May 2025 arXiv papers — page 19

Michael A. Ramirez-Sierra, Thomas R. Sokolowski

Self-organization is a fundamental process of complex biological systems, particularly during the early stages of development. In the mammalian embryo, blastocyst formation exemplifies a self-organized system, involving the correct spatio-temporal segregation of three distinct cell fates: trophectoderm (TE), epiblast (EPI), and primitive endoderm (PRE). Desp

Haim Kaplan, Yishay Mansour, Kobbi Nissim, Uri Stemmer

We introduce a new Bayesian perspective on the concept of data reconstruction, and leverage this viewpoint to propose a new security definition that, in certain settings, provably prevents reconstruction attacks. We use our paradigm to shed new light on one of the most notorious attacks in the privacy and memorization literature - fingerprinting code attacks

Hongxiang Zhang, Hao Chen, Muhao Chen, Tianyi Zhang

Recent decoding methods improve the factuality of large language models (LLMs) by refining how the next token is selected during generation. These methods typically operate at the token level, leveraging internal representations to suppress superficial patterns. Nevertheless, LLMs remain prone to hallucinations, especially over longer contexts. In this paper

Xiangdong Zhang, Jiaqi Liao, Shaofeng Zhang, Fanqing Meng

Recent advancements in text-to-video (T2V) diffusion models have enabled high-fidelity and realistic video synthesis. However, current T2V models often struggle to generate physically plausible content due to their limited inherent ability to accurately understand physics. We found that while the representations within T2V models possess some capacity for ph

Peter David Fagan

Neural network inference typically operates on raw input data, increasing the risk of exposure during preprocessing and inference. Moreover, neural architectures lack efficient built-in mechanisms for directly authenticating input data. This work introduces a novel encryption method for ensuring the security of neural inference. By constructing key-condition

Mohamed Elaraby, Diane Litman

We introduce Argument Representation Coverage (ARC), a bottom-up evaluation framework that assesses how well summaries preserve salient arguments, a crucial issue in summarizing high-stakes domains such as law. ARC provides an interpretable lens by distinguishing between different information types to be covered and by separating omissions from factual error

Jiaran Ye, Zijun Yao, Zhidian Huang, Liangming Pan

Recent work suggests that large language models (LLMs) can perform multi-hop reasoning implicitly -- producing correct answers without explicitly verbalizing intermediate steps -- but the underlying mechanisms remain poorly understood. In this paper, we study how such implicit reasoning emerges by training transformers from scratch in a controlled symbolic e

Yuehaw Khoo, Mathias Oster, Yifan Peng

Diffusion models have emerged as a powerful framework in generative modeling, typically relying on optimizing neural networks to estimate the score function via forward SDE simulations. In this work, we propose an alternative method that is both optimization-free and forward SDE-free. By expanding the score function in a sparse set of eigenbasis of the backw

Juncheol Shin, Minsang Seok, Seonggon Kim, Eunhyeok Park

Model merging has emerged as a powerful technique for combining task-specific weights, achieving superior performance in multi-target domain adaptation. However, when applied to practical scenarios, such as quantized models, new challenges arise. In practical scenarios, quantization is often applied to target-specific data, but this process restricts the dom

Michael A. Ramirez-Sierra, Thomas R. Sokolowski

Luminogenesis, the formation of a fluid-filled cavity (lumen), is an essential process in early mammalian embryonic development, coinciding with the second cell-fate decision that differentiates the inner-cell-mass (ICM) into epiblast (EPI) and primitive endoderm (PRE) tissues. Based on experiments, the blastocyst lumen is hypothesized to influence EPI-PRE t

Halil Alperen Gozeten, M. Emrullah Ildiz, Xuechen Zhang, Hrayr Harutyunyan

Modern language models generate chain-of-thought traces by autoregressively sampling tokens from a finite vocabulary. While this discrete sampling has achieved remarkable success, conducting chain-of-thought with continuously-valued tokens (CoT2) offers a richer and more expressive alternative. Our work provides new theoretical guarantees and algorithms for

Matteo Lulli, Emily S. C. Ching

Tuning the interface properties of multiphase models is of paramount importance to the final goal of achieving a one-to-one matching with nucleation and cavitation experiments. The surface tension, at the leading order, and the Tolman length, at higher order, play a crucial role in the estimation of the free-energy barrier determining the experimentally obse

Zijun Yao, Yantao Liu, Yanxu Chen, Jianhui Chen

Recently evolved large reasoning models (LRMs) show powerful performance in solving complex tasks with long chain-of-thought (CoT) reasoning capability. As these LRMs are mostly developed by post-training on formal reasoning tasks, whether they generalize the reasoning capability to help reduce hallucination in fact-seeking tasks remains unclear and debated.

