April 2026 arXiv papers — page 165

Ori Yonay, Tracy Hammond, Tianbao Yang

Self-supervised music foundation models underperform on key detection, which requires pitch-sensitive representations. In this work, we present the first systematic study showing that the design of self-supervised pretraining directly impacts pitch sensitivity, and demonstrate that masked contrastive embeddings uniquely enable state-of-the-art (SOTA) perform

Duncan Dauvergne, Lingfu Zhang

We prove that two half-space models in the KPZ universality class, exponential last-passage percolation and a family of Poisson-avoiding metrics generalizing colored TASEP, converge to a common scaling limit. This scaling limit is the directed landscape in half-space, a random directed metric in the half-plane indexed by a parameter which determines the stre

Naafis Ahnaf Shahed, Himanshu Mavani, Zhonglin He, Kai Huang

Magnetism in transition-metal systems emerges from exchange interactions that depend sensitively on carrier density. Yet leveraging this sensitivity to deliberately engineer exchange frustration and associated topological spin textures remains largely unexplored. Here, combining first-principles calculations with atomistic Monte Carlo simulations, we demonst

Vanesa Reinoso, Danilo Alvares, Jonathan Acosta, Isabelle S. Beaudry

Respondent-Driven Sampling (RDS) is a chain-referral design used for collecting data from hidden or hard-to-reach populations through their social networks. In RDS, respondents recruit their peers from the population of interest. As such, inference with RDS data commonly relies on estimated sampling probabilities derived from specific recruitment assumptions

Shaobo Liu, Haobo Xiong, Kai Liu, Yuna Lin

Parameter-efficient fine-tuning of pre-trained codecs is a promising direction in image compression for human and machine vision. While most existing works have primarily focused on tuning the feature structure within the encoder-decoder backbones, the adaptation of the statistical semantics within the entropy model has received limited attention despite its

Jialiang Li, Vasyl Yurchyshyn, Jason T. L. Wang, Haimin Wang

Solar eruptions, including flares and coronal mass ejections (CMEs), have a significant impact on Earth. Some flares are associated with CMEs, and some flares are not. The association between flares and CMEs is not always obvious. In this study, we propose a new deep learning method, specifically a hybrid neural network (HNN) that combines a vision transform

Yupeng Cao, Haohang Li, Weijin Liu, Wenbo Cao

Recent studies demonstrate that tool-calling capability enables large language models (LLMs) to interact with external environments for long-horizon financial tasks. While existing benchmarks have begun evaluating financial tool calling, they focus on limited scenarios and rely on call-level metrics that fail to capture trajectory-level reasoning quality. To

Alaa Elobaid

This paper provides a comprehensive evaluation of demographic and linguistic biases in omnimodal language models that process text, images, audio, and video within a single framework. Although these models are being widely deployed, their performance across different demographic groups and modalities is not well studied. Four omnimodal models are evaluated o

Siyuan Zhang, Chengde Qian, Xin Liu, Changliang Zou

To defend against Byzantine attacks in decentralized learning, most existing methods rely on robust aggregation rules to mitigate the influence of malicious machines. However, these strategies inherently introduce bias, leading to inexact convergence with non-vanishing steady-state errors. In this paper, we propose a strategic shift from passive aggregation

Tianxiao Sun, Noah Schwarzkopf

Managing stock efficiently remains a core issue in modern logistics, where companies must reconcile cost efficiency with dependable service despite unpredictable market conditions. Conventional models often overlook the direct connection between investment in inventory and overall financial performance. This study introduces a data-driven decision framework

Daichen Zou, Xudong Wang, Bin Qi

The structure of dark matter admixed neutron stars (DANSs) are investigated, adopting a non-annihilating self-interacting fermionic dark matter (DM) model, with a particular focus on the case of the light DM particle mass $m_D \in [10^{-10}, 1]$ GeV. The DANSs become DM-dominated configurations when $m_D <10^{-1}$ GeV, where a compact neutron star core becom

Alexander Petrov

The sensitivity of experiments searching for the electron electric dipole moment (eEDM) using the symmetric top molecules can be greatly enhanced by laser cooling. A detailed understanding of the Zeeman structure of the eEDM-sensitive levels is crucial for controlling systematic effects. We have developed a method for calculating the $g$-factors of $K$-doubl

