November 2025 arXiv papers — page 169

Zeyan Liu, Liping Chen, Kong Aik Lee, Zhenhua Ling

Facilitated by the speech generation framework that disentangles speech into content, speaker, and prosody, voice anonymization is accomplished by substituting the original speaker embedding vector with that of a pseudo-speaker. In this framework, the pseudo-speaker generation forms a fundamental challenge. Current pseudo-speaker generation methods demonstra

Haijun Xiong, Bin Feng, Bang Wang, Xinggang Wang

Gait recognition offers a non-intrusive biometric solution by identifying individuals through their walking patterns. Although discriminative models have achieved notable success in this domain, the full potential of generative models remains largely underexplored. In this paper, we introduce \textbf{CoD$^2$}, a novel framework that combines the data distrib

Shamika Likhite, Santiago López-Tapia, Aggelos K. Katsaggelos

Motion blur, caused by relative movement between camera and scene during exposure, significantly degrades image quality and impairs downstream computer vision tasks such as object detection, tracking, and recognition in dynamic environments. While deep learning-based motion deblurring methods have achieved remarkable progress, existing approaches face fundam

Jeffrey Zhang

In observational studies, exposures are often continuous rather than binary or discrete. At the same time, sensitivity analysis is an important tool that can help determine the robustness of a causal conclusion to a certain level of unmeasured confounding, which can never be ruled out in an observational study. Sensitivity analysis approaches for continuous

Roberto da Silva, Henrique A. Fernandes, Paulo G. Freitas, Sebastian Gonçalves

Visibility graphs are spatial interpretations of time series. When derived from the time evolution of physical systems, the graphs associated with such series may exhibit properties that can reflect aspects such as ergodicity, criticality, or other dynamical behaviors. It is important to describe how the criticality of a system is manifested in the structure

M. R. Ahmadi Zand, N. Baimani

In this work, we present several new findings regarding the concepts of orbit-transitivity, strict orbit-transitivity, $\omega$-transitivity, and $\mu$-open-set transitivity for self-maps on generalized topological spaces. Let $(X,\mu)$ denote a generalized topological space. A point $x \in X$ is said to be \textit{quasi-$\mu$-isolated} if there exists a $\m

Tzu-Jung Lin, Jia-Fong Yeh, Hung-Ting Su, Chung-Yi Lin

In open-vocabulary mobile manipulation (OVMM), task success often hinges on the selection of an appropriate base placement for the robot. Existing approaches typically navigate to proximity-based regions without considering affordances, resulting in frequent manipulation failures. We propose Affordance-Guided Coarse-to-Fine Exploration, a zero-shot framework

Byoungwoo Park, Juho Lee, Guan-Horng Liu

Learning-based methods for sampling from the Gibbs distribution in finite-dimensional spaces have progressed quickly, yet theory and algorithmic design for infinite-dimensional function spaces remain limited. This gap persists despite their strong potential for sampling the paths of conditional diffusion processes, enabling efficient simulation of trajectori

Ruihao Xia, Junhong Cai, Luziwei Leng, Liuyi Wang

Event cameras offer unique advantages for vision tasks in challenging environments, yet processing asynchronous event streams remains an open challenge. While existing methods rely on specialized architectures or resource-intensive training, the potential of leveraging modern Visual Foundation Models (VFMs) pretrained on image data remains under-explored for

Zhitao Li, Yiqiu Dong, Xueying Zeng

This paper presents a comprehensive analysis of hyperparameter estimation within the empirical Bayes framework (EBF) for sparse learning. By studying the influence of hyperpriors on the solution of EBF, we establish a theoretical connection between the choice of the hyperprior and the sparsity as well as the local optimality of the resulting solutions. We sh

Mojtaba Noghabaei

Pre-trained models for natural language inference (NLI) often achieve high performance on benchmark datasets by using spurious correlations, or dataset artifacts, rather than understanding language touches such as negation. In this project, we investigate the performance of an ELECTRA-small model fine-tuned on the Stanford Natural Language Inference (SNLI) d

Sushant Mehta, Ishan Gupta

In-context learning (ICL) enables large language models to adapt to new tasks from demonstrations without parameter updates. Despite extensive empirical studies, a principled understanding of ICL emergence at scale remains more elusive. We present a unified theoretical framework connecting scaling laws to ICL emergence in transformers. Our analysis establish

Henok Ademtew, Israel Goytom

Human-computer interaction increasingly demands systems that recognize not only explicit user inputs but also implicit emotional states. While substantial progress has been made in affective computing, most emotion recognition systems rely on cloud-based inference, introducing privacy vulnerabilities and latency constraints unsuitable for real-time applicati

