October 2025 arXiv papers — page 223

Balder ten Cate, Jesse Comer

In this chapter we give a basic overview of known results regarding Craig interpolation for first-order logic as well as for fragments of first-order logic. Our aim is to provide an entry point into the literature on interpolation theorems for first-order logic and fragments of first-order logic, and their applications. In particular, we cover a range of kno

Venkata Narendra Kotyada, Revanth Eranki, Nagesh Bhattu Sristy

Unpaired image-to-image translation involves learning mappings between source domain and target domain in the absence of aligned or corresponding samples. Score based diffusion models have demonstrated state-of-the-art performance in generative tasks. Their ability to approximate complex data distributions through stochastic differential equations (SDEs) ena

Nicola Fabiano

We extend the fixed point result for Path-Averaged Contractions (PA-contractions) from complete metric spaces to complete b-metric spaces. We prove that every PA-contraction on a complete b-metric space has a unique fixed point, provided the contraction constant $ \alpha $ satisfies $s \alpha^{1/N} < 1$, where $ s \geq 1 $ is the b-metric coefficient and $N$

Lulu Song, Di Zhang, Tingting Zhang

Source polar coding is a potential solution for short blocklength-based low-latency key generation with limited sources, which is a critical aspect of six generation (6G) Internet of things. However, existing source coding schemes still suffer from significant degradation in key generation rate and reconciliation reliability in short blocklength regime. To a

Philipp Becker, Niklas Freymuth, Serge Thilges, Fabian Otto

Reinforcement Learning (RL) with PPO-like clip objectives has become the standard choice for reward-based fine-tuning of large language models (LLMs). Although recent work has explored improved estimators of advantages and normalization, the clipping mechanism itself has remained untouched. Originally introduced as a proxy for principled KL-based trust regio

Wei Wang

Using the spectral theorem for symmetric matrices over a real closed field, we give a quick answer to a problem of Godsil and Sun on degree-similarity of graphs.

Yue wu

There are limitations of traditional methods and deep learning methods in terms of interpretability, generalization, and quantification of uncertainty in industrial fault diagnosis, and there are core problems of insufficient credibility in industrial fault diagnosis. The architecture performs preliminary analysis through a Bayesian network-based diagnostic

Lukas Eisenmann, Alena Brändle, Zahra Monfared, Daniel Durstewitz

Recurrent Neural Networks (RNNs) have found widespread applications in machine learning for time series prediction and dynamical systems reconstruction, and experienced a recent renaissance with improved training algorithms and architectural designs. Understanding why and how trained RNNs produce their behavior is important for scientific and medical applica

Byungjun Kim, Soobin Um, Jong Chul Ye

Text-to-image (T2I) diffusion models have demonstrated impressive performance in generating high-fidelity images, largely enabled by text-guided inference. However, this advantage often comes with a critical drawback: limited diversity, as outputs tend to collapse into similar modes under strong text guidance. Existing approaches typically optimize intermedi

Changhong Li, Clément Bled, Rosa Fernandez, Shreejith Shanker

Denoising is a core operation in modern video pipelines. In codecs, in-loop filters suppress sensor noise and quantisation artefacts to improve rate-distortion performance; in cinema post-production, denoisers are used for restoration, grain management, and plate clean-up. However, state-of-the-art deep denoisers are computationally intensive and, at scale,

Aya Laajil, Abduragim Shtanchaev, Sajan Muhammad, Eric Moulines

Designing mRNA sequences is a major challenge in developing next-generation therapeutics, since it involves exploring a vast space of possible nucleotide combinations while optimizing sequence properties like stability, translation efficiency, and protein expression. While Generative Flow Networks are promising for this task, their training is hindered by sp

Shankar Prasad Sastry

I present a regression algorithm that provides a continuous, piecewise-smooth function approximating scattered data. It is based on composing and blending linear functions over Voronoi cells, and it scales to high dimensions. The algorithm infers Voronoi cells from seed vertices and constructs a linear function for the input data in and around each cell. As

William Hao-Cheng Huang

In high-dimensional learning, models remain stable until they collapse abruptly once the sample size falls below a critical level. This instability is not algorithm-specific but a geometric mechanism: when the weakest Fisher eigendirection falls beneath sample-level fluctuations, identifiability fails. Our Fisher Threshold Theorem formalizes this by proving

Mehedi Hasan Emon

This research explores the annotation of rhetorical relations in discourse using the INCEpTION tool and compares manual annotation with automatic approaches based on large language models. The study focuses on sports reports (specifically cricket news) and evaluates the performance of BERT, DistilBERT, and Logistic Regression models in classifying rhetorical