Zahir Shah, Athar A. Dar, Sikandar Akbar, Anjum Peer

In this study, we conducted a systematic analysis of long-term Fermi-LAT \gamma-ray data for a sample of blazars, including FSRQs, BL\,Lacs, and BCUs, to investigate their $\gamma$-ray variability. We focused on light curves binned in 3-, 7-, and 30-day intervals to assess the impact of binning, using data with TS >4 as a detection threshold. We calculated f

Melissa J. Smith, Ihsan E. Buker, Kristina M. Zierold, Lonnie Sears

The field of environmental epidemiology has placed an increasing emphasis on understanding the health effects of mixtures of metals, chemicals, and pollutants in recent years. Bayesian Kernel Machine Regression (BKMR) is a statistical method that has gained significant traction in environmental mixture studies due to its ability to account for complex non-li

Manuel Costa, Boris Köpf, Aashish Kolluri, Andrew Paverd

As AI agents become increasingly autonomous and capable, ensuring their security against vulnerabilities such as prompt injection becomes critical. This paper explores the use of information-flow control (IFC) to provide security guarantees for AI agents. We present a formal model to reason about the security and expressiveness of agent planners. Using this

Nathaniel Burgdorfer, Philippos Mordohai

We introduce an inference-time scene optimization algorithm utilizing triangle soup, a collection of disconnected translucent triangle primitives, as the representation for the geometry and appearance of a scene. Unlike full-rank Gaussian kernels, triangles are a natural, locally-flat proxy for surfaces that can be connected to achieve highly complex geometr

Ke Nie, J. Nofear Bradford, Supriya Mandal, Aayam Bista

Bosonic codes in superconducting resonators are a hardware-efficient avenue for quantum error correction and benefit from favorable error hierarchies provided by long-lived cavities compared to typical superconducting qubits. The required coupling to an ancillary control qubit, however, can negate these benefits by inducing highly detrimental effects such as

Yilin Xie, Shiqiang Zhang, Jixiang Qing, Ruth Misener

Graph Bayesian optimization (BO) has shown potential as a powerful and data-efficient tool for neural architecture search (NAS). Most existing graph BO works focus on developing graph surrogates models, i.e., metrics of networks and/or different kernels to quantify the similarity between networks. However, the acquisition optimization, as a discrete optimiza

Jeremy Butterfield

This book is a philosopher's introduction to the idea that our universe is just one of many universes. I present and assess three versions of the idea: one version from philosophy, and two from physics. In short, they are: all the logically possible worlds; all the branches of the quantum state, in an Everettian interpretation of quantum theory; and all the

Alfred Benedito, Germán Sierra

We present a graphical framework to represent entanglement in three-qubit states. The geometry associated with each entanglement class and type is analyzed, revealing distinct structural features. We explore the connection between this geometric perspective and the tangle, deriving bounds that depend on the entanglement class. Based on these insights, we con

Dashti A. Ali, Richard K. G. Do, William R. Jarnagin, Aras T. Asaad

In medical image analysis, feature engineering plays an important role in the design and performance of machine learning models. Persistent homology (PH), from the field of topological data analysis (TDA), demonstrates robustness and stability to data perturbations and addresses the limitation from traditional feature extraction approaches where a small chan

Bitap Raj Thakuria, Trishna Kalita, Javed Akhtar, Himangshu Prabal Goswami

We investigate a nonequilibrium donor-acceptor quantum rectifier system coupled to an anharmonic vibrational mode, treating the vibrational dynamics both as a two-level system and as multilevel system. The time-dependent Fischer information is then calculated by deriving a quantum master equation for the reduced system dynamics. We estimate some key rectifie

Daniel Luckhardt, Harsh Beohar, Clemens Kupke

A Markov decision process (MDP) is a state-based dynamical system capable of describing probabilistic behaviour with rewards. In this paper, we view MDPs as coalgebras living in the category of analytic spaces, a very general class of measurable spaces. Note that analytic spaces were already studied in the literature on labelled Markov processes and bisimula

John Halloran

The model context protocol (MCP) has been widely adapted as an open standard enabling the seamless integration of generative AI agents. However, recent work has shown the MCP is susceptible to retrieval-based "falsely benign" attacks (FBAs), allowing malicious system access and credential theft, but requiring that users download compromised files directly to