Thomas Xiao

This study investigates temporal variability in U.S. climate using harmonic decomposition techniques, specifically Fourier and wavelet transforms. Monthly temperature, precipitation, and drought index data from the National Oceanic and Atmospheric Administration (NOAA) U.S. Climate Divisional Dataset (nClimDiv, 1895--2024) were analyzed to detect periodic st

Sed Centeno, Christopher Sprague, Arnab A Purkayastha, Ray Simar

Speculative backpropagation has emerged as a promising technique to accelerate the training of neural networks by overlapping the forward and backward passes. Leveraging speculative weight updates when error gradients fall within a specific threshold reduces training time without substantially compromising accuracy. In this work, we implement speculative bac

E. C. Tredenick, A. M. Boyce, R. Drummond, S. R. Duncan

Heterogeneities in lithium ion batteries can be significant factors in electrode under utilisation and degradation while charging. Bilayer electrodes have been proposed as a convenient and scalable way to homogenise the electrode response. In this paper, the design of a bilayer cathode for Li-ion batteries composed of separate layers of lithium nickel mangan

Anamul Haque Mollah, Ahmed Aljohani, Hyunsook Do

Unit tests often lack concise summaries that convey test intent, especially in auto-generated or poorly documented codebases. Large Language Models (LLMs) offer a promising solution, but their effectiveness depends heavily on how they are prompted. Unlike generic code summarization, test-code summarization poses distinct challenges because test methods valid

Xingcheng Liu, Yanchen Guan, Haicheng Liao, Zhengbing He

Accurate accident anticipation is essential for enhancing the safety of autonomous vehicles (AVs). However, existing methods often assume ideal conditions, overlooking challenges such as sensor failures, environmental disturbances, and data imperfections, which can significantly degrade prediction accuracy. Additionally, previous models have not adequately a

Jie Fan, Francesco Visentin, Barbara Mazzolai, Emanuela Del Dottore

Tendrils coil their shape to anchor the plant to supporting structures, allowing vertical growth toward light. Although climbing plants have been studied for a long time, extracting information regarding the relationship between the temporal shape change, the event that triggers it, and the contact location is still challenging. To help build this relation,

Shu Zhao, Nilesh Ahuja, Tan Yu, Tianyi Shen

Pre-trained vision language models have shown remarkable performance on visual recognition tasks, but they typically assume the availability of complete multimodal inputs during both training and inference. In real-world scenarios, however, modalities may be missing due to privacy constraints, collection difficulties, or resource limitations. While previous

Anmar Kareem, Alexander Aue

This paper evaluates the performance of classical time series models in forecasting Bitcoin prices, focusing on ARIMA, SARIMA, GARCH, and EGARCH. Daily price data from 2010 to 2020 were analyzed, with models trained on the first 90 percent and tested on the final 10 percent. Forecast accuracy was assessed using MAE, RMSE, AIC, and BIC. The results show that

Heeseung Bang, Andreas A. Malikopoulos

Classical Bayesian persuasion assumes that senders fully understand how receivers form beliefs and make decisions--an assumption that rarely holds when receivers possess private information or exhibit non-Bayesian behavior. In this paper, we develop a learning-based framework that integrates neural networks with conformal prediction to achieve robust persuas

Yue Huang, Xiangqi Wang, Xiangliang Zhang

In high-stakes scenarios-such as self-harm, legal, or medical queries-LLMs must be both trustworthy and helpful. However, these goals often conflict. We propose priority alignment, a new alignment paradigm that enforces a strict "trustworthy-before-helpful" ordering: optimization of helpfulness is conditioned on first meeting trustworthy thresholds (e.g., ha

Sen Xu, Yi Zhou, Wei Wang, Jixin Min

Challenging the prevailing consensus that small models inherently lack robust reasoning, this report introduces VibeThinker-1.5B, a 1.5B-parameter dense model developed via our Spectrum-to-Signal Principle (SSP). This challenges the prevailing approach of scaling model parameters to enhance capabilities, as seen in models like DeepSeek R1 (671B) and Kimi k2

Mohammad Farhad, Sabbir Rahman, Shuvalaxmi Dass

Software security testing, particularly when enhanced with deep learning models, has become a powerful approach for improving software quality, enabling faster detection of known flaws in source code. However, many approaches miss post-fix latent vulnerabilities that remain even after patches typically due to incomplete fixes or overlooked issues may later l

Yu-Yan Wang, Meng-Die Zhao, Xin-Yu Wang, Guo-Ping Li

In this paper, we apply numerical backward ray-tracing to study the observational appearance of Kerr-Taub-NUT (KTN) black holes illuminated by thin accretion disk flows. We obtained the inner shadow, redshift characteristics, and intensity distribution of thin-disk images of the KTN black hole, as observed by a common observer located at different positions.

