April 2026 arXiv papers — page 48

Khalid Zaman, Masashi Unoki

Human-imitated speech poses a greater challenge than AI-generated speech for both human listeners and automatic detection systems. Unlike AI-generated speech, which often contains artifacts, over-smoothed spectra, or robotic cues, imitated speech is produced naturally by humans, thereby preserving a higher degree of naturalness that makes imitation-based spe

Kevin Riehl, Anastasios Kouvelas, Michail A. Makridis

Reliable benchmarking is essential for progress in intelligent traffic control research. While microscopic traffic simulators such as SUMO enable detailed modelling of individual vehicle interactions, many published control studies still rely on single-run evaluations and project-specific baseline implementations, limiting reproducibility and comparability.

Xudong Jiang, Mingshan Loo, Hanchen Yang, Wengen Li

Accurate long-term time series forecasting (LTSF) requires the capture of complex long-range dependencies and dynamic periodic patterns. Recent advances in frequency-domain analysis offer a global perspective for uncovering temporal characteristics. However, real-world time series often exhibit pronounced cross-domain heterogeneity where variables that appea

Max Hartman, Vidhata Jayaraman, Moulik Choraria, Yash Savani

Distillation via sampling reasoning traces exposes closed-source frontier models to adversarial third parties who can bypass their guardrails and misappropriate their capabilities. Antidistillation methods aim to address this by poisoning reasoning traces to hinder student model learning while preserving teacher performance. However, current methods overlook

Tianci Zhang, Aobo Liu, Zhengchuan Chen, Zhong Tian

This paper studies information timeliness in multi-source wireless Internet of Things (IoT) status updating systems under a truncated Automatic Repeat reQuest (ARQ) protocol. We propose a source-aware truncated ARQ (SATARQ) scheme that allows differentiated maximum transmission times (MTTs) tailored to different sources. This work focuses on a wireless syste

Amelia Ford, Harry Desmond, Deaglan J Bartlett, Pedro G Ferreira

The galaxy luminosity and stellar mass function (LF, SMF), and halo mass function (HMF), are fundamental quantities in astrophysics and crucial inputs to a range of astrophysical and cosmological analyses. They are typically parametrised by fitting functions that have been chosen "by eye" to match observed or simulated data. We apply symbolic regression -- s

Harry Lu

Diffusion language models expose an explicit denoising trajectory, making it possible to ask when different kinds of information become measurable during generation. We study three independent 32-step runs of LLaDA-8B-Base on masked WikiText-103 text, each with 1{,}000 probe-training sequences and 200 held-out evaluation sequences. From saved trajectories, w

Juan P. Aguilera, David Fernández-Duque, Leonardo Pacheco

We develop polytopological semantics for various constructive, intuitionistic, and G\"odel--Dummett variations of $\mathsf{K4}$ and $\mathsf{S4}$. In our models, intuitionistic and modal operators are interpreted via various topologies over a single set, equipped with either the closure or derivative operators. We identify regularity conditions to ensure tha

A. Besnard, G. Conenna, V. Sauvage, K. Karatsu

Kinetic Inductance Detectors (KIDs) are a promising technology for future space missions, where exposure to high-energy particles may affect detector performance. In this work, we irradiated two types of KID arrays, absorber coupled and antenna coupled, with high-energy protons at 120 mK. We used a total dose equivalent to approximately 10 years of operation

Cédric Arhancet, Lei Li

We investigate the metric structure of nonassociative $\mathrm{L}^p$-spaces associated with tracial $\mathrm{JW}^*$-algebras. While noncommutative $\mathrm{L}^p$-spaces arising from von Neumann algebras enjoy a unique natural norm, the situation in the Jordan setting is more subtle. We compare two canonical definitions: the interpolation norm, arising from t

Xiao Yang, Yuni Lai, Gaolei Li, Jun Wu

Semantic Communication (SC) backdoor attacks aim to utilize triggers to manipulate the system into producing predetermined outputs via backdoored shared knowledge. Current SC backdoors adopt monomorphic paradigms with single attack target, which suffers from limited attack diversity, efficiency, and flexibility in heterogeneous downstream scenarios. To overc

Ratul Ali

Organisations operating within information-intensive environments face intensifying pressure to formalise the governance of information security. The ISO/IEC 27001:2022 standard provides a globally recognised framework for establishing, implementing, maintaining, and continually improving an Information Security Management System (ISMS). This article analyse

Tihomir G. Tenev, Nayden P. Nedev, Nikolay V. Vitanov

We investigate the performance of Grover's algorithm on three different generations of IBM Heron QPUs. On Heron family of IBM QPUs the success probabilities for three, four and five qubits without dynamical decoupling is better than results reported for previous generations of QPUs. The success probability as function of number of iterations of Grover operat

Philipp Sürig

We consider on Riemannian manifolds the Leibenson equation $$\partial _{t}u=\Delta _{p}u^{q}.$$ We prove that a certain upper bound for weak solutions of this equation is equivalent to a euclidean-type Sobolev inequality.