Juan Segundo Hevia, Facundo Arredondo, Vishesh Kumar

The integration of large language models (LLMs) into education offers significant potential to enhance accessibility and engagement, yet their high computational demands limit usability in low-resource settings, exacerbating educational inequities. To address this, we propose an offline Retrieval-Augmented Generation (RAG) pipeline that pairs a small languag

Vaskar Chakma, Wooyeol Choi

Current Cyber-Physical Systems (CPS) integrated with Digital Twin (DT) technology face critical limitations in achieving real-time performance for mission-critical industrial applications. Existing 5G-enabled systems suffer from latencies exceeding 10ms, which are inadequate for applications requiring sub-millisecond response times, such as autonomous indust

Sara Behnamian, Fatemeh Fogh

We introduce and study twisted triangular Banach algebras T_sigma(A,B;X), built from Banach algebras A,B, a Banach A-B bimodule X, and a pair of automorphisms sigma=(sigma_A,sigma_B). This construction extends the classical triangular framework by incorporating twisted module actions on the off-diagonal block. We obtain a complete isomorphism classification:

Canhui Wu, Qiong Cao, Chang Li, Zhenfang Wang

Large Reasoning Models (LRMs) demonstrate strong performance on complex tasks but often suffer from excessive verbosity, known as "overthinking." Existing solutions via reinforcement learning (RL) typically penalize generated tokens to promote conciseness. However, these methods encounter two challenges: responses with fewer tokens do not always correspond t

Henrik Kaiser, Wolfgang Trutschnig

Driven by the interest on how uniformity of marginal distributions propa\-gates to properties of regression functions, in this contribution we tackle the following questions: Given a $(d-1)$-dimensional random vector $\textbf{X}$ and a random variable $Y$ such that all univariate marginals of $(\textbf{X},Y)$ are uniformly distributed on $[0,1]$, how large c

Chenglong Bao, Zanyu Li, Yunan Yang

This work analyzes the inverse optimal transport (IOT) problem under Bregman regularization. We establish well-posedness results, including existence, uniqueness (up to equivalence classes of solutions), and stability, under several structural assumptions on the cost matrix. On the computational side, we investigate the existence of solutions to the optimiza

Xiaochen Zhao, Chengting Yu, Kairong Yu, Lei Liu

Spiking Neural Networks (SNNs) exhibit exceptional energy efficiency on neuromorphic hardware due to their sparse activation patterns. However, conventional training methods based on surrogate gradients and Backpropagation Through Time (BPTT) not only lag behind Artificial Neural Networks (ANNs) in performance, but also incur significant computational and me

Gilberto Recupito, Vincenzo De Martino, Dario Di Nucci, Fabio Palomba

The rapid adoption of Deep Learning (DL)-enabled systems has revolutionized software development, driving innovation across various domains. However, these systems also introduce unique challenges, particularly in maintaining software quality and performance. Among these challenges, Self-Admitted Technical Debt (SATD) has emerged as a growing concern, signif

Hanwen Shen, Alexander Ushakov

Let $G=F\ast_\varphi t$ be an HNN extension of a free group $F$ with two equal associated normal subgroups $H_1 = H_2$ of finite index. We prove that the word problem in $G$ is decidable in polynomial time. This result extends to the case where the subgroups $H_1=H_2$ are not normal, provided that the isomorphism $\varphi:H_1\to H_2$ satisfies an additional

Samanta Pal, Kaustuv Chatterjee, Jyotirmoy Sau, Biswarup Satpati

Spontaneous formation of charge density wave (CDW) superstructures in monolayers (MLs) of a two-dimensional (2D) crystal lattice is fundamental in understanding its complex quantum states. We report a successful top-down liquid phase exfoliation and stamp transfer process (LPESTP) to create ML VS\textsubscript{2}, undergoing a CDW transition at room temperat

Hadi Asghari, Sami Nenno

This paper explores the ability of large language models to generate and recognize deep cognitive frames, particularly in socio-political contexts. We demonstrate that LLMs are highly fluent in generating texts that evoke specific frames and can recognize these frames in zero-shot settings. Inspired by mechanistic interpretability research, we investigate th

Rasel Hossen, Diptajoy Mistry, Mushiur Rahman, Waki As Sami Atikur Rahman Hridoy

Urban safety and infrastructure maintenance are critical components of smart city development. Manual monitoring of road damages is time-consuming, highly costly, and error-prone. This paper presents a deep learning approach for automated road damage and manhole detection using the YOLOv9 algorithm with polygonal annotations. Unlike traditional bounding box

