April 2026 arXiv papers — page 46

Tobias Kröll, Stephan Kleber, Frank Kargl, Matthias Hollick

Wireless chips and interfaces expose a substantial remote attack surface. As of today, most cellular baseband security research is performed on the Android ecosystem, leaving a huge gap on Apple devices. With iOS jailbreaks, last-generation wireless chips become fairly accessible for performance and security research. Yet, iPhones were never intended to be u

M. Andrade, H. Baltazar, A. da Silva, D. Tavares

In this article, we derive an integral formula involving the tensor $D_{ijk}$ for compact Einstein-type manifolds with constant scalar curvature. As an application, we classify three-dimensional compact Einstein-type manifolds satisfying the cyclic parallel Ricci tensor condition, obtaining rigidity results that extend and unify previous work in the literatu

Haoming Meng

Automated failure diagnosis requires correlating browser-visible symptoms with backend observability signals, yet existing benchmarks do not evaluate this cross-modal reasoning task. Constructing one is non-trivial: multi-modal failure scenarios are costly to annotate, and live-environment capture introduces stochasticity that makes cross-run agent compariso

Xingche Guo

Sequential latent-variable models with subject-specific random effects provide a flexible framework for modeling temporally structured data with both local latent dynamics and stable between-subject heterogeneity. In such models, conditional inference for the local latent process is often tractable, but integrating over subject-specific random effects can be

Muhammad Afzal, Naveed Ahmed, Volker John

A residual-based a posteriori error estimator is proposed for the incompressible Oseen problem in the convection-dominated regime. The SUPG/PSPG/grad-div stabilized finite element method is used as discretization. The error estimator estimates the global error in a norm that is used in the a priori error analysis of the method. Based on several hypotheses co

Jainum Sanghavi

Vision Transformers trained only on image classification routinely transfer to tasks that demand spatial understanding, yet they receive no spatial supervision during pretraining. We ask where and how robustly such structure is encoded. Probing a frozen ViT-B/16 layerwise for two complementary properties, local patch boundaries (BSDS500) and per-patch depth

Shabnam Sodagari

Causal discovery from observational data underpins applications in finance, climate modeling, and machine learning. Constraint-based causal discovery reduces structure learning to a sequence of conditional independence (CI) tests, where each test decides independence by estimating conditional mutual information $I(X;Y \mid Z)$ to additive precision $\tau$ an

Shamik Das, Sudipa Mondal

A positive square-free integer is called a \textit{congruent number} if it arises as the area of a right triangle with rational side lengths. Let $ n = p_1p_2 \cdots p_t q $ be a square-free integer, where each $ p_i \equiv 1 \pmod{8} $ and $ q \equiv 3 \pmod{8} $, with the $ p_i $ and $ q $ being distinct primes. In this article, we present a congruence rel

Jennifer Kleiman, Yizhu Gao, Xin Xia, Zhaoji Wang

Argumentation is a core practice in STEM education, but its productivity depends on who participates and how they interact. Higher-achieving students often dominate the talk and decision-making, while lower-achieving peers may disengage, defer, or comply without contributing substantive reasoning. Forming groups strategically based on students' stances and a

Daniel Lao

We present CRC-SAM, a unified framework for colorectal cancer segmentation across colonoscopy, CT, and histopathology images. Unlike prior single-modality methods, CRC-SAM provides consistent, modality-agnostic segmentation throughout the clinical workflow. Built on MedSAM, it incorporates low-rank adaptation (LoRA) layers into a frozen encoder, enabling eff

Matson Garza, Mary Anne Limbach, Rachel Bowens-Rubin, Matthew De Furio

Epsilon Indi A b is a directly imaged $\sim6 M_{\rm Jup}$ exoplanet orbiting a nearby (3.6 pc) K-dwarf at $\sim 30$ AU. We analyze archival JWST/MIRI 15 $\mu$m coronagraphic imaging of this planet to search for directly imaged satellites orbiting Eps Ind A b. Within the planet's Hill sphere (radius $R_H \approx 2.3$ AU or $1.3 \lambda/D$), we compare single-

Rajkamal Nailwal

In this paper, we introduce a $3$-Brownian shift $T_{\sigma, \theta}$ on the Hilbert space $H^2(\mathbb D^2)\oplus H^2(\mathbb D)\oplus \mathbb C,$ which is a natural extension of the classical Brownian shift $B_{\sigma, \theta}$ on $H^2(\mathbb D)\oplus \mathbb C$. This is motivated by Brownian extensions in the context of 3-isometries recently developed by

