May 2025 arXiv papers — page 57

Sirui Chen, Shuqin Ma, Shu Yu, Hanwang Zhang

Consciousness stands as one of the most profound and distinguishing features of the human mind, fundamentally shaping our understanding of existence and agency. As large language models (LLMs) develop at an unprecedented pace, questions concerning intelligence and consciousness have become increasingly significant. However, discourse on LLM consciousness rem

Jérémy Scanvic, Quentin Barthélemy, Julián Tachella

The design of convolutional neural architectures that are exactly equivariant to continuous translations is an active field of research. It promises to benefit scientific computing, notably by making existing imaging systems more physically accurate. Most efforts focus on the design of downsampling/pooling layers, upsampling layers and activation functions,

Siyuan Li, Jian Chen, Rui Yao, Xuming Hu

Nowadays, regulatory compliance has become a cornerstone of corporate governance, ensuring adherence to systematic legal frameworks. At its core, financial regulations often comprise highly intricate provisions, layered logical structures, and numerous exceptions, which inevitably result in labor-intensive or comprehension challenges. To mitigate this, recen

Fuze Sun, Lingyu Li, Shixiangyue Meng, Xiaoming Teng

This study investigates the integration of individual human traits into an empathetically adaptive educational robot tutor system designed to improve student engagement and learning outcomes with corresponding Engagement Vector measurement. While prior research in the field of Human-Robot Interaction (HRI) has examined the integration of the traits, such as

Zhiyu Wang, Yang Liu, Hatice Gunes

Understanding pain-related facial behaviors is essential for digital healthcare in terms of effective monitoring, assisted diagnostics, and treatment planning, particularly for patients unable to communicate verbally. Existing data-driven methods of detecting pain from facial expressions are limited due to interpretability and severity quantification. To thi

Ram Manohar, S. M. Mallikarjunaiah

The rigorous convergence analysis of adaptive finite element methods for regularized variational models of quasi-static brittle fracture in strain-limiting elastic solids is presented. This work introduces two novel adaptive mesh refinement algorithms, based on robust local error indicators, designed to solve the underlying energy minimization problem effici

Zaid Alyafeai, Maged S. Al-Shaibani, Bernard Ghanem

Metadata extraction is essential for cataloging and preserving datasets, enabling effective research discovery and reproducibility, especially given the current exponential growth in scientific research. While Masader (Alyafeai et al.,2021) laid the groundwork for extracting a wide range of metadata attributes from Arabic NLP datasets' scholarly articles, it

Yulu Bai, Jiahong Fu, Qi Xie, Deyu Meng

Equivariant and invariant deep learning models have been developed to exploit intrinsic symmetries in data, demonstrating significant effectiveness in certain scenarios. However, these methods often suffer from limited representation accuracy and rely on strict symmetry assumptions that may not hold in practice. These limitations pose a significant drawback

Ankit Butola, Luis E. Villegas-Hernández, Dhivya B. Thiyagarajan, Bartłomiej Zapotoczny

The primary function of intestinal microvilli is to increase the surface area of the intestinal lining to maximize nutrient absorption. This is especially important as fish, like other animals, need to efficiently absorb proteins, carbohydrates, lipids, vitamins, and minerals from their digested food to support their growth and energy needs. Despite its impo

Yiqun Zhang, Hao Li, Chenxu Wang, Linyao Chen

Proprietary giants are increasingly dominating the race for ever-larger language models. Can open-source, smaller models remain competitive across a broad range of tasks? In this paper, we present the Avengers -- a simple recipe that leverages the collective intelligence of these smaller models. The Avengers builds upon four lightweight operations: (i) embed

Sajjad Shahabodini, Mobina Mansoori, Farnoush Bayatmakou, Jamshid Abouei

Image segmentation remains a challenging task in computer vision, demanding robust mask generation and precise classification. Recent mask-based approaches yield high-quality masks by capturing global context. However, accurately classifying these masks, especially in the presence of ambiguous boundaries and imbalanced class distributions, remains an open ch

Alessandro Alla, Alessandra De Luca, Raffaele Folino, Marta Strani

In this paper we study a convection-reaction-diffusion equation of the form \begin{equation*} u_t=\varepsilon(h(u)u_x)_x-f(u)_x+f'(u), \quad t>0, \end{equation*} with a nonlinear diffusion in a bounded interval of the real line. In particular, we first focus our attention on the existence of stationary solutions with at most one zero inside the interval, stu

