October 2025 arXiv papers — page 104

Ivan Molodetskikh, Kirill Malyshev, Mark Mirgaleev, Nikita Zagainov

Generative single-image super-resolution (SISR) is advancing rapidly, yet even state-of-the-art models produce visual artifacts: unnatural patterns and texture distortions that degrade perceived quality. These defects vary widely in perceptual impact--some are barely noticeable, while others are highly disturbing--yet existing detection methods treat them eq

Erik Riise, Mehmet Onurcan Kaya, Dim P. Papadopoulos

While inference-time scaling through search has revolutionized Large Language Models, translating these gains to image generation has proven difficult. Recent attempts to apply search strategies to continuous diffusion models show limited benefits, with simple random sampling often performing best. We demonstrate that the discrete, sequential nature of visua

Binyu Tan, Zhiyuan Wang, Jinhao Duan, Kaidi Xu

Medical image segmentation serves as a critical component of precision medicine, enabling accurate localization and delineation of pathological regions, such as lesions. However, existing models empirically apply fixed thresholds (e.g., 0.5) to differentiate lesions from the background, offering no statistical guarantees on key metrics such as the false nega

Eeshan Modak, Sivaraman Balakrishnan, Ananda Theertha Suresh

We study a variant of the simple hypothesis testing problem where observed samples do not necessarily come from either of the specified distributions, but rather from a close variant of them. In this setting, we require a test that is robust to misspecification and identifies which distribution is closer in Hellinger distance. If the underlying distribution

Petr Naryshkin, Spyridon Petrakos

We prove that any two $\mathbb{Z}$-odometers are sub-$L^1$-orbit equivalent, greatly strengthening previous results and giving a definitive picture of quantitative orbit equivalence for these systems.

Jiaogen Zhang

In this manuscript, we investigate a priori estimates for the solution to the Dirichlet eigenvalue problem for a broad class of concave elliptic Hessian operators of the form \[ F(D^2u)=-\Lambda u \quad \textrm{in} \, \Omega, \qquad u=0 \quad \textrm{on} \, \partial \Omega. \] These operators encompass the Monge-Amp\`ere operator, the $k$-Hessian operators,

Danish Nazir, Gowtham Sai Inti, Timo Bartels, Jan Piewek

Modern automotive systems leverage deep neural networks (DNNs) for semantic segmentation and operate in two key application areas: (1) In-car, where the DNN solely operates in the vehicle without strict constraints on the data rate. (2) Distributed, where one DNN part operates in the vehicle and the other part typically on a large-scale cloud platform with a

Rohan Sen

We propose a kernel-based nonparametric framework for mean-variance optimization that enables inference on economically motivated shape constraints in finance, including positivity, monotonicity, and convexity. Many central hypotheses in financial econometrics are naturally expressed as shape relations on latent functions (e.g., term premia, CAPM relations,

Srinivas Vivek

Ride-Hailing Services (RHS) match a ride request initiated by a rider with a suitable driver responding to the ride request. A Privacy-Preserving RHS (PP-RHS) aims to facilitate ride matching while ensuring the privacy of riders' and drivers' location data w.r.t. the Service Provider (SP). At NSS 2022, Xie et al. proposed a PP-RHS. In this work, we demonstra

Viktoria Schram, Markus Hiller, Daniel Beck, Trevor Cohn

The prediction of learning curves for Natural Language Processing (NLP) models enables informed decision-making to meet specific performance objectives, while reducing computational overhead and lowering the costs associated with dataset acquisition and curation. In this work, we formulate the prediction task as a multitask learning problem, where each task'

Paul Saves, Pramudita Satria Palar, Muhammad Daffa Robani, Nicolas Verstaevel

Complex systems are increasingly explored through simulation-driven engineering workflows that combine physics-based and empirical models with optimization and analytics. Despite their power, these workflows face two central obstacles: (1) high computational cost, since accurate exploration requires many expensive simulator runs; and (2) limited transparency

Yotam Kenneth-Mordoch, Robert Krauthgamer

We present the first non-trivial algorithm for the all-pairs minimum cut problem in the cut-query model. Given cut-query access to an unweighted graph $G=(V,E)$ with $n$ vertices, our randomized algorithm constructs a Gomory-Hu tree of $G$, and thus solves the all-pairs minimum cut problem, using $\tilde{O}(n^{7/4})$ cut queries.

