October 2025 arXiv papers — page 211

Ethan Davis

We introduce a framework for applying metaheuristic algorithms, such as ant colony optimization (ACO), to combinatorial optimization problems (COPs) like the traveling salesman problem (TSP). The framework consists of three sequential stages: broad exploration of the parameter space, exploitation of top-performing parameters, and uncertainty quantification (

Yaya Sy, Christophe Cerisara, Irina Illina

Pruning large pre-trained transformers in a data-scarce scenario is challenging, as it often requires massive retraining data to recover performance. For instance, Distill-Whisper prunes Whisper by 40 and retrains on 21,000 hours of speech, far beyond what is available for most languages. Can Whisper be made lighter and faster for edge devices in data-scarce

David Beauchemin, Yan Tremblay, Mohamed Amine Youssef, Richard Khoury

The tasks of idiom understanding and dialect understanding are both well-established benchmarks in natural language processing. In this paper, we propose combining them, and using regional idioms as a test of dialect understanding. Towards this end, we propose three new benchmark datasets for the Quebec dialect of French: QFrCoRE, which contains 4,633 instan

Kuofeng Gao, Yiming Li, Chao Du, Xin Wang

Jailbreaking attacks on the vision modality typically rely on imperceptible adversarial perturbations, whereas attacks on the textual modality are generally assumed to require visible modifications (e.g., non-semantic suffixes). In this paper, we introduce imperceptible jailbreaks that exploit a class of Unicode characters called variation selectors. By appe

Nevan Wichers, Aram Ebtekar, Ariana Azarbal, Victor Gillioz

Large language models are sometimes trained with imperfect oversight signals, leading to undesired behaviors such as reward hacking and sycophancy. Improving oversight quality can be expensive or infeasible, motivating methods that improve learned behavior despite an imperfect training signal. We introduce Inoculation Prompting (IP), a simple but counterintu

Weixin Wang, Haoyang Zheng, Guang Lin, Wei Deng

Most existing approximate Thompson Sampling (TS) algorithms for multi-armed bandits use Stochastic Gradient Langevin Dynamics (SGLD) or its variants in each round to sample from the posterior, relaxing the need for conjugacy assumptions between priors and reward distributions in vanilla TS. However, they often require approximating a different posterior dist

Daewoong Cheong, Thang Pham, Dung The Tran

We establish functional Loomis--Whitney type inequalities in the finite Heisenberg group $\mathbb{H}^n(\mathbb{F}_q)$. For $n=1$, we determine the sharp region of exponents $(u_1,u_2)$ for which the Heisenberg Loomis--Whitney inequality \[ \frac{1}{q^3}\sum_{(x,t)\in \mathbb{H}^1(\mathbb{F}_q)} f_1(\pi_1(x,t))\,f_2(\pi_2(x,t)) \;\lesssim\; \|f_1\|_{L^{u_1}(\

Artur Olejarz, Wenyi Huo, Anna Kosinska, Maciej Zielinski

Multi-principal element alloys (MPEAs) can potentially offer exceptional material properties, but their complex, costly manufacturing limits their scalability. Chemical complexity and complex manufacturing processes lead to the formation of some secondary phases, which have a significant impact on the final properties. In this work, chromium compound dispers

Nada Alghamdi, Paolo de Angelis, Pietro Asinari, Eliodoro Chiavazzo

Machine learning force fields (MLFFs) are transforming materials science and engineering by enabling the study of complex phenomena, such as those critical to battery operation. In this work, we explore the predictive capabilities of pre-trained and fine-tuned MACE MLFF and compare different fine-tuning strategies for predicting interstitial lithium diffusiv

Aziz Fall, Kaushik Dayal

Recent theoretical work has predicted that dislocation patterning induces anisotropic flat bands in the electronic band diagram, which can lead to unusual effects such as unconventional superconductivity. This work develops a reduced-dimensional framework to provide insights into their origin. An effective one-dimensional dislocation potential is constructed

Zihao Huang, Chenchao Xu, Yande Que, Yi Liu

The interplay of charge and spin orders lies at the heart of correlated electron physics and plays a critical role in the emergence of unconventional quantum phases. Kagome magnets provide a particularly promising platform to investigate these phenomena, owing to their geometrically frustrated lattice structure. However, resolving spin and charge orders micr

E. Plachy, H. Netzel, A. Bódi

Classical Cepheid stars that pulsate in the first overtone radial mode often exhibit additional periodicities at the millimagnitude level. Extensive studies of the OGLE data of the Magellanic Clouds have revealed distinct groups based on their period ratio with the first overtone mode. These groups are similar to those found in overtone RR Lyrae stars. Theor