Li Fang, Hao Zhu, Longlong Chen, Fei Hu

Recent advancements in generalizable novel view synthesis have achieved impressive quality through interpolation between nearby views. However, rendering high-resolution images remains computationally intensive due to the need for dense sampling of all rays. Recognizing that natural scenes are typically piecewise smooth and sampling all rays is often redunda

Claire Ott, Frank Jäkel

In order to behave intelligently both humans and machines have to represent their knowledge adequately for how it is used. Humans often use analogies to transfer their knowledge to new domains, or help others with this transfer via explanations. Hence, an important question is: What representation can be used to construct, find, and evaluate analogies? In th

Ioannis Markou

In this paper we formulate a continuous opinion model that takes into account population growth, i.e. increase with time in the number of interacting agents $N(t)$. In our setting the population growth is governed by a generic growth rate function $b(t, N(t))$. The two main components of our model are the growth rate $b(t, N(t))$, as well as the opinions of

Faruk Alpay

This paper introduces a formal framework for modeling observer-dependent collapse dynamics and temporal identity drift within artificial and mathematical systems, grounded entirely in the symbolic foundations of Alpay Algebra. Building upon the fixed-point emergence structures developed in Alpay Algebra I and II, this third installment formalizes the observe

Jijia Liu, Feng Gao, Bingwen Wei, Xinlei Chen

Large Vision-Language Action (VLA) models have shown significant potential for embodied AI. However, their predominant training via supervised fine-tuning (SFT) limits generalization due to susceptibility to compounding errors under distribution shifts. Reinforcement learning (RL) offers a path to overcome these limitations by optimizing for task objectives

Zihao Zeng, Xuyao Huang, Boxiu Li, Hao Zhang

Large Reasoning Models (LRMs) are criticized for the excessively lengthy Chain-of-Thought (CoT) to derive the final answer, suffering from high first-token and overall latency. Typically, the CoT of LRMs mixes multiple thinking units; each unit attempts to produce a candidate answer to the original query. Hence, a natural idea to improve efficiency is to red

Xing Huang, Panpan Ren, Feng-Yu Wang

For a class of McKean-Vlasov stochastic differential equations with singular interactions, which include the Coulomb/Riesz/Biot-Savart kernels as typical examples (Examples 2.1 and 2.2), we derive the well-posedness and regularity estimates by establishing the entropy-cost inequality. To measure the singularity of interactions, we introduce a new probability

M. M. Bosschaert, B. Lentjes, L. Spek, Yu. A. Kuznetsov

Recent work in [53, 54] by the authors on periodic center manifolds and normal forms for bifurcations of limit cycles in delay differential equations (DDEs) motivates the derivation of explicit computational formulas for the critical normal form coefficients of all codimension one bifurcations of limit cycles. In this paper, we derive such formulas via an ap

Qianyi Xu, Gousia Habib, Feng Wu, Dilruk Perera

Timely and personalized treatment decisions are essential across a wide range of healthcare settings where patient responses can vary significantly and evolve over time. Clinical data used to support these treatment decisions are often irregularly sampled, where missing data frequencies may implicitly convey information about the patient's condition. Existin

F. Tavecchio, F. Bolis, E. Sobacchi, S. Boula

Recent multifrequency polarimetric observations of the eponymous blazar BL Lac reveal an extremely large degree of polarization in the optical band (average of $25\%$, reaching $45\%$), together with a small ($\lesssim 7\%$) degree of polarization in the X-ray band. This has been interpreted as evidence that the X-rays are produced through inverse Compton em

Walter H. Aschbacher

For the general class of quasifree fermionic right mover/left mover systems over the infinitely extended two-sided discrete line introduced in [8] within the algebraic framework of quantum statistical mechanics, we study the von Neumann entropy of a contiguous subsystem of finite length in interaction with its environment. In particular, under the assumption

Areesha Rehman, Zikria Saleem, Jarrar Amjad, Syed Rehan Shah

Bacterial colony enumeration is an essential stage in microbiological research, allowing susceptibility to antibiotics assessment, monitoring of the environment, and clinical diagnostics. Traditional manual counting methods are costly and susceptible to human mistakes, prompting the creation of automated detection systems. This research compares the efficacy

Francesco Grotto, Umberto Pappalettera

We consider the generalized Surface Quasi-Geostrophic point vortices dynamics, and identify a sufficient condition implying existence of bursts out of (and collapses into) any given initial configuration of vortices. The condition is related to the stability of the linearized dynamics around three vortices evolving in a self-similar fashion.