Yaru Liu, Michael K. Ng, Yiqi Gu

This paper investigates the deep learning optimization problem with softmax cross-entropy loss. We propose a layer separation strategy to alleviate the strong nonconvexity encountered during training deep networks. For cross-entropy models with fully connected and convolutional neural networks, we introduce auxiliary variables associated with hidden layer ou

Flavien Mabilat

We will show in this text that, for all non-negative integers $n$ and $l$, the following equality is verified: \[\sum_{i=0}^{l} {n-i \choose i}{l+i \choose 2i+1}=\sum_{i=0}^{l} {n-i \choose i-1}{l+i \choose 2i}.\] We will first address the case where $l \leq n$, for which both sums contain only classical binomial coefficients. Then, we will consider the gene

Vladislav Kurichenko, Parul Rani, Björgvin Hjörvarsson

Profound finite size effects are observed in both the moment and ordering temperature in thin Co$_{70}$Zr$_{30}$ layers. The results are consistent with the presence of interface regions with reduced magnetic interactions and moment. The extension of this region is determined to be around 1 nm thick at each interface. Above and near the apparent critical tem

Benjamin Hopton, Alexander Abbey, David Johnson, Daniele Baldolini

Magneto-optical traps are central to atomic and molecular quantum technologies and precision tests of fundamental physics, where both sensitivity and bandwidth scale strongly with atom number and loading rate. We demonstrate that employing multiple, closely spaced optical frequency components in the cooling light of a $^{87}$Rb magneto-optical trap -- withou

Vasudevarao Allu, Shobhit Kumar

Let $\mathcal{A}$ denote the class of normalized analytic functions $f$ in the open unit disk defined as $ \mathbb{D}:=\{z\in\mathbb{C}:|z|<1\} $ with $f(0)=0$ and $f'(0)=1$. A function $f\in\mathcal{A}$ is said to be starlike if $f(\mathbb{D})$ is starlike domain. By using the Bernstein polynomial method to obtain the required maximum estimate, we establish

Maryam Mirsadeghi, Mojtaba Mirbagheri, Saeed Reza Kheradpisheh

Spiking Neural Networks (SNNs) offer a biologically inspired foundation for low-power, event-driven intelligence, yet their direct on-chip supervised training remains a key hardware challenge. This paper presents a multiplication-free, spike-time-based learning algorithm specifically designed for efficient FPGA realization. The proposed approach eliminates f

Julian Gootzen, Michelle S. Chong

Motivated by the need for real-time health monitoring of power distribution grids, we propose a secure state estimator design for continuous time Lur'e type systems with non-uniformly and synchronously sampled outputs which have potentially been maliciously corrupted. The secure state estimator provides state estimates with accuracy independent of the sensor

Valentin Sauvage, Anaïs Besnard, Giulia Conenna, Kenichi Karatsu

DRACuLA (Detector irRAdiation Cryogenic faciLity for Astrophysics) is a mobile dilution refrigerator platform developed at the Institut d'Astrophysique Spatiale (IAS) to expose sub-Kelvin detectors to particle beams at their nominal operating temperature, in the range 50-300 mK. We report on its design, beam-line integration at the Particle Therapy Research

Damiano Anselmi

We investigate formulations of quantum field theories whose kinetic terms involve fractional or continuous powers of the d'Alembert operator. The primary requirements are perturbative unitarity and a well-defined classical limit with a finite number of initial conditions. A direct approach consists of continuing the correlation functions from Euclidean space

Qiyuan Jin

Memes convey meaning through the interaction of visual and textual signals, often combining humor, irony, and offense in subtle ways. Detecting harmful or sensitive content in memes requires accurate modeling of these multimodal cues. Existing CLIP-based approaches rely on static fusion, which struggles to capture fine grained dependencies between modalities

Tom Eaves

Minimal seeds, the smallest amplitude perturbations that trigger transition to turbulence, are presented in the Stokes boundary layer, the oscillating flow of a viscous fluid above a flat plate. The minimal seed trajectories are dominated by the Stokes boundary layer's large linear transient growth at early times, but only 73% of the initial energy is formed

Weihao Lu, Qian Lin, Yingcun Xia, Dongming Huang

Existing large-dimensional theory for spectral algorithms resolves either the optimally tuned point or the interpolation limit, but leaves the under-regularized regime unexplored. We study the learning curve and benign overfitting of spectral algorithms in the large-dimensional setting where the sample size and dimension are of comparable order, i.e., $n \as