Biswabrata Pradhan, Rathin Das

We obtain a reliability acceptance sampling plan for independent competing risk data under interval censoring schemes using the Bayesian approach. At first, the Bayesian reliability acceptance sampling plan is obtained where the decision criteria of accepting a lot is pre-fixed. For large samples, computing Bayes risk is computationally intensive. Therefore,

Shingo Kukita, Yuichiro Matsuzaki

Quantum sensing leverages non-classical resources to enhance precision. In particular, Greenberger-Horne-Zeilinger (GHZ) states can, in principle, attain the Heisenberg limit that surpasses the standard quantum limit. While many studies have examined how open-system noise-typically modeled with Lindblad master equations-degrades GHZ-based metrology, coherent

Matteo El-Hariry, Vittorio Franzese, Miguel Olivares-Mendez

This paper presents a comprehensive analysis of how excitation design influences the identification of the inertia properties of rigid nano- and micro-satellites. We simulate nonlinear attitude dynamics with reaction-wheel coupling, actuator limits, and external disturbances, and excite the system using eight torque profiles of varying spectral richness. Two

Puja Porel, Archana Soam, Janik Karoly, Eun Jung Chung

SFO 38, located in the Cepheus molecular cloud within the northern part of the HII region IC 1396, is shaped by intense ultraviolet radiation from the nearby O6.5V-type star HD 206267 and represents a classic example of a bright-rimmed cloud (BRC) undergoing radiatively driven implosion (RDI). While previous studies have examined the southern globule using C

Patrizio Dazzi, William Guglielmo, Franco Maria Nardini, Raffaele Perego

This paper investigates the usage of FPGA devices for energy-efficient exact kNN search in high-dimension latent spaces. This work intercepts a relevant trend that tries to support the increasing popularity of learned representations based on neural encoder models by making their large-scale adoption greener and more inclusive. The paper proposes two differe

Aniket Agrawal, Harsharanga Patil

This paper introduces a control-theoretic framework for dynamic payment routing, implemented within JUSPAY's Payment Orchestrator to maximize transaction success rate. The routing system is modeled as a closed-loop feedback controller continuously sensing gateway performance, computing corrective actions, and dynamically routes transactions across gateway to

Siddharth Kasthurirangan, Meet Panchal, Aparna Joshi, Ganesh Pawar

We present a detailed analysis of AGN identification diagnostics and host galaxy evolution using optical spectral diagnostics using ESO-GOODS-S data. We employ traditional Baldwin-Phillips-Terlevich (BPT) diagrams along with their modern extensions, the Mass-Excitation (MEx) and Colour-Excitation (CEx) diagrams, to classify AGNs from among up to 600+ candida

Tao Bu, Qiangang Wang, Bowen Zeng, Hanwen Sun

Transformer-based large language models (LLMs) have achieved remarkable success, yet their standard attention mechanism incurs quadratic computation and memory costs with respect to sequence length, posing a major bottleneck for long-context training. Prior work tackles this challenge along two directions: (1) kernel-level optimizations, which accelerate den

Tianyang Dou, Ming Li, Jiangying Qin, Xuan Liao

Coral reefs are vital yet fragile ecosystems that require accurate large-scale mapping for effective conservation. Although global products such as the Allen Coral Atlas provide unprecedented coverage of global coral reef distri-bution, their predictions are frequently limited in spatial precision and semantic consistency, especially in regions requiring fin

Jianbiao Mei, Yu Yang, Xuemeng Yang, Licheng Wen

End-to-end autonomous driving systems increasingly rely on vision-centric world models to understand and predict their environment. However, a common ineffectiveness in these models is the full reconstruction of future scenes, which expends significant capacity on redundantly modeling static backgrounds. To address this, we propose IR-WM, an Implicit Residua

Christian Bayer, Davit Gogolashvili, Luca Pelizzari

We study nonparametric regression and classification for path-valued data. We introduce a functional Nadaraya-Watson estimator that combines the signature transform from rough path theory with local kernel regression. The signature transform provides a principled way to encode sequential data through iterated integrals, enabling direct comparison of paths in

Lei Zhang, Zhaosheng Li, Yuanyue Pan, Wenhui Yu

We presented a detailed analysis of seven thermonuclear X-ray bursts from Terzan 5 X-3/Swift J174805.3-244637, detected by NICER during the source's 2023 outburst. Our analysis reveals a clear evolution of burst properties, identifying four non-photospheric radius expansion (non-PRE) bursts, one PRE candidate occurring in a mixed hydrogen/helium environment,

Yongchun Bi, Panyu Deng, Jun Zheng, Guchuan Zhu

In this paper, we prove comparison principles for nonlinear differential equations with time-varying coefficients and develop Lyapunov analytical tools for the integral input-to-state stability (iISS) analysis of nonlinear non-autonomous infinite-dimensional systems, which involve nonlinearities satisfying a superlinear growth, {bringing} difficulties to the