R. M. V. Röhrs, R. N. Bisset

We investigate the formation of bound states between dark-antidark solitary waves in two-component dipolar Bose-Einstein condensates. The excitation spectrum contains density and spin branches, and a rotonic feature of the spin branch enables long-range soliton interactions, giving rise to multiple bound states for a single pair, each with a distinct separat

Simon Lupart, Daniël van Dijk, Eric Langezaal, Ian van Dort

Personalized Conversational Information Retrieval (CIR) has seen rapid progress in recent years, driven by the development of Large Language Models (LLMs). Personalized CIR aims to enhance document retrieval by leveraging user-specific information, such as preferences, knowledge, or constraints, to tailor responses to individual needs. A key resource for thi

Farid Bozorgnia, Avetik Arakelyan

We study partially segregated elliptic systems through the use of penalized energy functionals. These systems arise from the minimization of Gross-Pitaevskii-type energies that capture the behavior of multi-component ultracold gas mixtures and other systems involving multiple interacting fluid or gas species. In the case when the domain is planar, i.e., in $

Galina Weinstein

This paper provides a systematic response to the criticisms raised by Jean-Marc Ginoux in response to my review of his book on the history of relativity. Whereas my review was written in a strictly academic manner, Ginoux's commentary intermingles mathematical objections with ad hominem insinuations about both Einstein and me. The purpose of this reply is tw

Suat Koç

An $A$-module $E$ is said to be an \textit{annihilator multiplication module} if for each $e\in E$, there exists a finitely generated ideal $I$ of $A$ such that $ann(e)=ann(IE)$. This class of modules is quite large, as it contains multiplication modules, von Neumann regular modules, finitely generated Baer modules, torsion-free modules, and simple modules.

Misha Bialy, Andrey E. Mironov

This paper surveys our results on integrable billiards. We consider various models of billiards, including Birkhoff, outer, magnetic, and Minkowski billiards. Also, we discuss wire billiards and billiards in cones. For four models of convex plane billiards, we also discuss an isoperimetric-type inequality for the Mather $\beta$-function. We conclude with a s

Eridan Domoratskiy, Dmitrii Kosarev, Dmitry Boulytchev

We present a study of unification for rational trees in the context of miniKanren. We give the definition of rational trees, specify the unification algorithm and prove some of its properties. We also introduce a number of heuristic optimizations and evaluate them for a number of relevant benchmarks. Finally we discuss the relations between rational and conv

Abukar Ali

Following the success of Transformer architectures in language modeling, particularly their ability to capture long-range dependencies, researchers have explored how these architectures can be adapted for time-series forecasting. Transformer-based models have been proposed to handle both short- and long-term dependencies when predicting future values from hi

G. Messa, G. Acconciaioco, S. Ripani, L. Bozzelli

The present work proposes a novel two phases model-based strategy for leakage detection. The two phases are: the identification of the district metering area (DMA) and the pipe pre-localization into the identified DMA. The strategy is based on detecting and pre-localizing the punctual leakage as anomaly with respect to the normal working conditions. A furthe

Jacopo Pegoraro, Gianmaria Ventura, Dario Tagliaferri, Marco Mezzavilla

Frequency Range 3 (FR3) in the 7-24 GHz band will be the new spectrum for 6G wireless networks. The bandwidth availability and diversity of FR3 offer unprecedented opportunities for coherent multiband Integrated Sensing and Communications (ISAC), which aggregates the carrier phase information from multiple frequency bands to increase the sensing resolution t

Tonmoy Ghosh

Password security has been compelled to evolve in response to the growing computational capabilities of modern systems. However, this evolution has often resulted in increasingly complex security practices that alienate users, leading to poor compliance and heightened vulnerability. Consequently, individuals remain exposed to attackers through weak or improp

T-Mai Bui, Fares Bougourzi, Fadi Dornaika, Vinh Truong Hoang

In recent years, deep learning has shown near-expert performance in segmenting complex medical tissues and tumors. However, existing models are often task-specific, with performance varying across modalities and anatomical regions. Balancing model complexity and performance remains challenging, particularly in clinical settings where both accuracy and effici

Ruoxi Yu, Haotian Jiang, Jingpu Cheng, Penghao Yu

Transformers have achieved remarkable successes across a wide range of applications, yet the theoretical foundation of their model efficiency remains underexplored. In this work, we investigate how the model parameters -- mainly attention heads and head dimensions -- should be allocated across layers to balance expressivity and efficiency. We first provide m