Paul Gölz, Ayumi Igarashi, Pasin Manurangsi, Warut Suksompong

We study the fair allocation of indivisible goods with variable groups. In this model, the goal is to partition the agents into groups of given sizes and allocate the goods to the groups in a fair manner. We show that for any number of groups and corresponding sizes, there always exists an envy-free up to one good (EF1) outcome, thereby generalizing an impor

Bingyu Cui, Yanting Cao

We analyze finite-sample statistics of Bohmian trajectories for single spinless and spin-1/2 particles. Equivariance ensures agreement with $|\psi|^2$ in the quantum equilibrium limit, yet experiments and simulations necessarily use finite ensembles. We show that in regular flows (e.g., wavepackets or low-mode superpositions of eigenstates of harmonic oscill

Yanan Zhao, Feng Ji, Jingyang Dai, Jiaze Ma

Graph contrastive learning (GCL) learns node and graph representations by contrasting multiple views of the same graph. Existing methods typically rely on fixed, handcrafted views-usually a local and a global perspective, which limits their ability to capture multi-scale structural patterns. We present an augmentation-free, multi-view GCL framework grounded

Puzhen Su, Yongzhu Miao, Chunxi Guo, Jintao Tang

Detecting Alzheimer's Disease (AD) from narrative transcripts remains a challenging task for large language models (LLMs), particularly under out-of-distribution (OOD) and data-scarce conditions. While in-context learning (ICL) provides a parameter-efficient alternative to fine-tuning, existing ICL approaches often suffer from task recognition failure, subop

Hosna Sultana

Dielectric resonant metasurfaces can be utilized for efficient high harmonic generation, which has been explored here in this effort for a high-efficient Germanium metasurface for upconverted full-Stokes imaging at 1.33-micron wavelength by the third harmonic generation (THG) from the 4-micron fundamental wavelength. The internal TH conversion efficiency for

Xian-Jin Gui

Click-Through Rate (CTR) prediction is a core task in online personalization platform. A key step for CTR prediction is to learn accurate user representation to capture their interests. Generally, the interest expressed by a user is time-variant, i.e., a user activates different interests at different time. However, most previous CTR prediction methods overl

Shuhang Chen, Hangjie Yuan, Pengwei Liu, Hanxue Gu

The Segment Anything Model (SAM) has demonstrated significant potential in medical image segmentation. Yet, its performance is limited when only a small amount of labeled data is available, while there is abundant valuable yet often overlooked hierarchical information in medical data. To address this limitation, we draw inspiration from self-supervised learn

Seif Ikbarieh, Kshitiz Aryal, Maanak Gupta

The rapid expansion of the Internet of Things (IoT) is reshaping communication and operational practices across industries, but it also broadens the attack surface and increases susceptibility to security breaches. Artificial Intelligence has become a valuable solution in securing IoT networks, with Large Language Models (LLMs) enabling automated attack beha

Gautam Chandrasekaran, Raghu Meka, Konstantinos Stavropoulos

Sparse linear regression is one of the most basic questions in machine learning and statistics. Here, we are given as input a design matrix $X \in \mathbb{R}^{N \times d}$ and measurements or labels ${y} \in \mathbb{R}^N$ where ${y} = {X} {w}^* + {\xi}$, and ${\xi}$ is the noise in the measurements. Importantly, we have the additional constraint that the unk

Shihong Liao, Yizhou Liu, Haonan Zheng, Ming Li

Traditional cosmological hydrodynamical simulations usually assume equal-numbered but unequal-mass dark matter and baryonic particles, which can lead to spurious collisional heating due to energy equipartition. To avoid such a numerical heating effect, a simulation setup with equal-mass dark matter and baryonic particles, which corresponds to a particle numb

Jingwei Ni, Ekaterina Fadeeva, Tianyi Wu, Mubashara Akhtar

LLMs can solve complex tasks by generating long, multi-step reasoning chains. Test-time scaling (TTS) can further improve performance by sampling multiple variants of intermediate reasoning steps, verifying their correctness, and selecting the best steps for continuation. However, existing verification approaches, such as Process Reward Models (PRMs), are co

Zetian Shen, Hongjun Wang, Jiyuan Chen, Xuan Song

Supply chains are integral to global economic stability, yet disruptions can swiftly propagate through interconnected networks, resulting in substantial economic impacts. Accurate and timely inference of supply chain resilience the capability to maintain core functions during disruptions is crucial for proactive risk mitigation and robust network design. How