Minhua Lin, Zongyu Wu, Zhichao Xu, Hui Liu

The advent of large language models (LLMs) has transformed information access and reasoning through open-ended natural language interaction. However, LLMs remain limited by static knowledge, factual hallucinations, and the inability to retrieve real-time or domain-specific information. Retrieval-Augmented Generation (RAG) mitigates these issues by grounding

Hyeonbin Moon, Hanbin Cho, Wabi Demeke, Byungki Ryu

Characterizing the temperature-dependent thermal conductivity is challenging because the property varies strongly with temperature and reliable heat flow measurement, not just temperature sensing, is difficult under experimental conditions. Here, we present a physics informed deep learning framework that infers conductivity solely from sparse electric potent

Fahimeh Khosh-Ahang Ghasr

We introduce and investigate generalizations of interval and proper interval graphs to simplicial complexes, including strong interval, unit interval, and under closed variants. Through equivalent combinatorial and algebraic characterizations, we uncover hierarchies among these classes and extend key results to higher dimensions, such as the equivalence of c

Qiuhan Jia, Jiuyang Shi, Jian Sun

The nudged elastic band (NEB) method is one of the most widely used techniques for determining minimum-energy reaction pathways and activation barriers between known initial and final states. However, conventional implementations face steep computational scaling with system size, which makes nucleation-type transitions in realistically large supercells pract

Hiroyuki Tajima, Hideyuki Nagao, Kenya Tanaka, Hideaki Nishikawa

We theoretically investigate the biomechanical aspects of Olympic weightlifting within the framework of optimal control theory. The squared force and the rate of force development (RFD) defined by the time derivative of the force are taken into account in the evaluation functions of the first and second pull phases of the snatch motion. Focusing on the verti

Jitao Sang, Jinlin Xiao, Jiarun Han, Jilin Chen

The rapid evolution of agentic AI marks a new phase in artificial intelligence, where Large Language Models (LLMs) no longer merely respond but act, reason, and adapt. This survey traces the paradigm shift in building agentic AI: from Pipeline-based systems, where planning, tool use, and memory are orchestrated by external logic, to the emerging Model-native

Zak Ressler, Marcus Grijalva, Angelica Marie Ignacio, Melanie Torres

This paper presents a framework for processing EV charging load data in order to forecast future load predictions using a Recurrent Neural Network, specifically an LSTM. The framework processes a large set of raw data from multiple locations and transforms it with normalization and feature extraction to train the LSTM. The pre-processing stage corrects for m

Xusheng Yang, Long Zhou, Wenfu Wang, Kai Hu

We propose \textbf{U-Codec}, an \textbf{U}ltra low frame-rate neural speech \textbf{Codec} that achieves high-fidelity reconstruction and fast speech generation at an extremely low frame-rate of 5Hz (5 frames per second). Extreme compression at 5Hz typically leads to severe intelligibility and spectral detail loss, we introduce a Transformer-based inter-fram

Johan F. Hoorn

This paper introduces the correlation-of-divergency coefficient, c-delta, a custom statistical measure designed to quantify the similarity of internal divergence patterns between two groups of values. Unlike conventional correlation coefficients such as Pearson or Spearman, which assess the association between paired values, c-delta evaluates whether the way

Asmita Mohanty, Gezheng Kang, Lei Gao, Murali Annavaram

Large Language Models (LLMs) have demonstrated strong performance across diverse tasks, but fine-tuning them typically relies on cloud-based, centralized infrastructures. This requires data owners to upload potentially sensitive data to external servers, raising serious privacy concerns. An alternative approach is to fine-tune LLMs directly on edge devices u

Ziyue Feng, Tianjia Dong, Zheya Lei

In January 2025, the U.S. government enacted a nationwide ban on TikTok, prompting a wave of American users -- self-identified as ``TikTok Refugees'' -- to migrate to alternative platforms, particularly the Chinese social media app RedNote (Xiaohongshu). This paper examines how these digital migrants navigate cross-cultural platform environments and develop

Zulun Zhu, Haoyu Liu, Mengke He, Siqiang Luo

Question answering in temporal knowledge graphs requires retrieval that is both time-consistent and efficient. Existing RAG methods are largely semantic and typically neglect explicit temporal constraints, which leads to time-inconsistent answers and inflated token usage. We propose STAR-RAG, a temporal GraphRAG framework that relies on two key ideas: buildi