Chenhui Zuo, Guohao Lin, Chen Zhang, Shanning Zhuang

Coordinated human movement depends on the integration of multisensory inputs, sensorimotor transformation, and motor execution, as well as sensory feedback resulting from body-environment interaction. Building dynamic models of the sensory-musculoskeletal system is essential for understanding movement control and investigating human behaviours. Here, we repo

Dongyoung Kim, Sumin Park, Huiwon Jang, Jinwoo Shin

Large Vision-Language Models (LVLMs) have recently shown great promise in advancing robotics by combining embodied reasoning with robot control. A common approach involves training on embodied reasoning tasks related to robot control using Supervised Fine-Tuning (SFT). However, SFT datasets are often heuristically constructed and not explicitly optimized for

Xiao-Dong Lin, Long Zhang

The accurate determination of non-Hermitian (NH) topological invariants plays a central role in the study of NH topological phases. In this work, we propose a general framework for directly measuring NH topological invariants in even-dimensional systems with real line gaps through quench dynamics. Our approach hinges on constructing an auxiliary Hermitian ma

Xingyu Liu, Yubin Li, Guozhang Chen

A central idea in understanding brains and building artificial intelligence is that structure determines function. Yet, how the brain's complex structure arises from a limited set of genetic instructions remains a key question. The ultra high-dimensional detail of neural connections vastly exceeds the information storage capacity of genes, suggesting a compa

Boning Zhao, Xinnuo Li, Yutong Hu

Assessing student depression in sensitive environments like special education is challenging. Standardized questionnaires may not fully reflect students' true situations. Furthermore, automated methods often falter with rich student narratives, lacking the crucial, individualized insights stemming from teachers' empathetic connections with students. Existing

Enzo Doyen, Amalia Todirascu

A significant portion of the textual data used in the field of Natural Language Processing (NLP) exhibits gender biases, particularly due to the use of masculine generics (masculine words that are supposed to refer to mixed groups of men and women), which can perpetuate and amplify stereotypes. Gender rewriting, an NLP task that involves automatically detect

Xiang Xiang Wang, Tin-Yau Tam

We propose a new method for recognizing color image sets using quaternionic Grassmannians, which use the power of quaternions to capture color information and represent each color image set as a point on the quaternionic Grassmannian. We provide a direct formula to calculate the shortest distance between two points on the quaternionic Grassmannian, and use t

Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong

We present AutoSchemaKG, a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. Our system leverages large language models to simultaneously extract knowledge triples and induce comprehensive schemas directly from text, modeling both entities and events while employing conceptualization to organize inst

Griffin Dietz Smith, Dianna Yee, Jennifer King Chen, Leah Findlater

Identifying mistakes (i.e., miscues) made while reading aloud is commonly approached post-hoc by comparing automatic speech recognition (ASR) transcriptions to the target reading text. However, post-hoc methods perform poorly when ASR inaccurately transcribes verbatim speech. To improve on current methods for reading error annotation, we propose a novel end-

Francesco M. D. Pellegrino, Francesco Buccheri, G. G. N. Angilella

In this study, we investigate the localized surface plasmon modes of a sub-wavelength spherical nanoparticle composed of a Weyl semimetal, taking into account the axion modification of electrodynamics. We derive analytical solutions for dipole and quadrupole normal modes by employing the quasistatic approximation. The axion term leads to modified Fr\"ohlich

Chao Huang, Yuesheng Ma, Junxuan Huang, Susan Liang

Audio source separation is fundamental for machines to understand complex acoustic environments and underpins numerous audio applications. Current supervised deep learning approaches, while powerful, are limited by the need for extensive, task-specific labeled data and struggle to generalize to the immense variability and open-set nature of real-world acoust

Giacomo Bergami, Emma Packer, Kirsty Scott, Silvia Del Din

This paper offers a hybrid explainable temporal data processing pipeline, DataFul Explainable MultivariatE coRrelatIonal Temporal Artificial inTElligence (EMeriTAte+DF), bridging numerical-driven temporal data classification with an event-based one through verified artificial intelligence principles, enabling human-explainable results. This was possible thro

Jiaoda Li, Ryan Cotterell

Transformer-based language models (LMs) have achieved widespread empirical success, but their theoretical expressive power remains only partially understood. In this work, we analyze a restricted idealization of fixed-precision transformers with strict future masking, soft attention, and no positional encodings. We establish that this class of models is exac

