March 2025 arXiv papers — page 175

Matthew Shen, Aliyah Hsu, Abhineet Agarwal, Bin Yu

Concept bottleneck models (CBM) aim to improve model interpretability by predicting human level "concepts" in a bottleneck within a deep learning model architecture. However, how the predicted concepts are used in predicting the target still either remains black-box or is simplified to maintain interpretability at the cost of prediction performance. We propo

Nimisha Karnatak, Adrien Baranes, Rob Marchant, Triona Butler

Small business owners (SBOs) often lack the resources and design experience needed to produce high-quality advertisements. To address this, we developed ACAI (AI Co-Creation for Advertising and Inspiration), an GenAI-powered multimodal advertisement creation tool, and conducted a user study with 16 SBOs in London to explore their perceptions of and interacti

Sioneh Eyvazi, Evgeny A. Mamonov, Rebecca Heilmann, Javier Cuerda

Photonic flat bands are crucial for enabling strong localization of light and enhancing light-matter interactions, as well as tailoring the angular distribution of emission from photonic structures. These unique properties open pathways for developing robust photonic devices, efficient nonlinear optical processes, and novel platforms for exploring topologica

M. Andriamirado, A. B. Balantekin, C. D. Bass, O. Benevides Rodrigues

The Precision Reactor Oscillation and Spectrum Experiment, PROSPECT, was a segmented antineutrino detector that successfully operated at the High Flux Isotope Reactor in Oak Ridge, TN, during its 2018 run. Despite challenges with photomultiplier tube base failures affecting some segments, innovative machine learning approaches were employed to perform positi

CMS Collaboration

A first search for beyond the standard model physics in jet multiplicity patterns of multilepton events is presented, using a data sample corresponding to an integrated luminosity of 138 fb$^{-1}$ of 13 TeV proton-proton collisions recorded by the CMS detector at the LHC. The search uses observed jet multiplicity distributions in one-, two-, and four-lepton

Pouya Agheli, Nikolaos Pappas, Marios Kountouris

This paper addresses query scheduling for goal-oriented semantic communication in pull-based status update systems. We consider a system where multiple sensing agents (SAs) observe a source characterized by various attributes and provide updates to multiple actuation agents (AAs), which act upon the received information to fulfill their heterogeneous goals a

Juan Soria-Postigo, Luis F Seoane

Syntax connects words to each other in very specific ways. Two words are syntactically connected if they depend directly on each other. Syntactic connections usually happen within a sentence. Gathering all those connection across several sentences gives birth to syntax networks. Earlier studies in the field have analysed the structure and properties of synta

Giuliana Fusco

In this paper we study the asymptotic behavior of a family of discrete functionals as the lattice size, $\varepsilon>0$, tends to zero. We consider pairwise interaction energies satisfying $p$-growth conditions, $p<d$, $d$ being the dimension of the reference configuration, defined on discrete functions subject to Dirichlet conditions on a $\delta$-periodic

Luigi Caputi, Giuliamaria Menara

In this paper we explore the algebraic structure and combinatorial properties of eulerian magnitude homology. First, we analyze the diagonality conditions of eulerian magnitude homology, providing a characterization of complete graphs. Then, we construct the regular magnitude-path spectral sequence as the spectral sequence of the (filtered) injective nerve o

Muhammad Umer, Muhammad Ahmed Mohsin, Ali Arshad Nasir, Hatem Abou-Zeid

Integrating non-terrestrial networks (NTNs) with terrestrial networks (TNs) is key to enhancing coverage, capacity, and reliability in future wireless communications. However, the multi-tier, heterogeneous architecture of these integrated TN-NTNs introduces complex challenges in spectrum sharing and interference management. Conventional optimization approach

Payel Nandi, C. S. Stalin, D. J. Saikia

The driving force behind outflows, often invoked to understand the correlation between the supermassive black holes powering active galactic nuclei (AGN) and their host galaxy properties, remains uncertain. We provide new insights into the mechanisms that trigger warm ionized outflows in AGN, based on findings from the MaNGA survey. Our sample comprises 538

Stephen Bartell, Paul Seymour

Kuratowski's theorem says that the minimal (under subgraph containment) graphs that are not planar are the subdivisions of $K_5$ and of $K_{3,3}$. Here we study the minimal (under subdigraph containment) strongly-connected digraphs that are not planar. We also find the minimal strongly-connected non-outerplanar digraphs and the minimal strongly-connected non