Oleksiy Kapustyan, Olha Martynyuk, Oleksandr Misiats, Oleksandr Stanzhytskyi

We consider the stochastic thin-film equation with linear deterministic and stochastic It\^o perturbations. The existence of nonnegative weak martingale solutions on the semi-axis is established, and their asymptotic behavior as $t \to \infty$ is investigated. It is shown that in square mean the $L^\infty$ norm of the solution converges to the spatial mean v

Kening Wang, Di Wen, Yufan Chen, Ruiping Liu

Automatic sleep staging is a multimodal learning problem involving heterogeneous physiological signals such as EEG and EOG, which often suffer from domain shifts across institutions, devices, and populations. In practice, these data are also affected by noisy annotations, yet label-noise-robust multi-source domain generalization remains underexplored. We pre

Alexandra Irger, Ella Hugie, Minghao Guo, Simon Warchol

Visualization is central to scientific discovery, yet authoring tools remain split between information and scientific visualization, and expertise in one rarely transfers to the other. Large Language Model (LLM) based systems promise to bridge this gap through natural language, but current approaches generate code non-deterministically, with no guarantee of

Sajjad Lakzian

We present two characterizations of smooth compact Ricci flow solutions solely in terms of metrics and measures (one of them only works under positive scalar curvature along the flow); thus, provide weak formulations that are generalized to the singular setting in a straightforward manner. These formulations are achieved by weakly formulating super Ricci flo

Wilfried Youmbi Fotso, Xun Chen

We study delegated Bayesian persuasion: a principal incentivizes an intermediary to design information via outcome-contingent transfers, while the intermediary privately chooses the experiment subject to convex costs. We characterize first-best implementability through a pair of alignment conditions on the principal's and intermediary's payoff indices. A loc

Sean D Lawley

Many biophysical processes begin when the fastest searcher finds a target out of many random searchers, which is called an extreme or fastest first passage time (fFPT). In some models, (i) the fFPT vanishes logarithmically as the number of searchers grows, and (ii) the fFPT can be faster for subdiffusive search compared to normal diffusion. Though mathematic

Michael M. Dunham, Aina Palau, Nuria Huélamo, Eduard I. Vorobyov

The luminosities of protostars provide one of the only indirect methods of measuring their masses and mass accretion rates in their earliest stages of evolution. Accurate measurements of protostellar luminosities traditionally requires assembling complete spectral energy distributions (SEDs) from the near-infrared through millimeter wavelengths. In this work

Sajjad Lakzian

We present a generalization of the inverse mapping theorem, where variations of a weaker non-expansiveness property (referred to as property ${\sf A}$) replace the key $\mathsf{C}^1$ condition. We also obtain inverse mapping theorems that can be applied to non-smooth maps. Also as a by-product of the generalized inverse mapping theorem, we prove generalizati

Worasait Suwannik

We present a two-stage pipeline for AI-assisted improvement of published algorithm implementations. In the first stage, a large language model with research capabilities identifies recently published algorithms satisfying explicit experimental criteria. In the second stage, Claude Code is given a prompt to reproduce the reported baseline and then iterate an

Ashfak Yeafi, Parthaw Goswami, Md Khairul Islam, Ashifa Islam Shamme

Precise medical image segmentation is fundamental for enabling computer aided diagnosis and effective treatment planning. Traditional models that rely solely on visual features often struggle when confronted with ambiguous or low contrast patterns. To overcome these limitations, we introduce SwinTextUNet, a multimodal segmentation framework that incorporates

Kiran Thorat, Nicole Meng, Mostafa Karami, Caiwen Ding

IR drop analysis is essential in physical chip design to ensure the power integrity of on-chip power delivery networks. Traditional Electronic Design Automation (EDA) tools have become slow and expensive as transistor density scales. Recent works have introduced machine learning (ML)-based methods that formulate IR drop analysis as an image prediction proble

Trong Duong, Matthias Möller, Norbert Hosters

The Lattice Boltzmann method (LBM) is a well-established mesoscopic approach for simulating fluid dynamics by evolving particle distribution functions on discrete lattices. While the LBM is highly parallelizable on classical hardware, its translation to quantum algorithms is impeded by the collision process, which is intrinsically nonlinear and irreversible.