Mateusz Guzik, Giulio Cengarle, Daniel Arteaga

Spatial aliasing affects spaced microphone arrays, causing directional ambiguity above certain frequencies, degrading spatial and spectral accuracy of beamformers. Given the limitations of conventional signal processing and the scarcity of deep learning approaches to spatial aliasing mitigation, we propose a novel approach using a U-Net architecture to predi

Mobina Mansoori, Sajjad Shahabodini, Farnoush Bayatmakou, Jamshid Abouei

Using massive datasets, foundation models are large-scale, pre-trained models that perform a wide range of tasks. These models have shown consistently improved results with the introduction of new methods. It is crucial to analyze how these trends impact the medical field and determine whether these advancements can drive meaningful change. This study invest

Jesus Cañas, Mohamed Yassine, Oliver Ambacher

The discovery of ferroelectricity in wurtzite nitrides has paved the way for measuring and understanding spontaneous polarization in III-V semiconductors. However, the calculation of polarization effects at heterointerfaces - crucial for numerous electronic and photonic applications - remains a topic of debate. The need for a reference structure to calculate

Tao Yu, Yunsong Ning, Yi Yuan, Shihan Zhao

Muography, traditionally recognized as a potent instrument for imaging the internal structure of gigantic objects, has initialized various interdisciplinary applications. As the financial and labor costs of muography detector development hinder their massive applications, we develop a novel muon detector called MuGrid by coupling a monolithic plastic scintil

Akram Elbouanani, Evan Dufraisse, Adrian Popescu

Political biases encoded by LLMs might have detrimental effects on downstream applications. Existing bias analysis methods rely on small-size intermediate tasks (questionnaire answering or political content generation) and rely on the LLMs themselves for analysis, thus propagating bias. We propose a new approach leveraging the observation that LLM sentiment

Yue Su, Nan Wang, Long-cheng Gui, Jun Hua

By fitting the two-point correlation functions of topological charge density operators calculated on two $2+1$-flavor gauge ensembles with physical pion mass, we determine both the $\eta$ and $\eta'$ masses and also the mixing angle to be $m_\eta = 0.505(72)(75)$ GeV, $m_{\eta'}=0.952(47)(40)$ GeV, and $\theta_1 = -8.9(2.1)(1.8)^\circ$, respectively, where t

Prabash Reddy Male, Swayambhu Nath Ray, Harish Arsikere, Akshat Jaiswal

Recent advancements in speech encoders have drawn attention due to their integration with Large Language Models for various speech tasks. While most research has focused on either causal or full-context speech encoders, there's limited exploration to effectively handle both streaming and non-streaming applications, while achieving state-of-the-art performanc

Sangyeop Kim, Yohan Lee, Yongwoo Song, Kimin Lee

We investigate long-context vulnerabilities in Large Language Models (LLMs) through Many-Shot Jailbreaking (MSJ). Our experiments utilize context length of up to 128K tokens. Through comprehensive analysis with various many-shot attack settings with different instruction styles, shot density, topic, and format, we reveal that context length is the primary fa

Clemens Possel, Walter Hahn, Reza Shirazi, Marina Walt

Quantum computing has the potential to revolutionize quantum chemistry and material science by offering solutions to complex problems unattainable with classical computers. However, the development of efficient quantum algorithms that are efficient under noisy conditions remains a major challenge. This paper introduces the truncated Variational Hamiltonian A

Santiago Torres-Borda, Ahlem Mifdaoui

Time-sensitive networks are designed to meet stringent Quality of Service (QoS) requirements for mixed-criticality traffic with diverse performance demands. Ensuring deterministic guarantees for such traffic while reducing deployment costs remains a significant challenge. This paper proposes a cost-efficient partial deployment strategy for Time Sensitive Net

Ruizhe Shi, Minhak Song, Runlong Zhou, Zihan Zhang

We present a fine-grained theoretical analysis of the performance gap between two-stage reinforcement learning from human feedback~(RLHF) and direct preference optimization~(DPO). Our study decomposes this gap into two sources: the explicit representation gap under exact optimization and the implicit representation gap under finite samples. In the exact opti

Yuhui Chen, Haoran Li, Zhennan Jiang, Haowei Wen

Developing scalable and generalizable reward engineering for reinforcement learning (RL) is crucial for creating general-purpose agents, especially in the challenging domain of robotic manipulation. While recent advances in reward engineering with Vision-Language Models (VLMs) have shown promise, their sparse reward nature significantly limits sample efficie

Xing Cui, Yueying Zou, Zekun Li, Peipei Li

Real-world multimodal misinformation often arises from mixed forgery sources, requiring dynamic reasoning and adaptive verification. However, existing methods mainly rely on static pipelines and limited tool usage, limiting their ability to handle such complexity and diversity. To address this challenge, we propose \method, a novel misinformation detection a