Xiongkun Linghu, Jiangyong Huang, Ziyu Zhu, Baoxiong Jia

Existing research on 3D Large Language Models (LLMs) still struggles to achieve grounded question-answering, primarily due to the under-exploration of the mechanism of human-like scene-object grounded reasoning. This paper bridges the gap by presenting a novel framework. We first introduce a grounded Chain-of-Thought reasoning method in 3D scenes (SCENECOT),

Sriharsh Bhyravajjula, Melanie Walsh, Anna Preus, Maria Antoniak

Whitespace is a critical component of poetic form, reflecting both adherence to standardized forms and rebellion against those forms. Each poem's whitespace distribution reflects the artistic choices of the poet and is an integral semantic and spatial feature of the poem. Yet, despite the popularity of poetry as both a long-standing art form and as a generat

Nishat Fiza, Mehedi Masud, Kim Siyeon, Guang Yang

The Electron-Ion Collider (EIC) is a next-generation accelerator primarily designed to study the internal structure of nucleons through high-precision electron-hadron collisions. In this work, we explore the feasibility of employing a 1 MW fraction of the EIC proton beam to generate a high-intensity GeV-scale neutrino beam for long-baseline oscillation studi

Liu Haojie, Gao Suixiang

We present HumanCM, a one-step human motion prediction framework built upon consistency models. Instead of relying on multi-step denoising as in diffusion-based methods, HumanCM performs efficient single-step generation by learning a self-consistent mapping between noisy and clean motion states. The framework adopts a Transformer-based spatiotemporal archite

Qile Su, Shoutai Zhu, Shuai Zhang, Baoyu Liang

Script event induction, which aims to predict the subsequent event based on the context, is a challenging task in NLP, achieving remarkable success in practical applications. However, human events are mostly recorded and presented in the form of videos rather than scripts, yet there is a lack of related research in the realm of vision. To address this proble

Yuhan Tang

Type 2 diabetes prevention and treatment can benefit from personalized lifestyle prescriptions. However, the delivery of personalized lifestyle medicine prescriptions is limited by the shortage of trained professionals and the variability in physicians' expertise. We propose an offline contextual bandit approach that learns individualized lifestyle prescript

Kailai Yang, Yan Leng, Xin Zhang, Tianlin Zhang

Cardiovascular diseases are becoming increasingly prevalent in modern society, with a profound impact on global health and well-being. These Cardiovascular disorders are complex and multifactorial, influenced by genetic predispositions, lifestyle choices, and diverse socioeconomic and clinical factors. Information about these interrelated factors is disperse

Yisheng Zhong, Zhengbang Yang, Zhuangdi Zhu

Large Language Models (LLMs) are increasingly integrated into real-world applications, raising concerns about privacy, security and the need to remove undesirable knowledge. Machine Unlearning has emerged as a promising solution, yet faces two key challenges: (1) practical unlearning needs are often continuous and heterogeneous, and (2) they involve decentra

Hongjie Zhang, Zhiqi Zhao, Hanzhou Wu, Zhihua Xia

Feature embedding has become a cornerstone technology for processing high-dimensional and complex data, which results in that Embedding as a Service (EaaS) models have been widely deployed in the cloud. To protect the intellectual property of EaaS models, existing methods apply digital watermarking to inject specific backdoor triggers into EaaS models by mod

Larissa Jerrim, Stas Shabala, Ross Turner, Patrick Yates-Jones

We investigate the effect of turbulent magnetic fields on the observed spectral properties of synchrotron radio emission in large-scale radio galaxy lobes. We use three-dimensional relativistic magnetohydrodynamic simulations of fast, high-powered jets to study the structure of the lobe magnetic fields and how this structure affects the radio spectrum of the

Tianxin Wei, Yifan Chen, Xinrui He, Wenxuan Bao

Distribution shifts between training and testing samples frequently occur in practice and impede model generalization performance. This crucial challenge thereby motivates studies on domain generalization (DG), which aim to predict the label on unseen target domain data by solely using data from source domains. It is intuitive to conceive the class-separated

Huy Minh Nhat Nguyen, Triet Hoang Minh Dao, Chau Vinh Hoang Truong, Cuong Tuan Nguyen

Optical Coherence Tomography (OCT) is a widely used non-invasive imaging technique that provides detailed three-dimensional views of the retina, which are essential for the early and accurate diagnosis of ocular diseases. Consequently, OCT image analysis and processing have emerged as key research areas in biomedical imaging. However, acquiring paired datase

Ni Zhang, Zhiguang Cao, Jianan Zhou, Cong Zhang

Complex vehicle routing problems (VRPs) remain a fundamental challenge, demanding substantial expert effort for intent interpretation and algorithm design. While large language models (LLMs) offer a promising path toward automation, current approaches still rely on external intervention, which restrict autonomy and often lead to execution errors and low solu