Zhenyu Xiu, Zhengyuan Si

In this paper, we provide some structures of uninorms on bounded lattices via t-conorms, closure operators and t-subnorms, subject to certain constraints on the closure operators and t-subnorms. Importantly, these constraints are shown to be both sufficient and necessary. That is, the proposed methods reveal clear relationships between the structure of the r

Dachao Han, Teng Huang, Han Ding, Cui Zhao

With the rise of voice-enabled technologies, loudspeaker playback has become widespread, posing increasing risks to speech privacy. Traditional eavesdropping methods often require invasive access or line-of-sight, limiting their practicality. In this paper, we present mmSpeech, an end-to-end mmWave-based eavesdropping system that reconstructs intelligible sp

Hyun Jung Koo, Aaron J. Molstad

Popular technologies for generating spatially resolved transcriptomic data measure gene expression at the resolution of a "spot", i.e., a small tissue region 55 microns in diameter. Each spot can contain many cells of different types. In typical analyses, researchers are interested in using these data to identify and profile discrete spatial domains in the t

Zezhang Yang, Zitong Yu, Nuri Choi, Janice Tania

Single-photon emission computed tomography for myocardial perfusion imaging (MPI SPECT) is a widely used diagnostic tool for coronary artery disease. However, the procedure requires considerable scanning time, leading to patient discomfort and the potential for motion-induced artifacts. Reducing the number of projection views while keeping the time per view

Shahram Najam Syed, Yatharth Ahuja, Arthur Jakobsson, Jeff Ichnowski

Vision-Language-Action (VLA) models like OpenVLA demonstrate impressive zero-shot generalization across robotic manipulation tasks but struggle to adapt to specific deployment environments where consistent high performance on a limited set of tasks is more valuable than broad generalization. We present EXPierence replayed, REtrieval augmented, Specialized VL

Rodrigo Gallardo, Oz Fishman, Alexander Htet Kyaw

This paper introduces a human-in-the-loop computer vision framework that uses generative AI to propose micro-scale design interventions in public space and support more continuous, local participation. Using Grounding DINO and a curated subset of the ADE20K dataset as a proxy for the urban built environment, the system detects urban objects and builds co-occ

Sara Antonijevic, Danielle Sitalo, Brani Vidakovic

Dental caries remain a persistent global health challenge, and fluoride varnish is widely used as a preventive intervention. This study synthesizes evidence from multiple clinical trials to evaluate the effectiveness of fluoride varnish in reducing Decayed-Missing-Filled (DMF) surfaces. The principal measure of efficacy is the Prevented Fraction (PF), repres

Shiqi Liu, Hang Song, Bo Wei, Nopphon Keerativoranan

Integrated Sensing and Communication (ISAC) is considered as a key component of future 6G technologies, especially in the millimeter-wave (mmWave) bands. Recently, the performances of ISAC were experimentally evaluated and demonstrated in various scenarios by developing ISAC systems. These systems generally consist of coherent transmitting (Tx) and receiving

Vienna B. Rossmanith

Ideal gases can be modeled by the Boltzmann equation from statistical physics. Instead of trying to track the position and velocity of a large number of gas molecules, it is possible to describe the particles with a particle distribution function. The Boltzmann equation provides the rule for evolving the distribution function over time, allowing one to simul

Shoto Aoki, Yoshio Kikukawa, Toshinari Takemoto

We consider Kogut-Susskind fermions (also known as staggered fermions) in a $(3+1)$-dimensional Hamiltonian formalism and examine a chiral transformation and its associated chiral anomaly. The Hamiltonian of the massless Kogut-Susskind fermion has symmetry under the shift transformations in each space direction $S_k \, (k=1,2,3)$, and the product of the thre

Dilli Prasad Sharma, Liang Xue, Xiaowei Sun, Xiaodong Lin

The rapid proliferation of Internet of Things (IoT) devices has transformed numerous industries by enabling seamless connectivity and data-driven automation. However, this expansion has also exposed IoT networks to increasingly sophisticated security threats, including adversarial attacks targeting artificial intelligence (AI) and machine learning (ML)-based

Xiao Fang, Yang Xie, Yi-Kun Zhao

We use martingale embeddings to prove a central limit theorem (CLT) for one-dimensional projections of high-dimensional random vectors in $\{-1,1\}^n$ satisfying a Poincar\'e inequality. We obtain a non-asymptotic error bound involving two-point and three-point functions for the CLT in 2-Wasserstein distance. We present three illustrative applications: Ising

Mira Winick, Naisha Agarwal, Chiheb Boussema, Ingrid Lee

Interfaces for contemporary large language, generative media, and perception AI models are often engineered for single user interaction. We investigate ritual as a design scaffold for developing collaborative, multi-user human-AI engagement. We consider the specific case of an immersive staging of the musical Xanadu performed at UCLA in Spring 2025. During a