Junyang Shu, Zhiwei Lin, Yongtao Wang

Vision-Language-Action (VLA) models have demonstrated significant potential in the field of embodied intelligence, enabling agents to follow human instructions to complete complex tasks in physical environments. Existing embodied agents are often trained through behavior cloning, which requires expensive data and computational resources and is constrained by

Masoomali Fatehkia, Enes Altinisik, Mohamed Osman, Husrev Taha Sencar

Large language models (LLMs) remain vulnerable to misalignment and jailbreaks, making external safeguards like moderation filters essential, yet existing filters often focus narrowly on safety, falling short of the broader alignment needs seen in real-world deployments. We introduce Policy Aligned Moderation (PAM), a flexible framework for training custom mo

Nick E. Mavromatos, Andreas Mershin, Dimitri V. Nanopoulos

We examine the quantum coherence properties of tubulin heterodimers arranged into the protofilaments of cytoskeletal microtubules. In the physical model proposed by the authors, the microtubule interiors are treated as high-Q quantum electrodynamics (QED) cavities that can support decoherence-resistant entangled states under physiological conditions, with de

Juan Pablo Borthagaray, Patrick Ciarlet

We study problems in which a local model is coupled with a nonlocal one. We propose two energies: both of them are based on the same classical weighted $H^1$-semi norm to model the local part, while two different weighted $H^s$-semi norms, with $s \in (0,1)$, are used to model the nonlocal part. The corresponding strong formulations are derived. In doing so,

Patara Trirat, Wonyong Jeong, Sung Ju Hwang

Large language models (LLMs) have demonstrated remarkable capabilities across diverse tasks, but optimizing LLM-based agentic systems remains challenging due to the vast search space of agent configurations, prompting strategies, and communication patterns. Existing approaches often rely on heuristic-based tuning or exhaustive evaluation, which can be comput

Jean-Marie Malherbe

The Solar Maximum Mission of NASA was one of the first satellites with on board digitization of observations. It was launched for the solar maximum of cycle 21 (1980) in order to study the solar activity. It carried many instruments, such as coronagraphs, X and $\gamma$ ray detectors, an Ultra Violet spectrometer and a radiometer. Ground based support was of

Thomas Hamelryck, Kanti V. Mardia

The seminal breakthrough of AlphaFold in protein structure prediction relied on a learned potential energy function parameterized by deep models, in contrast to its successors AlphaFold2 and AlphaFold3, which lack an explicit probabilistic interpretation. While AlphaFold's potential was originally justified by heuristic analogy to physical potentials of mean

Dawei Cheng, Wenjun Wang, Mingjian Guang

Graph neural networks (GNNs) have become a standard paradigm for graph representation learning, yet their message passing mechanism implicitly assumes that messages can be represented by source node embeddings, an assumption that fails in heterophilic graphs. While existing methods attempt to address heterophily through graph structure refinement or adaptati

Zican Hu, Wei Liu, Xiaoye Qu, Xiangyu Yue

While showing sophisticated reasoning abilities, large language models (LLMs) still struggle with long-horizon decision-making tasks due to deficient exploration and long-term credit assignment, especially in sparse-reward scenarios. Inspired by the divide-and-conquer principle, we propose an innovative framework **GLIDER** (**G**rounding **L**anguage Models

Matteo Torcoli, Mhd Modar Halimeh, Emanuël A. P. Habets

Perceptual Evaluation of Speech Quality (PESQ) is an objective quality measure that remains widely used despite its withdrawal by the International Telecommunication Union (ITU). PESQ has evolved over two decades, with multiple versions and publicly available implementations emerging during this time. Different versions and their updates can be overwhelming,

Mario Abundo

We address the problem of minimizing the expected first-passage time of a Brownian motion with Poissonian resetting, with respect to the resetting rate $r.$ We consider both the one-boundary and the two-boundary cases.We investigate the first-passage time (FPT) and first-exit time (FET) of a one-dimensional, time-homogeneous diffusion process subject to Pois

Yu-Shuai Li

In this work, we study the nonleptonic decays $\Xi_{cc}^{++}\to\Xi_{c}^{(\prime)+}\pi^{+}$ with considering $\Xi_{c}-\Xi_{c}^{\prime}$ mixing. The relevant decay amplitudes are evaluated within the framework of nonrelativistic quark model, combining the baryon spatial wave functions adopted from solving the Schr\"{o}dinger equation with a nonrelativistic pot

