October 2025 arXiv papers — page 163

Anand D. Sarwate, Flavio P. Calmon, Oliver Kosut, Lalitha Sankar

Since being proposed in 2006, differential privacy has become a standard method for quantifying certain risks in publishing or sharing analyses of sensitive data. At its heart, differential privacy measures risk in terms of the differences between probability distributions, which is a central topic in information theory. A differentially private algorithm is

Gabe Cunningham, Igor Minevich

For a graph $\Gamma$ and group $G$, $G^\Gamma$ is the subgroup of $G^{|\Gamma|}$ generated by elements with $g$ in the coordinates corresponding to $v$ and its neighbors in $\Gamma$. There is a natural epimorphism $G^\Gamma \to (G/[G,G])^\Gamma$ with kernel $[G,G]^n \cap G^\Gamma$. When $[G,G]^n \leq G^\Gamma$, the structure of $G^\Gamma$ is easily described

Luiz Fernando M. Arruda, Moises Ferber, Diego Greff

This article presents a study on the application of artificial neural networks (ANNs) for maximum power point tracking (MPPT) in photovoltaic (PV) systems using low-cost pyranometer sensors. The proposed approach integrates pyranometers, temperature sensors, and an ANN to estimate the duty cycle of a DC/DC converter, enabling the system to consistently opera

Mark J. Ablowitz, Ramesh Gupta, Ziad H. Musslimani, Nicholas J. Ossi

The multi-dimensional six-wave interaction system is derived in the context of nonlinear optics. Starting from Maxwell's equations, a reduced system of equations governing the dynamics of the electric and polarization fields are obtained. Using a space-time multi-scale asymptotic expansion, a hierarchy of coupled equations describing the spatio-temporal evol

Jingcheng Dong, Sebastien Palcoux

A generalization of an argument due to Etingof-Nikshych-Ostrik yields a highly efficient necessary criterion for integral modular categorification. This criterion allows us to complete the classification of categorifiable integral modular data up to rank 14, and up to rank 25 in the odd-dimensional case.

Aristaa Bhardwaj, Adrian Boyer-Paulet, Wade Hindes, Emma Qiu

Let $G=\langle x^d+c_1,\dots,x^d+c_s\rangle$ be a semigroup generated under composition for some $c_1,\dots,c_s\in\mathbb{Z}$ and some $d\geq2$. Then we prove that, outside of an exceptional one-parameter family, $G$ contains a large and explicit subset of irreducible polynomials if and only if it contains at least one irreducible polynomial. In particular,

Shashank Gupta, Steven S. -L. Zhang

Unidirectional magnetoresistance (UMR) in metallic bilayers arises from nonlinear spin-charge transport mediated by broken time-reversal and inversion symmetries, yet the role of magnons remains unsettled. We develop a theoretical framework that incorporates coupled electron-magnon dynamics, revealing cross diffusion and spin-angular-momentum transfer betwee

Yinuo Zhang, Demao Liu, Zhichao Liang, Jiani Cheng

Recent breakthroughs in artificial intelligence (AI) are reshaping the way we construct computational counterparts of the brain, giving rise to a new class of ``surrogate brains''. In contrast to conventional hypothesis-driven biophysical models, the AI-based surrogate brain encompasses a broad spectrum of data-driven approaches to solve the inverse problem,

Sven Hirsch, Lan-Hsuan Huang

We address two problems concerning the ADM mass-minimizing initial data sets. First, we show that the equality case of the positive mass theorem embeds into a pp-wave spacetime. Second, we show that positive Bartnik mass minimizers embed into strongly stationary vacuum spacetimes, thereby confirming the Bartnik stationary vacuum conjecture. A key ingredient

Rustam Sadykov

In [MaII] Mather proved that a smooth proper infinitesimally stable map is stable. This result is the key component of the Mather stability theorem [MaV], which can be reformulated as follows: a smooth proper map $f: M\to N$ is stable if and only if it is infinitesimally stable if and only if it satisfies the Mather normal crossing condition. The latter cond

Michael Y. Hu, Benjamin Van Durme, Jacob Andreas, Harsh Jhamtani

Language model (LM) agents deployed in novel environments often exhibit poor sample efficiency when learning from sequential interactions. This significantly hinders the usefulness of such agents in environments where interaction is costly (for example, when they interact with humans or reset physical systems). While a number of existing LM agent architectur

Akila Kadambi, Lisa Aziz-Zadeh, Antonio Damasio, Marco Iacoboni

Multimodal Large Language Models (MLLMs) have demonstrated extraordinary progress in bridging textual and visual inputs. However, MLLMs still face challenges in situated physical and social interactions in sensorally rich, multimodal and real-world settings where the embodied experience of the living organism is essential. We posit that next frontiers for ML