Shiyao Wang, Shiwan Zhao, Jiaming Zhou, Yong Qin

Dysarthric speech recognition (DSR) research has witnessed remarkable progress in recent years, evolving from the basic understanding of individual words to the intricate comprehension of sentence-level expressions, all driven by the pressing communication needs of individuals with dysarthria. Nevertheless, the scarcity of available data remains a substantia

Xin Mu, Frank Weiss, Hongyao Chua, Robert Lawrowski

Visible-spectrum photonic integrated circuits (PICs) present compact and scalable solutions for emerging technologies including quantum computing, biosensing, and virtual/augmented reality. Realizing their full potential requires the development of scalable visible-light-source integration methods compatible with high-volume manufacturing and capable of deli

Rafichha Yasmin, Ishrat Jahan, Abdelrahman Omar, Md. Zunaid Baten

This work introduces an electromagnetic metastructure based interconnect design that could address the critical need for electrical bandwidth and heat dissipation in high-speed, chiplet integration. We leverage silicon as the substrate for its superior thermal properties, and to counteract its high dielectric constant that typically causes high mutual capaci

Yan Liu, Jiantao Wang, Hongkun Deng, Yan Sun

Multi-principal element alloys (MPEAs) exhibit exceptional properties but face significant challenges in developing accurate machine-learning potentials (MLPs) due to their vast compositional and configurational complexity. Here, we introduce an efficient small-cell sampling (SCS) method, which allows for generating diverse and representative training datase

Jierui Hu, Hao Yuan, Joshua Akin, A. K. M. Naziul Haque

Quantum frequency conversion (QFC) is essential for interfacing quantum systems operating at different wavelengths and for realizing scalable quantum networks. Despite extensive progress, achieving QFC with simultaneous high efficiency, low pump power, minimal noise, broad bandwidth, and pump-wavelength flexibility remains challenging. Here, we demonstrate e

Iman Deznabi, Peeyush Kumar, Madalina Fiterau

Zero-shot forecasting aims to predict outcomes for previously unseen conditions without direct historical data, posing a significant challenge for traditional forecasting methods. We introduce a Resolution-Aware Retrieval-Augmented Forecasting model that enhances predictive accuracy by leveraging spatial correlations and temporal frequency characteristics. B

Anthony DiMaggio, Raghav Sharma, Gururaj Saileshwar

Secure federated learning (FL) preserves data privacy during distributed model training. However, deploying such frameworks across heterogeneous devices results in performance bottlenecks, due to straggler clients with limited computational or network capabilities, slowing training for all participating clients. This paper introduces the first straggler miti

Ayan Das, Anushka Sharma, Anamitra Pal

A variety of algorithms have been proposed to address the power system state estimation problem in the presence of uncertainties in the data. However, less emphasis has been given to handling perturbations in the model. In the context of linear state estimation (LSE), which is the focus of this paper, perturbations in the model come from variations in the li

Tianshu Ruan, Zoe Betta, Georgios Tzoumas, Rustam Stolkin

This study investigates First Responders' (FRs) attitudes toward the use of semantic information and Situational Awareness (SA) in robotic systems during emergency operations. A structured questionnaire was administered to 22 FRs across eight countries, capturing their demographic profiles, general attitudes toward robots, and experiences with semantics-enha

Maksym Mohorian, Devika Kamath, Meghna Menon, Hans Van Winckel

Post-AGB and post-RGB binaries with stable circumbinary discs provide key insights into late stellar and disc evolution, revealing how binary interactions shape disc structure and stellar surface composition. A defining trait of such systems is the observed underabundance of refractory elements in the stellar photosphere relative to volatile elements -- phot

Jinwoo Baek

Matrix Phylogeny introduces compact spectral fingerprints (CSF/ASF) that characterize matrices at the family level. These fingerprints are low-dimensional, eigendecomposition-free descriptors built from Chebyshev trace moments estimated by Hutchinson sketches. A simple affine rescaling to [-1,1] makes them permutation/similarity invariant and robust to globa

Anna L. F. Lucchi, Jean H. Y. Passos, Max Jauregui, Renio S. Mendes

Many efforts have been made to explore systems that show significant deviations from predictions related to the standard statistical mechanics. The present work introduces a unified formalism that connects divergences, generalized free energies, generalized Fokker-Planck equations, and H-theorem. This framework is applied here in a range of scenarios, illust