Maria Dziuba, Valentin Malykh

Effective generation of structured code comments requires robust quality metrics for dataset curation, yet existing approaches (SIDE, MIDQ, STASIS) suffer from limited code-comment analysis. We propose CIDRe, a language-agnostic reference-free quality criterion combining four synergistic aspects: (1) relevance (code-comment semantic alignment), (2) informati

Ruihan Gong, Yue Liu, Wenjie Qu, Mingzhe Du

Large Reasoning Models (LRMs) achieve promising performance but compromise token efficiency due to verbose reasoning processes. Unconscious Thought Theory (UTT) posits that complex problems can be solved more efficiently through internalized cognitive processes. Inspired by UTT, we propose a new reasoning paradigm, termed Chain of Unconscious Thought (CoUT),

Junyan Qiu, Ze Wang, Fan Zhang, Zuowu Zheng

Modern industrial advertising systems commonly employ Multi-stage Cascading Architectures (MCA) to balance computational efficiency with ranking accuracy. However, this approach presents two fundamental challenges: (1) performance inconsistencies arising from divergent optimization targets and capability differences between stages, and (2) failure to account

Ruisheng Cao, Hanchong Zhang, Tiancheng Huang, Zhangyi Kang

The increasing number of academic papers poses significant challenges for researchers to efficiently acquire key details. While retrieval augmented generation (RAG) shows great promise in large language model (LLM) based automated question answering, previous works often isolate neural and symbolic retrieval despite their complementary strengths. Moreover, c

Dennis Bonatsos, Andriana Martinou, S. K. Peroulis, D. Petrellis

Triaxial shapes in even-even nuclei have been considered since the early days of the nuclear collective model. Although many theoretical approaches have been used over the years for their description, no effort appears to have been made for grouping them together and identifying regions on the nuclear chart where the appearance of triaxiality might be favore

Hengli Li, Yuxuan Wang, Song-Chun Zhu, Ying Nian Wu

Discrete diffusion has recently emerged as a promising paradigm in discrete data modeling. However, existing methods typically rely on a fixed rate transition matrix during training, which not only limits the expressiveness of latent representations, a fundamental strength of variational methods, but also constrains the overall design space. To address these

Hala Djeghim, Nathan Piasco, Luis Roldão, Moussab Bennehar

Intrinsic image decomposition aims at separating an image into its underlying albedo and shading components, isolating the base color from lighting effects to enable downstream applications such as virtual relighting and scene editing. Despite the rise and success of learning-based approaches, intrinsic image decomposition from real-world images remains a si

Huaiyuan Zhang, Hang Chen, Yu Cheng, Shunyi Wu

In this technical report, we present our solution to the CVPR 2025 Visual Anomaly and Novelty Detection (VAND) 3.0 Workshop Challenge Track 1: Adapt & Detect: Robust Anomaly Detection in Real-World Applications. In real-world industrial anomaly detection, it is crucial to accurately identify anomalies with physical complexity, such as transparent or reflecti

Yang Liu, Guochun Wu, Xin Zhong

We study the global stability of large solutions to the compressible isentropic magnetohydrodynamic equations in a three-dimensional (3D) bounded domain with Navier-slip boundary conditions. It is shown that the solutions converge to an equilibrium state exponentially in the $L^2$-norm provided the density is essentially uniform-in-time bounded from above. M

Yilin Ye, Denis S. Grebenkov

We investigate the boundary local time on polygonal boundaries such as finite generations of the Koch snowflake. To reveal the role of angles, we first focus on wedges and obtain the mean boundary local time, its variance, and the asymptotic behavior of its distribution. Moreover, we establish the coupled partial differential equations for higher-order momen

Ligong Bian, Rong-Gen Cai, Yu-Qi Dong, Qing Gao

Gravitational waves (GWs) originating from cosmological sources offer direct insights into the physics of the primordial Universe, the fundamental nature of gravity, and the cosmic expansion of the Universe. In this review paper, we present a comprehensive overview of our recent advances in GW cosmology, supported by the national key research and development

Jakov Samardžija, Donik Vršnak, Sven Lončarić

Accurate identification of acute cellular rejection (ACR) in endomyocardial biopsies is essential for effective management of heart transplant patients. However, the rarity of high-grade rejection cases (3R) presents a significant challenge for training robust deep learning models. This work addresses the class imbalance problem by leveraging synthetic data

Nicolas Bergmann, Nicéphore Bonnet, Nicola Marzari, Karsten Reuter

We present a response-augmented machine learning (ML) approach to the energetics of electrified metal surfaces. We leverage local descriptors to learn the work function as the first-order energy change to introduced bias charges and stabilize this learning through Born effective charges. This permits the efficient extension of ML interatomic potential archit