Taj Kumar, Aviral Kumar Pandey, Anand Kumar, Devendra Kumar Mishra

We propose a novel method for enhancing phase estimation in the displacement-assisted SU(1,1) (DSU(1,1)) interferometer by incorporating the photon recycling technique, evaluated under single-intensity detection (SID) and homodyne detection (HD) schemes. Our analysis showed that utilizing the photon recycling technique, the photon-recycled DSU(1,1) interfero

Guofu Xie, Chen Zhang, Xiao Zhang, Yunsheng Shi

Adapting to diverse user needs at test time is a key challenge in controllable multi-objective generation. Existing methods are insufficient: merging-based approaches provide indirect, suboptimal control at the parameter level, often disregarding the impacts of multiple objectives. While decoding-based guidance is more direct, it typically requires aggregati

Majid Asgari-Bidhendi, Muhammad Amin Ghaseminia, Alireza Shahbazi, Sayyed Ali Hossayni

This paper presents the development of Rezwan, a large-scale AI-assisted Hadith corpus comprising over 1.2M narrations, extracted and structured through a fully automated pipeline. Building on digital repositories such as Maktabat Ahl al-Bayt, the pipeline employs Large Language Models (LLMs) for segmentation, chain--text separation, validation, and multi-la

Yiqiao Chen

Cardiovascular disease (CVD) is a major pediatric health burden, and early screening is of critical importance. Electrocardiography (ECG), as a noninvasive and accessible tool, is well suited for this purpose. This paper presents the first benchmark study of deep learning for multi-label pediatric CVD classification on the recently released ZZU-pECG dataset,

Sonej Alam, Md. Wali Hossain

We introduce two three-parameter extensions of the minimal Akhtar-Hossain (mAH) dark energy parametrization, termed modified minimal AH (MmAH1 and MmAH2), which provide a smooth and bounded evolution of the dark energy equation of state while retaining $\Lambda$CDM as a limiting case. Using a joint analysis of the CMB compressed likelihood, DESI DR2 BAO, $H(

Maatank Parashar, Tejas Dhulipalla

We make a complete variational treatment of rank-one Proper Generalised Decomposition for separable fractional partial differential equations with conformable derivatives. The setting is Hilbertian, the energy is induced by a symmetric coercive bilinear form, and the residual is placed in the dual space. A greedy rank-one update is obtained by maximizing an

Divij Handa, Mihir Parmar, Aswin RRV, Md Nayem Uddin

Repeated Sampling (RS) is a simple inference-time algorithm that has been shown to improve model performance on complex tasks. Although it is an effective way of scaling inference time, it often struggles to generate diverse solution candidates, frequently relying on the same underlying approach to solve the problem and thus producing redundant samples. To a

Tiago Rodrigues de Almeida, Yufei Zhu, Andrey Rudenko, Tomasz P. Kucner

Robots and other intelligent systems navigating in complex dynamic environments should predict future actions and intentions of surrounding agents to reach their goals efficiently and avoid collisions. The dynamics of those agents strongly depends on their tasks, roles, or observable labels. Class-conditioned motion prediction is thus an appealing way to red

Takehiro Ishikawa

Recent advancements in QML and SNNs have generated considerable excitement, promising exponential speedups and brain-like energy efficiency to revolutionize AI. However, this paper argues that they are unlikely to displace DNNs in the near term. QML struggles with adapting backpropagation due to unitary constraints, measurement-induced state collapse, barren

Dinh Van Hoang, Phan Thanh Toan

The classical Noether Normalization Lemma states that if $S$ is a finitely generated algebra over a field $k$, then there exist elements $x_1,\dots,x_n$ which are algebraically independent over $k$ such that $S$ is a finite module over $k[x_1,\dots,x_n]$. This lemma has been studied intensively in different flavors. In 2024, Elad Paran and Thieu N. Vo succes

Changchi Huang, Jigen Peng, Yuchao Tang

Let $E$ be a Banach space, and let $J: E \to E^{*}$ denote the normalized duality mapping. In this paper, we establish an upper bound for $\|Jx - Jy\|$ in $q$-uniformly smooth Banach spaces, where the bound is expressed in terms of a relatively simple function of $\|x - y\|$. Subsequently, we derive the H\"{o}lder property of mappings of firmly nonexpansive