Yihe Huang, Sizhe Cui, Jiaqi Wang, Jujian Zhang

Combinatorial curve neighborhoods are somewhat foundational when setting up the quantum Schubert calculus for affine flag manifolds. In the specific case of type $A_1^{(1)}$, you can encode these neighborhoods entirely within the moment graph of the infinite dihedral group $D_\infty$. Building on the framework developed by Mihalcea and Norton, this paper pre

Víctor Gallego

Can large language model agents discover hidden safety objectives through experience alone? We introduce EPO-Safe (Experiential Prompt Optimization for Safe Agents), a framework where an LLM iteratively generates action plans, receives sparse binary danger warnings, and evolves a natural language behavioral specification through reflection. Unlike standard L

Huyen Thanh Phan, Katsunori Wakabayashi

We numerically study topological effects of electromagnetic (EM) waves in a two-dimensional (2D) non-Hermitian photonic crystal (PhC) composed of lossy magneto-optical materials. In this system, not only the EM wavefunctions but also the complex eigenfrequencies exhibit nontrivial topological properties. We demonstrate that the non-Hermitian skin effect, pro

Umair Zulfiqar

This paper considers large-scale nonsymmetric continuous-time algebraic Riccati equations (NAREs) that admit low-rank solutions. Low-rank alternating direction implicit (ADI) methods have proven to be an efficient approach for solving several matrix equations, including Lyapunov equations, Sylvester equations, and symmetric Riccati equations. Although a low-

Jianquan Ge, Lixin Xiao, Wenjin Zhang

This paper investigates the variational problems associated with the $L^2$-norms of the normal and tangent curvature tensors for submanifolds immersed in a unit sphere. We define the critical points of these functionals under normal variations as Normal-Yang-Mills and Tangent-Yang-Mills submanifolds, for which we explicitly establish the Euler-Lagrange equat

Tong Niu

For every nonzero integer $m$ and every integer $n \ge 1$, the $n$\textsuperscript{th} harmonic number $H_n = 1 + \tfrac12 + \dots + \tfrac1n$ satisfies the identity \[ H_n \;=\; \frac{1}{m}\,\sum_{k=1}^{n} \frac{(-1)^{k+1}}{k}\, \binom{m k}{k}\binom{n + (m-1)k}{n - k}. \] The cases $m = 1$ and $m = 2$ are classical; for general nonzero integer $m$ the ident

Animan Naskar

Deploying proprietary Deep Neural Networks (DNNs) on commodity edge devices demands hardware-backed Digital Rights Management (DRM) capable of withstanding both software-level and physical adversaries. In Unified Memory Architecture (UMA) systems, the host CPU and Neural Processing Unit (NPU) share physical DRAM, leaving plaintext model weights directly read

Chao Deng, Lipeng Zhu, Chang Liu, Hefeng Zhai

The emerging deep learning (DL) technology has recently exhibited great potential in data-driven short-term voltage stability (SVS) assessment of complex power grids. However, without sufficient attention to the time-varying topological structures of today's power grids, the majority of existing DL-based SVS assessment schemes could experience severe perform

Haotian Xu, Bohui Wang, Shuai Liu, Chao Shen

This paper focuses on the multi-agent synchronization problem with an open-loop unstable leader and followers under the switching topologies. For this issue, the typical approach is intermittent communication (including a spanning tree intermittently) or fast switching strategy. We here consider a more general scenario where each communication link between t

Yuchen WU, Xiaoping Yuan

In this paper, we consider a derivative nonlinear Schr\"odinger equation $$ \mathrm{i}\partial_{t}u+\partial_{xx}u-V\ast u+\mathrm{i}\vert u\vert^{2}\partial_{x}u=0 $$ on the torus $\mathbb{T}$, depending on some potential $V$. We prove that for `almost all' potentials $V$, this equation admits an almost-periodic solution.

Zixing Zhuang, Chenglong Fang, Liwen Cao

For $\alpha\in(0, n)$ and a growth function $\varphi:[0,\infty)\rightarrow [0,\infty)$, it is proved that the commutator $[b,I_\alpha]$ generated by fractional integral operator $I_\alpha$ and Orlicz $\mathrm{BMO}$ function $b$ is bounded from Orlicz-Hardy space $H_{b}^{\varphi}(\mathbb{R}^{n})$ to Lebesgue space $L^{\frac{n}{n-\alpha}}(\mathbb{R}^{n})$, whe

Marco De Paoli, Sergio Pirozzoli

We investigate how models of fluid properties and boundary conditions influence predictions of convective mixing in confined porous media, with relevance to subsurface carbon dioxide storage. Using high-resolution simulations at high Rayleigh-Darcy numbers (O($10^4$)), we analyze miscible fluids with linear, nonlinear, and non-monotonic density-concentration