Lucas Carrit Delgado Pinheiro, Ziru Chen, Bruno Caixeta Piazza, Ness Shroff

While task-specific demonstrations show early success in applying large language models (LLMs) to automate some astronomical research tasks, they only provide incomplete views of all necessary capabilities in solving astronomy problems, calling for more thorough understanding of LLMs' strengths and limitations. So far, existing benchmarks and evaluations foc

Nabil Daiyan, Md Rakibul Haque

Parkinson's disease (PD) is a progressive neurodegenerative condition characterized by the death of dopaminergic neurons, leading to various movement disorder symptoms. Early diagnosis of PD is crucial to prevent adverse effects, yet traditional diagnostic methods are often cumbersome and costly. In this study, a machine learning-based approach is proposed u

Xuanming Cui, Jianpeng Cheng, Hong-you Chen, Satya Narayan Shukla

There is a growing interest in Universal Multimodal Embeddings (UME), where models are required to generate task-specific representations. While recent studies show that Multimodal Large Language Models (MLLMs) perform well on such tasks, they treat MLLMs solely as encoders, overlooking their generative capacity. However, such an encoding paradigm becomes le

Marten Berger, Johannes Braathen, Gudrid Moortgat-Pick, Georg Weiglein

A $\gamma\gamma$ collider, either in conjunction with an $e^+e^-$ linear collider or as a stand-alone facility, offers a very attractive Higgs physics programme at relatively low centre-of-mass (c.m.) energies. While the Higgs boson that has been discovered at the LHC can be studied in detail in resonant production at 125~GeV, a c.m.\ energy as low as 280~Ge

R. R. Metsaev

In the framework of Lorentz covariant on-shell approach, interacting continuous-spin fields and integer-spin fields in flat space are investigated. Continuous-spin fields are considered by using a Lorentz vector superspace formulation, while integer-spin fields are considered by using an oscillator formulation. All parity-even cubic vertices for self-interac

Sofía Perón Santana, Ariel Fiuri, Omar Osenda, Martín Domínguez

Quantum state transfer (QST) via homogeneous spin chains plays a crucial role in building scalable quantum hardware. A basic quantum state transmission protocol prepares a state in one qubit and transfers it to another through a channel, seeking to minimize the time and avoid information loss. The fidelity of the process is measured by functions proportional

Thomas Pawlaschyk

In the spirit of Lelong and Bochner, we show that an upper semi-continuous function defined on a open tube set $\Omega=\omega + i\mathbb{R}^n$ in $\mathbb{C}^n$, where $\omega$ is an open set in $\mathbb{R}^n$, and which is invariant in its imaginary part, is $q$-plurisubharmonic on $\Omega$ (in the sense of Hunt and Murray) if and only if it is real $q$-con

Tenzan Araki, Joseph F. Goodwin, Zhenyu Cai

Classical error-correcting codes are powerful but incompatible with quantum noise, which includes both bit-flips and phase-flips. We introduce Hadamard-based Virtual Error Correction (H-VEC), a protocol that empowers any classical bit-flip code to correct Pauli noise with the addition of only a single control qubit and two layers of controlled-Hadamard gates

Giovanni Cerulli, Francesco Caracciolo

This paper develops a risk-adjusted alternative to standard optimal policy learning (OPL) for observational data by importing Roy's (1952) safety-first principle into the treatment assignment problem. We formalize a welfare functional that maximizes the probability that outcomes exceed a socially required threshold and show that the associated pointwise opti

Koen Vellenga, H. Joe Steinhauer, Jonas Andersson, Anders Sjögren

Deep neural networks (DNNs) are increasingly applied to safety-critical tasks in resource-constrained environments, such as video-based driver action and intention recognition. While last layer probabilistic deep learning (LL-PDL) methods can detect out-of-distribution (OOD) instances, their performance varies. As an alternative to last layer approaches, we

Shuolin Xiao, Xiaowei Zhu, Ghanesh Narasimhan, Dennice F Gayme

The atmospheric boundary layer undergoes significant changes throughout a diurnal cycle, affecting wind turbine performance and wakes in wind farms. Wind farm Large Eddy Simulations (LES) under such conditions provide rich datasets to study the underlying dynamics and identify important trends. Here, we describe a comprehensive open dataset generated using L

Hamza Adrat, Laurent Decreusefond

This paper establishes quantitative limit theorems for two classes of Cox point processes, quantifying their convergence to a Poisson point process (PPP). We employ Stein's method for PPP aproximation, leveraging the generator approach and the Stein-Dirichlet representation formula associated with the Glauber dynamics. First, we investigate a Cox-Poisson pro