Wentian Xu, Ziyun Liang, Harry Anthony, Yasin Ibrahim

Interactive segmentation uses real-time user inputs, such as mouse clicks, to iteratively refine model predictions. Although not originally designed to address distribution shifts, this paradigm naturally lends itself to such challenges. In medical imaging, where distribution shifts are common, interactive methods can use user inputs to guide models towards

Souptik Chakraborty, Utsab Sarkar

This paper investigates sharp stability estimates for the fractional Hardy-Sobolev inequality: $$\mu_{s,t}\left(\mathbb{R}^N\right) \left(\int_{\mathbb{R}^N} \frac{|u|^{2^*_s(t)}}{|x|^t} \,{\rm d}x \right)^{\frac{2}{2^*_s(t)}} \leq \int_{\mathbb{R}^N} \left|(-\Delta)^{\frac{s}{2}} u \right|^2 \,{\rm d}x, \quad \text{for all } u \in \dot{H}^s\left(\mathbb{R}^

Allen Zhang

We develop a technique to show the Morita equivalence of certain subrings of a ring with local units. We then apply this technique to develop conditions that are sufficient to show the Morita equivalence of subalgebras induced by partial subactions on generalized Boolean algebras and, subsequently, strongly $E^{\ast}$-unitary inverse subsemigroups. As an app

Selçuk Kayacan

A group $G$ with conjugation operation is a rack. We call such racks \emph{group racks}. In this paper we study finite group racks via their subrack lattices. Heckenberger, Shareshian, and Welker proved that the isomorphism type of the subrack lattice of a finite group determines whether the group is solvable. Our first result shows that if $G$ is a finite s

Rohollah Khoshlahni, Yousef Azizi, Ali Aftabi, Serban Lepadatu

Existing skyrmion nucleation methods lead to increased Joule heating, limiting the applicability to metallic Antiferromagnetic (AFM) systems. In this study, we propose a novel, energy-efficient mechanical method for nucleating AFM skyrmions using Surface Acoustic Waves (SAWs). SAWs, which propagate along material surfaces with minimal attenuation, generate d

DES Collaboration, T. M. C. Abbott, M. Acevedo, M. Adamow

The Dark Energy Survey (DES) recently released the final results of its two principal probes of the expansion history: Type Ia Supernovae (SNe) and Baryonic Acoustic Oscillations (BAO). We explore the cosmological implications of these data in combination with external Cosmic Microwave Background (CMB), Big Bang Nucleosynthesis (BBN), and age-of-the-Universe

Bertalan Pécsi

This paper touches on several interaction points of semigroups and constructions from category theory: An adjunction is established between categories with selected arrows and semigroups. Regular semigroups are characterized by split epi - split mono factorization of the Karoubi envelope. We investigate how semigroupads (monads without requirement of unit tr

Christopher Cedzich, Long Li

We solve the Dry Ten Martini Problem for the unitary almost Mathieu operator with Diophantine frequencies in the non-critical regime.

Hongshen Xu, Zixv yang, Zichen Zhu, Kunyao Lan

Large Language Models often generate factually incorrect but plausible outputs, known as hallucinations. We identify a more insidious phenomenon, LLM delusion, defined as high belief hallucinations, incorrect outputs with abnormally high confidence, making them harder to detect and mitigate. Unlike ordinary hallucinations, delusions persist with low uncertai

Hongshen Xu, Zihan Wang, Zichen Zhu, Lei Pan

Recent advancements in tool learning have enabled large language models (LLMs) to integrate external tools, enhancing their task performance by expanding their knowledge boundaries. However, relying on tools often introduces tradeoffs between performance, speed, and cost, with LLMs sometimes exhibiting overreliance and overconfidence in tool usage. This pape

Edwin Hamel-De le Court, Francesco Belardinelli, Alexander W. Goodall

In real-life scenarios, a Reinforcement Learning (RL) agent aiming to maximise their reward, must often also behave in a safe manner, including at training time. Thus, much attention in recent years has been given to Safe RL, where an agent aims to learn an optimal policy among all policies that satisfy a given safety constraint. However, strict safety guara

Brian Huge, Antoine Savine

We extend the scope of differential machine learning and introduce a new breed of supervised principal component analysis to reduce dimensionality of Derivatives problems. Applications include the specification and calibration of pricing models, the identification of regression features in least-square Monte-Carlo, and the pre-processing of simulated dataset

Ming Zhang, Yuhui Wang, Yujiong Shen, Tingyi Yang

Process-driven dialogue systems, which operate under strict predefined process constraints, are essential in customer service and equipment maintenance scenarios. Although Large Language Models (LLMs) have shown remarkable progress in dialogue and reasoning, they still struggle to solve these strictly constrained dialogue tasks. To address this challenge, we