Liangkun Chen, Zijian Wen, Tian Wu, Xiaoxi Zhang

The Mixture-of-Experts (MoE) architecture has been widely adopted in large language models (LLMs) to reduce computation cost through model sparsity. Employing speculative decoding (SD) can further accelerate MoE inference by drafting multiple tokens per step and verifying them in parallel. However, combining MoE with SD inflates GPU memory and aggravates CPU

Boris Kazarnovskii

The Fundamental Theorem of Algebra (FTA) asserts that every complex polynomial has as many complex roots, counted with multiplicities, as its degree. A probabilistic analogue of this theorem for real roots of real polynomials, commonly referred to as the Kac theorem, was introduced in 1938 by J. Littlewood and A. Offord. In this paper, we present the Kac the

Giulio Ruffini

The regulator theorem states that, under certain conditions, any optimal controller must embody a model of the system it regulates, grounding the idea that controllers embed, explicitly or implicitly, internal models of the controlled. This principle underpins neuroscience and predictive brain theories like the Free-Energy Principle or Kolmogorov/Algorithmic

Charles Bordenave

Sparse graphs with bounded average degree form a rich class of discrete structures where local geometry strongly influences global behavior. The Benjamini-Schramm (BS) convergence offers a natural framework to describe their asymptotic local structure. In this note, we survey spectral aspects of BS convergence and their applications, with a focus on random S

Alexandr Fonari, Farshad Fallah, Michael Rauch

The use of several open source scientific packages in the Schr\"odinger Materials Science Suite will be discussed. A typical workflow for materials discovery will be described, discussing how open source packages have been incorporated at every stage. Some recent implementations of machine learning for materials discovery will be discussed, as well as how op

Emmanouil T. Kokkinakis, Konstantinos G. Makris, Eleftherios N. Economou

The process of dephasing during wave evolution has traditionally been viewed as an obstacle to localization, leading to diffusion even in strongly disordered Hermitian lattices. In contrast, here we demonstrate how the interplay of dephasing with non-Hermitian defects can be harnessed to engineer wave localization. Specifically, we identify a novel dynamical

Graeme W. Milton

For two-dimensional polycrystals the effective elasticity tensor $C_*$ as a function $C_*(C_0)$ of the elasticity tensor $C_0$ of the constituent crystal is considered. It is shown that this function can be approximated by one with a continued fraction expansion resembling that associated with a class of microstructure known as sequential laminates. These ar

Mohammad Noormohammadi, Atefeh Javadi, Mehdi Khakian Ghomi

Multiple structures within stellar groups are an intriguing subject for theoretical and observational studies of stellar formation. With the accuracy and completeness of data from Gaia Data Release 3, we now have new opportunities to detect reliable members of stellar groups across a larger field of view than in previous studies. In this work, using machine

Franz Lemmermeyer

In this article we explain how to construct cyclic octic unramfied extensions of the real quadratic number field $k = {\mathbb Q}(\sqrt{2p}\,)$, where $p \equiv 1 \bmod 8$ is a prime number such that $h_2(k) \equiv 0 \bmod 8$. The construction only requires solving the diophantine equation $eu^2 = t^2 + 2ps^2$ in integers.

Yiyang Li, Zehong Wang, Zhengqing Yuan, Zheyuan Zhang

Illicit drug use among teenagers and young adults (TYAs) remains a pressing public health concern, with rising prevalence and long-term impacts on health and well-being. To detect illicit drug use among TYAs, researchers analyze large-scale surveys such as the Youth Risk Behavior Survey (YRBS) and the National Survey on Drug Use and Health (NSDUH), which pre

Hongwei Chen, Yishu Lei, Dan Zhang, Bo Ke

Test-time scaling has emerged as a promising paradigm in language modeling, wherein additional computational resources are allocated during inference to enhance model performance. Recent approaches, such as DeepConf, have demonstrated the efficacy of this strategy, however, they often incur substantial computational overhead to achieve competitive results. I

Zhirong Chou, Quan Qin, Shi Li

The emergence of foundation models in healthcare has opened new avenues for learning generalizable representations from large scale clinical data. Yet, existing approaches often struggle to reconcile the tabular and event based nature of Electronic Health Records (EHRs) with the sequential priors of natural language models. This structural mismatch limits th

Joy Hsu, Emily Jin, Jiajun Wu, Niloy J. Mitra

Real-world scenes, such as those in ScanNet, are difficult to capture, with highly limited data available. Generating realistic scenes with varied object poses remains an open and challenging task. In this work, we propose FactoredScenes, a framework that synthesizes realistic 3D scenes by leveraging the underlying structure of rooms while learning the varia