Imran Mansha

Large Language Models (LLMs) such as GPT-4 and LLaMA have demonstrated remarkable reasoning abilities but require significant computational resources for fine-tuning. This paper presents a resource-efficient fine-tuning approach for LLaMA-3.2-3B to enhance medical chain-of-thought reasoning while operating under constrained GPU and memory settings. Using par

John Rankin, Sergio Campana, Giovanni Pareschi, Daniele Spiga

The localization of X-ray counterparts to gravitational wave events requires a telescope with accurate localization capability in a field of view comparable to the region constrained by the gravitational wave detectors. In the context of a small, dedicated, mission, we investigate which optical design could satisfy this capability. We compare the possible op

Aditya Sripada, Abhishek Warrier

Robotic locomotion research typically draws from biologically inspired leg designs, yet many human-engineered settings can benefit from non-anthropomorphic forms. TARS3D translates the block-shaped 'TARS' robot from Interstellar into a 0.25 m, 0.99 kg research platform with seven actuated degrees of freedom. The film shows two primary gaits: a bipedal-like w

Xiang-Gen Xia

In this short article, I would like to briefly summarize my research in the first 5 years in my university academia life in USA. I think that my research results obtained in these 5 years are the best in my career, at least which I like the most by myself. I wish that my experience in my junior academia career could be of some help to young researchers.

Nilay Kumar, Priyansh Bhandari, G. Maragatham

Text-to-video (T2V) generation technology holds potential to transform multiple domains such as education, marketing, entertainment, and assistive technologies for individuals with visual or reading comprehension challenges, by creating coherent visual content from natural language prompts. From its inception, the field has advanced from adversarial models t

Daniel Majaess

S$H_0$ES 2016-2022 $HVI$ data for classical Cepheids in the keystone galaxy NGC4258 yield doubly discordant Wesenheit Leavitt functions:~$\Delta W_{0,H-VI} = -0.13\pm0^{m}.02$ ($-0^{m}.17$ unweighted) and that is paired with a previously noted $\Delta W_{0,I-VI}\simeq-0^{m}.3$, which in concert with complimentary evidence suggest the 2016 S$H_0$ES NGC4258-an

Jiongchi Yu, Weipeng Jiang, Xiaoyu Zhang, Qiang Hu

Understanding software faults is essential for empirical research in software development and maintenance. However, traditional fault analysis, while valuable, typically involves multiple expert-driven steps such as collecting potential faults, filtering, and manual investigation. These processes are both labor-intensive and time-consuming, creating bottlene

Wei Xiong, Chenlu Ye, Baohao Liao, Hanze Dong

Reinforcement learning (RL) for large language model reasoning is frequently hindered by signal loss, a phenomenon where standard uniform sampling with small group sizes fails to uncover informative learning signals for difficult prompts. We demonstrate that this collapse is a statistical artifact of undersampling rather than an inherent model limitation. To

Xuefeng Xu, Graham Cormode

Power transforms are popular parametric methods for making data more Gaussian-like, and are widely used as preprocessing steps in statistical analysis and machine learning. However, we find that direct implementations of power transforms suffer from severe numerical instabilities, which can lead to incorrect results or even crashes. In this paper, we provide

Sjoerd Dost

Concurrent logic programming predates miniKanren, but concurrent implementations of miniKanren have remained largely unexplored. In this work we present a parallel implementation of miniKanren in Go, demonstrating its feasibility and potential for performance improvements. Our approach leverages implicit parallelism allowing legacy programs to benefit from p

Zhiyang He, Luke Robitaille, Xinyu Tan

The Clifford hierarchy is a fundamental structure in quantum computation whose mathematical properties are not fully understood. In this work, we characterize permutation gates -- unitaries which permute the $2^n$ basis states -- in the third level of the hierarchy. We prove that any permutation gate in the third level must be a product of Toffoli gates in w

Prabhanjan Ananth, Eli Goldin

Quantum cryptographic definitions are often sensitive to the number of copies of the cryptographic states revealed to an adversary. Making definitional changes to the number of copies accessible to an adversary can drastically affect various aspects including the computational hardness, feasibility, and applicability of the resulting cryptographic scheme. Th

D. Schwartz, K. Kondo, J. P. How

We present a novel high-level planning framework that leverages vision-language models (VLMs) to improve autonomous navigation in unknown indoor environments with many dead ends. Traditional exploration methods often take inefficient routes due to limited global reasoning and reliance on local heuristics. In contrast, our approach enables a VLM to reason dir