Muhammad Usama, Mohammad Sadil Khan, Didier Stricker, Muhammad Zeshan Afzal

Generating editable 3D CAD models from natural language remains challenging, as existing text-to-CAD systems either produce meshes or rely on scarce design-history data. We present NURBGen, the first framework to generate high-fidelity 3D CAD models directly from text using Non-Uniform Rational B-Splines (NURBS). To achieve this, we fine-tune a large languag

Michael Jaemin Kim, Seungmin Baek, Jumin Kim, Hwayong Nam

A decade after its academic introduction, RowHammer (RH) remains a moving target that continues to challenge both the industry and academia. With its potential to serve as a critical attack vector, the ever-decreasing RH threshold now threatens DRAM process technology scaling, with a superlinearly increasing cost of RH protection solutions. Due to their gene

Soujanya Dash, Kenjiro Ide, Rikuhei Umemoto, Kai Amino

Defensive organization is critical in soccer, particularly during negative transitions when teams are most vulnerable. The back-four defensive line plays a decisive role in preventing goal-scoring opportunities, yet its collective coordination remains difficult to quantify. This study introduces interpretable spatio-temporal indicators namely, space control,

Sangmook Lee, Dohyung Kim, Hyukhun Koh, Nakyeong Yang

Recent advances in Large Language Models (LLMs) - particularly model scaling and test-time techniques - have greatly enhanced the reasoning capabilities of language models at the expense of higher inference costs. To lower inference costs, prior works train router models or deferral mechanisms that allocate easy queries to a small, efficient model, while for

Navonil Deb, Raaz Dwivedi, Sumanta Basu

We address the challenge of forecasting counterfactual outcomes in a panel data with missing entries and temporally dependent latent factors -- a common scenario in causal inference, where estimating unobserved potential outcomes ahead of time is essential. We propose Forecasting Counterfactuals under Stochastic Dynamics (FOCUS), a method that extends tradit

Zhihao Tao, Athina P. Petropulu

In our prior work we have proposed the use of GFlowNets, a generative AI (GenAI) framework, for designing a secure communication system comprising a time-modulated intelligent reflecting surface (TM-IRS). However, GFlowNet-based approaches assume static environments, limiting their applicability in mobile wireless networks. In this paper, we proposes a novel

Mathew Joseph, Tanush Savadi, Abel Souza

Over recent decades, electricity demand has experienced sustained growth through widespread electrification of transportation and the accelerated expansion of Artificial Intelligence (AI). Grids have managed the resulting surges by scaling generation capacity, incorporating additional resources such as solar and wind, and implementing demand-response mechani

Lakshmi Priya Bodepudi, Yutong Zhao, Ming Quan Fu, Yuanyuan Wu

Building Android applications reliably remains a persistent challenge due to complex dependencies, diverse configurations, and the rapid evolution of the Android ecosystem. This study conducts an empirical analysis of 200 open-source Android projects written in Java and Kotlin to diagnose and resolve build failures. Through a five-phase process encompassing

Xinyuan Wang, Hongyu Cao, Kunpeng Liu, Yanjie Fu

The growing demand for artificial intelligence (AI) applications in materials discovery, molecular modeling, and climate science has made data preparation a critical but labor-intensive bottleneck. Raw data from diverse sources must be cleaned, normalized, and transformed to become AI-ready, where effective feature transformation and selection are essential

Swetapadma Sahoo, Péter Udvarhelyi, Jaden Li, Darwon Kim

Single-photon emitters are fundamental building blocks for quantum information processing, communication and sensing. However, unwanted interactions with bulk phonons in their host environment strongly limit their coherence and controllability. We report single color centers in nanodiamonds that are strongly and comprehensively decoupled from the bulk phonon

Ryuhei Miyazato, Ting-Ruen Wei, Xuyang Wu, Hsin-Tai Wu

Aspect-based summarization aims to generate summaries that highlight specific aspects of a text, enabling more personalized and targeted summaries. However, its application to books remains unexplored due to the difficulty of constructing reference summaries for long text. To address this challenge, we propose BookAsSumQA, a QA-based evaluation framework for

Peican Lin, Gan Sun, Chenxi Liu, Fazeng Li

Vision-language models (VLMs) have been widely-applied in ground-based vision-language navigation (VLN). However, the vast complexity of outdoor aerial environments compounds data acquisition challenges and imposes long-horizon trajectory planning requirements on Unmanned Aerial Vehicles (UAVs), introducing novel complexities for aerial VLN. To address these