Luca Claude Gino Lebon, Claudio Altafini

In this paper we show how to formulate and solve disturbance decoupling problems over networks while choosing a minimal number of input and output nodes. Feedback laws that isolate and eliminate the impact of disturbance nodes on specific target nodes to be protected are provided using state, output, and dynamical feedback. For that, we leverage the fact tha

Yejie Guo, Yunzhong Hou, Wufei Ma, Meng Tang

Spatial reasoning, the ability to ground language in 3D understanding, remains a persistent challenge for Vision-Language Models (VLMs). We identify two fundamental bottlenecks: inadequate 3D understanding capabilities stemming from 2D-centric pre-training, and reasoning failures induced by redundant 3D information. To address these, we first construct a Min

Shurong Lin, Eric D. Kolaczyk, Adam Smith, Elliot Paquette

The interplay between optimization and privacy has become a central theme in privacy-preserving machine learning. Noisy stochastic gradient descent (SGD) has emerged as a cornerstone algorithm, particularly in large-scale settings. These variants of gradient methods inject carefully calibrated noise into each update to achieve differential privacy, the gold

Wenhang Shi, Shuqing Bian, Yiren Chen, Xinyi Zhang

Chain-of-thought (CoT) rationales, which provide step-by-step reasoning to derive final answers, benefit LLMs in both inference and training. Incorporating rationales, either by generating them before answering during inference, or by placing them before or after the original answers during training - significantly improves model performance on mathematical,

Serena Jinchen Xie, Shumenghui Zhai, Yanjing Liang, Jingyi Li

Large Language Model (LLM)-based conversational agents offer promising solutions for mental health support, but lack cultural responsiveness for diverse populations. This study evaluated the effectiveness of cultural prompting in improving cultural responsiveness and perceived empathy of LLM-generated therapeutic responses for Chinese American family caregiv

Damin Zhang, Julia Rayz

Large language models (LLMs) increasingly show strong performance on temporally grounded tasks, such as timeline construction, temporal question answering, and event ordering. However, it remains unclear how their behavior depends on the way time is anchored in language. In this work, we study LLMs' temporal understanding through temporal frames of reference

Devin Zhao, Rephael Wenger

Let $f: \mathbb{R}^3 \rightarrow \mathbb{R}$ be a scalar field. An isosurface is a piecewise linear approximation of a level set $f^{-1}(\sigma)$ for some $\sigma \in \mathbb{R}$ built from some regular grid sampling of $f$. Isosurfaces constructed from scanned data such as CT scans or MRIs often contain extremely small components that distract from the visu

Xiaohong Chen, Haitian Xie

This paper studies nonparametric local (over-)identification and the semiparametric efficiency in modern causal frameworks. We develop a unified approach that begins by translating structural models with latent variables into their induced statistical models of observables and then analyzes local overidentification through conditional moment restrictions. We

Omer Bahadir Eryilmaz, Cihan Katar, Max A. Little

We introduce ellipsoidal filtration, a novel method for persistent homology, and demonstrate its effectiveness in denoising recurrent signals. Unlike standard Rips filtrations, which use isotropic neighbourhoods and ignore the signal's direction of evolution, our approach constructs ellipsoids aligned with local gradients to capture trajectory flow. The deat

Sukjin Han, Haiqing Xu

This paper develops a nonparametric framework to identify and estimate distributional treatment effects under nonseparable endogeneity. We begin by revisiting the widely adopted \emph{rank similarity} (RS) assumption and characterizing it by the relationship it imposes between observed and counterfactual potential outcome distributions. The characterization

Nathan R. Krause

We study the topdrop map, a mapping on permutations in $S_n$ related to card shuffling. We show this map is bijective and study its orbit structure. We introduce the notion of the topdrop-necklace as a way of classifying the orbits of the map and establish a general theorem to count orbits using topdrop-necklaces. We then provide exact counts for orbits of s

Feyza Duman Keles, Lisa Hellerstein, Kunal Marwaha, Christopher Musco

Consider $n$ independent, biased coins, each with a known probability of heads. Presented with an ordering of these coins, flip (i.e., toss) each coin once, in that order, until we have observed both a *head* and a *tail*, or flipped all coins. The Unanimous Vote problem asks us to find the ordering that minimizes the expected number of flips. Gkenosis et al

Yunhan Qiao, Md Istiak Hossain Shihab, Christopher Hundhausen

The ability to comprehend code has long been recognized as an essential skill in software engineering. As programmers lean more heavily on generative artificial intelligence (GenAI) assistants to develop code solutions, it is becoming increasingly important for programmers to comprehend GenAI solutions so that they can verify their appropriateness and proper