Narda Cordero-Michel, Mika Olsen

Given a digraph $D$ with no loops, the \textit{dicoloring graph} of $D$, denoted by $\mathcal{D}_k(D)$, is the graph whose vertices are the acyclic $k$-colorings of $D$ and two colorings are adjacent in $\mathcal{D}_k(D)$ if they differ in color on exactly one vertex. In this paper, we prove that there is no expression $\phi(\vec{\chi})$ in terms of the dich

Bo Peng

We show that the isomorphism of ergodic measure-preserving transformations is not Borel reducible to the relation induced by the conjugacy action of the full group of an ergodic measure-preserving transformation on itself. This answers a question of Le Ma\^{i}tre in the negative and gives a positive indication towards a conjecture of Sabok. In fact, we prove

Kristi Topollai, Anna Choromanska

The vast majority of modern deep learning models are trained with momentum-based first-order optimizers. The momentum term governs the optimizer's memory by determining how much each past gradient contributes to the current convergence direction. Fundamental momentum methods, such as Nesterov Accelerated Gradient and the Heavy Ball method, as well as more re

Avilash Rath, Weiliang Qi, Youpeng Li, Xinda Wang

Graph-based models learn rich code graph structural information and present superior performance on various code analysis tasks. However, the robustness of these models against adversarial example attacks in the context of vulnerability detection remains an open question. This paper proposes NatGVD, a novel attack methodology that generates natural adversari

Jade Hak, Nathaniel Lam Johnson, Matin Amoozadeh, Amin Alipour

Large Language Models (LLMs) such as ChatGPT have quickly become part of student programmers' toolkits, whether allowed by instructors or not. This paper examines how introductory programming (CS1) students integrate LLMs into their problem-solving processes. We conducted a mixed-methods study with 14 undergraduates completing three programming tasks while t

Carlos Améndola, Tobias Boege, Benjamin Hollering, Pratik Misra

We prove two characterizations of model equivalence of acyclic graphical continuous Lyapunov models (GCLMs) with uncorrelated noise. The first result shows that two graphs are model equivalent if and only if they have the same skeleton and equivalent induced 4-node subgraphs. We also give a transformational characterization via structured edge reversals. The

J. E. M. Scanlon, A. Pelzer, M. Gharleghi, K. C. Fuhrmeister

Electroencephalogram monitoring devices and online data repositories hold large amounts of data from individuals participating in research and medical studies without direct reference to personal identifiers. This paper explores what types of personal and health information have been detected and classified within task-free EEG data. Additionally, we investi

Khalid Mehtab Khan, Anagha Kulkarni

Identifying cultural capital (CC) themes in student reflections can offer valuable insights that help foster equitable learning environments in classrooms. However, themes such as aspirational goals or family support are often woven into narratives, rather than appearing as direct keywords. This makes them difficult to detect for standard NLP models that pro

Aakash Ahmad, Muhammad Waseem, Bakheet Aljedaani, Mahdi Fahmideh

Quantum systems have started to emerge as a disruptive technology and enabling platforms - exploiting the principles of quantum mechanics via programmable quantum bits (QuBits) - to achieve quantum supremacy in computing. Academic research, industrial projects (e.g., Amazon Braket, IBM Qiskit), and consortiums like 'Quantum Flagship' are striving to develop

Aida Behmard, Casey L. Brinkman, Soichiro Hattori, Ryan A. Rubenzahl

Planets and their host stars form from the same cloud of gas and dust, so we assume that their chemical compositions are linked. However, a clear correlation between rocky planet interior properties and host star chemistry remains elusive for planets around FGK dwarfs, and non-existent for planets around M dwarfs because cool stars frequently lack detailed c

Fangzhou Liang, Tianshi Zheng, Chunkit Chan, Yauwai Yim

Effective multi-agent collaboration requires agents to infer the rationale behind others' actions, a capability rooted in Theory-of-Mind (ToM). While recent Large Language Models (LLMs) excel at logical inference, their ability to infer rationale in dynamic, collaborative settings remains under-explored. This study introduces LLM-Hanabi, a novel benchmark th

Xuefeng Xu, Graham Cormode

Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves are fundamental tools for evaluating machine learning classifiers, offering detailed insights into the trade-offs between true positive rate vs. false positive rate (ROC) or precision vs. recall (PR). However, in Federated Learning (FL) scenarios, where data is distributed across multip