Xuanyue Zhong, Yuqiang Xie, Guanqun Bi, Jiangping Yang

Current video moment retrieval excels at action-centric tasks but struggles with narrative content. Models can see \textit{what is happening} but fail to reason \textit{why it matters}. This semantic gap stems from the lack of \textbf{Theory of Mind (ToM)}: the cognitive ability to infer implicit intentions, mental states, and narrative causality from surfac

Xinyue Zhang, Yuanhao Ding, Xiang Ao

Delayed feedback poses a core challenge for online CVR prediction, forcing a trade-off between label accuracy and data freshness. Existing methods address this through delay modeling or sample reweighting, yet neglect how post-click behaviors evolve over the observation period. To overcome this limitation, we formalize this evolution as feedback trajectory a

ElMouatez Billah Karbab

Deep learning malware detectors achieve high classification accuracy but suffer from severe interpretability limitations, typically returning probabilistic verdicts that lack forensic context. We introduce AsmRAG, a framework performing malware analysis through Assembly-Level Retrieval-Augmented Generation. Unlike classifiers built on global statistical feat

Yihan Wang, Lei Li, Yao Lai, Jing Wang

Analog circuit design relies heavily on reusing existing intellectual property (IP), yet searching across heterogeneous representations such as SPICE netlists, schematics, and functional descriptions remains challenging. Existing methods are largely limited to exact matching within a single modality, failing to capture cross-modal semantic relationships. To

Haoran Tan, Zeyu Zhang, Chen Ma, Tianze Liu

Large language model-based agents have recently emerged as powerful approaches for solving dynamic and multi-step tasks. Most existing agents employ planning mechanisms to guide long-term actions in dynamic environments. However, current planning approaches face a fundamental limitation that they operate at a fixed granularity level. Specifically, they eithe

Shabarish Chenakkod, Michał Dereziński, Xiaoyu Dong, Mark Rudelson

Perturbing a deterministic $n$-dimensional matrix with small Gaussian noise is a cornerstone of smoothed analysis of algorithms [Spielman and Teng, JACM 2004], as it reduces the condition number of the input to $O(n)$, and with it the complexity of many matrix algorithms. However, when deployed algorithmically, these perturbations are expensive due to the co

Sreemayee Aditya, Emanuele Tirrito, Piotr Sierant, Xhek Turkeshi

We study how conservation laws shape the spreading of quantum coherence in many-body dynamics. Focusing on $U(1)$-symmetric random circuits, charge-and-dipole conserving circuits, as well as ergodic Hamiltonian dynamics, we probe coherences both globally, via the participation entropy, and locally, via the relative entropy of coherence. Combining exact vecto

Pawan Kumar Pandey, KVS Chaithanya, Prateek K. Jha

Drug-Filled Porous Implants (DFPIs) are an innovative solution for delivering drugs in a controlled and sustained manner to target sites. To optimize their performance across various physiological conditions, it is essential to understand how fluid flow and porous media properties influence the drug release process. In this work, we numerically investigate a

Antonio Ortiz, Robert Vertesi

The enhancement of the baryon production relative to mesons in small-collision systems is considered a breakthrough result of the Large Hadron Collider since a similar effect in heavy-ion collisions is understood by invoking the formation of the strongly-interacting quark--gluon plasma. In this letter, a baryon enhancement is reported for $p_{\rm T}^{\rm ch,

Szilard Zsolt Fazekas, Adam Mammoliti, Robert Mercas, Jamie Simpson

Fici and Saarela ([2]) conjectured that a binary word of length n contains at least $\lfloor n/4 \rfloor$ abelian squares. We slightly extend this conjecture and show that it holds in some special cases. In all other cases we have the following: given a Parikh vector over a two letter alphabet we produce a word with that Parikh vector which we conjecture con

Niamh Belton, Victoria Joppin, Aonghus Lawlor, Catherine Masson

This work introduces DyABD, a novel and complex benchmark dataset of dynamic abdominal MRIs from patients with abdominal hernias and associated high quality abdominal muscle annotations. DyABD is the first-of-its-kind in four key ways; (1) it proposes the first abdominal muscle segmentation task, (2) the dynamic MRIs are acquired whilst the patients perform

Elina Kokurina, Dmitry Vagin, Eduardo Barredo-Alamilla, Maxim A. Gorlach

Recently, it was predicted that nonreciprocal magneto-electric effect in antiferromagnetic multilayered metamaterials occurs in two distinct versions. One is the conventional axion response, while another one is dual axion response captured by electrodynamics with magnetic charge. Here we investigate a model condensed matter system of spins coupled through a

