yanwei Yue, Guibin Zhang, Haoran Yang, Dawei Cheng

Graph Neural Networks (GNNs) demonstrate superior performance in various graph learning tasks, yet their wider real-world application is hindered by the computational overhead when applied to large-scale graphs. To address the issue, the Graph Lottery Hypothesis (GLT) has been proposed, advocating the identification of subgraphs and subnetworks, \textit{i.e.

Jennifer Noelle Kampe, Luca Alessandro Silva, Tomas Roslin, David Brian Dunson

Dynamic latent space models are widely used for characterizing changes in networks and relational data over time. These models assign to each node latent attributes that characterize connectivity with other nodes, with these latent attributes dynamically changing over time. Node attributes can be organized as a three-way tensor with modes corresponding to no

Darryl Z. Seligman, Davide Farnocchia, Marco Micheli, Olivier R. Hainaut

Small bodies are capable of delivering essential prerequisites for the development of life, such as volatiles and organics, to the terrestrial planets. For example, empirical evidence suggests that water was delivered to the Earth by hydrated planetesimals from distant regions of the Solar System. Recently, several morphologically inactive near-Earth objects

Mehrdad Nasernejad, Jonathan Toledo

In this paper we investigate the monomial ideals which satisfy the copersistence property or nearly copersistence property.

Conghao Wang, Jagath C. Rajapakse

De novo design of bioactive drug molecules with potential to treat desired biological targets is a profound task in the drug discovery process. Existing approaches tend to leverage the pocket structure of the target protein to condition the molecule generation. However, even the pocket area of the target protein may contain redundant information since not al

Keith Andrew, Eric Steinfelds, Kristopher Andrew

We investigate BZT shocks and the QCD phase transition in the dense core of a cold quark star in beta equilibrium subject to the multicomponent van der Waals (MvdW) equation of state (EoS) as a model of internal structure. When this system is expressed in terms of multiple components, it can be used to explore the impact of a phase transition from a hadronic

A. Ciach, O. Patsahan

Effect of an oscillatory decay of the charge density in concentrated ionic solutions and ionic liquids on the double-layer capacitance is studied in a framework of a mesoscopic theory. Only Coulomb and steric forces between the ions that are present in all ionic systems are taken into account. We show that the charge oscillations lead to a rescaled distance

Cillian Cockrell, Aleksandra Dragović

Heat transfer in liquids is a very challenging problem as it combines the competing effect of high frequency oscillations, which dominate liquid heat capacity, and diffusive motion, which enables transport macroscopic flow. This issue is compounded by the relatively junior state of dynamical theories of liquid thermodynamics. Nevertheless, molten salts are p

Qin-He Yang, Ling-Yun Dai, Ulf-G. Meißner

We study the properties of the $X(1880)$, the structure around the $\bar{p}p$ threshold that appears in the $3(\pi^+\pi^-)$ invariant mass spectrum in the decay process of $J/\psi \to \gamma 3(\pi^+\pi^-)$. Nucleon-antinucleon rescattering is taken into account in our analysis, and the decay amplitude of $J/\psi \to \gamma 3(\pi^+\pi^-)$ can be obtained by t

Yurina Nakazato, Andrea Ferrara

The James Webb Space Telescope (JWST) has discovered an overabundance of UV-bright ($M_{\rm UV} \lesssim -20$), massive galaxies at $z \gtrsim 10$ in comparison to pre-JWST theoretical predictions. Among the proposed interpretations, such excess has been explained by negligible dust attenuation conditions following radiation-driven outflows launched by young

Ragheed Alhyder, Victor E. Colussi, Matija Čufar, Joachim Brand

We develop a new theoretical framework for exploring a mobile impurity interacting strongly with a highly correlated bath of bosons in the quantum critical regime of a Mott insulator (MI) to superfluid (SF) quantum phase transition. Our framework is based on a powerful quantum Gutzwiller (QGW) description of the bosonic bath combined with diagrammatic field

Amaresh Sahu

An arbitrary Lagrangian--Eulerian finite element method and numerical implementation for curved and deforming lipid membranes is presented here. The membrane surface is endowed with a mesh whose in-plane motion need not depend on the in-plane flow of lipids. Instead, in-plane mesh dynamics can be specified arbitrarily. A new class of mesh motions is introduc

Jan H. Orkisz, Jouni Kainulainen

Volume density is a key physical quantity controlling the evolution of the interstellar medium (ISM) and star formation, but it cannot be accessed directly by observations of molecular clouds. We aim at estimating the volume density distribution in nearby molecular clouds, to measure the relation between column and volume densities and to determine their rol

Qikai Chang, Mingjun Chen, Changpeng Pi, Pengfei Hu

The primary objective of Optical Chemical Structure Recognition is to identify chemical structure images into corresponding markup sequences. However, the complex two-dimensional structures of molecules, particularly those with rings and multiple branches, present significant challenges for current end-to-end methods to learn one-dimensional markup directly.

Mingxuan Xiao, Wei Wang, Wenjing Liu, Zheng Li

Optical resonators have shown outstanding abilities to tailor chemical landscapes through enhanced light-matter interaction between confined optical modes and molecule vibrations. We propose a theoretical model to study cooperative vibrational strong coupling in an open quantum system. The non-equilibrium stochastic molecular dynamics in an optical cavity wi