Kun Xiang, Terry Jingchen Zhang, Yinya Huang, Jixi He

The rapid advancement of embodied intelligence and world models has intensified efforts to integrate physical laws into AI systems, yet physical perception and symbolic physics reasoning have developed along separate trajectories without a unified bridging framework. This work provides a comprehensive overview of physical AI, establishing clear distinctions

Abhishek Abhishek, Shotaro Abe, Tomohiro Inada, Sofia Ventura

Dark Matter remains a great mystery in modern physics. Among various candidates, the weakly interacting massive particles (WIMPs) scenario stands out and is under extensive study. The detection of the hypothetical gamma-ray emission from WIMP annihilation could act as a direct probe of electroweak-scale interactions, complementing DM collider searches and ot

A. Koujok, A. Hamadeh, L. Martins, F. Kohl

Non-conventional beyond-the-state-of-the-art signal processing schemes require parallelism, scalability, robustness and energy efficiency to meet the demands of complex data-driven applications. With further research, magnonic and spintronic circuits can potentially help to fulfill these requirements. We present an experimental proof-of-concept of a hybrid d

R. Kumar, A. Ortega

In this paper, we analyze the existence of solution for a fractional elliptic system coupled by critical nonlinearities and endowed with mixed Dirichlet-Neumann boundary conditions. By means of variational methods and an orthogonalization-like process in the corresponding Sobolev space, we establish the existence of at least one weak solution.

Allen Daniel Sunny

We present StructuralDecompose, an R package for modular and interpretable time series decomposition. Unlike existing approaches that treat decomposition as a monolithic process, StructuralDecompose separates the analysis into distinct components: changepoint detection, anomaly detection, smoothing, and decomposition. This design provides flexibility and rob

Daqian Shi, Xiaolei Diao, Jinge Wu, Honghan Wu

Timely and accurate analysis of population-level data is crucial for effective decision-making during public health emergencies such as the COVID-19 pandemic. However, the massive input of semi-structured data, including structured demographic information and unstructured human feedback, poses significant challenges to conventional analysis methods. Manual e

Arjan Cornelissen

In this work, we generalize the recently-introduced graph composition framework to the non-boolean setting. A quantum algorithm in this framework is represented by a hypergraph, where each hyperedge is adjacent to multiple vertices. The input and output to the quantum algorithm is represented by a set of boundary vertices, and the hyperedges act like switche

Nabarun Deb

In this paper, we study fluctuations of conditionally centered statistics of the form $$N^{-1/2}\sum_{i=1}^N c_i(g(\sigma_i)-\mathbb{E}_N[g(\sigma_i)|\sigma_j,j\neq i])$$ where $(\sigma_1,\ldots ,\sigma_N)$ are sampled from a dependent random field, and $g$ is some bounded function. Our first main result shows that under weak smoothness assumptions on the co

Uroš Šmajdek, Ciril Bohak

We present an interactive visualization system for exploring named entities and their relationships across document collections. The system is designed around a graph-based representation that integrates three types of nodes: documents, entity mentions, and entities. Connections capture two key relationship types: (i) identical entities across contexts, and

Marcel Wienöbst, Leonard Henckel, Sebastian Weichwald

We present FLOP (Fast Learning of Order and Parents), a score-based causal discovery algorithm for linear models. It pairs fast parent selection with iterative Cholesky-based score updates, cutting run-times over prior algorithms. This makes it feasible to fully embrace discrete search, enabling iterated local search with principled order initialization to f

Satrio Pambudi, Filippo Menolascina

The selection of appropriate medical imaging procedures is a critical and complex clinical decision, guided by extensive evidence-based standards such as the ACR Appropriateness Criteria (ACR-AC). However, the underutilization of these guidelines, stemming from the difficulty of mapping unstructured patient narratives to structured criteria, contributes to s

Allen Daniel Sunny

This study explores the integration of contextual explanations into AI-powered loan decision systems to enhance trust and usability. While traditional AI systems rely heavily on algorithmic transparency and technical accuracy, they often fail to account for broader social and economic contexts. Through a qualitative study, I investigated user interactions wi

Priscilla Muheki, Mirjana Pović, Somaya Saad, Salma Sylla Mbaye

In preparation for the International Astronomical Union (IAU) General Assembly (GA) 2024, the first GA held in Africa, the African Network of Women in Astronomy (AfNWA) embarked on a visionary project: the creation of an inspiring storytelling book that showcases the remarkable journeys of professional female astronomers in Africa. This book is not merely a

John Gough

We pose and solve the problem of quantum filtering based on continuous-in-time quadrature measurements (homodyning) for the case where the quantum process is in a thermal state. The standard construction of quantum filters involves the determination of the conditional expectation onto the von Neumann algebra generated by the measured observables with the non