Shaohong Shi, Jacco Heres, Simon H. Tindemans

Electrical grid congestion has emerged as an immense challenge in Europe, making the forecasting of load and its associated metrics increasingly crucial. Among these metrics, peak load is fundamental. Non-time-resolved models of peak load have their advantages of being simple and compact, and among them Velander's formula (VF) is widely used in distribution

Yang Zhang, Yu Yu, Bo Tang, Yu Zhu

With the rapid development of Large Language Models (LLMs), aligning these models with human preferences and values is critical to ensuring ethical and safe applications. However, existing alignment techniques such as RLHF or DPO often require direct fine-tuning on LLMs with billions of parameters, resulting in substantial computational costs and inefficienc

Jue Gong, Tingyu Yang, Jingkai Wang, Zheng Chen

Human-centered images often suffer from severe generic degradation during transmission and are prone to human motion blur (HMB), making restoration challenging. Existing research lacks sufficient focus on these issues, as both problems often coexist in practice. To address this, we design a degradation pipeline that simulates the coexistence of HMB and gener

Paul Liautaud, Pierre Gaillard, Olivier Wintenberger

We study online adversarial regression with convex losses against a rich class of continuous yet highly irregular prediction rules, modeled by Besov spaces $B\_{pq}^s$ with general parameters $1 \leq p,q \leq \infty$ and smoothness $s > \tfrac{d}{p}$. We introduce an adaptive wavelet-based algorithm that performs sequential prediction without prior knowledge

Weichen Si, Yihao Ou, Zhen Tian

In this study, we propose a machine learning-based method for noise reduction and disease-causing gene feature extraction in gene sequencing DeepSeqDenoise algorithm combines CNN and RNN to effectively remove the sequencing noise, and improves the signal-to-noise ratio by 9.4 dB. We screened 17 key features by feature engineering, and constructed an integrat

Donatien Schmitz, Guillaume Rosinosky, Etienne Rivière

Distributed Stream Processing (DSP) engines analyze continuous data via queries expressed as a graph of operators. Auto-scalers adjust the number of parallel instances of these operators to support a target rate. Current auto-scalers couple CPU and memory scaling, allocating resources as one-size-fits-all packages. This contrasts with operators' high diversi

Arshyn Altybay

In this paper, we numerically address the inverse problem of identifying a time-dependent coefficient in the time-fractional diffusion equation. An a priori estimate is established to ensure uniqueness and stability of the solution. A fully implicit finite-difference scheme is proposed and rigorously analysed for stability and convergence. An efficient algor

Luc Pronzato, Maria-João Rendas

We present a weighted version of Leave-One-Out (LOO) cross-validation for estimating the Integrated Squared Error (ISE) when approximating an unknown function by a predictor that depends linearly on evaluations of the function over a finite collection of sites. The method relies on the construction of the best linear estimator of the squared prediction error

Sabinakhon Akbarova, Felix Dobslaw, Francisco Gomes de Oliveira Neto, Robert Feldt

Software systems exhibit distinct behaviors based on input characteristics, and failures often occur at the boundaries between input domains. Traditional Boundary Value Analysis (BVA) relies on manual heuristics, while automated Boundary Value Exploration (BVE) methods typically optimize a single quality metric, risking a narrow and incomplete survey of boun

Marzia Bisi, Maria Groppi, Giorgio Martalò

We present a hybrid Boltzmann-BGK model for inert mixtures, where each kind of binary interaction may be described by a classical Boltzmann integral or by a suitable relaxation-type operator. We allow also the possibility of changing the option Boltzmann/BGK operator according to the space position. We prove that this model guarantees conservations of specie

Juxin Niu, Xiangfeng Liu, Dan Niu, Xi Wang

Coding with hardware description languages (HDLs) such as Verilog is a time-intensive and laborious task. With the rapid advancement of large language models (LLMs), there is increasing interest in applying LLMs to assist with HDL coding. Recent efforts have demonstrated the potential of LLMs in translating natural language to traditional HDL Verilog. Chisel

Alou Diakite, Cheng Li, Lei Xie, Yuanjing Feng

Accurately delineating the visual pathway (VP) is crucial for understanding the human visual system and diagnosing related disorders. Exploring multi-parametric MR imaging data has been identified as an important way to delineate VP. However, due to the complex cross-sequence relationships, existing methods cannot effectively model the complementary informat