Sebastián Barbieri, Kanéda Blot, Mathieu Sablik, Ville Salo

We introduce a new quasi-isometry invariant for finitely generated groups and show that every group with this property admits a subshift which is effectively closed by patterns and that cannot be realized as the topological factor of any subshift of finite type. We provide several examples of groups with the property, such as amenable groups, multi-ended gro

Sayan Mandal, Hua Jiang

Automated code review adoption lags in compliance-heavy settings, where static analyzers produce high-volume, low-rationale outputs, and naive LLM use risks hallucination and incurring cost overhead. We present a production system for grounded, PR-native review that pairs static-analysis findings with AST-guided context extraction and a single-GPU, on-demand

Ke Ma, Andrey Vlasov, Zeynep B. Simsek, Jinshui Zhang

Transcranial magnetic stimulation (TMS) with asymmetric electric field pulses, such as monophasic, offers directional selectivity for neural activation but requires excessive energy. Previous pulse shape optimisation has been limited to symmetric pulses or heavily constrained variations of conventional waveforms without achieving general optimality in energy

Sayan Mandal, Divyadarshini Karthikeyan, Manas Paldhe

We propose SAM2LoRA, a parameter-efficient fine-tuning strategy that adapts the Segment Anything Model 2 (SAM2) for fundus image segmentation. SAM2 employs a masked autoencoder-pretrained Hierarchical Vision Transformer for multi-scale feature decoding, enabling rapid inference in low-resource settings; however, fine-tuning remains challenging. To address th

Markus Käppeler, Özgün Çiçek, Daniele Cattaneo, Claudius Gläser

Camera-based 3D object detection and tracking are essential for perception in autonomous driving. Current state-of-the-art approaches often rely exclusively on either perspective-view (PV) or bird's-eye-view (BEV) features, limiting their ability to leverage both fine-grained object details and spatially structured scene representations. In this work, we pro

Wolfgang Kurz, Danny Baranes

Building on evidence of structural parallels between brain networks and the cosmic web [1], we apply AI-based geometric analysis to cultured neuronal networks. Isolated neurons self-organize into dendritic lattices shaped by reproducible wiring rules. These lattices show non-random features-frequent dendritic convergence, hub nodes, small-world connectivity,

Haolang Lu, Bolun Chu, WeiYe Fu, Guoshun Nan

Multimodal large reasoning models (MLRMs) often suffer from hallucinations that stem not only from insufficient visual grounding but also from imbalanced allocation between perception and reasoning processes. Building upon recent interpretability findings suggesting a staged division of attention across layers, we analyze how this functional misalignment lea

Saneesh Babu, Boštjan Brešar, Aparna Lakshmanan S, Babak Samadi

The concept of mutual-visibility (MV) has been extended in several directions. A vertex subset $S$ of a graph $G$ is a $k$-distance mutual-visibility ($k$DMV) set if for any two vertices in $S$, there is a geodesic between them of length at most $k$ whose internal vertices are not in $S$. In this paper, we combine this with the MV coloring as follows. For an

Zhen Guan, Xianxian Cao

In this paper, a second-order linearized discontinuous Galerkin method on general meshes, which treats the backward differentiation formula of order two (BDF2) and Crank-Nicolson schemes as special cases, is proposed for solving the two-dimensional Ginzburg-Landau equations with cubic nonlinearity. By utilizing the discontinuous Galerkin inverse inequality a

Jens Boos, Hao Hu

We study the gravitational microlensing of various static and spherically symmetric non-singular black holes (and horizonless, non-singular compact objects of similar size). For pointlike sources we extend the parametrized post-Newtonian lensing framework to fourth order, whereas for extended sources we develop a ray tracing approach via a simple radiative t

Guan-Yan Yang, Tzu-Yu Cheng, Ya-Wen Teng, Farn Wanga

The integration of Large Language Models (LLMs) into computer applications has introduced transformative capabilities but also significant security challenges. Existing safety alignments, which primarily focus on semantic interpretation, leave LLMs vulnerable to attacks that use non-standard data representations. This paper introduces ArtPerception, a novel

Yihong Liu, Mingyang Wang, François Yvon, Hinrich Schütze

Multilingual large language models (LLMs) are expected to recall factual knowledge consistently across languages. However, the factors that give rise to such crosslingual consistency -- and its frequent failure -- remain poorly understood. In this work, we hypothesize that these inconsistencies may arise from failures in entity alignment, the process of mapp

Vanessa Sochat, Daniel Milroy

Cloud is now the leading software and computing hardware innovator, and is changing the landscape of compute to one that is optimized for artificial intelligence and machine learning (AI/ML). Computing innovation was initially driven to meet the needs of scientific computing. As industry and consumer usage of computing proliferated, there was a shift to sati

Lei Ni

A result of R. Hamilton asserts that any convex hypersurface in an Euclidian space with pinched second fundamental form must be compact. Partly inspired by this result, twenty years ago, in \cite{Ancient}, Remark 3.1 on page 650, the author formulated a problem asking if a complete Riemannian manifold with positively pinched Ricci curvature must be compact.