Anna Chistyakova, Mikhail Pautov

Being trained on large and vast datasets, visual foundation models (VFMs) can be fine-tuned for diverse downstream tasks, achieving remarkable performance and efficiency in various computer vision applications. The high computation cost of data collection and training motivates the owners of some VFMs to distribute them alongside the license to protect their

Albert Solà Vilalta, Ignasi Mañé, F. - Javier Heredia

We propose a multi-stage stochastic programming model for the optimal participation of energy communities in electricity markets. The multi-stage aspect captures the different times at which variable renewable generation and electricity prices are observed. This results in large-scale optimization problem instances containing large scenario trees with 34 sta

Kelechi G. Kalu, James C. Davis

Software signing provides a formal mechanism for provenance by ensuring artifact integrity and verifying producer identity. It also imposes tooling and operational costs to implement in practice. In an era of centralized registries such as PyPI, npm, Maven Central, and Hugging Face, it is reasonable to ask whether hardening registry security controls obviate

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

A study of charm mixing and CP violation in $D^0\to K^\pm\pi^\mp\pi^\pm\pi^\mp$ decays is performed using data collected by the LHCb experiment in proton-proton collisions from 2015 to 2018, corresponding to an integrated luminosity of 6$\text{fb}^{-1}$. The ratio of promptly produced $D^0\to K^+\pi^- \pi^+\pi^-$ to $D^0\to K^-\pi^+ \pi^-\pi^+$ decay rates i

Michele Pizzochero

An influential argument for scientific realism posits that, if scientific theories were not true, their empirical success would be a coincidence. Here, I show that the false Drude's theory succeeds in explaining the Wiedemann-Franz law by coincidence--a fortuitous compensation of errors.

Yannick Léa Tenkeu Jeufack, Leonard Kwuida

In this paper, we show that given a weakly dicomplemented lattice (WDL) $\mathcal{L}=(L; \vee, \wedge, ^{\Delta}, ^{\nabla}, 0, 1)$, $^{\Delta}$ induces a structure of a dual weakly complemented lattice in the lattice $(F(L), \subseteq)$ of filters of $\mathcal{L}$. We prove that the set of dense elements of $F(L)$ forms a nearlattice, and the set of princip

Jia-Lin Pan, Zi-Fan Zhu, Shixuan Chen, Yu Su

In recent years, the study of Majorana signatures in quantum transport has become a central focus in condensed matter physics. Here, we present a rigorous and systematic derivation of the fermionic superoperator describing the open quantum dynamics of electron transport through Majorana zero modes, building on the techniques introduced in Phys. Rev. B 105, 0

Rongrong Liu, Eric W. Peng, Kaixiang Wang, Laura Ferrarese

Galaxy model subtraction removes the smooth light of nearby galaxies so that fainter sources (e.g., stars, star clusters, background galaxies) can be identified and measured. Traditional approaches (isophotal or parametric fitting) are semi-automated and can be challenging for large data sets. We build a convolutional denoising autoencoder (DAE) for galaxy m

Anni Tziakouri, Filippo Menolascina

Medical imaging has revolutionized diagnosis, yet unnecessary procedures are rising, exposing patients to radiation and stress, limiting equitable access, and straining healthcare systems. The American College of Radiology Appropriateness Criteria, developed through extensive multidisciplinary review, provide evidence-based guidance but remain underutilized.

Bi-Cheng Yan, Ming-Kang Tsai, Berlin Chen

Computer-assisted pronunciation training (CAPT) manages to facilitate second-language (L2) learners to practice pronunciation skills by offering timely and instructive feedback. To examine pronunciation proficiency from multiple facets, existing methods for CAPT broadly fall into two categories: mispronunciation detection and diagnosis (MDD) as well as autom

G. Dréau, Y. Lebreton, B. Mosser, D. Stello

The high-frequency resolution of the four-year $\textit{Kepler}$ time series allows detailed study of seismic modes in luminous giants. Seismic observables help infer interior structures via comparisons with stellar models. We aim to investigate differences between H-shell (Red-Giant Branch; RGB) and He-burning (red clump and Asymptotic-Giant Branch; AGB) st

Štěpán Šmíd, Richard Meister, Mario Berta, Roberto Bondesan

Dissipative quantum algorithms for state preparation in many-body systems are increasingly recognised as promising candidates for achieving large quantum advantages in application-relevant tasks. Recent advances in algorithmic, detailed-balance Lindbladians enable the efficient simulation of open-system dynamics converging towards desired target states. Howe