Jarosław Adam Miszczak

The emergence of cooperation in the groups of interacting agents is one of the most fascinating phenomena observed in many complex systems studied in social science and ecology, even in the situations where one would expect the agent to use a free-rider policy. This is especially surprising in the situation where no external mechanisms based on reputation or

Divij Handa, David Blincoe, Orson Adams, Yinlin Fu

Deploying capable and user-aligned LLM-based systems necessitates reliable evaluation. While LLMs excel in verifiable tasks like coding and mathematics, where gold-standard solutions are available, adoption remains challenging for subjective tasks that lack a single correct answer. E-commerce Query Rewriting (QR) is one such problem where determining whether

David Megias

Ensuring the trustworthiness of data from distributed and resource-constrained environments, such as Wireless Sensor Networks or IoT devices, is critical. Existing Reversible Data Hiding (RDH) methods for scalar data suffer from low embedding capacity and poor intrinsic entanglement between host data and watermark. This paper introduces Hiding in the Imagina

Shimaa Elbana, Ahmad Kamal, Shahd Ahmed Ali, Ahmad Al-Kabbany

The increasing size and complexity of medical imaging datasets, particularly in 3D formats, present significant barriers to collaborative research and transferability. This study investigates whether the ZFP compression technique can mitigate these challenges without compromising the performance of automated cerebrovascular segmentation, a critical first ste

Aydin Ahmadi, Baris Akgun

Data-driven planar pushing methods have recently gained attention as they reduce manual engineering effort and improve generalization compared to analytical approaches. However, most prior work targets narrow capabilities (e.g., side switching, precision, or single-task training), limiting broader applicability. We present a model-based framework for non-pre

Yiheng Dong, Yi Lin, Xin Yang

The transparency of deep learning models is essential for clinical diagnostics. Concept Bottleneck Model provides clear decision-making processes for diagnosis by transforming the latent space of black-box models into human-understandable concepts. However, concept-based methods still face challenges in concept capture capabilities. These methods often rely

Abbas Askar, Marcelo C. Vergara, Sohaib Ali

Dense star clusters are promising nurseries for the formation and growth of intermediate-mass black holes (IMBHs; $\sim 10^2-10^5\,\mathrm{M}_{\odot}$), with increasing observational evidence pointing to their presence in massive star clusters and stripped dwarf-galaxy nuclei. During the early evolution of compact clusters, massive stars can rapidly segregat

Giulia Elena Aliffi, Giovanni Nastasi, Vittorio Romano

We propose a Schr\"odinger equation of arbitrary order for modeling charge transport in semiconductors operating in the ballistic regime. This formulation incorporates non-parabolic effects through the Kane dispersion relation, thereby extending beyond the conventional effective mass approximation. Building upon the framework introduced in G.E. Aliffi, G. Na

Junade Ali

The defence of hacking (sometimes referred to as the "Trojan Horse Defence" or the "SODDI Defence", Some Other Dude Did It Defence) is prevalent in computer cases and a challenge for those working in the criminal justice system. Historical reviews of cases have demonstrated the defence operating to varying levels of success. However, there remains an absence

Jiaxin Deng, Junbiao Pang

Sharpness-Aware Minimization (SAM) improves model generalization but doubles the computational cost of Stochastic Gradient Descent (SGD) by requiring twice the gradient calculations per optimization step. To mitigate this, we propose Adaptively sampling-Reusing-mixing decomposed gradients to significantly accelerate SAM (ARSAM). Concretely, we firstly discov

Deshan Sumanathilaka, Nicholas Micallef, Julian Hough

Recent advances in Large Language Models (LLMs) have significantly reshaped the landscape of Natural Language Processing (NLP). Among the various prompting techniques, few-shot prompting has gained considerable attention for its practicality and effectiveness. This study investigates how few-shot prompting strategies impact the Word Sense Disambiguation (WSD

Richard A. Dubniczky, Bertalan Borsos, Tamas Bisztray, Norbert Tihanyi

The widespread use of preprint repositories such as arXiv has accelerated the communication of scientific results but also introduced overlooked security risks. Beyond PDFs, these platforms provide unrestricted access to original source materials, including LaTeX sources, auxiliary code, figures, and embedded comments. In the absence of sanitization, submiss

Ping Guo, Chenyu Zhu, Siyuan Chen, Fei Liu

CUDA kernel optimization has become a critical bottleneck for AI performance, as deep learning training and inference efficiency directly depends on highly optimized GPU kernels. Despite the promise of Large Language Models (LLMs) for automating kernel optimization, this field suffers from a fragmented ecosystem of isolated and incomparable approaches with u