Chao Zhang, Rong-gen Cai, Guoyang Fu, Yungui Gong

The numerical calculation of gravitational self-force in extreme mass ratio inspiral systems is fundamentally challenging due to the singular nature of point-particle sources. To overcome these difficulties, the effective source method offers an innovative alternative by replacing traditional regularization techniques with a reformulation of the problem. In

Daniil Tiapkin, Daniele Calandriello, Denis Belomestny, Eric Moulines

Traditional Reinforcement Learning from Human Feedback (RLHF) often relies on reward models, frequently assuming preference structures like the Bradley--Terry model, which may not accurately capture the complexities of real human preferences (e.g., intransitivity). Nash Learning from Human Feedback (NLHF) offers a more direct alternative by framing the probl

Amit Kumar Singh, Alvaro Gomez-Iglesias, Stefan Schulz

UV emitters based on the semiconductor alloy aluminium gallium nitride, (Al,Ga)N, have attracted significant interest in recent years due to their potential for optoelectronic devices. To guide the design of such devices with improved efficiencies, theoretical frameworks based on so-called k.p methods have found widespread application in the literature. Give

Rizwan Abbas, Ali Raza Mirza, Adam Zaman Chaudhry

Quantum sensors allow us to measure weak oscillating fields with incredible precision. One common approach is to use the time evolution of a single two-level system (or a qubit) in conjunction with applied control pulses to measure the oscillating field. For high-frequency fields, the time interval required between the applied pulses decreases, meaning that

Mingyue Guo, Zhenhua Shi

In this paper, we study the problem of local isometric immersion of pseudospherical surfaces determined by the solutions of a class of third order nonlinear partial differential equations with the type $u_t - u_{xxt} = \lambda u^2 u_{xxx} + G(u, u_x, u_{xx}),(\lambda\in\mathbb{R})$. We prove that there is only two subclasses of equations admitting a local is

Yu Fu, Min-Chun Hong, Gang Tian

The biharmonic flow of hypersurfaces $M^n$ immersed in the Euclidean space $\mathbb {R}^{n+1}$ for $n\geq 2$ is given by a fourth order geometric evolution equation, which is similar to the Willmore flow. We apply the Michael-Simon-Sobolev inequality to establish new Gagliardo-Nirenberg inequalities on hypersurfaces. Based on these Gagliardo-Nirenberg inequa

Hongjun Guo, François Hamel, Luca Rossi

This paper is concerned with reaction-diffusion-advection equations in spatially periodic media. Under an assumption of weak stability of the constant states 0 and 1, and of existence of pulsating traveling fronts connecting them, we show that fronts' profiles appear, along sequences of times and points, in the large-time dynamics of the solutions of the Cau

Hajime Kobayashi, Shinji Mukohyama, Naritaka Oshita, Kazufumi Takahashi

A set of tidal dissipation numbers (TDNs) quantifies the absorption of the tidal force exerted by a companion during an inspiralling phase of a binary compact object. This tidal dissipation generally affects the gravitational waveform, and measuring the TDNs of a black hole (BH) allows us to test the nature of gravity in the strong-field regime. In this pape

Mitsuaki Obara, Takayuki Okuno, Akiko Takeda

We consider Riemannian optimization problems with inequality and equality constraints and analyze a class of Riemannian interior point methods for solving them. The algorithm of interest consists of outer and inner iterations. We show that, under standard assumptions, the algorithm achieves local superlinear convergence by solving a linear system at each out

Šimon Bräuer, Jan Provazník, Vojtěch Kala, Petr Marek

Superposed coherent states are central to quantum technologies, yet their reliable identification remains a challenge, especially in noisy or resource-constrained settings. We introduce a novel, directly measurable criterion for detecting cat-like features in quantum states, rooted in the concept of nonlinear squeezing. This approach bypasses the need for fu

Yihao Ai, Zhiyuan Ning, Weiwei Dai, Pengfei Wang

Biomedical entity linking aims to map nonstandard entities to standard entities in a knowledge base. Traditional supervised methods perform well but require extensive annotated data to transfer, limiting their usage in low-resource scenarios. Large language models (LLMs), especially closed-source LLMs, can address these but risk stability issues and high eco

Fumi Nakanishi, Ken'ichiro Nakazato, Masayuki Harada, Yusuke Koshio

Neutrinos from supernovae, especially those emitted during the late phase of core collapse, are essential for understanding the final stages of massive star evolution. We have been dedicated to developing methods for the analysis of neutrinos emitted during the late phase and observed at Super-Kamiokande (SK). Our previous studies have successfully demonstra