Abhishek Agarwal, Lachlan P. Lindoy, Deep Lall, Sebastian E. de Graaf

Qubit noise and fluctuations of the noise over time are key factors limiting the performance of quantum computers. Characterising them with high temporal resolution is challenging due to multiple overlapping stochastic processes such as discrete jumps and continuous drifts. Hence, experiments typically probe individual sources of fluctuations rather than con

Zheyuan Yang, Lyuhao Chen, Arman Cohan, Yilun Zhao

In this work, we present the first study to explore inference-time scaling on table reasoning tasks. We develop and evaluate two post-training strategies to enable inference-time scaling: distillation from frontier model reasoning traces and reinforcement learning with verifiable rewards (RLVR). For distillation, we introduce a large-scale dataset of reasoni

Jiayuan Ye, Vitaly Feldman, Kunal Talwar

We study the fundamental problem of estimating an unknown discrete distribution $p$ over $d$ symbols, given $n$ i.i.d. samples from the distribution. We are interested in minimizing the KL divergence between the true distribution and the algorithm's estimate. We first construct minimax optimal private estimators. Minimax optimality however fails to shed ligh

Neta Glazer, David Chernin, Idan Achituve, Sharon Gannot

Recent advancements in Text-to-Speech (TTS) models, particularly in voice cloning, have intensified the demand for adaptable and efficient deepfake detection methods. As TTS systems continue to evolve, detection models must be able to efficiently adapt to previously unseen generation models with minimal data. This paper introduces ADD-GP, a few-shot adaptive

Amir Said, Hilmi E. Egilmez, Yung-Hsuan Chao

Recent video codecs with multiple separable transforms can achieve significant coding gains using asymmetric trigonometric transforms (DCTs and DSTs), because they can exploit diverse statistics of residual block signals. However, they add excessive computational and memory complexity on large transforms (32-point and larger), since their practical software

Chenhao Zheng, Jieyu Zhang, Mohammadreza Salehi, Ziqi Gao

Effective video tokenization is critical for scaling transformer models for long videos. Current approaches tokenize videos using space-time patches, leading to excessive tokens and computational inefficiencies. The best token reduction strategies degrade performance and barely reduce the number of tokens when the camera moves. We introduce grounded video to

Vladimír Kučera

This paper addresses the problem of row-by-row (or diagonal) decoupling of discrete-time linear multi-input multi-output systems with periodic time-varying coefficients using periodic state feedback. Previous solutions have tackled row-by-row decoupling using dynamic compensation for square systems and block-decoupling through regular state feedback for nons

Chang Yue, Niraj K. Jha

Neural networks (NNs) achieve outstanding performance in many domains; however, their decision processes are often opaque and their inference can be computationally expensive in resource-constrained environments. We recently proposed Differentiable Logic Networks (DLNs) to address these issues for tabular classification based on relaxing discrete logic into

Xiangcheng Zhang, Haowei Lin, Haotian Ye, James Zou

Classical search algorithms have long underpinned modern artificial intelligence. In this work, we tackle the challenge of inference-time control in diffusion models -- adapting generated outputs to meet diverse test-time objectives -- using principles from classical search. We propose a general framework that orchestrates local and global search to efficien

Matthew Harrison-Trainor

Given a countable structure $\mathcal{A}$, the degree spectrum of $\mathcal{A}$ is the set of all Turing degrees which can compute an isomorphic copy of $\mathcal{A}$. One of the major programs in computable structure theory is to determine which (upwards closed, Borel) classes of degrees form a degree spectrum. We resolve one of the major open problems in t

Caio Cheohen, Vinnícius M. S. Gomes, Manuela L. da Silva

The COVID-19 pandemic, caused by SARS-CoV-2, highlighted the critical need for accurate prediction of disease severity to optimize healthcare resource allocation and patient management. The spike protein, which facilitates viral entry into host cells, exhibits high mutation rates, particularly in the receptor-binding domain, influencing viral pathogenicity.