Christopher Chiu, Silviu Pitis, Mihaela van der Schaar

Clinical reasoning in medicine is a hypothesis-driven process where physicians refine diagnoses from limited information through targeted history, physical examination, and diagnostic investigations. In contrast, current medical benchmarks for large language models (LLMs) primarily assess knowledge recall through single-turn questions, where complete clinica

Nikolaus Salvatore, Hao Wang, Qiong Zhang

The performance of Large Language Models (LLMs) often degrades when crucial information is in the middle of a long context, a "lost-in-the-middle" phenomenon that mirrors the primacy and recency effects in human memory. We propose that this behavior is not simply a flaw indicative of information loss but an adaptation to different information retrieval deman

Sujoy Majumder, Nabadwip Sarkar

In the paper, we investigate the uniqueness problem of entire function concerning its derivative and shift and obtain two results. On of our result solves the open problem posed by Majumder et al. (On a conjecture of Li and Yang, Hiroshima Math. J., 53 (2023), 199-223) and the other result improves and generalizes the recent result due to Huang and Fang (Uni

Jinliang Zheng, Jianxiong Li, Zhihao Wang, Dongxiu Liu

Successful generalist Vision-Language-Action (VLA) models rely on effective training across diverse robotic platforms with large-scale, cross-embodiment, heterogeneous datasets. To facilitate and leverage the heterogeneity in rich, diverse robotic data sources, we propose a novel Soft Prompt approach with minimally added parameters, by infusing prompt learni

Vincent Schoenbach, Marvin Wiedemann, Raphael Memmesheimer, Malte Mosbach

Efficient physics simulation has significantly accelerated research progress in robotics applications such as grasping and assembly. The advent of GPU-accelerated simulation frameworks like Isaac Sim has particularly empowered learning-based methods, enabling them to tackle increasingly complex tasks. The PAL Robotics TIAGo++ Omni is a versatile mobile manip

Mykola Makhortykh, Aytalina Kulichkina, Kateryna Maikovska

This study examines elite-driven political communication on Telegram during the ongoing Russo-Ukrainian war, the first large-scale European war in the social media era. Using a unique dataset of Telegram public posts from Ukrainian and Russian policymakers (2019-2024), we analyze changes in communication volume, thematic content, and actor engagement followi

Haiyuan Feng, Rong-Jia Yang, Jinjun Zhang

Based on the classical limit of relativistic scattering amplitudes, we compute the coupling between a general super-renormalizable gravity and massive scalar particles. This allows us to derive the $D$-dimensional metric corrections at both tree-level and one-loop level-the latter containing the first calculation by using newly derived three-graviton Feynman

Donglin Huang, Yongyuan Li, Tianhang Liu, Junming Huang

Existing for audio- and pose-driven human animation methods often struggle with stiff head movements and blurry hands, primarily due to the weak correlation between audio and head movements and the structural complexity of hands. To address these issues, we propose VividAnimator, an end-to-end framework for generating high-quality, half-body human animations

Jiafang Song, Sandipan Pramanik, Abhirup Datta

In many applications, one must propagate parameter uncertainty from an earlier (upstream) analysis, available as samples, to subsequent (downstream) analyses without feedback. This problem is called cutting feedback or cut-Bayes, and the cut-posterior, the optimal posterior preserving information-flow constraints, is well characterized. However, sampling fro

Qin-Mei Li, Qi-Bin Sun, Sheng-Bang Qian, Fu-Xing Li

The origin of extragalactic fast X-ray transients (EFXTs) remains a fundamental open question in high-energy astrophysics. The Einstein Probe (EP) mission provides a transformative opportunity to investigate their nature. While mounting observations of EP-discovered EFXTs (EP-EFXTs) suggest a possible connection to long gamma-ray bursts (lGRBs), an in-depth

Pranay Jaiswal, Ivar S. Haugerud, Hidde D. Vuijk, Christoph A. Weber

Life relies on a sophisticated metabolic molecular machinery that turns over high-energy molecules to evolve complex macromolecules and assemblies. At the molecular origin of life, such machinery was absent, implying the need for simple yet robust physical mechanisms to harvest energy from the environment and perform chemical work or produce chemical power.