Tomohiro Hakamata, Hirokazu Odaka, Ryota Tomaru, Hironori Matsumoto

Cygnus X-3, hosting a Wolf-Rayet (WR) star whose dense wind produces various spectral lines due to photoionization by X-rays from a compact object, provides an ideal laboratory for studying wind dynamics and density structure. We measured the orbital modulations of the Fe, Ca, Ar, and S Ly$\alpha$ lines observed with the X-ray microcalorimeter (Resolve) onbo

Zhongkai Ji, Dawei Li, Yong Zhao, Wenhe Liao

Recent years have seen growing application potential for Lattice-skin Plate Structures in advanced manufacturing fields such as aerospace and automotive engineering. For multiscale performance evaluation of such structures, conventional homogenization methods for lattice-filled volume structures are often used for equivalent analysis. However, in finite-thic

Feng Hao, Yang Su, Jianqiang Yang

In this article, we find finitely many numerical invariants to classify the diffeomorphism types of three dimensional simply connected Mori fibre spaces with torsion free homology groups.

Kanghoon Lee, Matthew M. Sato, Jinnyeong Yang, Seungro Lee

Monitoring human activity in indoor environments is important for applications such as facility management, safety assessment, and space utilization analysis. While mobile robot teams offer the potential to actively improve observation quality, existing multi-robot monitoring and active perception approaches typically rely on coverage or visitation based obj

Zhiwen Zhao, Bishnu Karki, Bo Yu, Andrew Smith

We present the development of readout electronics and artificial-intelligence-based particle-identification methods for the SoLID Cherenkov detectors at Jefferson Lab. To operate in the high-rate, high-background SoLID environment, we designed a MAROC sum readout system for multianode photomultiplier tubes that provides simultaneous pixel, quadrant-sum, and

Isaiah Andrews, Ricky Li, Yucheng Shang

We study optimal estimation when the likelihood may be misspecified. Building on tools from the theory of decision-making under uncertainty, we analyze a class of axiomatically grounded optimality criteria which nests several existing misspecification-robust objectives. Within this class, we introduce the constrained multiplier criterion, which allows for fl

Yifei Xu, Yuzhang Lin

Power system state estimation (SE) is foundational for grid monitoring, yet conventional centralized solvers face increasing computational pressure as the system scale and real-time requirements grow. This paper presents a GPU-native framework for hierarchical multi-area state estimation (MASE) that addresses these bottlenecks through a single-instruction, m

Anija C. R, Smitha S, Sudheesh K. Kattumannil

In this paper, we introduce the weighted cumulative residual Mathai--Haubold entropy and establish its fundamental properties. A dynamic version is developed, and its behavior under linear transformations is studied. Bounds and explicit expressions for some lifetime distributions are derived. Characterization results based on the associated measure are obtai

Balaji Darur, Amanmeet Garg, Makarand Tapaswi

Video Situation Recognition (VidSitu) addresses the challenging problem of "who did what to whom, with what, how, and where" in a video. It tests thorough video understanding by requiring identification of salient actions and associated short descriptions for event roles across multiple events. Grounding with VidSitu requires spatio-temporal localization of

Terry Gou, Puneet Gupta

In this work, we developed and tested 3 techniques for vector quantization (VQ) based model weight compression. To mitigate codebook collapse and enable end-to-end training, we adopted cosine similarity-based assignment. Building on ideas from attention-based formulations in Differentiable K-Means (DKM), we further improved this approach by using cosine simi

Mark de Berg, Bart M. P. Jansen, Jeroen S. K. Lamme

We study SINGLE-SOURCE SHORTEST PATH (SSSP) on unweighted intersection graphs whose node set corresponds to a set of $n$ constant-complexity objects in the plane. We prove SSSP can be solved in $O(U(n)\ \mathrm{polylog}\,n)$ expected time for any class of objects whose union complexity is $U(n)$. In particular, we obtain an $O(n 2^{\alpha(n)}\log^2 n)$ algor

Matthew Tassava, Cameron Kolodjski, Jordan Milbrath, Jeremy Straub

Core logic and processing improvements were made to the software for operations and network attack results review (SONARR) and are presented, herein. Previous SONARR versions' Boolean-only logic, derived from the Blackboard Architecture, was replaced with generic logic that allows any .NET type (e.g., integers, decimals, strings) to be utilized within facts.