Mohammed Asad, Ajai Kumar Gautam, Priyanshu Dhiman, Rishi Raj Prajapati

Accurate apple detection in orchard images is important for yield prediction, fruit counting, robotic harvesting, and crop monitoring. However, changing illumination, leaf clutter, dense fruit clusters, and partial occlusion make detection difficult. To provide a fair and reproducible comparison, this study establishes a controlled benchmark for single-class

Qinjuan Wang, Shan Yang, Yongli Zhu

This paper introduces an LLM agent that automates power grid static analysis by converting natural language into MATPOWER scripts. The framework utilizes DeepSeek-OCR to build an enhanced vector database from MATPOWER manuals. To ensure reliability, it devises a three-tier error-correction system: a static pre-check, a dynamic feedback loop, and a semantic v

Tong Xie, Zuodong Zhang, Chao Yang, Yuan Wang

Deep neural networks (DNNs) have showcased remarkable performance across various tasks and are widely deployed on AI accelerators fabricated in advanced technology nodes for efficiency. As aging effects become more pronounced, timing and voltage guardbands are increasingly applied. Aging-aware adaptive voltage scaling (AVS), which adjusts supply voltage base

Samuel C. Buckner, Purnanand Elango

Contact-implicit trajectory optimization (CITO) enables the automatic discovery of contact sequences, but most methods rely on fine time discretization to capture all contact events accurately, which increases problem size and runtime while tying solution quality to grid resolution. We extend the recently proposed sequential convex programming (SCP) approach

Beichen Zheng, Lili Wen

In cross-section-space probability table constructions, reaction-channel levels are reconstructed on fixed total-subgroup nodes and probabilities. Although the standard full-matching reconstruction is uniquely determined, it does not in general preserve componentwise nonnegativity of the channel levels. We impose nonnegativity both for physical interpretabil

Hao Li, Man Fung Zhuo

Infrared small target detection still faces two persistent challenges: training instability from non-monotonic scale loss functions, and inadequate spatial attention due to generic convolution kernels that ignore the physical imaging characteristics of small targets. In this paper, we revisit both aspects. For the loss side, we propose a \emph{diff-based sca

Sasmita Harini S, Anshoo Tandon

As data-privacy regulations tighten and statistical models are increasingly deployed on sensitive human-sourced data, privacy-preserving linear regression has become a critical necessity. For the add-remove DP model, Kulesza et al. (2024) and Fitzsimons et al. (2024) have independently shown that the size of the dataset -- an important statistic for linear r

Mohammed Asad, Dinesh Kumar Vishwakarma

Gait recognition is a biometric modality that identifies individuals from their characteristic walking patterns. Unlike conventional biometric traits, gait can be acquired at a distance and without active subject cooperation, making it suitable for surveillance and public safety applications. Nevertheless, silhouette-based temporal models remain sensitive to

Yuchen Zou, Huikai Shao, Lihuang Fang, Zhipeng Xiong

Recently, synthetic palmprints have been increasingly used as substitutes for real data to train recognition models. To be effective, such synthetic data must reflect the diversity of real palmprints, including both style variation and geometric variation. However, existing palmprint generation methods mainly focus on style translation, while geometric varia

Federico Formica, Andrea Rota, Aurora Francesca Zanenga, Andrea Bombarda

Deep Neural Networks (DNNs) are widely used by engineers to solve difficult problems that require predictive modeling from data. However, these models are often massive, with millions or billions of parameters, and require substantial computational power, RAM, and storage. This becomes a limitation in practical scenarios where strict size and resource constr

Wei-Yang Wang, Shunshun Cao, Zhipeng Huang, Jiguang Lu

The radiative mechanism of coherent radio emission has remained an enigma since the discovery of pulsars, even the emergence of fast radio bursts (FRBs), which exhibit similarities to the single-pulse behavior of pulsars and have opened a new view for deciphering the long-standing mystery. Besides tremendous efforts in modelling, advanced facilities matter f

Alec Kercheval, Ololade Sowunmi

We study the long-only minimum variance (LOMV) portfolio under a one-factor covariance model with asset betas of arbitrary sign. We provide an explicit solution in terms of the set of active (positive weight) assets, and provide an explicit and computable characterization of the active set. As a corollary we resolve an open question of \citet{qi2021} concern

Yiyu Liu, Shuo Ye, Chao Hao, Zitong Yu

Video Camouflaged Object Detection (VCOD) is currently constrained by the scarcity of challenging benchmarks and the limited robustness of models against erratic motion dynamics. Existing methods often struggle with Motion-Induced Appearance Instability and Temporal Feature Misalignment caused by complex motion scenarios. To address the data bottleneck, we p

Marco Pollanen

A credit rating of AAA asserts near-certainty of repayment. This paper asks whether the pre-crisis information environment could have supported that assertion for structured products. Bayes' theorem implies that any reliability target requires a minimum level of statistical discrimination between instruments that will repay and those that will not. At struct