Zhongmu Li, Rainer Spurzem

It is well-known that some star clusters contain composite stellar populations (CSPs), in which the metallicities or (and) ages of stars are different. The formation and evolution of such clusters and their stellar populations remain unclear. Both single and binary cluster channels may lead to such CSPs. In order to simulate the formation and evolution of su

Wenhui Ren, Liwei Zhang

This paper investigates minimax quadratic programming problems with coupled inequality constraints. By leveraging a duality theorem, we develop a dual algorithm that extends the dual active set method to the minimax setting, transforming the original inequality constrained problem into a sequence of equality constrained subproblems. Under a suitable assumpti

Joel Ward

We introduce MemoriesDB, a unified data architecture designed to avoid decoherence across time, meaning, and relation in long-term computational memory. Each memory is a time-semantic-relational entity-a structure that simultaneously encodes when an event occurred, what it means, and how it connects to other events. Built initially atop PostgreSQL with pgvec

Congyu Liao, Jason P. Stockmann, Zhitao Li, Zhixing Wang

Purpose: We developed a dynamic B0 shimming approach using a 46-channel AC/DC shim array to correct phase errors caused by eddy currents from diffusion-encoding gradients in diffusion-prepared MRI, enabling high b-value imaging without the SNR loss from the use of magnitude stabilizer. Methods: A 46-channel AC/DC shim array and corresponding amplifier system

Dmitrii Vlasiuk, Mikhail Smirnov

We test the hypothesis that consecutive intraday price changes in the most liquid U.S. equity ETF (SPY) are conditionally nonrandom. Using NBBO event-time data for about 1,500 regular trading days, we form for every lag L ordered pairs of a backward price increment ("push") and a forward price increment ("response"), standardize them, and estimate the expect

Sang Hoon Lee

The friendship paradox -- the observation that, on average, one's friends have more friends than oneself -- admits two common formulations depending on whether averaging is performed over edges or over nodes. These two definitions, the "alter-based" and "ego-based" means, are often treated as distinct but related quantities. This paper establishes their exac

Kaijie Xu, Fandi Meng, Clark Verbrugge, Simon Lucas

In Social Deduction Games (SDGs) such as Avalon, Mafia, and Werewolf, players conceal their identities and deliberately mislead others, making hidden-role inference a central and demanding task. Accurate role identification, which forms the basis of an agent's belief state, is therefore the keystone for both human and AI performance. We introduce CSP4SDG, a

Zifan He, Shengyu Ye, Rui Ma, Yang Wang

The rapid development of large language models (LLM) has greatly enhanced everyday applications. While many FPGA-based accelerators, with flexibility for fine-grained data control, exhibit superior speed and energy efficiency compared to GPUs, recent GPU-specific optimizations have diminished this advantage. When limited to arithmetic-based computation, FPGA

Zhenzhong Wang, Xin Zhang, Jun Liao, Min Jiang

Multiphase flow simulation is critical in science and engineering but incurs high computational costs due to complex field discontinuities and the need for high-resolution numerical meshes. While Neural Operators (NOs) offer an efficient alternative for solving Partial Differential Equations (PDEs), they struggle with two core challenges unique to multiphase

Liyang Lu, Haochen Wu, Wenbo Xu, Zhaocheng Wang

We provide new recovery bounds for hierarchical compressed sensing (HCS) based on prior support information (PSI). A detailed PSI-enabled reconstruction model is formulated using various forms of PSI. The hierarchical block orthogonal matching pursuit with PSI (HiBOMP-P) algorithm is designed in a recursive form to reliably recover hierarchically block-spars

Andrés Ledesma, Karo Michaelian

From the non-equilibrium thermodynamic perspective of the origin of life as a photochemical dissipative structuring (entropy driven) process, we assess the probability of carbon-based life arising on Earth-like analogues orbiting different main-sequence stellar types (O7 V to M2 V). Using black-body spectra normalized to Earth's solar constant, we calcul

Hua Chang, Xin Xu, Wei Liu, Wei Wang

Chinese opera is celebrated for preserving classical art. However, early filming equipment limitations have degraded videos of last-century performances by renowned artists (e.g., low frame rates and resolution), hindering archival efforts. Although space-time video super-resolution (STVSR) has advanced significantly, applying it directly to opera videos rem

Xi Chen, Anindya De, Yizhi Huang, Shivam Nadimpalli

Several recent works [DHLNSY25, CPPS25a, CPPS25b] have studied a model of property testing of Boolean functions under a \emph{relative-error} criterion. In this model, the distance from a target function $f: \{0,1\}^n \to \{0,1\}$ that is being tested to a function $g$ is defined relative to the number of inputs $x$ for which $f(x)=1$; moreover, testing algo