Ran Tong, Jiaqi Liu, Su Liu, Xin Hu

We present a compact, strictly causal benchmark for streaming clinical time series on the MIT--BIH Arrhythmia Database using per-second heart rate. Two tasks are studied under record-level, non-overlapping splits: near-term tachycardia risk (next ten seconds) and one-step heart rate forecasting. We compare a GRU-D (RNN) and a Transformer under matched traini

Anindya Sarkar, Binglin Ji, Yevgeniy Vorobeychik

In many scientific and engineering fields, where acquiring high-quality data is expensive--such as medical imaging, environmental monitoring, and remote sensing--strategic sampling of unobserved regions based on prior observations is crucial for maximizing discovery rates within a constrained budget. The rise of powerful generative models, such as diffusion

Daniel Augusto de Souza, Yuchen Zhu, Harry Jake Cunningham, Yuri Saporito

A variety of infinitely wide neural architectures (e.g., dense NNs, CNNs, and transformers) induce Gaussian process (GP) priors over their outputs. These relationships provide both an accurate characterization of the prior predictive distribution and enable the use of GP machinery to improve the uncertainty quantification of deep neural networks. In this wor

Azam Shirali, Giri Narasimhan

Protein-protein docking tools help in studying interactions between proteins, and are essential for drug, vaccine, and therapeutic development. However, the accuracy of a docking tool depends on a robust scoring function that can reliably differentiate between native and non-native complexes. PIsToN is a state-of-the-art deep learning-based scoring function

Yuki Ohnishi, Michael J. Daniels, Lei Yang, Fan Li

Causal mediation analysis in cluster-randomized trials (CRTs) is essential for explaining how cluster-level interventions affect individual outcomes, yet it is complicated by interference, post-treatment confounding, and hierarchical covariate adjustment. We develop a Bayesian nonparametric framework that simultaneously accommodates interference and a post-t

Jiahan Du

This paper investigates a refinement of Marstrand's projection theorem; more specifically, let $\Pi_t, t\in[0,1]$ be a family of $m$ dimensional subspaces of the Euclidean space $\mathbb{R}^n$ and let $P_t:\mathbb{R}^4\mapsto \Pi_t$ be the orthogonal projections onto $\Pi_t$. We hope to determine the conditions on $\Pi_t$ under which, for any Borel $A\subset

Yiyang Liu, James C. Liang, Heng Fan, Wenhao Yang

Prompt-based learning has emerged as a parameter-efficient finetuning (PEFT) approach to facilitate Large Language Model (LLM) adaptation to downstream tasks by conditioning generation with task-aware guidance. Despite its successes, current prompt-based learning methods heavily rely on laborious grid searching for optimal prompt length and typically require

Luis F. G. Campos, Leonardo C. Marques, Walter T. Nakamura

Usability inspection is a well-established technique for identifying interaction issues in software interfaces, thereby contributing to improved product quality. However, it is a costly process that requires time and specialized knowledge from inspectors. With advances in Artificial Intelligence (AI), new opportunities have emerged to support this task, part

Hisham A. Shehadeh, Mohd Yamani Idna Idris, Iqbal H. Jebril

In this paper, a novel bio-inspired optimization algorithm is proposed, called Bombardier Beetle Optimizer (BBO). This type of species is very intelligent, which has an ability to defense and escape from predators. The principles of the former one is inspired by the defense mechanism of Bombardier Beetle against the predators, which the Bombardier Beetle tri

M. Mahmoudzadeh Baghbani, K. Atazadeh, M. Mousavi

Symmetry plays a crucial role in theoretical physics, especially Noether symmetry, which is a powerful approach for identifying the models at the fundamental level. The exact solution is provided within the point-like Lagrangian framework. In this work, we study one of the alternative theories of gravity based on the non-metricity scalar $Q$, namely $f(Q)$ g

V. E. Timofeev, D. N. Aristov

A regular lattice of magnetic skyrmions is the ground state of thin ferromagnetic films with Dzyaloshinskii-Moriya interaction in a relatively wide range of external magnetic fields. It was previously theoretically shown that upon the increase of magnetic field a topological transition in the magnon spectrum of such skyrmion crystal (SkX) may occur. Non-unif

Alberto De Toni, Aynur Cemre Aka, Costantino Agnesi, Davide Giacomo Marangon

Quantum Key Distribution (QKD) leverages the principles of quantum mechanics to exchange a secret key between two parties. Unlike classical cryptographic systems, the security of QKD is not reliant on computational assumptions but is instead rooted in the fundamental laws of physics. In a QKD protocol, any attempt by an eavesdropper to intercept the key is d