Jianbo Zhao, Taiyu Ban, Xiyang Wang, Qibin Zhou

Controllable trajectory generation guided by high-level semantic decisions, termed meta-actions, is crucial for autonomous driving systems. A significant limitation of existing frameworks is their reliance on invariant meta-actions assigned over fixed future time intervals, causing temporal misalignment with the actual behavior trajectories. This misalignmen

Koki Ho, Masafumi Isaji, Malav Patel, Kayla Garoust

The paper develops a novel design optimization framework and associated computational techniques for staged deployment optimization of complex systems under operational uncertainties. It proposes a local scenario discretization method that offers a computationally efficient approach to optimize staged co-deployment of multiple coupled subsystems by decouplin

Yannick De Bruijn, Erik Orvehed Hiltunen

Using the Bloch-Floquet theory, we propose an innovative technique to obtain the eigenvectors of tridiagonal k-Toeplitz operators. This method offers a more extensive and quantitative basis for describing localised eigenvectors beyond the non-trivial winding zone, yielding sharp decay bounds. The validity of our results is confirmed numerically in one-dimens

Armando Bellante, Martin Plávala, Alessandro Luongo

This paper proposes a family of permutation-invariant graph embeddings, generalizing the Skew Spectrum of graphs of Kondor & Borgwardt (2008). Grounded in group theory and harmonic analysis, our method introduces a new class of graph invariants that are isomorphism-invariant and capable of embedding richer graph structures - including attributed graphs, mult

Adam Peichl, Matěj Kuře, Wim Michiels, Tomáš Vyhlídal

The problem of non-collocated vibration absorption by a delayed resonator is addressed with emphasis on system fatigue resistance and energy efficiency of control actions. The analysis is performed for a system consisting of an arbitrary large series of flexibly linked single-degree-of-freedom masses. For the stage where the vibration of the target mass is f

Carolina Fortuna, Gregor Cerar, Blaz Bertalanic, Andrej Campa

The transition from traditional power grids to smart grids, significant increase in the use of renewable energy sources, and soaring electricity prices has triggered a digital transformation of the energy infrastructure that enables new, data driven, applications often supported by machine learning models. However, the majority of the developed machine learn

Uttaran Ghosh, Sarbari Guha

We examine the structure scalars constructed from the orthogonal splitting of the Riemann tensor for the spacetime metric describing the interior of a charged matter configuration undergoing dissipative collapse in the framework of $f(R,T)$ gravity (where $R$ and $T$ are the Ricci scalar and the trace of energy-momentum tensor, respectively), and also the wa

Guangtao Zeng, Maohao Shen, Delin Chen, Zhenting Qi

Language models (LMs) perform well on standardized coding benchmarks but struggle with real-world software engineering tasks such as resolving GitHub issues in SWE-Bench, especially when model parameters are less than 100B. While smaller models are preferable in practice due to their lower computational cost, improving their performance remains challenging.

Nitin Jha, Abhishek Parakh

Quantum networks use principles of quantum physics to create secure communication networks. Moving these networks off the ground using drones, balloons, or satellites could help increase the scalability of these networks. This article reviews how such aerial links work, what makes them difficult to build, and the possible solutions that can be used to overco

Federico Rottoli, Vincenzo Alba

We investigate off-diagonal matrix elements of local operators in integrable spin chains, focusing on the isotropic spin-$1/2$ Heisenberg chain ($XXX$ chain). We employ state-of-the-art Algebraic Bethe Ansatz results, which allow us to efficiently compute matrix elements of operators with support up to two sites between generic energy eigenstates. We conside

Shengyuan Liu, Boyun Zheng, Wenting Chen, Zhihao Peng

Endoscopic procedures are essential for diagnosing and treating internal diseases, and multi-modal large language models (MLLMs) are increasingly applied to assist in endoscopy analysis. However, current benchmarks are limited, as they typically cover specific endoscopic scenarios and a small set of clinical tasks, failing to capture the real-world diversity

N. N. Dattatreya

In this article, we study the asymptotic behavior of large solutions for a quasi-linear equation involving the p-Laplacian, defined on a sequence of finite cylindrical domains converging to an infinite cylinder. We demonstrate that the sequence of solutions converges locally, in the Sobolev norm, to a solution of the corresponding cross-sectional problem. Mo

Eitan Levin, Yuxin Ma, Mateo Díaz, Soledad Villar

Many modern learning tasks require models that can take inputs of varying sizes. Consequently, dimension-independent architectures have been proposed for domains where the inputs are graphs, sets, and point clouds. Recent work on graph neural networks has explored whether a model trained on low-dimensional data can transfer its performance to higher-dimensio

Radzim Sendyka, Christian Cabrera, Andrei Paleyes, Diana Robinson

This paper investigates the ability of large language models (LLMs) to recognise and solve tasks which have been obfuscated beyond recognition. Focusing on competitive programming and benchmark tasks (LeetCode and MATH), we compare performance across multiple models and obfuscation methods, such as noise and redaction. We demonstrate that all evaluated LLMs