Murat Tas

We present a calculation of the magnon spectra and chiral lifetimes of altermagnons in bulk CrSb using the many-body perturbation theory. The spin-split band structure is evident in the magnon spectra. Altermagnons attain an energy of 275 meV at the K point of the Brillouin zone. Due to large spin splitting at a specific ${\bf q}$ point along the A - M direc

Ilias Tougui, Mehdi Zakroum, Mounir Ghogho

Parkinson's Disease (PD) affects over 10 million people worldwide, with speech impairments in up to 89% of patients. Current speech-based detection systems analyze entire utterances, potentially overlooking the diagnostic value of specific phonetic elements. We developed a granularity-aware approach for multilingual PD detection using an automated pipeline t

Sophie Beck, Matthew Shammami, Lorenzo Van Muñoz, Jason Kaye

We reveal a mechanism that may explain the non-metallic out-of-plane resistivity in layered metals. By carefully examining how the Drude-Boltzmann expression for the $c$-axis conductivity emerges out of the Kubo formula, we find, besides the standard metallic term proportional to the carrier lifetime $\tau$, a non-Drude contribution proportional to $1/\tau$.

Roham Koohestani, Parham Bateni, Aydin Ebrahimi, Behdad Etezadi

The adoption of AI-powered code completion tools in software development has increased substantially, yet the user interaction data produced by these systems remain proprietary within large corporations. This creates a barrier for the academic community, as researchers must often develop dedicated platforms to conduct studies on human--AI interaction, making

Cristina Acciarri, Pavel Shumyatsky

By a coprime commutator in a profinite group $G$ we mean any element of the form $[x, y]$, where $x,y\in G$ and $(|x|,|y|)=1$. It is well-known that the subgroup generated by the coprime commutators of $G$ is precisely the pronilpotent residual $\gamma_\infty(G)$. There are several recent works showing that finiteness conditions on the set of coprime commuta

L. A. Harland-Lang, T. Cridge, M. Reader, R. S. Thorne

We present updates within the MSHT global PDF fit that focus on the high $x$ region, and on improving our understanding of the interplay of various theoretical contributions and experimental constraints here. We revisit the question of target mass and higher twist corrections, considering their impact for the first time at approximate N$^3$LO order in a glob

Rohit Chatterjee, Changrui Mu, Prashant Nalini Vasudevan

We construct a public-key encryption scheme from the hardness of the (planted) MinRank problem over uniformly random instances. This corresponds to the hardness of decoding random linear rank-metric codes. Existing constructions of public-key encryption from such problems require hardness for structured instances arising from the masking of efficiently decod

Mubariz Zaffar, Liangliang Nan, Sebastian Scherer, Julian F. P. Kooij

Given a query image, Visual Place Recognition (VPR) is the task of retrieving an image of the same place from a reference database with robustness to viewpoint and appearance changes. Recent works show that some VPR benchmarks are solved by methods using Vision-Foundation-Model backbones and trained on large-scale and diverse VPR-specific datasets. Several b

Hanwen Zhang, Kun Fang, Ziyu Wang, Ichiro Fujinaga

Evaluation for continuous piano pedal depth estimation tasks remains incomplete when relying only on conventional frame-level metrics, which overlook musically important features such as direction-change boundaries and pedal curve contours. To provide more interpretable and musically meaningful insights, we propose an evaluation framework that augments stand

Fanghao Xia, Zesong Fei, Xinyi Wang, Nanchi Su

In this paper, sensing-assisted secure communication in a multi-user multi-eavesdropper integrated sensing and communication (ISAC) system is investigated. Confidential communication signals and dedicated sensing signals are jointly transmitted by a base station (BS) to simultaneously serve users and sense aerial eavesdroppers (AEs). A sum rate maximization

Ramtin Kakavand, Ebrahim Ansari

Large Language Models (LLMs) have consistently demonstrated strong performance in machine translation, especially when guided by high-quality prompts. Few-shot prompting is an effective technique to improve translation quality; however, most existing example selection methods focus solely on query-to-example similarity and do not account for the quality of t

Zuomin Qu, Yimao Guo, Qianyue Hu, Wei Lu

Deepfakes pose significant societal risks, motivating the development of proactive defenses that embed adversarial perturbations in facial images to prevent manipulation. However, in this paper, we show that these preemptive defenses often lack robustness and reliability. We propose a novel approach, Low-Rank Adaptation (LoRA) patching, which injects a plug-