Shivam Nadimpalli, Mingda Qiao, Ronitt Rubinfeld

The classic *priced query model*, introduced by Charikar et al. (STOC 2000), captures the task of computing a known function on an unknown input when each input variable can only be revealed by paying an associated cost. The goal is to design a query strategy that determines the function's value while minimizing the total cost incurred. However, all prior wo

Sanskar Amgain, Prashant Shrestha, Bidur Khanal, Alina Devkota

Federated learning on clients with noisy labels is a challenging problem, as such clients can infiltrate the global model, impacting the overall generalizability of the system. Existing methods proposed to handle noisy clients assume that a sufficient number of clients with clean labels are available, which can be leveraged to learn a robust global model whi

Boxuan Wang, Zhuoyun Li, Xinmiao Huang, Xiaowei Huang

This paper primarily demonstrates a method to quantitatively assess the alignment between multi-step, structured reasoning in large language models and human preferences. We introduce the Alignment Score, a semantic-level metric that compares a model-produced chain of thought traces with a human-preferred reference by constructing semantic-entropy-based matr

Assaf Singer, Noam Rotstein, Amir Mann, Ron Kimmel

Diffusion-based video generation can create realistic videos, yet existing image- and text-based conditioning fails to offer precise motion control. Prior methods for motion-conditioned synthesis typically require model-specific fine-tuning, which is computationally expensive and restrictive. We introduce Time-to-Move (TTM), a training-free, plug-and-play fr

Chao Yang, Yiyi Zhu

Let $V$ be a vertex operator algebra equipped with two commuting finite-order automorphisms $g_1$ and $g_2$, and set $g_3 = g_1 g_2$. For $k = 1, 2, 3$, let $W^k$ be a $g_k$-twisted $V$-module. Assuming that $W^1$ and $W^2$ are $C_1$-cofinite and that there exists a surjective twisted logarithmic intertwining operator of type $\binom{W^3}{W^1 \ W^2}$, we pro

Jonas D. Gerber, Efe Ersoy, Michele Masseroni, Markus Niese

We report a well-resolved 0.7 conductance anomaly at $G = 0.7\times(2e^2/h)$ in bilayer graphene/WSe$_2$ quantum point contacts. Proximity-enhanced spin-orbit coupling splits the four-fold ground state of bilayer graphene into well-separated spin-valley locked Kramers doublets. The anomaly emerges between these opposite spin-valley states. Despite fundamenta

Shota Nishida, Shunsuke Kitou, Shingo Toyoda, Yuiga Nakamura

We investigate displacive short-range order in pyrochlore Y2Nb2O7, which exhibits a nonmagnetic insulating state despite the presence of formally tetravalent Nb4+ (S = 1/2) ions on the pyrochlore network. Synchrotron x-ray diffraction on a single crystal reveals a characteristic x-ray diffuse scattering (XDS) pattern primarily around q = {0.5, 0.5, 2}. Rever

Sergey V. Gusev

An element $x$ of a lattice $L$ is modular if $L$ has no five-element sublattice isomorphic to the pentagon in which $x$ would correspond to the lonely midpoint. In the present work, we classify all modular elements of the lattice of all monoid varieties.

Yifang Xu, Yilong Zhou, Lida Sun, Hongwei Huang

High-fidelity two-logical-qubit gates are essential for realizing fault-tolerant quantum computation with bosonic codes, yet experimentally reported fidelities have rarely exceeded 90\%. Here, we propose a geometric phase engineering approach for implementing controlled-phase gates for binomially encoded logical qubits. This method leverages the structural s

Abbas Zaidi, Rina Friedberg, Samir Khan, Yao-Yang Leow

The widespread adoption of online randomized controlled experiments (A/B Tests) for decision-making has created ongoing capacity constraints which necessitate interim analyses. As a consequence, platform users are increasingly motivated to use ad-hoc means of optimizing limited resources via peeking. Such processes, however, are error prone and often misalig

Ali Abbasian Ardakani, Afshin Mohammadi, Alisa Mohebbi, Anushya Vijayananthan

Background: The 2022 update of the Ovarian-Adnexal Reporting and Data System (O-RADS) ultrasound classification refines risk stratification for adnexal lesions, yet human interpretation remains subject to variability and conservative thresholds. Concurrently, deep learning (DL) models have demonstrated promise in image-based ovarian lesion characterization.