Euclid Collaboration, S. Vinciguerra, F. Bouchè, N. Martinet

This is the second paper in the HOWLS (higher-order weak lensing statistics) series exploring the usage of non-Gaussian statistics for cosmology inference within Euclid. With respect to our first paper, we develop a full tomographic analysis based on realistic photometric redshifts that allows us to derive Fisher forecasts in the ($\sigma_8$, $w_0$) plane fo

Ailiya Borjigin, Cong He

We present a cross-market algorithmic trading system that balances execution quality with rigorous compliance enforcement. The architecture comprises a high-level planner, a reinforcement learning execution agent, and an independent compliance agent. We formulate trade execution as a constrained Markov decision process with hard constraints on participation

Jayanta Mandi, Marianne Defresne, Senne Berden, Tias Guns

When some parameters of a constrained optimization problem (COP) are uncertain, this gives rise to a predict-then-optimize (PtO) problem, comprising two stages: the prediction of the unknown parameters from contextual information and the subsequent optimization using those predicted parameters. Decision-focused learning (DFL) implements the first stage by tr

Om Dobariya, Akhil Kumar

The wording of natural language prompts has been shown to influence the performance of large language models (LLMs), yet the role of politeness and tone remains underexplored. In this study, we investigate how varying levels of prompt politeness affect model accuracy on multiple-choice questions. We created a dataset of 50 base questions spanning mathematics

E. E. Nokhrina, I. N. Pashchenko, V. A. Frolova, R. V. Todorov

We developed the full magnetohydrodynamical analytical jet model that allows accurate reproducing of a transversal and longitudinal structure for a highly collimated relativistic jets. This model can be used as a setup for convenient solution of radiative transfer equations and modelling the total intensity and polarization maps. We show that the analytical

Wentao Fan, Boris Hasselblatt

We show ergodicity of (asymmetric) lemon billiards, billiard tables that are the intersection of two circles of which one contains the centers of both. These do not satisfy the Wojtkowski criteria for hyperbolicity, but we establish \emph{uniform} expansion of vectors in an invariant cone family and alignment of singularity curves. Both of these are difficul

Xin Li, Kaixiang Yang, Qiang Li, Zhiwei Wang

Dual-view mammography, including craniocaudal (CC) and mediolateral oblique (MLO) projections, offers complementary anatomical views crucial for breast cancer diagnosis. However, in real-world clinical workflows, one view may be missing, corrupted, or degraded due to acquisition errors or compression artifacts, limiting the effectiveness of downstream analys

Cédrick Perron, Yves Bérubé-Lauzière, Victor Drouin-Touchette

In this work, we develop new pulse designs and embedding strategies to improve the analog quantum subroutines of hybrid column generation (CG) algorithms based on neutral-atoms quantum computers (NAQCs). These strategies are designed to improve the quality and diversity of the samples generated. We apply these to an important combinatorial optimization (CO)

Juan-José Guzmán-Landa, Juan-Manuel Torres-Moreno, Miguel Figueroa-Saavedra, Ligia Quintana-Torres

In this article we introduce a context-free grammar (CFG) for the Nawatl language. Nawatl (or Nahuatl) is an Amerindian language of the $\pi$-language type, i.e. a language with few digital resources, in which the corpora available for machine learning are virtually non-existent. The objective here is to generate a significant number of grammatically correct

Jerry Yao-Chieh Hu, Xiwen Zhang, Ali ElSheikh, Weimin Wu

Structured State-Space Duality (SSD) [Dao & Gu, ICML 2024] is an equivalence between a simple Structured State-Space Model (SSM) and a masked attention mechanism. In particular, a state-space model with a scalar-times-identity state matrix is equivalent to a masked self-attention with a $1$-semiseparable causal mask. Consequently, the same sequence transform

Marco Fanizza, Larissa Kroell, Arthur Mehta, Connor Paddock

We show that it is undecidable to determine whether the commuting operator value of a nonlocal game is strictly greater than 1/2. Specifically, there is a computable mapping from Turing machines to /boolean constraint system (BCS) nonlocal games in which the halting property of the machine is encoded as a decision problem for the commuting operator value of

Raktim Bhattacharya

This paper develops a robust estimation framework for cislunar navigation that embeds the Circular Restricted Three-Body Problem (CR3BP) dynamics and bearing-only optical measurements within a Linear Fractional Transformation (LFT) representation. A full-order $\mathcal{H}_\infty$ observer is synthesized with explicit $\mathcal{L}_2$ performance bounds. The