M. S. Terekhov

It is known that if $n$ vertices can be removed from a connected graph $\Gamma$ so that no subgraphs isomorphic to the graph $K$ remain, then no more than $|V(K)|\cdot n$ vertices can be removed, forming a set invariant with respect to all automorphisms of the graph $\Gamma$, so that no subgraphs isomorphic to the graph $K$ remain. We construct an infinite s

Qianfei Fan, Jiayu Wei, Peijun Zhu, Wensheng Ye

Accurate decade-scale daily runoff forecasting in small watersheds is difficult because signals blend drifting trends, multi-scale seasonal cycles, regime shifts, and sparse extremes. Prior deep models (DLinear, TimesNet, PatchTST, TiDE, Nonstationary Transformer, LSTNet, LSTM) usually target single facets and under-utilize unlabeled spans, limiting regime a

Zachary Eberhart, Collin McMillan

Large-language-model assistants are suitable for explaining popular APIs, yet they falter on niche or proprietary libraries because the multi-turn dialogue data needed for fine-tuning are scarce. We present APIDA-Chat, an open-source pipeline that converts symbolic dialogue-act "scripts" into realistic, domain-grounded API Search conversations using a lightw

Ruchika, Purba Mukherjee, Arianna Favale

We investigate uncertainties in the estimation of the Hubble constant ($H_0$) arising from Gaussian Process (GP) reconstruction, demonstrating that the choice of kernel introduces systematic variations comparable to those arising from different cosmological models. To address this limitation, we introduce the Generalized Gaussian Process (Gen GP) framework,

Benoît Giniès, Xiaoyu Bie, Olivier Fercoq, Gaël Richard

While neural-based models have led to significant advancements in audio feature extraction, the interpretability of the learned representations remains a critical challenge. To address this, disentanglement techniques have been integrated into discrete neural audio codecs to impose structure on the extracted tokens. However, these approaches often exhibit st

Yu Xiao, Can Zhang

In this paper, we focus on the rapid boundary stabilization of 1D nonlinear parabolic equations via the modal decomposition method. The nonlinear term is assumed to satisfy certain local Lipschitz continuity and global growth conditions. Through the modal decomposition, we construct a feedback control that modifies only the unstable eigenvalues to achieve sp

Xueqing Wan, Zhenlong Zhang, Charles Paillard, Jinyang Ni

Sliding ferroelectrics, which exhibit out-of-plane polarization arising from specific stacking rather than conventional ionic displacements, are new types of ferroelectrics whose underdeveloped physics needs to be explored. Here, we investigate the electro-optic (EO) response of these materials using first-principles calculations, focusing on ZrI$_{2}$ as a

Federico Maria Cau, Lucio Davide Spano

Human-AI collaboration outcomes depend strongly on human self-confidence calibration, which drives reliance or resistance toward AI's suggestions. This work presents two studies examining whether calibration of self-confidence before decision tasks, low versus high levels of Need for Cognition (NFC), and Actively Open-Minded Thinking (AOT), leads to differen

Dongyang Zhan, Zhaofeng Yu, Xiangzhan Yu, Hongli Zhang

With the development of Internet of Things (IoT), it is gaining a lot of attention. It is important to secure the embedded systems with low overhead. The Linux Seccomp is widely used by developers to secure the kernels by blocking the access of unused syscalls, which introduces less overhead. However, there are no systematic Seccomp configuration approaches

Rak-Hee Kim, Yeongmin Jang, Bob M. Wang, Dong Yu

Majorana zero modes (MZMs) in topological superconductors are promising for quantum computing, yet their unambiguous detection remains challenging. We fabricated Josephson junctions (JJs) using Cd$_3$As$_2$ Dirac semimetal nanoribbons with NbTi superconducting electrodes to investigate topological supercurrents through Fraunhofer pattern analysis. The JJs ex

Benoit Ginies, Xiaoyu Bie, Olivier Fercoq, Gaël Richard

In neural-based audio feature extraction, ensuring that representations capture disentangled information is crucial for model interpretability. However, existing disentanglement methods often rely on assumptions that are highly dependent on data characteristics or specific tasks. In this work, we introduce a generalizable approach for learning disentangled f

Nirjhar Das, Mohit Sharma, Praharsh Nanavati, Kirankumar Shiragur

We study the problem of auditing the fairness of a given classifier under partial feedback, where true labels are available only for positively classified individuals, (e.g., loan repayment outcomes are observed only for approved applicants). We introduce a novel cost model for acquiring additional labeled data, designed to more accurately reflect real-world