Takumi Kano, Yutaro Yamaguchi

We present a deterministic linear-time algorithm for finding an odd/even cycle through two specified vertices in an undirected graph. This is shown in a generalized form as follows: Let $Γ$ be any group in which every element is of order at most $2$. For a given $Γ$-labeled graph with two specified vertices (or edges), we can determine in linear time whether

Ryo Suzuki, Yutaro Yamaguchi

In the sliding window model, we are required to maintain the target statistics over the most recent $n$ elements of a data stream, which is captured by a window of size $n$ sliding over the data stream. Exact computation usually requires space linear in $n$, and the central goal is approximate maintenance using sublinear space. In this paper, we study the pr

Shafeequdheen Palengara, Jyotiranjan Nayak, Vijayakrishna Rowthu

A levelset free but a hybrid image segmentation approach based on a modified version of the piece wise constant shape gradient of an Mumford Shah shape functional and a repulsive function is considered. The segmentation is performed a non-local shape based through an evolution of discrete curves driven by a non local shape based energy to segment images cont

Zhuo Zheng, Iván Higuera-Mendieta, Richard Lee, David Newhouse

Poverty statistics guide social policy, but in many low- and middle-income countries, censuses and household surveys that collect these data are costly, infrequent, quickly outdated, and sometimes error-prone. Satellite imagery offers global coverage and the possibility of predicting economic livelihoods at scale, yet existing approaches to predicting liveli

Jiong Li, Jun-ling Wang, Qing-Hu Chen, Hai-Qing Lin

Spectral collapse in the two-photon quantum Rabi model (tpQRM) has long been regarded as incompatible with quantum criticality due to the absence of a vanishing excitation gap. We show that, in the anisotropic tpQRM, spectral collapse constitutes a genuine continuous quantum phase transition governed by a single soft mode. The excitation gap within the same

Dan Zhang, Shulan Li, Guoyang Fu, Jian-Pin Wu

This letter presents an assessment of quantum gravity effects on extreme-mass-ratio inspirals (EMRIs) for the rotating quantum Oppenheimer-Snyder (qOS) black hole. Employing the adiabatic evolution, we compute the gravitational wave (GW) dephasing, which quantifies the cumulative phase shift induced by the quantum correction {\alpha} . We further generate th

Eduard Curcă, Michał Wojciechowski

Let $d\geq 2$, $l\geq 0$ and suppose $X$ is one of the function spaces $W^{l,1}(\mathbb{T}^{d})$, $W^{l,\infty }(\mathbb{T}^{d})$ or $C^{l}(\mathbb{T}^{d})$. We extend a result of Henkin (1967), showing that, for appropriate $N\times N$ matrix operators $A(D)$, the subspace of $X^{N}$ consisting of $A(D)-$free elements is noncomplemented. In order to prove t

Agung Budiyono, Sebastian Deffner

Any protocol to process quantum information has to conclude with a measurement, aimed at producing a specific set of probabilities of measurement outcomes. In this work, we investigate the time, energy and importantly the genuine quantum resources necessary for transforming a set of measurement probabilities generated by a positive-operator-valued measure (P

Eduard Curcă

In 2002, Bourgain and Brezis proved that for the space $X=W^{1,d}$ (on $\mathbb{T}^{d}$, with $d\geq2$) we have the equality of images \begin{equation} \operatorname{div} (L^{\infty}\cap X)=\operatorname{div} X, \tag{$\ast$} \end{equation} i.e., given a vector field $v\in X$ there exists a vector field $u\in L^{\infty }\cap X$ such that $\operatorname{div} u

Yangyang Luan, Camilla Ancona, Carmela Bernardo, Valentina Pansanella

The concept of homophily is pervasive in online social media. While many empirical studies have relied on external sociodemographic traits to investigate it, significantly less is known about homophily at the cognitive level, that is, at the level of shared opinions or values. For such "value homophily", in this paper we study interval-based patterns of opin

Tian He, Chen Yang, Jiawei Zhang, Lin Guo

Generative recommendation systems are increasingly adopted in local service platforms, where semantic relevance alone is insufficient without strict geographic feasibility. A key technical challenge lies in semantic ID (SID) tokenization, which directly impacts the recommendation performance. However, existing semantic codebooks neglect geographic constraint

Nanjia Li, Weibo Liu, Kang Ma, Elsayed E. E. Qupasie

This paper presents recent advances in the KBmini Kibble balance, a tabletop system for E2-accuracy mass calibration up to 1 kg. The $Bl(z)$ profile is characterized by manually setting the magnet at different vertical positions, and the extremum point is selected as the weighing position. The spring constant of the weighing cell around this point is measure

Masaki Hino, Shogo Kato, Takeshi Emura

In biomedical studies, paired survival data arise naturally when two event times are observed within the same subject. Existing statistical models seldom accommodate both cure fractions and complex dependence structures. In this paper, we propose a novel bivariate cure frailty-copula model for paired survival data with a cure fraction. By incorporating a zer