Georgios Voulgaris

The accurate semantic segmentation of tree crowns within remotely sensed data is crucial for scientific endeavours such as forest management, biodiversity studies, and carbon sequestration quantification. However, precise segmentation remains challenging due to complexities in the forest canopy, including shadows, intricate backgrounds, scale variations, and

Robert Hankache, Kingsley Nketia Acheampong, Liang Song, Marek Brynda

As large language models (LLMs) are increasingly deployed in multi-turn dialogue and other sustained interactive scenarios, it is essential to understand how extended context affects their performance. Popular benchmarks, focusing primarily on single-turn question answering (QA) tasks, fail to capture the effects of multi-turn exchanges. To address this gap,

Linqiang Guo, Wei Liu, Yi Wen Heng, Tse-Hsun

Mobile Graphical User Interface (GUI) agents aim to autonomously complete tasks within or across apps based on user instructions. While recent Multimodal Large Language Models (MLLMs) enable these agents to interpret UI screens and perform actions, existing agents remain fundamentally reactive. They reason over the current UI screen but lack a structured rep

Youssef Mohamed, Noran Mohamed, Khaled Abouhashad, Feilong Tang

While Multi-Task Learning (MTL) offers inherent advantages in complex domains such as medical imaging by enabling shared representation learning, effectively balancing task contributions remains a significant challenge. This paper addresses this critical issue by introducing DeepChest, a novel, computationally efficient and effective dynamic task-weighting f

Xi Chen, Soham Jana, Christopher A. Metzler, Arian Maleki

Multilook coherent imaging is a widely used technique in applications such as digital holography, ultrasound imaging, and synthetic aperture radar. A central challenge in these systems is the presence of multiplicative noise, commonly known as speckle, which degrades image quality. Despite the widespread use of coherent imaging systems, their theoretical fou

Calum S. Skene, Keaton J. Burns

We present a general and automated approach for computing model gradients for PDE solvers built on sparse spectral methods, and implement this capability in the widely used open-source Dedalus framework. We apply reverse-mode automatic differentiation to symbolic graph representations of PDEs, efficiently constructing adjoint solvers that retain the speed an

Jing Lei

Modern data analysis and statistical learning are marked by complex data structures and black-box algorithms. Data complexity stems from technologies such as imaging, remote sensing, wearable devices, and genomic sequencing. At the same time, black-box models, especially deep neural networks, have achieved impressive results. This combination raises new chal

Matan Eilat

Let $M$ be a $C^{2}$-smooth Riemannian surface. A classical theorem in differential geometry states that the Gauss curvature function $K : M \to \mathbb{R}$ vanishes everywhere if and only if the surface is locally isometric to the Euclidean plane. We give an asymptotically sharp quantitative version of this theorem with respect to an isothermal coordinate c

Zifu Wang, Junyi Zhu, Bo Tang, Zhiyu Li

The application of rule-based reinforcement learning (RL) to multimodal large language models (MLLMs) introduces unique challenges and potential deviations from findings in text-only domains, particularly for perception-heavy tasks. This paper provides a comprehensive study of rule-based visual RL, using jigsaw puzzles as a structured experimental framework.

Bihai Sun, Callum Atkinson, Julio Soria

This paper investigates the turbulent--non-turbulent interface (TNTI) in a zero-pressure-gradient turbulent boundary layer (ZPG-TBL) using a novel, threshold-free method based on the uniform momentum zone (UMZ) concept. Requiring only planar streamwise velocity data, the method is directly applicable to experimental PIV and ensures consistent TNTI detection

Mrinmay Sen, Sidhant R Nair, C Krishna Mohan

This paper explores second-order optimization methods in Federated Learning (FL), addressing the critical challenges of slow convergence and the excessive communication rounds required to achieve optimal performance from the global model. While existing surveys in FL primarily focus on challenges related to statistical and device label heterogeneity, as well

Christian Schmidt, Heinrich Martin Overhoff

In medical image segmentation, limited external validity remains a critical obstacle when models are deployed across unseen datasets, an issue particularly pronounced in the ultrasound image domain. Existing solutions-such as domain adaptation and GAN-based style transfer-while promising, often fall short in the medical domain where datasets are typically sm

Ziyong Wang, Charith Abhayaratne

The explosive growth of digital images and the widespread availability of image editing tools have made image manipulation detection an increasingly critical challenge. Current deep learning-based manipulation detection methods excel in achieving high image-level classification accuracy, they often fall short in terms of interpretability and localization of