Apostolos Angelis, George Kousiouris

Cloud-native applications have significantly advanced the development and scalability of online services through the use of microservices and modular architectures. However, achieving adaptability, resilience, and efficient performance management within cloud environments remains a key challenge. This work systematically reviews 111 publications from the las

Parinya Karndumri

We study five-dimensional $N=4$ gauged supergravity coupled to three vector multiplets with $SO(2)_D\times SO(3)$ gauge group. The gauged supergravity admits two supersymmetric $AdS_5$ vacua. One of the vacua, at the origin of the scalar manifold $\mathbb{R}^+\times SO(5,3)/SO(5)\times SO(3)$, preserves $N=4$ supersymmetry and the full $SO(2)_D\times SO(3)$

Sergi Liesegang, Stefano Buzzi, Carmen D'Andrea

This paper investigates a cell-free massive MIMO (multiple-input multiple-output) system where distributed access points (APs) perform integrated sensing and communications (ISAC) tasks, enabling simultaneous user communication and target detection/tracking. A unified framework and signal model are developed for the detection of potential targets and trackin

Albert S. Berahas, Jiahao Shi, Baoyu Zhou

We propose and analyze a sequential quadratic programming algorithm for minimizing a noisy nonlinear smooth function subject to noisy nonlinear smooth equality constraints. The algorithm uses a step decomposition strategy and, as a result, is robust to potential rank-deficiency in the constraints, allows for two different step size strategies, and has an ear

Omar Mameche, Abdelhadi Abedou, Taqwa Mezaache, Mohamed Tadjine

This paper explores the application of reinforcement learning to optimize the parameters of a Type-1 Takagi-Sugeno fuzzy controller, designed to operate as an artificial pancreas for Type 1 diabetes. The primary challenge in diabetes management is the dynamic nature of blood glucose levels, which are influenced by several factors such as meal intake and timi

Chenfei Liao, Xu Zheng, Yuanhuiyi Lyu, Haiwei Xue

Research has focused on Multi-Modal Semantic Segmentation (MMSS), where pixel-wise predictions are derived from multiple visual modalities captured by diverse sensors. Recently, the large vision model, Segment Anything Model 2 (SAM2), has shown strong zero-shot segmentation performance on both images and videos. When extending SAM2 to MMSS, two issues arise:

Junhao Cao, Nicolas Folastre, Gozde Oney, Edgar Rauch

This study presents a novel integration of unsupervised learning and decision-making strategies for the advanced analysis of 4D-STEM datasets, with a focus on non-negative matrix factorization (NMF) as the primary clustering method. Our approach introduces a systematic framework to determine the optimal number of components (k) required for robust and interp

Xavier Thomas, Deepti Ghadiyaram

Domain Generalization aims to develop models that can generalize to novel and unseen data distributions. In this work, we study how model architectures and pre-training objectives impact feature richness and propose a method to effectively leverage them for domain generalization. Specifically, given a pre-trained feature space, we first discover latent domai

Han Guo, Ding Lin

Accurate probabilistic load forecasting is crucial for maintaining the safety and stability of power systems. However, the mainstream approach, multi-step prediction, is hindered by cumulative errors and forecasting lags, which limits its effectiveness in probabilistic day-ahead load forecasting (PDALF). To overcome these challenges, we introduce DALNet, a n

A T M Omor Faruq, Md Ataur Rahman Chowdhury

This study explores the role of big data adoption and financial market development in driving ESG investments in developing countries, using an instrumental variable (IV) approach to address endogeneity. The results show that big data adoption significantly enhances ESG investing, as data-driven analytics improve sustainability assessments and capital alloca

Amin Hosseinkhani, Fedor Šimkovic, Alessio Calzona, Emiliano Godinez-Ramirez

Error mitigation is essential for extracting reliable results from quantum computations performed on noisy intermediate-scale quantum hardware. Here we introduce Noise-Robust Estimation (NRE), a noise-agnostic framework that suppresses estimation bias through a two-stage post-processing protocol. The method combines measurement data from a target circuit and

Maosen Xu, Huangjie Yu, Lipeng Luo

In this paper, we prove that uniformly bounded simple Lie conformal algebra must be finitely generated. Furthermore, we give a completely classification of simple uniformly bounded Lie conformal algebras with upper bound one.