Sana Kordoghli, Abdelhakim Settar, Oumayma Belaati, Mohammad Alkhatib

This work contributes to advancing sustainable energy and waste management strategies by investigating the thermochemical conversion of food-based biomass through pyrolysis, highlighting the role of artificial intelligence (AI) in enhancing process modelling accuracy and optimization efficiency. The main objective is to explore the potential of underutilized

Andrey V. Sadakov, Vladimir A. Vlasenko, A. Yu. Levakhova, I. V. Zhuvagin

We present a comprehensive investigation of the field-dependent critical current density and pinning force, combined with a detailed analysis of the nanostructural defect landscape in single crystal of underdoped PrFeAs(O,F) superconductor. Our study demonstrates that for both in-plane and out-of-plane magnetic field orientations critical current density exh

Moona Kanwal, Muhammad Sami Siddiqui, Syed Anael Ali

Profiling gamers provides critical insights for adaptive game design, behavioral understanding, and digital well-being. This study proposes an integrated, data-driven framework that combines psychological measures, behavioral analytics, and machine learning to reveal underlying gamer personas. A structured survey of 250 participants, including 113 active gam

Jingwen Li, Zhiguang Cao, Yaoxin Wu, Tang Liu

Exploring machine learning techniques for addressing vehicle routing problems has attracted considerable research attention. To achieve decent and efficient solutions, existing deep models for vehicle routing problems are typically trained and evaluated using instances of a single size. This substantially limits their ability to generalize across different p

Xiang Chen, Xin Guan, Chuan-Qi He, Yan-Qing Ma

We present a next-to-next-to-leading-order calculation within the nonrelativistic QCD framework for the process of $e^{+}e^{-} \rightarrow Z^{\ast} \rightarrow J/\psi+J/\psi$ . We find that the NNLO contribution is 2-3 times larger than the next-to-leading-order contribution, which itself is already 3-5 times larger than the leading-order result. In the high

Junyan Ye, Hoi Ying Wong, Kyunghyun Park

We propose and analyze a continuous-time robust reinforcement learning framework for optimal stopping under ambiguity. In this framework, an agent chooses a robust exploratory stopping time motivated by two objectives: robust decision-making under ambiguity and learning about the unknown environment. Here, ambiguity refers to considering multiple probability

Isabelle Hupont, Marisa Ponti, Sven Schade

Citiverses hold the potential to support regulatory learning by offering immersive, virtual environments for experimenting with policy scenarios and technologies. This paper proposes a science-for-policy agenda to explore the potential of citiverses as experimentation spaces for regulatory learning, grounded in a consultation with a high-level panel of exper

Yishen Jiang, Xin Wang, Wenqiang Zhu, Ming Wei

The evolutionary mechanisms of cooperative behavior represent a fundamental topic in complex systems and evolutionary dynamics. Real-world collective interactions, particularly in multi-agent systems, are often characterized by behavior-dependent mechanism switching where the environmental state is endogenously shaped by group strategies. However, existing m

Shuai Guo, Dawei Liu, Tiantian Zheng

This paper critiques the limits of human-centered design in HCI, proposing a shift toward Interface-Centered Design. Drawing on Hookway's philosophy of interfaces, phenomenology, and embodied interaction, we created Umbilink, an umbilical interaction device simulating a uterine environment with tactile sensors and rhythmic feedback to induce a pre-subjectivi

Abdelrhman Elrawy, Emad A. Mohammed

3D Gaussian Splatting (3DGS) struggles in few-shot scenarios, where its standard adaptive density control (ADC) can lead to overfitting and bloated reconstructions. While state-of-the-art methods like FSGS improve quality, they often do so by significantly increasing the primitive count. This paper presents a framework that revises the core 3DGS optimization

Gonzalo Gomez-Nogales, Zhen Chen, Rosalie Martin, Elena Garces

In this work we analyze and address a fundamental restriction that blocks the reliable application of codimensional yarn-level and shell models with thickness, to simulate real-world woven and knit fabrics. As discretizations refine toward practical and accurate physical modeling, such models can generate non-physical contact forces with stencil-neighboring

Daniil Kochergin

We studied single-particle Anderson localization in ensembles of graphs that correspond to chiral and Bogoliubov-de Gennes (BdG) symmetry classes. For a random biregular bipartite graph with chiral symmetry, the density of states was found using the cavity approach. Calculating the fractal dimension shows the effects of disordered zero modes. For Bogoliubov-