Yaru Hao, Li Dong, Xun Wu, Shaohan Huang

Reinforcement learning algorithms are fundamental to align large language models with human preferences and to enhance their reasoning capabilities. However, current reinforcement learning algorithms often suffer from training instability due to loose on-policy constraints and computational inefficiency due to auxiliary models. In this work, we propose On-Po

Sumbal Malik, Majid Khonji, Khaled Elbassioni, Jorge Dias

The rapid growth of e-commerce and the increasing demand for timely, cost-effective last-mile delivery have increased interest in collaborative logistics. This research introduces a novel collaborative synchronized multi-platform vehicle routing problem with drones and robots (VRP-DR), where a fleet of $\mathcal{M}$ trucks, $\mathcal{N}$ drones and $\mathcal

Zhiding Liu, Mingyue Cheng, Guanhao Zhao, Jiqian Yang

Time series forecasting plays a vital role in various real-world applications and has attracted significant attention in recent decades. While recent methods have achieved remarkable accuracy by incorporating advanced inductive biases and training strategies, we observe that instance-level variations remain a significant challenge. These variations--stemming

Davide Palitta, Valeria Simoncini

Sketching techniques have gained popularity in numerical linear algebra to accelerate the solution of least squares problems. The so-called $\varepsilon$-subspace embedding property of a sketching matrix $S$ has been largely used to characterize the problem residual norm, since the procedure is no longer optimal in terms of the (classical) Frobenius or Eucli

Nitin Jha, Abhishek Parakh

The Hilbert transform has been one of the foundational transforms in signal processing, finding it's way into multiple disciplines from cryptography to biomedical sciences. However, there does not exist any quantum analogue for the Hilbert transform. In this work, we introduce a formulation for the quantum Hilbert transform (QHT)and apply it to a quantum ste

Ramit Aditya, Razvan Bunescu, Smita Nannaware, Erfan Al-Hossami

A restaurant dinner or a hotel stay may lead to memorable experiences when guests encounter unexpected aspects that also match their interests. For example, an origami-making station in the waiting area of a restaurant may be both surprising and enjoyable for a customer who is passionate about paper crafts. Similarly, an exhibit of 18th century harpsichords

Adibvafa Fallahpour, Andrew Magnuson, Purav Gupta, Shihao Ma

Unlocking deep and interpretable biological reasoning from complex genomic data remains a major AI challenge limiting scientific progress. While current DNA foundation models excel at representing sequences, they struggle with multi-step reasoning and lack transparent, biologically meaningful explanations. BioReason addresses this by tightly integrating a DN

Tianyi Chu, Benjamin Wilfong, Timothy Koehler, Ryan M. McMullen

Fluid--fluid interfacial instability and subsequent fluid mixing are ubiquitous in nature and engineering. The hydrodynamic instability of fluid interfaces has long centered on the pressure gradient-driven long-wavelength Rayleigh--Taylor instability and the resonance-induced short-wavelength Faraday instability. However, neither instability alone can explai

Aaron Fainman, Stefan Vlaski

Algorithms for decentralized optimization and learning rely on local optimization steps coupled with combination steps over a graph. Recent works have demonstrated that using a time-varying sequence of matrices that achieves finite-time consensus can improve the communication and iteration complexity of decentralized optimization algorithms based on gradient

Jane Cleland-Huang, Pedro Antonio Alarcon Granadeno, Arturo Miguel Russell Bernal, Demetrius Hernandez

Small Uncrewed Aerial Systems (sUAS) are increasingly deployed as autonomous swarms in search-and-rescue and other disaster-response scenarios. In these settings, they use computer vision (CV) to detect objects of interest and autonomously adapt their missions. However, traditional CV systems often struggle to recognize unfamiliar objects in open-world envir

Benjamin Arnav, Pablo Bernabeu-Pérez, Nathan Helm-Burger, Tim Kostolansky

As AI models are deployed with increasing autonomy, it is important to ensure they do not take harmful actions unnoticed. As a potential mitigation, we investigate Chain-of-Thought (CoT) monitoring, wherein a weaker trusted monitor model continuously oversees the intermediate reasoning steps of a more powerful but untrusted model. We compare CoT monitoring t

Adam J. Czarnecki, Nikola L. Kolev, Patrick See, Nick Sullivan

As silicon-based devices continue to shrink to the nanoscale, traps at the Si-SiO$_2$ interface pose increasing challenges to device performance. These traps reduce channel carrier mobility and shift threshold voltages in integrated circuits, and introduce charge noise in quantum systems, reducing their coherence times. Knowledge of the precise location of s