Pau Escofet, Santiago Rodrigo, Artur Garcia-Sáez, Eduard Alarcón

Fidelity is one of the most valuable and commonly used metrics for assessing the performance of quantum circuits on error-prone quantum processors. Several approaches have been proposed to estimate circuit fidelity without executing it on quantum hardware, but they often face limitations in scalability or accuracy. In this work, we present a comprehensive th

Yuchen Yan, Yongliang Shen, Yang Liu, Jin Jiang

Advanced reasoning in large language models has achieved remarkable performance on challenging tasks, but the prevailing long-context reasoning paradigm faces critical limitations: quadratic computational scaling with sequence length, reasoning constrained by maximum context boundaries, and performance degradation beyond pre-training context windows. Existin

Jaroslav I. Borodavka, Sebastian Krumscheid

We present two limit theorems, a mean ergodic and a central limit theorem, for a specific class of one-dimensional diffusion processes that depend on a small-scale parameter $\varepsilon$ and converge weakly to a homogenized diffusion process in the limit $\varepsilon \rightarrow 0$. In these results, we allow for the time horizon to blow up such that $T_\va

Animesh Kumar Paul, Russell Greiner

Dynamic Treatment Regimes (DTRs) provide a systematic approach for making sequential treatment decisions that adapt to individual patient characteristics, particularly in clinical contexts where survival outcomes are of interest. Censoring-Aware Tree-Based Reinforcement Learning (CA-TRL) is a novel framework to address the complexities associated with censor

Junjia Du, Yadi Liu, Hongcheng Guo, Jiawei Wang

While large language models (LLMs) have shown considerable promise in code generation, real-world software development demands advanced repository-level reasoning. This includes understanding dependencies, project structures, and managing multi-file changes. However, the ability of LLMs to effectively comprehend and handle complex code repositories has yet t

Pijush Patra, Anubhab Roy

We investigate the collisions of uncharged, conducting droplets settling under gravity in the presence of an external electric field. Previous studies have derived a near-field asymptotic expression for the electric-field-induced attraction, suggesting that this force can overcome lubrication resistance and drive surface-to-surface contact between two spheri

Gongbo Zhang, Yanting Li, Renqian Luo, Pipi Hu

Function in natural systems arises from one-dimensional sequences forming three-dimensional structures with specific properties. However, current generative models suffer from critical limitations: training objectives seldom target function directly, discrete sequences and continuous coordinates are optimized in isolation, and conformational ensembles are un

Eli Bogdanov, Izack Cohen, Avigdor Gal

Long event sequences (termed traces) and large data logs that originate from sensors and prediction models are becoming increasingly common in our data-rich world. In such scenarios, conformance checking-validating a data log against an expected system behavior (the process model) can become computationally infeasible due to the exponential complexity of fin

Sheng Song, Yiting Chen, Duo Xu, Songhan Ge

Freehand 3D ultrasound enables volumetric imaging by tracking a conventional ultrasound probe during freehand scanning, offering enriched spatial information that improves clinical diagnosis. However, the quality of reconstructed volumes is often compromised by tracking system noise and irregular probe movements, leading to artifacts in the final reconstruct

Daisuke A. Takahashi

The closed-form expressions of electric potentials and field lines for a uniformly-charged tube and cylinder are presented using elliptic integrals and Appell's hypergeometric functions, where field lines are depicted by introducing the concept of the field line potential in axisymmetric systems, whose contour lines represent electric field lines outside the

Zhaowei Chen, Borui Zhao, Yuchen Ge, Yuhao Chen

Online Knowledge Distillation (OKD) methods streamline the distillation training process into a single stage, eliminating the need for knowledge transfer from a pretrained teacher network to a more compact student network. This paper presents an innovative approach to leverage intermediate spatial representations. Our analysis of the intermediate features fr

Xuechao Zou, Yue Li, Shun Zhang, Kai Li

Remote sensing image segmentation faces persistent challenges in distinguishing morphologically similar categories and adapting to diverse scene variations. While existing methods rely on implicit representation learning paradigms, they often fail to dynamically adjust semantic embeddings according to contextual cues, leading to suboptimal performance in fin

Samik Mitra

In this proceeding, we provide a novel approach to study the General Relativistic Magnetohydrodynamic (GRMHD) accretion flows around rotating black holes (BHs). In doing so, we choose a sub-Keplerian distribution of angular momentum of the flow, which is necessary for the accreting matter to reach the event horizon of the BH. Further, we consider the converg