Yue Zhang, Wenpei Long, Junzhe Cao, Kun Xu

Simulating multiscale flows with moving boundaries, such as hypersonic multi-body separation and flows in micro-electro-mechanical systems (MEMS), requires robust numerical methods that couple mesh deformation with complex flow physics. This paper presents a hybrid overlapping moving-mesh technique developed within the unified gas-kinetic scheme (UGKS). To m

Santosh Poudel, Binil Aryal

We investigate the morphological and physical properties of ring galaxies residing within cosmic voids. Using void catalogs from VoidFinder, ring candidates identified via the Galaxy Zoo 2 decision tree, and morphological classifications from the Buta (2017) CVRHS based catalog, we analyze a sample of 341 void ring galaxies and find a radial preference, with

Utshab Kumar Ghosh, Ashish David, Shubham Chatterjee

Reproducibility must validate architectural robustness, not just numerical accuracy. We evaluate ColBERT-v2 and ConstBERT across five dimensions, finding that while ConstBERT reproduces within 0.05% MRR@10 on MS-MARCO, both models show a drop of 86-97% on long, narrative queries (TREC ToT 2025). Ablations prove this failure is architectural: performance plat

Guangchen Wang, Zhifeng Tang, Nan Yang, Xin Hao

In this paper, we propose a distributed optimization-learning framework for terahertz (THz) cell-free integrated sensing and communication (CF-ISAC) systems, termed Distributed Optimization-Learning with Graph Transformers (DOLG). We first formulate a highly non-convex joint scheduling and signal design problem for THz CF-ISAC systems, jointly optimizing acc

He Wang, Jinyang Yao

Boolean satisfiability (SAT) problem is of fundamental importance in computer science and many application domains. For Grover's algorithm, solving the SAT problem requires $\mathcal{O}(\sqrt{2^n})$ queries--where n denotes the number of logic variables in the problem. However, Grover's algorithm suffers from the Souffle problem: specifically, when the numbe

Louie Hong Yao, Yuhao Li, Shengchao Liu

Self-supervised representation learning is central to modern machine learning because it extracts structured latent features from unlabeled data and enables robust transfer across tasks and domains. However, it can suffer from representation collapse, a widely observed failure mode in which embeddings lose discriminative structure and distinct inputs become

Mohamed-Amine Lahmeri, Ata Khalili, Yujiao Liu, Anke Schmeink

Synthetic aperture radar (SAR) imaging can be exploited to enhance wireless communication performance through high-precision environmental awareness. However, integrating sensing and communication functionalities in such wideband systems remains challenging, motivating the development of a joint SAR and communication (JSARC) framework. We propose a dynamic t

D. V. Belskiy

In this paper, the inverse spectral problem is applied to the integration of a periodic Volterra chain. A generalization of the Lagrange interpolation formula has been made.

K. N. Singh, S. K. Maurya, A. Errehymy, A. Altaibayeva

We construct a gravitationally decoupled anisotropic strange star model using the minimal geometric deformation approach with a MIT bag equation of state and an additional source sector controlled by a deformation parameter $\beta$ and a radial perturbation scale $\Psi$ through $g(r)=\sin(\Psi r^{2})$. The resulting Einstein system is consistently split into

Yufan Zhu, Chao Jin, Khin Mi Mi Aung, Xiaokui Xiao

Transformer inference in machine-learning-as-a-service (MLaaS) raises privacy concerns for sensitive user inputs. Prior secure solutions that combine fully homomorphic encryption (FHE) and secure multiparty computation (MPC) are bottlenecked by inefficient FHE kernels, communication-heavy MPC protocols, and expensive FHE-MPC conversions. We present EncFormer

Qianhu Zhou, Kun Zhang

In this paper, we classify all $2$-blocks for which the defect groups are abelian and the inertial quotient has prime order. As a consequence, we prove that Brou\'e's abelian defect group conjecture holds for all blocks under consideration here.