Marco Rando, Cesare Molinari, Lorenzo Rosasco, Silvia Villa

Finite-difference methods are widely used for zeroth-order optimization in settings where gradient information is unavailable or expensive to compute. These procedures mimic first-order strategies by approximating gradients through function evaluations along a set of random directions. From a theoretical perspective, recent studies indicate that imposing str

Juntong Wang, Xiyuan Wang, Muhan Zhang

Common Neighbors (CNs) and their higher-order variants are important pairwise features widely used in state-of-the-art link prediction methods. However, existing methods often struggle with the repetition across different orders of CNs and fail to fully leverage their potential. We identify that these limitations stem from two key issues: redundancy and over

D. Sebastian, I. Boisse, A. Santerne, A. H. M. J. Triaud

Eclipsing binaries are perfect laboratories to measure precise, accurate and model-independent stellar radii and stellar masses, so long as both components are spectroscopically resolved. Resolving both components is difficult in high-contrast binaries, for instance, those composed of an FGK main-sequence star with an M-type companion. In those cases, the se

Quentin Rouxel, Clemente Donoso, Fei Chen, Serena Ivaldi

Imitation learning is a promising approach for enabling generalist capabilities in humanoid robots, but its scaling is fundamentally constrained by the scarcity of high-quality expert demonstrations. This limitation can be mitigated by leveraging suboptimal, open-ended play data, often easier to collect and offering greater diversity. This work builds upon r

Yifan Wu, Jingze Shi, Bingheng Wu, Jiayi Zhang

Existing chain-of-thought (CoT) distillation methods can effectively transfer reasoning abilities to base models but suffer from two major limitations: excessive verbosity of reasoning traces and inadequate adaptability to problem difficulty. Long reasoning traces significantly increase inference costs, and uniform-length solutions prevent base models from l

Chunyang Jiang, Chi-min Chan, Yiyang Cai, Yulong Liu

Recently, the pretrain-finetune paradigm has become a cornerstone in various deep learning areas. While in general the pre-trained model would promote both effectiveness and efficiency of downstream tasks fine-tuning, studies have shown that not all knowledge acquired during pre-training is beneficial. Some of the knowledge may actually bring detrimental eff

Zhaopeng Feng, Yupu Liang, Shaosheng Cao, Jiayuan Su

Text Image Machine Translation (TIMT)-the task of translating textual content embedded in images-is critical for applications in accessibility, cross-lingual information access, and real-world document understanding. However, TIMT remains a complex challenge due to the need for accurate optical character recognition (OCR), robust visual-text reasoning, and h

Johannes Hertrich, Antonin Chambolle, Julie Delon

This paper investigates the connections between rectified flows, flow matching, and optimal transport. Flow matching is a recent approach to learning generative models by estimating velocity fields that guide transformations from a source to a target distribution. Rectified flow matching aims to straighten the learned transport paths, yielding more direct fl

Jiahui Geng, Qing Li, Zongxiong Chen, Yuxia Wang

The rapid advancement of vision-language models (VLMs) has brought a lot of attention to their safety alignment. However, existing methods have primarily focused on model undersafety, where the model responds to hazardous queries, while neglecting oversafety, where the model refuses to answer safe queries. In this paper, we introduce the concept of $\textit{

A. S. Mikhaylov, A. S. Mikhaylov

We consider the inverse problem for the dynamical system with discrete Schr\"odinger operator and discrete time. As an inverse data we take a \emph{response operator}, the natural analog of the dynamical Dirichlet-to-Neumann map. We derive two types of equations of inverse problem and answer a question on the characterization of the inverse data, i.e. we des

A. S. Mikhaylov, V. S. Mikhaylov

We consider a dynamic inverse problem for a dynamical system which describes the propagation of waves in a Krein string. The problem is reduced to an integral equation and an important special case is considered when the string density is determined by a finite number of point masses distributed over the interval. We derive an equation of Krein type, with th

Runxin Zhang, Yulin Shao, Jian Xiong, Lu Lu

Since commercial LEDs are primarily designed for illumination rather than data transmission, their modulation bandwidth is inherently limited to a few MHz. This becomes a major bottleneck in the implementation of visible light communication (VLC) systems necessiating the design of pre-equalizers. While state-of-the-art equalizer designs primarily focus on th

Paul Janicot, Alex Vinyas

Decentralized Finance (DeFi) on Ethereum has undergone significant transformations since its emergence during the DeFi summer of 2020. With the introduction of Proof of Stake (PoS) and Proposer-Builder Separation (PBS), the transaction supply chain on Ethereum has shifted from relying entirely on the public mempool for DeFi interactions to an astonishing 80%