Ravis Shirkhani, Reshma Prasad, Leonardo Bonati, Tommaso Melodia

Software-driven O-RAN architectures enable rapid innovation through frequent, independent updates to virtualized components. However, attributing performance variations to specific software changes is challenging due to the stochastic nature of wireless systems, where channel conditions, interference, and hardware variability confound analysis. Traditional t

Pawat Akara-pipattana, Sergei Nechaev

We study the statistics of rewired random regular graphs (RRGs) in a mixed ensemble, where the average number of triangles is controlled by the fugacity $\lambda$, while the number of vertices and the vertex degree are fixed. This model exhibits a phase transition at critical fugacity $\lambda_{cr}$ from a triangle-poor phase (TPP), in which the number of tr

Balagopal Nair, Arshed Nabeel, Danny Raj M

Coordinated movement and self-organisation of active self-driven agents is common in nature and is seen across different scales, from herds of animals to collective motion in bacteria. Often, these systems are heterogeneous in composition, with different agents having different intrinsic motilities. Inferring these intrinsic characteristics and quantifying t

Abhimanyu Bambhaniya, Geonhwa Jeong, Jason Park, Jiecao Yu

Most recent state-of-the-art (SOTA) large language models (LLMs) use Mixture-of-Experts (MoE) architectures to scale model capacity without proportional per-token compute, enabling higher-quality outputs at manageable serving costs. However, MoE inference at scale is fundamentally bottlenecked by expert load imbalance and inefficient token routing, especiall

Zimeng Li, Ning Wu

This paper considers an $N$-site spin-1/2 $J_1$-$J_2$ ring with $N=6$ and $8$. With the help of a set of exact few-magnon Bloch states, we obtain the block-diagonalized Hamiltonian consisting of block matrices of at most four dimensions. Partial of the eigenstates are analytically solved. For the six-site anisotropic ring, we reveal a subset of eigenstates t

Tianlong Yu, Yang Yang, Ziyi Zhou, Jiaying Xu

The emerging threat of AR-LLM-based Social Engineering (AR-LLM-SE) attacks (e.g. SEAR) poses a significant risk to real-world social interactions. In such an attack, a malicious actor uses Augmented Reality (AR) glasses to capture a target visual and vocal data. A Large Language Model (LLM) then analyzes this data to identify the individual and generate a de

Xuejie Li, Zhongxu Zhai, Xiaohu Yang, Andrew Benson

Understanding how galaxy populations emerge and evolve from the growth of dark matter structure is a central challenge in galaxy formation theory. Semi-analytic models (SAMs) provide an efficient framework to address this problem, but exploring large ensembles of merger trees across broad parameter spaces remains computationally demanding. We develop a condi

Farzad Razi, Mehran Moghadam, Sercan Aygun, M. Hassan Najafi

Today's high-performance architectures are increasingly constrained by data movement latency and energy overhead, as the slowdown of single-core performance scaling coincides with the rise of highly data-intensive workloads. In-memory architectures have emerged as a complementary solution to conventional von Neumann systems by alleviating memory bandwidth bo

Jason Nguyen, Ameet Rao, Alexander Chang, Ishaan Kumar

Video Question Answering (VideoQA) demands models that jointly reason over spatial, temporal, and linguistic cues. However, the task's inherent complexity often requires multi-step reasoning that current large multimodal models (LMMs) perform implicitly, leaving their internal decision process opaque. In contrast, large reasoning models (LRMs) explicitly gen

Boxiang Wang, Zhengding Luo, Dongyuan Shi, Junwei Ji

Directional Selective Fixed-Filter Active Noise Control (D-SFANC) can effectively attenuate noise from different directions by selecting the suitable pre-trained control filter based on the Direction-of-Arrival (DoA) of the current noise. However, this method is weak at tracking the direction variations of non-stationary noise, such as that from a moving sou

Naveen Dinujaya, Suhas Eswarappa Prameela

Deformation mechanisms in CrCoNi-based oxide-dispersion-strengthened multi-principal element alloys (CrCoNi-based ODS-MPEA) have been extensively studied under quasi-static and low strain rate loading over a wide temperature range, yet their behavior at high strain rates and elevated temperatures remains poorly understood. In this work, we investigate the hi

Alexey Bobtsov, Jose Guadalupe Romero, Romeo Ortega, Anton Pyrkin

In this paper we present a radically new approach to design state observers for nonlinear systems, with particular emphasis on physical ones. Our objective is to obtain an algebraic relation between the unmeasurable part of the state and filtered versions of the systems inputs and outputs, which holds true for all $t \geq 0$. The latter qualifier should be c