Rhea Palak Bakshi, Thang T. Q. Lê, Józef H. Przytycki

We determine the structure of the Kauffman bracket skein module of the connected sum of two genus one handlebodies over the ring of Laurent polynomials $\mathbb Z[q^{\pm 1}]$, thereby proving a conjecture posed by the first and third authors. Our results lay the groundwork for computing the Kauffman bracket skein module of arbitrary connected sums over the r

Wei Liu, Anweshit Panda, Ujwal Pandey, Haven Cook

In the decentralized distributed learning, achieving fast convergence and low communication cost is essential for scalability and high efficiency. Adaptive gradient methods, such as Adam, have demonstrated strong practical performance in deep learning and centralized distributed settings. However, their convergence properties remain largely unexplored in dec

River Robles, Veronica Guo, David Cesar, Paris Franz

Attosecond pulses from free-electron lasers have opened the doors to atomic site-specific studies of bound electronic dynamics on their natural, sub-femtosecond timescales. Key to their success has been electron beam shaping techniques enabling the generation of sub-femtosecond current spikes with peak currents on the order of 10 kA. We demonstrate in an RF

Peng Gao, Liangyi Zhao

Let $\lambda(n)$ denote the Fourier coefficients of a fixed modular form and $h(n)$ a Steinhaus or Rademacher random multiplicative function. In this paper, we determine the order of magnitude of \[ \E|\sum_{n \leq x} h(n)\lambda(n)|^{2q} \] for real $x$, $q$ with $0 \leq q \leq 1$.

Ruoqi Jin

AI coding agents spend a substantial fraction of their tool calls on undirected codebase exploration. We investigate whether providing agents with formal architecture descriptors can reduce this navigational overhead. We present three complementary studies. First, a controlled experiment (24 code localization tasks x 4 conditions, Claude Sonnet 4.6, temperat

Russell K. Standish

A fragmented landscape reduces the impact of interspecies connectivity, leading to higher diversity levels than otherwise possible in a connected landscape. Reconnecting a previously fragmented landscape initiates an extinction event, preferentially weeding out more highly connected species. A sequence of fragmentation-coalescence events will drive the ecosy

Rumen Dimitrov, Valentina Harizanov, Henry J. Klatt, Keshav Srinivasan

We develop the foundations of effective ultraproducts of fields and their Galois groups using the methods of computability theory. These computability-theoretic analogs of ultraproducts are called cohesive products, since the role of an ultrafilter is played by a cohesive set. A set of natural numbers is cohesive if it is infinite and cannot be partitioned i

James Nguyen

We present a family of Kaczmarz-based preference learning algorithms for real-time personalized matchmaking in reciprocal recommender systems. Post-step L2 normalization, common in Kaczmarz-inspired online learners, induces exponential recency bias: the influence of the t-th interaction decays as eta^(n - t), reaching approximately 1e-6 after just 20 swipes

Laurent Bindschaedler

Microreboot enables fast recovery by restarting only the failing component, but in modern microservices naive restarts are unsafe: dense dependencies mean rebooting one service can disrupt many callers. Autonomous remediation agents compound this by actuating raw infrastructure commands without safety guarantees. We make microreboot practical by separating p

Jiun-Cheng Chen, Hsian-Hua Tseng

For a simple flop $X\dashrightarrow X'$, we construct a correspondence between genus $0$ descendant Gromov-Witten theories of $X$ and $X'$. We show that the Fourier-Mukai equivalence induced by $X\dashrightarrow X'$ is compatible, in a precise sense, with the descendant correspondence.

Marko Kostic, Halis Can Koyuncuoglu, Youssef N. Raffoul

In this paper, we present several new results concerning multiparameter $C$-semigroups. We introduce and systematically analyze the class of multiparameter $C$-cosine functions, providing several new structural results and applications to abstract multiparameter Cauchy problems of first/second order in locally convex spaces. We also consider automatic extens

Tyra Girdwood, Saba Kheirinejad, Parnian Kheirkhah Rahimabad, Brianna M. White

For patients experiencing cancer, nurse navigation can ease the burden of complex care by enhancing coordination of health services and patient outcomes. However, in under-resourced areas, trained nurse navigators may be limited or non-existent. In the United States, artificial intelligence (AI)-enabled digital health tools are increasingly available and may

Supriya Khadka, Sanchari Das

Advancements in augmented reality (AR) technologies offer immense potential for mobile experiences. However, most commercial and educational AR systems assume a baseline of predictable user behavior and stationary interaction. Preschoolers and children in early childhood education, specifically ages 3 to 8, are naturally erratic, physically dynamic, and pron

Yuyang Yao, Shaocheng Luo

The Intelligent Driver Model (IDM) is a cornerstone of Adaptive Cruise Control (ACC), valued for its interpretable parameters and effectiveness in car-following behavior modeling. However, its inherent conservatism leads to prolonged stabilization and reduced traffic efficiency, which have received limited attention. In this paper, we propose SEIDM (Safe and