Yuxiang Lai, Jike Zhong, Ming Li, Yuheng Li

Recent advances in large generative models have shown that simple autoregressive formulations, when scaled appropriately, can exhibit strong zero-shot generalization across domains. Motivated by this trend, we investigate whether autoregressive video modeling principles can be directly applied to medical imaging tasks, despite the model never being trained o

Sagnik Das, Yunfeng Jiang

We study certain equivariant deformation components of minimally elliptic surface singularities under finite group actions. Interesting examples include cyclic quotients of simple elliptic singularities and finite group quotients of cusp singularities, where the resulting quotients remain simple elliptic and cusp singularities, respectively. In cases where t

Samir Abdaljalil, Erchin Serpedin, Khalid Qaraqe, Hasan Kurban

Large language models (LLMs) often generate reasoning traces that appear coherent but rest on unsupported assumptions, leading to hallucinated conclusions. Prior work mainly addresses factual hallucinations or relies on post-hoc verification, leaving reasoning-induced hallucinations largely unaddressed. We propose Audit-of-Understanding (AoU), a framework th

Gualtiero Piccinini

The Language of Thought (LOT) hypothesis posits that at least some important cognitive processes involve language-like representations. These representations must be processed by appropriate hardware. Since the organ of biological cognition is the nervous system, whether biological cognition relies on a LOT depends on how neural hardware works. I distinguish

Jack Krolik, Jake Lynn, John Henry Rudden, Dmytro Vremenko

This study explores the application of deep learning techniques in the automated detection and segmentation of brain tumors from MRI scans. We employ several machine learning models, including basic logistic regression, Convolutional Neural Networks (CNNs), and Residual Networks (ResNet) to classify brain tumors effectively. Additionally, we investigate the

Stephen Ni-Hahn, Chao Péter Yang, Mingchen Ma, Cynthia Rudin

Artificial Intelligence (AI) for music generation is undergoing rapid developments, with recent symbolic models leveraging sophisticated deep learning and diffusion model algorithms. One drawback with existing models is that they lack structural cohesion, particularly on harmonic-melodic structure. Furthermore, such existing models are largely "black-box" in

Jiaxi Zhuang, Yaorui Shi, Jue Hou, Yunong He

Molecular property prediction is crucial for drug discovery and materials science, yet existing approaches suffer from limited interpretability, poor cross-task generalization, and lack of chemical reasoning capabilities. Traditional machine learning models struggle with task transferability, while specialized molecular language models provide little insight

Chaofang Shi, Zhongwen Li, Xiaoqi Li

System passwords serve as critical credentials for user authentication and access control when logging into operating systems or applications. Upon entering a valid password, users pass verification to access system resources and execute corresponding operations. In recent years, frequent password cracking attacks targeting system passwords have posed a seve

Houssam Zenati, Bariscan Bozkurt, Arthur Gretton

Adaptive experiments improve efficiency by adjusting treatment assignments based on past outcomes, but this adaptivity breaks the i.i.d.\ assumptions that underpin classical asymptotics. At the same time, many questions of interest are distributional, extending beyond average effects. Kernel treatment effects (KTE) provide a flexible framework by representin

Qingwen Li, Xiaohang Zhao, Xiao Han, Hailiang Huang

Intensive Care Unit (ICU) mortality prediction, which estimates a patient's mortality status at discharge using EHRs collected early in an ICU admission, is vital in critical care. For this task, predictive accuracy alone is insufficient; interpretability is equally essential for building clinical trust and meeting regulatory standards, a topic that has attr

Ziyu Zhou, Keyan Hu, Ling Zhang, Zhaohui Xue

Soil moisture (SM) plays a critical role in hydrological and meteorological processes. High-resolution SM can be obtained by combining coarse passive microwave data with fine-scale auxiliary variables. However, the inversion of SM at the temporal scale is hindered by the incompleteness of surface auxiliary factors. To address this issue, first, we introduce

Jian Zhu, Zhidong Lin, Wensheng Gan, Philip S. Yu

Discovering valuable insights from rich data is a crucial task for exploratory data analysis. Sequential pattern mining (SPM) has found widespread applications across various domains. In recent years, low-utility sequential pattern mining (LUSPM) has shown strong potential in applications such as intrusion detection and genomic sequence analysis. However, ex

Kaiyu Zhou, Somasekhara Goud Sontti, Joe Zhou, Xuehua Zhang

The massive volume of oil sands tailings has been one of the most challenging environmental issues. In this work, we experimentally explore a simple and effective approach to bitumen residue separation from a highly concentrated slurry flow of the artificial oil sands tailings. By utilizing microbubbles from in-situ catalytic decomposition of H2O2 at low con