Omin Kwon, Kyungjun Oh, Jaeyong Lee, Myungsuk Kim

Although NAND flash memory has achieved continuous capacity improvements via advanced 3D stacking and multi-level cell technologies, these innovations introduce new reliability challenges, particularly lateral charge spreading (LCS), absent in low-capacity 2D flash memory. Since LCS significantly increases retention errors over time, addressing this problem

Juntao Li, Haobin Yuan, Ling Luo, Tengxiao Lv

Discharge medication recommendation plays a critical role in ensuring treatment continuity, preventing readmission, and improving long-term management for patients with chronic metabolic diseases. This paper present an overview of the CHIP 2025 Shared Task 2 competition, which aimed to develop state-of-the-art approaches for automatically recommending appro-

Jian Li, Xinsheng Wang, Jianjie Zhao

In this paper, we obtain the dichotomy for mean equicontinuity and mean sensitivity for a sequence of bounded linear operators from a Banach space to a normed linear space. The mean Li-Yorke chaos for sequences and submultiplicative sequences of bounded linear operators are also studied. Furthermore, several criteria for mean Li-Yorke chaos are established.

Tim L. Alderson

In this short communication, we generalize a classical result of Barlotti concerning the unique extendability of arcs in the projective plane to higher-dimensional projective spaces. Specifically, we show that for integers \( k \ge 3 \), \( s \ge 0 \), and prime power \( q \), any \((n, k + s - 1)\)-arc in PG\((k - 1, q)\) of size \( n = (s+1)(q+1) + k - 3 \

Mirco Felske, Jannik Redenius, Georg Happich, Julius Schöning

The agricultural sector increasingly relies on autonomous systems that operate in complex and variable environments. Unlike on-road applications, agricultural automation integrates driving and working processes, each of which imposes distinct operational constraints. Handling this complexity and ensuring consistency throughout the development and validation

Kenta Takata, Adam Mock, Masaya Notomi, Akihiko Shinya

Non-Hermitian systems can have peculiar degeneracies of eigenstates called exceptional points (EPs). An EP of $n$ degenerate states is said to have order $n$, and higher-order EPs (HEPs) with $n \ge 3$ exhibit intrinsic order-scaling responses potentially applied to superior sensing and state control. However, traditional eigenvalue-based searches for HEPs a

Francesco Vaselli, Chang Sun, Thea Aarrestad, Dimitrios Danopoulos

We present the first implementation of a Continuous Normalizing Flow (CNF) model for unsupervised anomaly detection within the realistic, high-rate environment of the Large Hadron Collider's L1 trigger systems. While CNFs typically define an anomaly score via a probabilistic likelihood, calculating this score requires solving an Ordinary Differential Equ

Salvatore Capozziello, Giuseppe Meluccio, Jonas R. Mureika

We discuss the Generalized Uncertainty Principle and the Extended Uncertainty Principle in the context of black hole solutions coming from non-local theories of gravity, focusing, specifically, on Infinite Derivative Gravity. We argue that these modifications of the Heisenberg Uncertainty Principle are effective descriptions arising from the non-local featur

Jordan T Kemp, Laura Fürsich, Luís M A Bettencourt

Growth is a multi-layered phenomenon in human societies, composed of socioeconomic and demographic change at many different scales. Yet, standard macroeconomic indicators average over most of these processes, blurring the spatial and hierarchical heterogeneity driving people's choices and experiences. To address this gap, we introduce here a framework based

Constantinos Daskalakis, Vardis Kandiros, Rui Yao

We study the problem of learning the topology of a directed Gaussian Graphical Model under the equal-variance assumption, where the graph has $n$ nodes and maximum in-degree $d$. Prior work has established that $O(d \log n)$ samples are sufficient for this task. However, an important factor that is often overlooked in these analyses is the dependence on the

Meike Neuwohner, Olha Silina, Michael Zlatin

We introduce and study a directed analogue of the weighted Tree Augmentation Problem (WTAP). In the weighted Directed Tree Augmentation Problem (WDTAP), we are given an oriented tree $T = (V,A)$ and a set of directed links $L \subseteq V \times V$ with positive costs. The goal is to select a minimum cost set of links which enters each fundamental dicut of $T

Ibna Kowsar, Kazi F. Akhter, Manar D. Samad

Transfer learning on tabular data is challenging due to disparate feature spaces across domains, in contrast to the homogeneous structures of image and text. Large language models (LLMs) offer a knowledge base to improve the limited effectiveness of cross-domain transfer learning for tabular data. However, LLM performance often stagnates due to subjective te

Rasman Mubtasim Swargo, Md Arifuzzaman

Modern scientific data acquisition generates petabytes of data that must be transferred to geographically distant computing clusters. Conventional tools either rely on preconfigured sessions, which are difficult to tune for users without domain expertise, or they adaptively optimize only concurrency while ignoring other important parameters. We present \name