Komal Gupta, Anand Hegde, Chen-Bin Huang

Plasmonic logic circuits combine ultrafast operation with nanoscale integration, making them a strong candidate for next-generation optical computing. Realizing this potential, however, requires overcoming practical challenges such as bulky interferometric designs and reliance on secondary control signals. This work advances plasmonic logic by introducing a

Yongfu Xue

The rapid development of parameter-efficient fine-tuning methods has noticeably improved the efficiency of adapting large language models. Among these, LoRA has gained widespread popularity due to its strong balance of effectiveness and parameter efficiency. However, LoRA relies on initializing two low-rank matrices whose product is zero, which limits its ab

Nathan A. Judd, Amy V. Tansell, Benjamin Costello, Liam Leonard

Linking crimes by modus operandi has long been employed as an effective tool for crime investigation. The standard statistical method that underpins statistical crime linkage has been logistic regression. The simplicity and interpretability of this approach has been seen as an advantage for law enforcement agencies using statistical crime linkage. In 2023, t

Jan O. Bauer

Sparse principal component analysis (sparse PCA) is a widely used technique for dimensionality reduction in multivariate analysis, addressing two key limitations of standard PCA. First, sparse PCA can be implemented in high-dimensional low sample size settings, such as genetic microarrays. Second, it improves interpretability as components are regularized to

Kuang Yuan, Yang Gao, Xilin Li, Xinhao Mei

Acoustic scene classification (ASC) models on edge devices typically operate under fixed class assumptions, lacking the transferability needed for real-world applications that require adaptation to new or refined acoustic categories. We propose ContrastASC, which learns generalizable acoustic scene representations by structuring the embedding space to preser

Xuehai He

Humans understand the world through the integration of multiple sensory modalities, enabling them to perceive, reason about, and imagine dynamic physical processes. Inspired by this capability, multimodal foundation models (MFMs) have emerged as powerful tools for multimodal understanding and generation. However, today's MFMs fall short of serving as effecti

Jiahao Zeng, Wolong Xing, Liangtao Shi, Xin Huang

Federated learning has received significant attention for its ability to simultaneously protect customer privacy and leverage distributed data from multiple devices for model training. However, conventional approaches often focus on isolated heterogeneous scenarios, resulting in skewed feature distributions or label distributions. Meanwhile, data heterogenei

Thomas Hallopeau, Joris Guérin, Laurent Demagistri, Youssef Fouzai

While deep learning methods for detecting informal settlements have already been developed, they have not yet fully utilized the potential offered by recent pretrained neural networks. We compare two types of pretrained neural networks for detecting the favelas of Rio de Janeiro: 1. Generic networks pretrained on large diverse datasets of unspecific images,

Weiying Liu, Yanran Yuan, Zhiqiang Sheng, Dandan Lian

Autism Spectrum Disorder (ASD) is marked by action imitation deficits stemming from visuomotor integration impairments, posing challenges to imitation-based learning, such as dance movement therapy in mixed reality (MR-DMT). Previous gaze-guiding interventions in ASD have mainly focused on optimizing gaze in isolation, neglecting the crucial "gaze-performanc

Martin Kocour, Martin Karafiat, Alexander Polok, Dominik Klement

We propose a speaker-attributed (SA) Whisper-based model for multi-talker speech recognition that combines target-speaker modeling with serialized output training (SOT). Our approach leverages a Diarization-Conditioned Whisper (DiCoW) encoder to extract target-speaker embeddings, which are concatenated into a single representation and passed to a shared deco

Qianxin Yi, Shao-Bo Lin, Jun Fan, Yao Wang

Reinforcement learning (RL) has been widely applied to sequential decision making, where interpretability and performance are both critical for practical adoption. Current approaches typically focus on performance and rely on post hoc explanations to account for interpretability. Different from these approaches, we focus on designing an interpretability-orie

Leander Girrbach, Stephan Alaniz, Genevieve Smith, Trevor Darrell

Vision-language models trained on large-scale multimodal datasets show strong demographic biases, but the role of training data in producing these biases remains unclear. A major barrier has been the lack of demographic annotations in web-scale datasets such as LAION-400M. We address this gap by creating person-centric annotations for the full dataset, inclu

Dongyang Zhan, Zhaofeng Yu, Xiangzhan Yu, Hongli Zhang

Linux Seccomp is widely used by the program developers and the system maintainers to secure the operating systems, which can block unused syscalls for different applications and containers to shrink the attack surface of the operating systems. However, it is difficult to configure the whitelist of a container or application without the help of program develo