Tianlong Yu, Yang Yang, Xiao Luo, Lihong Liu

Emerging AR-LLM-based Social Engineering attack (e.g., SEAR) is at the edge of posing great threats to real-world social life. In such AR-LLM-SE attack, the attacker can leverage AR (Augmented Reality) glass to capture the image and vocal information of the target, using the LLM to identify the target and generate the social profile, using the LLM agents to

Xin Zhou, Zhengsong Lu, Bo Zeng, Na Geng

Green manufacturing has become a strategic priority for many firms seeking to address sustainability and social responsibility, while improving production efficiency and profitability. However, integrating green technologies and renewable energy unavoidably introduces climate-related randomness that affects both product demand and renewable energy generation

Arefin Niam, Tevfik Kosar, M. S. Q. Zulkar Nine

Distributed GNN training is dominated by remote feature fetching, which can be very costly. Multi-hop neighborhood sampling crosses partition boundaries and triggers fine-grained RPCs whose fixed initiation cost and GPU-stall latency waste energy. Prior systems try to reduce this overhead with presampling and static caching, but cache policies cannot react t

Reuben Tate, Shamminuj Aktar, Stephan Eidenbenz

Trotterization is a technique that allows one to approximate a time evolution of a Hamiltonian by repeatedly evolving the individual terms of the Hamiltonian one-at-a-time for small time durations. Bounds on the error of this approximation exist; however, they are typically loose and moreover, it is known that the true error can be greatly influenced by the

Syed Ibad Hasnain, Muhammad Faris, Hafiza Syeda Yusra Tirmizi, Rabail Khowaja

Early detection and classifying brain tumors using Magnetic Resonance Imaging (MRI) images is highly important but difficult to extract in medical images. Convolutional Neural Networks (CNNs) are good at capturing both local texture and spatial information whereas Vision Transformers (ViTs) are good at capturing long-range global dependencies. We propose a n

Sanjana Gautam, Houjiang Liu, Yujin Choi, Matthew Lease

In the early stages of scientific research, researchers rely on core scholarly judgments to identify relevant literature, assess credible evidence, and determine which directions merit pursuit. As AI tools become increasingly integrated into these early-stage workflows, the scholarly judgments that were once transparent and attributable to individual researc

Dustin M. Haggett

Technical traders have long relied on visual analysis of candlestick charts to identify market patterns and predict price movements. While deep learning has achieved remarkable success in image classification, its application to financial chart images remains underexplored. This paper presents a systematic study comparing different visual representations for

Yanru Qu, Yijie Zhang, Wenjuan Tan, Xiangzhe Kong

Accurate molecular representations are critical for drug discovery, and a central challenge lies in capturing the chemical environment of molecular fragments, as key interactions, such as H-bond and {\pi} stacking, occur only under specific local conditions. Most existing approaches represent molecules as atom-level graphs; however, atom-level representation

Tipaluck Krityakierne, Thotsaporn Aek Thanatipanonda

We study the expected number of rolls required for the cumulative sum of a fair six-sided die to first enter a prescribed target set $H\subset\mathbb{Z}_{\ge0}$. A one-variable dynamic-programming formulation is introduced that removes dependence on the roll count. Within this framework, the infinite process is truncated at a large cutoff $N$ and corrected b

Chunlin You

Recently, Arag\~{a}o, Marciano, and Mendon\c{c}a [\emph{European J. Combin.}, 2025] conjectured that for any graph $G$ on $n$ vertices satisfying $(r-1)(t-1)k < n \le (r-1)(t-1)(k+1)$, the minimum degree condition $\delta(G) \ge n - \left\lceil \frac{k}{k+1} \left\lceil \frac{n}{r-1} \right\rceil \right\rceil$ guarantees that $G \rightarrow (K_r, P_t)$. In t

Xiang Li, Cara Li, Emily Kuang, Can Liu

Knowledge workers face increasing challenges in synthesizing information from multiple documents into structured conceptual understanding. This process is inherently iterative: users explore content, identify relationships between concepts, and continuously reorganize their mental models. However, current approaches offer limited support. LLM-based systems l

Akibul Hasan Mazumder, Yuanrui Sang

Large data centers are being deployed in the U.S. at an unprecedented rate, introducing significant flexible load potential. A portion of data center workloads - best-effort (BE) jobs - can be scheduled flexibly to reduce power system operating costs and emissions. However, the system-level impacts of such scheduling remain underexplored. This paper investig

Showmitra Kumar Sarkar, Sai Ravela

Soil salinity is a major environmental challenge in coastal Bangladesh, threatening agricultural productivity and local livelihoods. This study develops a machine-learning-based framework to predict and map soil salinity in Satkhira district by integrating field observations with Landsat-derived spectral indices. A total of 205 soil samples collected during