Khalid M. Saqr

We revisit the classical Womersley solution for pulsatile viscous flow in a circular tube and reconstruct its full time-domain geometry from first principles. By combining harmonic decomposition with exact Bessel solutions, we derive a unified spectral spacetime analytical solution in which the instantaneous relationship between pressure gradient and velocit

Kangyang Luo, Yuzhuo Bai, Shuzheng Si, Cheng Gao

Coreference Resolution (CR) is a critical task in Natural Language Processing (NLP). Current research faces a key dilemma: whether to further explore the potential of supervised neural methods based on small language models, whose detect-then-cluster pipeline still delivers top performance, or embrace the powerful capabilities of Large Language Models (LLMs)

Boyan Wang

This paper reconstructs the derivation process from the Kerr metric to the adiabatic inspiral, transition, and plunge regimes, aiming to highlight the details and logical connections often overlooked in previous derivations. The first half provides a comprehensive roadmap for readers familiar with advanced general relativity to follow the entire logic of the

Sebastien Boucksom

These lecture notes are an introduction to the use of non-Archimedean geometry in the study of meromorphic degenerations of complex algebraic varieties. They provide a self-contained discussion of hybrid spaces, which fill in one-parameter degenerations with the associated non-Archimedean Berkovich space as a central fiber. The main focus is on the interplay

Zixuan Qin, Qingchen Yu, Kunlin Lyu, Zhaoxin Fan

Large Language Models (LLMs) have become foundational tools in natural language processing, powering a wide range of applications and research. Many studies have shown that LLMs share significant similarities with the human brain. Recent neuroscience research has found that a small subset of biological neurons in the human brain are crucial for core cognitiv

Baoxia Wang, Shuo Yang, Pengxuan Zhu

We investigate the discovery potential of the $3~{\rm TeV}$ Compact Linear Collider (CLIC) for a singlet vector-like bottom partner $B$ decaying via $B \to tW$. Focusing on the fully hadronic final state $B\bar{B} \to tW\,tW$, we reconstruct boosted top and $W$ candidates using large-$R$ Valencia jets, supplemented by a merging strategy for partially resolve

Dhrumil Bhatt, Siddharth Penumatsa, Vidushi Kumar

Flying Ad Hoc Networks (FANETs) present unique challenges due to high node mobility, dynamic topologies, and strict resource constraints. Existing routing protocols often optimize for a single metric, such as path length or energy, while neglecting the complex dependencies between network performance, security, and MAC layer efficiency. This paper introduces

Jinpeng Xu, Shuowen Zhang

In this paper, we investigate a novel multiple-input multiple-output (MIMO) radar system aided by phase shifter based polarization-reconfigurable antennas (PRAs). Specifically, a base station (BS) equipped with multiple PRAs at both the transmitter and the receiver aims to sense the unknown and random angular location parameter of a point target via sending

Xavier Blot

Wang recently constructed a quantization of the dispersionless KdV hierarchy using the Heisenberg vertex algebra. Independently, in joint work with Rossi, we obtained a quantization of the dispersionless KdV hierarchy as the trivial Cohomological Field Theory case of the meromorphic differential hierarchies. In this note, we prove that the two constructions

Zakk Heile, Peilin He, Jayson Tran, Alice Wang

Comparing probability measures modulo unknown rigid transformations is a central challenge in geometric data analysis. Classical optimal transport (OT) distances, including Wasserstein and sliced Wasserstein, are sensitive to rotations and reflections, whereas Gromov-Wasserstein (GW) and Procrustes-Wasserstein (PW) distances are invariant to isometries but c

Laura Sáez-Ortuño, Santiago Forgas-Coll, Massimiliano Ferrara

This work studies the feasibility of applying quantum kernel methods to a real consumer classification task in the NISQ regime. We present a hybrid pipeline that combines a quantum-kernel Support Vector Machine (Q-SVM) with a quantum feature extraction module (QFE), and benchmark it against classical and quantum baselines in simulation and with limited shall

Xuening Wu, Shenqin Yin, Yanlan Kang, Xinhang Zhang

Recursive self-modification is increasingly central in AutoML, neural architecture search, and adaptive optimization, yet no existing framework ensures that such changes are made safely. Godel machines offer a principled safeguard by requiring formal proofs of improvement before rewriting code; however, such proofs are unattainable in stochastic, high-dimens

Chuangchuang Tan, Xiang Ming, Jinglu Wang, Renshuai Tao

The rapid advancement of AI-generated content (AIGC) has enabled the synthesis of visually convincing images; however, many such outputs exhibit subtle \textbf{semantic anomalies}, including unrealistic object configurations, violations of physical laws, or commonsense inconsistencies, which compromise the overall plausibility of the generated scenes. Detect