Jianchao Zhang, Jun Suzuki

We develop a hybrid framework for quantum parameter estimation in the presence of nuisance parameters. In this Bayes-point scheme, the parameters of interest are treated as fixed non-random parameters while nuisance parameters are integrated out with respect to a prior (random parameters). Within this setting, we introduce the hybrid partial quantum Fisher i

M. Q. Dong, B. Liu, Z. H. Dai, Zhi-Xin Guo

Multiferroics, which combine ferroelectric and magnetic order, offer a transformative platform for next-generation electronic devices. However, the intrinsic competition between the mechanisms driving ferroelectricity and magnetism in single-phase materials severely limits their performance, typically resulting in weak magnetoelectric coupling at room temper

Alberto Giuseppe Catalano, Sven Benjamin Kožić, Gianpaolo Torre, Carola Ciaramelletti

We pursue the identification of quantum resources carried by topological order, by evaluating quantum magic, quantified through the rank-$2$ Stabilizer Rényi entropy $\mathcal{M}_2$, in one-dimensional systems hosting symmetry-protected topological phases (SPTP). Focusing on models with an exact duality between an SPTP and a trivial one, namely the dimerized

Alonso Perez-Lona, Eric Sharpe, Xingyang Yu, Hao Zhang

In this note we consider examples of decomposition (in which a local QFT is equivalent to a disjoint union of multiple independent theories, known as universes) where there is a continuous familiy of universes, rather than a finite or countably infinite collection. In particular, this allows us to consistently eliminate all instantons in a local QFT via a su

Xixi Hu, Runlong Liao, Keyang Xu, Bo Liu

Rectified Flow offers a simple and effective approach to high-quality generative modeling by learning a velocity field. However, we identify a limitation in directly modeling the velocity with an unconstrained neural network: the learned velocity often fails to satisfy certain boundary conditions, leading to inaccurate velocity field estimations that deviate

Jing Kong

This paper proposes a debiased estimator for causal effects in high-dimensional generalized linear models with binary outcomes and general link functions. The estimator augments a regularized regression plug-in with weights computed from a convex optimization problem that approximately balances link-derivative-weighted covariates and controls variance; it do

Gabriella Agazie, David L. Kaplan, Abhimanyu Susobhanan, Ingrid H. Stairs

Wideband timing of the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) datasets, where a single time-of-arrival (TOA) and a single dispersion measure (DM) are measured using the entire bandwidth of each observation, was first done for the 12.5-year dataset, and proved to be invaluable for characterizing the time-varying dispersion mea

Félix Parraud

In this paper we prove that the Haagerup inequality for non-homogeneous polynomials in free semicircular variables of degree $n$ is optimal with a constant of order $n^{3/2}$. We also show an operator valued Haagerup inequality which improves on existing results. Our main tool to do so are free Chebyshev polynomials also known as $0$-Hermite polynomials.

Maurice Almeida, Ravindra Pawar, Siddharth Gupta, Tarkeshwar Singh

For a simple graph G = (V, E) and a positive integer k greater than or equal to 2, a coloring of vertices of G using exactly k colors such that every vertex has an equal number of vertices of each color in its closed neighborhood is called closed neighborhood-balanced k-coloring, and the graph which admits such a coloring is called closed neighborhood balanc

Mohamed Sami Rakha, Andriy Miranskyy, Daniel Alencar da Costa

Software defect prediction (SDP) is crucial for delivering high-quality software products. Recent research has indicated that prediction performance improvements in SDP are achievable by applying hyperparameter tuning to a particular SDP scenario. However, the positive impact resulting from the hyperparameter tuning step may differ based on the targeted SDP

Christopher Thirgood, Oscar Mendez, Erin Ling, Jon Storey

Hyperspectral images (HSI) promise to support a range of new applications in computer vision. Recent research has explored the feasibility of generalizable Spectral Reconstruction (SR), the problem of recovering a HSI from a natural three-channel color image in unseen scenarios. However, previous Multi-Scale Attention (MSA) works have only demonstrated suffi

Arun Muthukkumar

Pose estimation is essential for many applications within computer vision and robotics. Despite its uses, few works provide rigorous uncertainty quantification for poses under dense or learned models. We derive a closed-form lower bound on the covariance of camera pose estimates by treating a differentiable renderer as a measurement function. Linearizing ima

Yiding Feng, Vahideh Manshadi, Rad Niazadeh, Saba Neyshabouri

We consider a natural dynamic staffing problem in which a decision-maker sequentially hires workers over a finite horizon to meet an unknown demand revealed at the end. Predictions about demand arrive over time and become increasingly accurate, while worker availability decreases. This creates a fundamental trade-off between hiring early to avoid understaffi