Adam Braun, Lucas Brivadis, Jean Auriol

We propose an observer design for a cascaded system composed of an arbitrary nonlinear ordinary differential equation (ODE) with a 1D heat equation. The nonlinear output of the ODE imposes a boundary condition on one side of the heat equation, while the measured output is on the other side. The observer design combines an infinitedimensional Kazantzis-Kravar

Suman Acharya, Balasubramanian Srinivasan, David Shanahan, Utz Roedig

Surface Acoustic Wave gas sensors have garnered increasing attention as highly sensitive, miniaturized, and wireless compatible platforms for molecular detection. Their unique ability to convert surface perturbations into measurable acoustic shifts makes them ideal for gas sensing across diverse environments. This review synthesizes reported SAW platforms ac

Alina Chertock, Arsen S. Iskhakov, Alexander Kurganov

In this paper, we study uncertainty quantification (UQ) in forward problems. Our objective is to construct accurate and robust surrogate models by incorporating the seventh-order central weighted essentially non-oscillatory (CWENO7) scheme into the stochastic collocation framework. A key focus is on mitigating the oscillatory behavior often encountered in tr

Yuxi Liu, Catherine Lalman, Yimin Yang

The effectiveness of modern deep learning models is predicated on the availability of large-scale, human-annotated datasets, a process that is notoriously expensive and time-consuming. While Active Learning (AL) offers a strategic solution by labeling only the most informative and representative data, its iterative nature still necessitates significant human

Shiwen Qin, Alexander Auras, Shay B. Cohen, Elliot J. Crowley

Neural architecture search (NAS) automates the design process of high-performing architectures, but remains bottlenecked by expensive performance evaluation. Most existing studies that achieve faster evaluation are mostly tied to cell-based search spaces and graph encodings tailored to those individual search spaces, limiting their flexibility and scalabilit

Ben Heritage, Fiona Ryder, Michael McLoughlin, Karolina Prawda

Immersion in virtual and augmented reality solutions is reliant on plausible spatial audio. However, plausibly representing a space for immersive audio often requires many individual acoustic measurements of source-microphone pairs with specialist spatial microphones, making the procedure time-consuming and expensive. In this study, we evaluate the plausibil

Abigail Hickok, Andrew J. Blumberg

We study the relationship between discrete analogues of Ricci and scalar curvature that are defined for point clouds and graphs. In the discrete setting, Ricci curvature is replaced by Ollivier-Ricci curvature. Scalar curvature can be computed as the trace of Ricci curvature for a Riemannian manifold; this motivates a new definition of a scalar version of Ol

Guoxin Chen, Zile Qiao, Wenqing Wang, Donglei Yu

Large Reasoning Models (LRMs) face two fundamental limitations: excessive token consumption when overanalyzing simple information processing tasks, and inability to access up-to-date knowledge beyond their training data. We introduce MARS (Multi-Agent System for Deep ReSearch), a novel co-evolution framework that jointly optimizes dual cognitive systems thro

Yves Achdou

Relying on the careful study of a related problem in the calculus of variations, we study a class of optimal control problems in which the control lies on the acceleration, with state constraints on the position variable. In dimension one, we find explicit formulas in the special case when the running cost is a power of the acceleration (in absolute value) a

Satvik Dixit, Soham Deshmukh, Bhiksha Raj

Audio Question Answering (AQA) is a key task for evaluating Audio-Language Models (ALMs), yet assessing open-ended responses remains challenging. Existing metrics used for AQA such as BLEU, METEOR and BERTScore, mostly adapted from NLP and audio captioning, rely on surface similarity and fail to account for question context, reasoning, and partial correctnes

Amir Hameed Mir

Large Language Models (LLMs) often produce fluent yet factually incorrect statements-a phenomenon known as hallucination-posing serious risks in high-stakes domains. We present Layer-wise Semantic Dynamics (LSD), a geometric framework for hallucination detection that analyzes the evolution of hidden-state semantics across transformer layers. Unlike prior met

Paul Fearnhead, Chris Sherlock

A state-space model is a time-series model that has an unobserved latent process from which we take noisy measurements over time. The observations are conditionally independent given the latent process and the latent process itself is Markovian. These properties lead to simplifications for the conditional distribution of the latent process given the paramete

B. Folkers, A. van Rossum, A. Brinkman, H. K. E. Stadermann

Quantum entanglement remains a challenging concept to teach and visualise due to its microscopic and non-classical nature. We present innovative educational demonstration material consisting of electronic dice that simulate the properties of quantum entanglement through haptic interaction. The system uses displays, orientation sensors, and wireless communica