Austin James, Xavier-Lewis Palmer, Lucas Potter, Celisha Oscar

Automated insulin delivery (AID) and artificial pancreas systems increasingly serve as safety-critical cyber-physical technologies in clinical care, integrating sensors, algorithms, software, and insulin-delivery hardware to automate a life-sustaining therapy. While regulated commercial systems are supported by formal approval pathways, manufacturer governan

Shuai Li, Chen Huang, Duanyu Feng, Wenqiang Lei

To augment Large Language Models (LLMs) for multi-hop question answering, a mainstream solution within Graph Retrieval Augmented Generation (GraphRAG) leverages lightweight retrievers to efficiently extract information from a given Knowledge Graph (KG). However, existing methods often overlook the inherent challenge of sparse semantic information in graphs.

Miles Q. Li, Benjamin C. M. Fung, Boyang Li, Heba Ismail

The rapid deployment of LLM-based autonomous agents has introduced safety risks that extend far beyond traditional LLM concerns, prompting a proliferation of safety benchmarks since late 2023. However, these benchmarks have developed independently, with inconsistent threat models, incompatible metrics, and overlapping yet incomplete risk coverage. We present

Navapat Nananukul, Mayank Kejriwal

Clinical diagnosis requires answers that are accurate, verifiable, and explicitly grounded in official guidelines. While large language models excel at natural language processing, their tendency to hallucinate undermines their utility in high-stakes medical contexts where precision is essential. Existing retrieval-augmented generation (RAG) systems treat al

Peter Ván, Réka Somogyfoki

In a recent work, Somogyfoki et al. (J. Non-Equilib. Thermodyn. 50, 59-76, 2025) analysed the linear stability of homogeneous equilibrium in third-order non-Fourier heat conduction within the framework of non-equilibrium thermodynamics with internal variables. They identified a stability condition, their equation (49), which could not be derived from the sta

Meng&#39;en Qin, Yu Song, Quanling Zhao, Xiaodong Yang

Learning multi-scale representations is the common strategy to tackle object scale variation in dense prediction tasks. Although existing feature pyramid networks have greatly advanced visual recognition, inherent design defects inhibit them from capturing discriminative features and recognizing small objects. In this work, we propose Asymptotic Content-Awar

Divya Ahuja, Jaya NN Iyer

We introduce a refinement of persistent homology that detects simple-homotopy-theoretic phenomena invisible to homology. Given a filtered simplicial complex, we define the Morse complexity profile as the minimal number of critical simplices at each filtration level. We prove that this profile is invariant under levelwise simple-homotopy equivalence and detec

Simon Wiesheier, Miguel Angel Moreno-Mateos, Paul Steinmann

Invariant-based models for incompressible isotropic hyperelasticity are typically formulated as functions of the first and second invariants, $W = W(\bar{I}_1, \bar{I}_2)$. A widely used class of models employs separable representations of the form $W(\bar{I}_1, \bar{I}_2) = W_1(\bar{I}_1) + W_2(\bar{I}_2)$, which enable efficient calibration and straightfor

Kazuki Ikeda

Quantum energy teleportation (QET) has been studied in continuum field theory and in lattice many-body systems, but the relation between the two within a single interacting model is still not well understood. To address this question, we consider the massive Thirring model, equivalently the sine--Gordon theory. In the continuum, the trigonometric measurement

Z. -H. Peng, S. Benetti, Y. -Z. Cai, A. Pastorello

We present optical photometric and spectroscopic observations of the Type~IIb supernova (SN)~2017ati. It reached the maximum light at about 27~d after the explosion and the light curve shows a broad, luminous peak with an absolute $r$-band magnitude of $M_{r} = -18.48 \pm 0.16$~mag. At about 50~d after maximum light, SN~2017ati exhibits a decline rate close

Mayank, Dawood Kothawala

Geodesic flows emanating from an arbitrary point $\mathscr{P}$ in a manifold $\mathscr{M}$ carry important information about the geometric properties of $\mathscr{M}$. These flows are characterized by Synge&#39;s world function and van Vleck determinant - important bi-scalars that also characterize quantum description of physical systems in $\mathscr{M}$. If

Guo Zhang, Yuanye Zhu, Ying Li

We propose a fault-tolerant quantum computation scheme that is broadly applicable to quantum low-density parity-check (qLDPC) codes. The scheme achieves constant qubit overhead and a time overhead of $O(d^{a+o(1)})$ for any $[[n,k,d]]$ qLDPC code with constant encoding rate and distance $d = Ω(n^{1/a})$. For good qLDPC codes, the time overhead is minimized a