Guowen Gong, Changmin Xiong, Lijun Zhu

Magnetic domain walls play a critical role in the nanoscale evolution of magnetic devices. Despite the early efforts, a complete understanding of the micromagnetic evolution of the width and the type of magnetic domain walls has still remained missing. Here, we report a combined analytical and micromagnetic simulation study and establish the scaling of the m

CMS Collaboration

Measurements in the highly Lorentz-boosted regime provoke increased interest in probing the Higgs boson properties and in searching for particles beyond the standard model at the LHC. In the CMS Collaboration, various boosted-object tagging algorithms, designed to identify hadronic jets originating from a massive particle decaying to $\mathrm{b\overline{b}}$

Greg Bodwin, Bernhard Haeupler, D Ellis Hershkowitz, Zihan Tan

Length-constrained expander decompositions are a new graph decomposition that has led to several recent breakthroughs in fast graph algorithms. Roughly, an $(h, s)$-length $\phi$-expander decomposition is a small collection of length increases to a graph so that nodes within distance $h$ can route flow over paths of length $hs$ while using each edge to an ex

Lars Fritz

How long a stochastic process survives before leaving a domain depends not only on its intrinsic dynamics but also on how it is observed. Classical first-passage theory assumes continuous monitoring with absorbing boundaries (``kill-on-touch''). In practice, however, measurements are often taken at discrete times. Between two checks, a trajectory may leave a

Jialin Sun, Yuchen Hu, Dean You, Yushu Du

Functional verification is a critical bottleneck in integrated circuit development, with CPU verification being especially time-intensive and labour-consuming. Industrial practice relies on differential testing for CPU verification, yet faces bottlenecks at nearly each stage of the framework pipeline: front-end stimulus generation lacks micro-architectural a

Ruize An, Richong Zhang, Zhijie Nie, Zhanyu Wu

Unsupervised text representation learning (TRL) is a fundamental task in natural language processing, which is beneficial for improving search and recommendations with the web's unlabeled texts. A recent empirical study finds that the high-quality representation aligns with the key token of the input text, uncovering the potential connection between represen

Yijie Xu, Huizai Yao, Zhiyu Guo, Pengteng Li

Large language models (LLMs) are increasingly deployed in specialized domains such as finance, medicine, and agriculture, where they face significant distribution shifts from their training data. Domain-specific fine-tuning can mitigate this challenge but relies on high-quality labeled data that is expensive and slow to collect in expertise-limited settings.

Clara C. de la Casa, Kelley M. Hess, Lourdes Verdes-Montenegro, Ralf Kotulla

We present a new catalog of 196 galaxies of the nearby Hydra I cluster out to $\sim$1.75$\rm r_{200}$, consisting of broad u,g,r,i,z along with narrowband H${\alpha}$ measurements. These deep optical images were obtained with the DECam camera (CTIO) and reach down to a surface brightness limit of $\mu( 3\sigma;10''\times10'')$=26.9 mag $\rm arcsec^2$ in the

Hyogo Hiruma, Hiroshi Ito, Hiroki Mori, Tetsuya Ogata

This study investigates the developmental interaction between top-down (TD) and bottom-up (BU) visual attention in robotic learning. Our goal is to understand how structured, human-like attentional behavior emerges through the mutual adaptation of TD and BU mechanisms over time. To this end, we propose a novel attention model $A^3 RNN$ that integrates predic

Ali I. Keskin, Mehmet Yaşar, K. Kurt

In this work, we study early-time inflation within a class of $f(R, \phi, X)$ gravity models under a constant-roll condition. Employing a generalized potential of the form $V(\phi)^\sigma$, we derive expressions for the spectral index $n_s$ and tensor-to-scalar ratio $r$, demonstrating that the inflationary dynamics are primarily governed by the parameter $\

Ruihao Li, Lizy K. John, Neeraja J. Yadwadkar

Memory allocators hide beneath nearly every application stack, yet their performance footprint extends far beyond their code size. Even small inefficiencies in the allocators ripple through caches and the rest of the memory hierarchy, collectively imposing what operators often call a "datacenter tax". At hyperscale, even a 1% improvement in allocator efficie

Gaurav Rai, Ojaswa Sharma

Sketch animation has emerged as a transformative technology, bridging art and science to create dynamic visual narratives across various fields such as entertainment, education, healthcare, and virtual reality. This survey explores recent trends and innovations in sketch animation, with a focus on methods that have advanced the state of the art. The paper ca