Marius Durea, Christian Günther, Radu Strugariu, Christiane Tammer

We consider vector and set optimization problems with respect to variable domination structures given by set-valued mappings acting between the preimage space and the image space of the objective mapping, as well as by set-valued mappings with the same input and output space, that coincides with the image space of the objective mapping. The aim of this paper

Nitesh Patnaik, Navdeep Nayak, Himani Bansal Agrawal, Moinak Chinmoy Khamaru

The emergence of small vision-language models (sVLMs) marks a critical advancement in multimodal AI, enabling efficient processing of visual and textual data in resource-constrained environments. This survey offers a comprehensive exploration of sVLM development, presenting a taxonomy of architectures - transformer-based, mamba-based, and hybrid - that highl

Wei Li, Xin Zhang, Zhongxin Guo, Shaoguang Mao

Implementing new features in repository-level codebases is a crucial application of code generation models. However, current benchmarks lack a dedicated evaluation framework for this capability. To fill this gap, we introduce FEA-Bench, a benchmark designed to assess the ability of large language models (LLMs) to perform incremental development within code r

V. A. Shestakov, M. M. Korshunov

We obtain solutions for Eliashberg equations within the Nambu representation for a two-band model of iron-based superconductors with nonmagnetic impurities. Two cases of a transition between $s_{\pm}$ and $s_{++}$ states are considered: (i) the transition is accompanied by the abrupt change of the order parameter sign within one of the bands and (ii) the cha

Hantao Zhou, Rui Yang, Longxiang Tang, Guanyi Qin

Image assessment aims to evaluate the quality and aesthetics of images and has been applied across various scenarios, such as natural and AIGC scenes. Existing methods mostly address these sub-tasks or scenes individually. While some works attempt to develop unified image assessment models, they have struggled to achieve satisfactory performance or cover a b

Di Wu, Liu Liu, Zhou Linli, Anran Huang

Articulated objects, as prevalent entities in human life, their 3D representations play crucial roles across various applications. However, achieving both high-fidelity textured surface reconstruction and dynamic generation for articulated objects remains challenging for existing methods. In this paper, we present REArtGS, a novel framework that introduces a

Chenyu Huang, Peng Ye, Xiaohui Wang, Shenghe Zheng

With transformer-based models and the pretrain-finetune paradigm becoming mainstream, the high storage and deployment costs of individual finetuned models on multiple tasks pose critical challenges. Delta compression attempts to lower the costs by reducing the redundancy of delta parameters (i.e., the difference between the finetuned and pre-trained model we

Faizuddin Ahmed, Nuray Candemir, Abdelmalek Bouzenada

In this paper, we investigate how the gravitational field generated by a four-dimensional electrovacuum cosmological space-time influences the dynamics of fermionic fields governed by the Dirac equation, while also considering the effects of topology. We derive the radial wave equation corresponding to the relativistic Dirac equation and subsequently obtain

Yihong Luo, Tianyang Hu, Jiacheng Sun, Yujun Cai

Accelerating diffusion model sampling is crucial for efficient AIGC deployment. While diffusion distillation methods -- based on distribution matching and trajectory matching -- reduce sampling to as few as one step, they fall short on complex tasks like text-to-image generation. Few-step generation offers a better balance between speed and quality, but exis

Daniel Danielski

We initiate systematic study of EZ-structures (and associated boundaries) of groups acting on spaces that admit consistent and conical (equivalently, consistent and convex) geodesic bicombings. Such spaces recently drew a lot of attention due to the fact that many classical groups act `nicely' on them. We rigorously construct EZ-structures, discuss their uni

Guangming Lu, Gustau Catalan, Ekhard K. H. Salje

Ferroelastic materials (materials with switchable spontaneous strain) often are centrosymmetric, but their domain walls are always polar, as their internal strain gradients cause polarization via flexoelectricity. This polarization is generally not switchable by an external electric field, because reversing the domain wall polarity would require reversing th

Dongheon Lee, Seokju Yun, Youngmin Ro

In this paper, we tackle the high computational overhead of Transformers for efficient image super-resolution~(SR). Motivated by the observations of self-attention's inter-layer repetition, we introduce a convolutionized self-attention module named Convolutional Attention~(ConvAttn) that emulates self-attention's long-range modeling capability and instance-d

Roni Goldshmidt

Interpretability in Vision-Language Models (VLMs) is crucial for trust, debugging, and decision-making in high-stakes applications. We introduce PixelSHAP, a model-agnostic framework extending Shapley-based analysis to structured visual entities. Unlike previous methods focusing on text prompts, PixelSHAP applies to vision-based reasoning by systematically p