Saverio Pasqualoni, Tommaso Bonato, Lorenzo Piarulli, Torsten Hoefler

Collective operations are cornerstones of both HPC applications and large-scale AI training and inference, yet benchmarking them in a systematic and reproducible way remains difficult on modern systems due to the complexity of their hardware and software stacks. Existing suites primarily report end-to-end timings and offer limited support for controlled algo

Sanguo Zhu

Let $E$ be a Lalley-Gatzouras carpet determined by a set of contractive affine mappings $\{f_{ij}\}_{(i,j)\in G}$. We study the asymptotics of quantization error for the self-affine measures $μ$ on $E$. We prove that the upper and lower quantization coefficient for $μ$ are both bounded away from zero and infinity in the exact quantization dimension. This sig

Alfredo Bermúdez, Iago Padín

With the growing global emphasis on sustainability and the implementation of contemporary environmental policies, photovoltaic (PV) generation is playing an increasingly important role in modern power systems, while its intrinsic variability poses challenges for real-time operation and electricity market participation. This paper proposes a continuous-time s

Yifu Cai, Xin Ren, Taotao Qiu, Mingzhe Li

Observations from DESI DR2 are challenging the $Λ$CDM paradigm by suggesting that the equation-of-state parameter of dark energy evolves across $w = -1$, a phenomenon known as the Quintom scenario. Inspired by this development, we present a staged review of Quintom cosmology including its theoretical foundations, observational supports, and implications as w

Divyum Sharma, L. Singhal

Let $α$ be a fixed quadratic irrational. Consider the Diophantine equation \[ y^a\ =\ q_{N_1} + \cdots + q_{N_K},\quad N_1 \geq \cdots \geq N_{K} \geq 0,\quad a, y \geq 2 \] where $(q_N)_{N\,\geq\,0}$ is the sequence of convergent denominators to $α$. We find two effective upper bounds for $y^a$ which depend on the Hamming weights of $y$ with respect to its

Krzysztof Urbanowski

Analyzing Heisenberg--Robertson (HR) and Schrödinger uncertainty relations we found, that there can exist a large set of states of the quantum system under considerations, for which the lower bound of the product of the standard deviations of a pair of non--commuting observables, $A$ and $B$, is zero, and which differ from those described in the literature.

Kazuki Okamura

We consider the conjugate equation driven by two families of finite maps on the unit interval satisfying a compatibility condition. This framework contains de Rham&#39;s functional equations. We give sufficient conditions for singularity of the solution with quantitative estimates in the case where the equation is driven by a family of non-affine maps and a

Sourav Chatterjee

The construction of non-Abelian Euclidean Yang-Mills theories in dimension four, as scaling limits of lattice Yang-Mills theories or otherwise, is a central open question of mathematical physics. This paper takes the following small step towards this goal. In any dimension $d\ge 2$, we construct a scaling limit of $\mathrm{SU}(2)$ lattice Yang-Mills theory c

Yong-Guang Zheng, Ying-Chao Shen, Wei-Yong Zhang, An Luo

Nonequilibrium dynamics of quantum many-body systems is challenging for classical computing, providing opportunities for demonstrating practical quantum computational advantage with analogue quantum simulators. Owing to the intimate connection with a random matrix ensemble, it is proposed to be classically intractable to sample the driven thermalized many-bo

Roland Leißa, Johannes Griebler

Dominance is a fundamental concept in compilers based on static single assignment (SSA) form. It underpins a wide range of analyses and transformations and defines a core property of SSA: every use must be dominated by its definition. We argue that this reliance on dominance has become increasingly problematic -- both in terms of precision and applicability

Samuel Jaeger, Calvin Ibeneye, Aya Vera-Jimenez, Dhrubajyoti Ghosh

The rapid adoption of large language models has introduced a new class of AI-generated fake news that coexists with traditional human-written misinformation, raising important questions about how these two forms of deceptive content differ and how reliably they can be distinguished. This study examines linguistic, structural, and emotional differences betwee

Thang Pham, Arindom Nag

Vapor liquid solid (VLS) growth is one of the most widely used routes for nanowire synthesis. For conventional semiconductor nanowires, here we refer to group IV and III-V systems, decades of work have established VLS growth across diverse vapor-phase methods and enabled substantial control over morphology, crystal phase, and structural modulation. In contra