Qingfeng Sun, Hui Wang, Yanxue Yu

Let $f$ be a fixed holomorphic primitive cusp form of even weight $k$, level $r$ and trivial nebentypus $\chi_r$. Let $q$ be an odd prime with $(q,r)=1$ and let $\chi$ be a primitive Dirichlet character modulus $q$ with $\chi\neq\chi_r$. In this paper, we prove an unconditional Selberg-type zero-density estimate for the family of twisted $L$-functions $L(s,

Maximilian Wackenhuth

In this article we obtain linear programming bounds for the maximal sphere packing density of commutative spaces. A special case of our results solves a conjecture by Cohn and Zhao on linear programming bounds for sphere packings in hyperbolic space.

William A. Smith, Fumiya Katsutani, Jin Hou, Hao Zhang

Layered hybrid perovskites containing larger organic cations have demonstrated superior environmental stability, but the presence of these insulating spacers also strengthens the exciton binding energy, which contributes to reduced carrier separation. The consequences of increased binding energy on device efficiency are still not fully documented, and bindin

Leonardo La Rocca, Francesco Corso, Francesco Pierri

As a leading online platform with a vast global audience, YouTube's extensive reach also makes it susceptible to hosting harmful content, including disinformation and conspiracy theories. This study explores the use of open-weight Large Language Models (LLMs), both text-only and multimodal, for identifying conspiracy theory videos shared on YouTube. Leveragi

Samo Hromadka, Kai Biegun, Lior Fox, James Heald

We introduce a novel unsupervised learning method for time series data with latent dynamical structure: the recognition-parametrized Gaussian state space model (RP-GSSM). The RP-GSSM is a probabilistic model that learns Markovian Gaussian latents explaining statistical dependence between observations at different time steps, combining the intuition of contra

Katy C. Molina, Joaquín Martínez-Minaya, Danilo Alvares, Vera D. Tomazella

Frailty survival models are widely used to capture unobserved heterogeneity among individuals in clinical and epidemiological research. This paper introduces a Bayesian survival model that features discrete frailty induced by the hurdle zero-modified power series (HZMPS) distribution. A key characteristic of HZMPS is the inclusion of a dispersion parameter,

Mark L. Turner, Earl T. Campbell, Ophelia Crawford, Neil I. Gillespie

Atomic, molecular and optical (AMO) approaches to quantum computing are promising due to their increased connectivity, long coherence times and apparent scalability. However, they have a significantly reduced cadence of syndrome extraction compared to superconducting devices, a potentially crippling slow-down given the substantial logical gate counts require

Yu Li, Jin Jiang, Jianhua Zhu, Shuai Peng

Handwritten Mathematical Expression Recognition (HMER) remains a persistent challenge in Optical Character Recognition (OCR) due to the inherent freedom of symbol layouts and variability in handwriting styles. Prior methods have faced performance bottlenecks by proposing isolated architectural modifications, making them difficult to integrate coherently into

Jiashuo Liu, Tianyu Wang, Henry Lam, Hongseok Namkoong

We introduce dro, an open-source Python library for distributionally robust optimization (DRO) for regression and classification problems. The library implements 14 DRO formulations and 9 backbone models, enabling 79 distinct DRO methods. Furthermore, dro is compatible with both scikit-learn and PyTorch. Through vectorization and optimization approximation t

Yiran Guo, Lijie Xu, Jie Liu, Dan Ye

Enhancing the reasoning capabilities of large language models effectively using reinforcement learning (RL) remains a crucial challenge. Existing approaches primarily adopt two contrasting advantage estimation granularities: token-level methods (e.g., PPO) aim to provide fine-grained advantage signals but suffer from inaccurate estimation due to difficulties

B. A. Nesterov

The paper studies the class of all metric spaces considered up to zero Gromov-Hausdorff distance between them. In this class, we examine clouds - classes of spaces situated at finite Gromov-Hausdorff distances from a reference space. We prove that all clouds are proper classes. The Gromov-Hausdorff distance is defined for clouds similarly with the case of th

Zenghui Yuan, Yangming Xu, Jiawen Shi, Pan Zhou

Model merging for Large Language Models (LLMs) directly fuses the parameters of different models finetuned on various tasks, creating a unified model for multi-domain tasks. However, due to potential vulnerabilities in models available on open-source platforms, model merging is susceptible to backdoor attacks. In this paper, we propose Merge Hijacking, the f