AgiBot-World-Contributors, Qingwen Bu, Jisong Cai, Li Chen

We explore how scalable robot data can address real-world challenges for generalized robotic manipulation. Introducing AgiBot World, a large-scale platform comprising over 1 million trajectories across 217 tasks in five deployment scenarios, we achieve an order-of-magnitude increase in data scale compared to existing datasets. Accelerated by a standardized c

Takeshi Nakamori, Kazuaki Hashiyama, Rin Sato, Masayoshi Shoji

We have developed an optical photon-counting imaging system, IMONY, as an instrument for short-scale time-domain astronomy. In this study, we utilized a Geiger avalanche photodiode array with a $4\times 4$ pixel configuration, with each pixel measuring \SI{100}{\micro m}. We developed a dedicated analog frontend board and constructed a data acquisition syste

Lorenzo Lasagni

In this work, image analysis techniques used in astrophysics to detect low-contrast signals have been adapted in the processing of Computed Tomography (CT) images, combining Centroidal Voronoi Tessellation (CVT) and machine learning techniques. Several CT acquisitions were performed using a phantom containing cylindrical inserts of different diameters produc

Antonio M. García-García, Zhongling Lu, Lucas Sá, Jacobus J. M. Verbaarschot

Quantum many-body scars have received much recent attention for being both intriguing non-ergodic states in otherwise quantum chaotic systems and promising candidates to encode quantum information efficiently. So far, these studies have mostly been restricted to Hermitian systems. Here, we study many-body scars in many-body quantum chaotic systems coupled to

Tommaso Bendinelli, Artur Dox, Christian Holz

High-quality, error-free datasets are a key ingredient in building reliable, accurate, and unbiased machine learning (ML) models. However, real world datasets often suffer from errors due to sensor malfunctions, data entry mistakes, or improper data integration across multiple sources that can severely degrade model performance. Detecting and correcting thes

Sahidul Islam, Wei Wei, Jishnu Banarjee, Chen Pan

Deep neural network (DNN) inference in energy harvesting (EH) devices poses significant challenges due to resource constraints and frequent power interruptions. These power losses not only increase end-to-end latency, but also compromise inference consistency and accuracy, as existing checkpointing and restore mechanisms are prone to errors. Consequently, th

Xiaoxing Ren, Michelangelo Bin, Ivano Notarnicola, Thomas Parisini

This paper studies a distributed algorithm for constrained consensus optimization that is obtained by fusing the Arrow-Hurwicz-Uzawa primal-dual gradient method for centralized constrained optimization and the Wang-Elia method for distributed unconstrained optimization. It is shown that the optimal primal-dual point is a semiglobally exponentially stable equ

Wenxin Ma, Xu Zhang, Qingsong Yao, Fenghe Tang

Anomaly detection (AD) identifies outliers for applications like defect and lesion detection. While CLIP shows promise for zero-shot AD tasks due to its strong generalization capabilities, its inherent Anomaly-Unawareness leads to limited discrimination between normal and abnormal features. To address this problem, we propose Anomaly-Aware CLIP (AA-CLIP), wh

Yang Zou, Zhaoshuai Qi, Yating Liu, Zihao Xu

Object pose estimation, which plays a vital role in robotics, augmented reality, and autonomous driving, has been of great interest in computer vision. Existing studies either require multi-stage pose regression or rely on 2D-3D feature matching. Though these approaches have shown promising results, they rely heavily on appearance information, requiring comp

Tianyang Wen, Xucheng Zhang, Zhirong Wan, Jing Zhao

Parkinson's Disease (PD) significantly impacts driving abilities, often leading to early driving cessation or accidents due to reduced motor control and increasing reaction times. To diminish the impact of these symptoms, we developed PANDA (Parkinson's Assistance and Notification Driving Aid), a multi-modality real-time alert system designed to monitor driv

Divyanshu Vashistha, Chaman Kumar

An explicit first-order drift-randomized Milstein scheme for a regime switching stochastic differential equation is proposed and its bi-stability and rate of strong convergence are investigated for a non-differentiable drift coefficient. Precisely, drift is Lipschitz continuous while diffusion along with its derivative is Lipschitz continuous. Further, we ex

Andrew Craig, Claudette Robinson, Wilmari Morton

We extend the work of Galatos (2004) on nested sums, originally called generalised ordinal sums, of residuated lattices. We show that the nested sum of an odd quasi relation algebra (qRA) satisfying certain conditions and an arbitrary qRA is again a qRA. In a recent paper by Craig and Robinson (2025) the notion of representability for distributive quasi rela