Rubén Gordillo-Hachuel, Erik Torrontegui, Cristina de Dios, Ricardo Puebla

Quantum correlations can be harnessed to improve the precision in parameter estimation beyond classical capabilities. Under a standard interferometric or rotation protocol, it is well established that the optimal single-mode Gaussian state is a standard squeezed vacuum, which enables Heisenberg limited precision. In this work, we investigate the potential me

Prasad Nimantha Madusanka Ukwatta Hewage, Midhun Chakkravarthy, Ruvan Kumara Abeysekara

The barren plateau phenomenon; where cost function gradients vanish exponentially with system size; remains a fundamental obstacle to training variational quantum circuits (VQCs) at scale. We demonstrate, both theoretically and numerically, that embedding partial differential equation (PDE) constraints into the VQC loss function provides a natural and effect

Cordell Mazzetti, Sayam Sethi, Rich Rines, Pranav Gokhale

Recent advancements in quantum computing have enabled practical use of quantum error detecting and correcting codes. However, current architectures and future proposals of quantum computer design suffer from limited qubit counts, necessitating the use of high-rate codes. Such codes, with their code parameters denoted as $[[n, k, d]]$, have more than $1$ logi

Tzu Ling Liu, Ian Stavness, Mrigank Rochan

Video Unsupervised Domain Adaptation (VUDA) poses a significant challenge in action recognition, requiring the adaptation of a model from a labeled source domain to an unlabeled target domain. Despite recent advances, existing VUDA methods often fall short of fully supervised performance, a key reason being the prevalence of static and uninformative backgrou

Noah Wisdom

In previous work, the author and David Chan computed the algebraic $K$-theory of the constant $C_2$-Tambara field with value the field with two elements, using a method which fails at odd primes. Herein we make progress towards the corresponding odd primary computations using a completely new idea. Particularly, we show that the $K$-theory groups of any cons

Michele Peruzzi

In Gaussian graphical models, conditional independence and partial correlations are natural inferential targets for understanding direct relationships in multivariate data. No comparable framework exists for spatial processes, where multivariate analysis defaults to modeling unconditional cross-covariance structure, even when direct relationships remain of s

Renjini R. Nair, Damian K. Kowalczyk, Marco Gaudesi, Chhaya Methani

Many applications today use large language models for code generation; however, production systems have strict latency requirements that can be difficult to meet with large models. Small language models with a few billion parameters are resource efficient but may suffer from limited reasoning, hallucinations, or poor retention of longer context. Fine tuning

Vojtech Honek, Pok Man Lo, Boris Tomasik

We calculate the baryon number--electric charge susceptibility at non-vanishing baryo-chemical potential within the model of hadron gas where pion-nucleon interaction is accounted for by the $S$-matrix formalism. The susceptibility is largely increased when the chemical potential grows within a phenomenologically relevant interval. The results are then evalu

Jonathan Ansari

Recently studied dependence measures take values in [0,1], where 0 characterizes independence of X and Y, and 1 characterizes perfect functional (not necessarily monotone) dependence of Y on X. The most prominent example is Chatterjee&#39;s rank correlation, which is based on the concept of conditional independence. In contrast, we show that dependence measu

Rishad Shahmurov

We study how the orientation and elliptic depth of swirl-generated vorticity response affect singularity formation in axisymmetric model equations. For a sign-reversed Hou--Li model on the whole line, the dynamics factorize into two real Riccati--diffusion channels. For every viscosity $ν>0$, every nontrivial smooth datum in an infinite-dimensional cone of e

Yimin Zhu, Lincoln Linlin Xu

Although hyperspectral image (HSI) classification is critical for supporting various environmental applications, it is a challenging task due to the spectral-mixture effect, the spatial-spectral heterogeneity and the difficulty to preserve class boundaries and details. This letter presents a novel unmixing-guided spatial-spectral Mamba with clustering tokens

Gonzalo Arranz, Adrian Lozano-Duran

We discover unified scaling laws for the mean wall shear stress and the mean velocity profile in turbulent boundary layers subject to favorable and adverse mean pressure gradients-including flows with separation and reattachment. We use the information-theoretic irreducible error theorem to identify, among all dimensionally consistent combinations, the dimen

Anna Marie Rezk, Patrizia Di Campli San Vito, Ayah Soufan, Graham McDonald

Search engines that present users with a ranked list of search results are a fundamental technology for providing public access to information. Evaluations of such systems are typically conducted by domain experts and focus on model-centric metrics, relevance judgments, or output-based analyses, rather than on how accountability, harm, or trust are experienc