Kurt Bryan, Michael S. Vogelius

Perfectly Matched Layers (PML) has become a very common method for the numerical approximation of wave and wave-like equations on unbounded domains. This technique allows one to obtain accurate solutions while working on a finite computational domain, and the technique is relatively simple to implement. Results concerning the accuracy of the PML method have

Francesco Verdiani, Emilio Bellini, Chiara Moretti, Emiliano Sefusatti

We study the Kaiser formula for biased tracers in massive neutrino cosmologies by comparing its predictions with a large set of N-body simulations. In particular, we examine the ambiguity in the definition of the peculiar velocity contribution at linear level, whether it should be expressed in terms of the total matter velocity field or only the cold-matter

Yanbin Zheng, Yang Zhang, Zhengbang Zha, Xiangyong Zeng

The generalized cyclotomic mappings over finite fields $\mathbb{F}_{q}$ are those mappings which induce monomial functions on all cosets of an index $\ell$ subgroup $C_0$ of the multiplicative group $\mathbb{F}_{q}^{*}$. Previous research has focused on the one-to-one property, the functional graphs, and their applications in constructing linear codes and be

Lena Jonas, Lutz Mattner

We prove a central limit error bound for convolution powers of laws with finite moments of order $r \in \mathopen]2,3\mathclose]$, taking a closeness of the laws to normality into account. Up to a universal constant, this generalises the case of $r=3$ of the sharpening of the Berry (1941) - Esseen (1942) theorem obtained by Mattner (2024), namely by sharpeni

Yihong Luo, Tianyang Hu, Yifan Song, Jiacheng Sun

While diffusion distillation has enabled one-step generation through methods like Variational Score Distillation, adapting distilled models to emerging new controls -- such as novel structural constraints or latest user preferences -- remains challenging. Conventional approaches typically requires modifying the base diffusion model and redistilling it -- a p

Tengjiao Wang, Zhenyu Kang, Ting Li, Zhihui Chen

This paper explores the emerging research direction of electromagnetic information theory (EIT), which aims to integrate traditional Shannon-based methodologies with physical consistency, particularly the electromagnetic properties of communication channels. We propose an EIT-based multiple-input multiple-output (MIMO) paradigm that enhances conventional spa

André Galligo, Joseph Najnudel

In this paper, we study the asymptotic macroscopic behavior of the root sets of iterated, randomized derivatives of polynomials. The randomization depend on a parameter of inverse temperature $\beta \in (0, \infty]$, the case $\beta = \infty$ corresponding to the situation where one considers the derivative of polynomials, without randomization. Our construc

Matteo Buzzegoli, Kirill Tuchin, Nandagopal Vijayakumar

We develop the quasi-classical (WKB) approximation of the synchrotron radiation by a fermion embedded into uniformly rotating system in external magnetic field. We show that it gives an accurate approximation of the exact expression that we recently obtained at a tiny fraction of the numerical cost. Our results can be used to compute the electromagnetic radi

Hender Lin

Standard NLP benchmarks often fail to capture vulnerabilities stemming from dataset artifacts and spurious correlations. Contrast sets address this gap by challenging models near decision boundaries but are traditionally labor-intensive to create and limited in diversity. This study leverages large language models to automate the generation of diverse contra

Hassan Kazemi Tehrani, Jun Cai, Abbas Yekanlou, Sylvia Santosa

Accurate food intake monitoring is crucial for maintaining a healthy diet and preventing nutrition-related diseases. With the diverse range of foods consumed across various cultures, classic food classification models have limitations due to their reliance on fixed-sized food datasets. Studies show that people consume only a small range of foods across the e

Jiaxin Liu, Yixuan Tang, Yi Yang, Kar Yan Tam

Large Language Models (LLMs) are increasingly used in decision-making scenarios that involve risk assessment, yet their alignment with human economic rationality remains unclear. In this study, we investigate whether LLMs exhibit risk preferences consistent with human expectations across different personas. Specifically, we assess whether LLM-generated respo

Jiangtao Duan, Jushan Bai, Xu Han

This paper proposes a quasi-maximum likelihood (QML) estimator for break points in high-dimensional factor models, specifically accounting for multiple structural breaks. We begin by establishing a necessary and sufficient condition to categorize two distinct types of breaks in factor loadings: singular changes and rotational changes. The analysis of the nea

Gary Y Ge, Charles M Weaver, Jie Zhang

The Centers for Medicare & Medicaid Services (CMS) has introduced CMS1074v2, a quality measure for computed tomography (CT) that assesses radiation dose and image quality across 18 CT exam categories. This measure mandates the calculation of size-adjusted dose (SAD) using patient effective diameter and predefined size-adjustment coefficients. However, variab

Koki Mizuno, Ai Yamakage

We study two-dimensional superconductivity mediated by magnetic fluctuations at the interface between a ferromagnetic insulator and the nonsymmorphic topological crystalline insulator with a fourfold-degenerate Dirac point, wallpaper fermion. We demonstrate that BCS pairing with zero center-of-mass momentum induces chiral $p$-wave superconductivity, and the

Shipeng Liu, Liang Zhao, Dengfeng Chen

Unsupervised image complexity representation often suffers from bias in positive sample selection and sensitivity to image content. We propose CLICv2, a contrastive learning framework that enforces content invariance for complexity representation. Unlike CLIC, which generates positive samples via cropping-introducing positive pairs bias-our shifted patchify

Youssef Mroueh

Group Relative Policy Optimization (GRPO) was introduced and used recently for promoting reasoning in LLMs under verifiable (binary) rewards. We show that the mean + variance calibration of these rewards induces a weighted contrastive loss in which the contrastive samples are synthetic data drawn from the previous policy. While GRPO was originally paired wit

Manru Yin, Shengqian Han, Chenyang Yang

In the paper the joint optimization of uplink multiuser power and resource block (RB) allocation are studied, where each user has quality of service (QoS) constraints on both long- and short-blocklength transmissions. The objective is to minimize the consumption of RBs for meeting the QoS requirements, leading to a mixed-integer nonlinear programming (MINLP)

Qikai Lu, Minbo Yang, Shunneng Zhao

In this paper, we study the following fractional nonlocal Sobolev-type inequality \begin{equation*} C_{HLS}\bigg(\int_{\mathbb{R}^n}\big(|x|^{-\mu} \ast |u|^{p_s}\big)|u|^{p_s} dx\bigg)^{\frac{1}{p_s}}\leq\|u\|_{\dot{H}^s(\mathbb{R}^n)}^2\quad \mbox{for all}~~u\in \dot{H}^s(\mathbb{R}^n), \end{equation*} induced by the classical fractional Sobolev inequality

Zhiyuan Ning, Zaitian Wang, Ran Zhang, Ping Xu

Graph clustering aims to divide the graph into different clusters. The recently emerging deep graph clustering approaches are largely built on graph neural networks (GNN). However, GNN is designed for general graph encoding and there is a common issue of representation collapse in existing GNN-based deep graph clustering algorithms. We attribute two main rea

Yajie Wen, Defu Zhang

In this study, we present a block-based heuristic search algorithm to address the nuclear waste container packing problem in the context of real-world nuclear power plants. Additionally, we provide a dataset comprising 1600 problem instances for future researchers to use. Experimental results on this dataset demonstrate that the proposed algorithm effectivel

Yuri A. Kordyukov

In our recent papers, we studied semiclassical spectral problems for the Bochner-Schr\"odinger operator on a manifold of bounded geometry. We survey some results of these papers in the setting of the magnetic Schr\"odinger operator in the Euclidean space and describe some ideas of the proofs.

Yu Zhou, Bingyan Liu

Federated Learning (FL) enables multiple clients to collaboratively develop a global model while maintaining data privacy. However, online FL deployment faces challenges due to distribution shifts and evolving test samples. Personalized Federated Learning (PFL) tailors the global model to individual client distributions, but struggles with Out-Of-Distributio

Mingxiao Li, Tingyu Qu, Tinne Tuytelaars, Marie-Francine Moens

Personalized image generation via text prompts has great potential to improve daily life and professional work by facilitating the creation of customized visual content. The aim of image personalization is to create images based on a user-provided subject while maintaining both consistency of the subject and flexibility to accommodate various textual descrip

Linpeng Wu, Qingfeng Zhang, Valéry Lainey, Nick Cooper

Astrometric measurements are significantly challenged by the relative motion between the point source and the telescope, primarily due to the difficulty in accurately determining the position of the point source at the mid-exposure moment. Especially when the trail is irregular in shape or results from nonuniform relative motion, determining the centroid of

Bogdan Maxim

In this paper, we study a generalization of the D\'iaz-Saa inequality and its applications to nonlinear elliptic problems. We first present the necessary hypotheses and preliminary results before introducing an improved version of the inequality, which holds in a broader functional setting and allows applications to problems with homogeneous Neumann boundary