Chathurangi Shyalika, Dhaval Patel, Amit Sheth

Industrial maintenance environments increasingly rely on AI systems to assist operators in understanding asset behavior, diagnosing failures, and evaluating interventions. Although large language models (LLMs) enable fluent natural-language interaction, deployed maintenance assistants routinely produce generic explanations that are weakly grounded in telemet

Suhas BN, Andrew M. Sherrill, Rosa I. Arriaga, Chris W. Wiese

Large language models are being deployed as mental health support agents at scale, yet only 16% of LLM-based chatbot interventions have undergone rigorous clinical efficacy testing, and simulations reveal psychological deterioration in over one-third of cases. We evaluate four generative models on 250 Prolonged Exposure (PE) therapy scenarios and 146 CBT cog

Zhichuan Liao, Ao Shen, Lai Zhou, Nan Jiang

Signal photons emitted by quantum nodes typically fall outside the low-loss telecom window of optical fibers, leading to severe transmission losses. Quantum frequency conversion (QFC) offers an effective optical interface that bridges quantum nodes with telecom-band channels, enabling long-distance quantum communication. In this work, we demonstrate a compac

Linyuan Wang, Haibo Yao, Te-Ming Tseng, Kelvin Betitame

Weeds compete with crops for light, water, and nutrients, reducing yield and crop quality. Efficient weed detection is essential for site-specific weed management (SSWM). Although deep learning models have been deployed on UAV-based edge systems, a systematic understanding of how different model architectures perform under real-world resource constraints is

Ioannis Makos, Jakub Benda, David Busto, Benjamin Steiner

Attosecond interferometry in a two-color field is central to attosecond metrology and spectroscopy. In this technique, a photoelectron wave packet is released when a single photon from an extreme ultraviolet comb is absorbed. The wave packet then either emits or absorbs one or more near-infrared photons, leading to the formation of sidebands of the main phot

John W. Moffat

We investigate whether Scalar-Tensor-Vector Gravity in its weak-field modified gravity form can account for the cluster-scale inverse-square force law inferred from recent kinematic Sunyaev-Zeldovich measurements of cluster pairwise motions. The starting point is the X-COP cluster fit of STVG-MOG, for which a representative baryonic cluster mass $M\sim 10^{1

Charalambos D. Charalambous, Umarbek Guvercin, Seddik Djouadi

This paper develops a dynamic programming (DP) approach for decentralized stochastic optimal control problems with delayed sharing information patterns, which exhibits the fundamental Properties of classical DP of centralized partially observable Markov decision problems (POMDPs): the value functions and information states depend on the actions of the minimi

Ritik Roshan Giri, Arnab Hazra

Climate change has become a significant global concern due to its capacity to cause substantial disruption to daily life by increasing the frequency and intensity of extreme weather events. Given the rising trend of human interventions in the climate system over recent decades, this study aims to quantify the relative contribution of anthropogenic forcing to

Fred Jalbert-Desforges

We present an audio-to-analysis pipeline that produces composer-level information-theoretic profiles : reflecting compositional vocabulary as it emerges from aggregated performances : from raw recordings, built on a transcription layer whose accuracy we certify on a standard benchmark (F1 = 0.9791 on the MAESTRO v3.0.0 test set). Applied to 1,238 pieces and

Abdul Azim, Ahmed Hossain, Soumyadip Maitra, Panick Kalambay

Tree-involved crashes represent a critical subset of run-off-road (ROR) collisions, often resulting in fatal or severe injuries due to high-energy impacts. This study develops a comprehensive analytical framework to identify and quantify risk factors contributing to crash severity in tree-involved collisions using the Crash Report Sampling System (CRSS) data

Lucky Verma

Dynamic Tanh (DyT) removes LayerNorm by bounding activations with a learned tanh(alpha x). We show that this bounding is a regime-dependent implicit regularizer, not a uniformly beneficial replacement. Across GPT-2-family models spanning 64M to 3.78B parameters and 1M to 118M tokens, with Llama and ViT cross-checks, DyT improves validation loss by 27.3% at 6

Necdet Duman, Özge Gönül, Burak Kaya, Jayatra Saxena

This paper is a contribution to the investigation of closed partition relations for pairs of countable ordinals. As our main result, we prove that \[\omega^4 \cdot (n-2)+1 < R^{cl}(\omega \cdot n+1,3)<\omega^5\] for every integer $n \geq 3$. This result significantly improves the existing upper and lower bounds for these closed Ramsey numbers. In addition, w

Soulayma Gazzeh, Giuseppe Mazzola, Liliana Lo Presti, Marco La Cascia

Reliable depth estimation from spherical images is crucial for 360{\deg} vision in robotic navigation and immersive scene understanding. However, the onboard spherical camera can experience unintentional pose variations in real-world robotic platforms that, along with the geometric distortions inherent in equirectangular projections, significantly impact the

Zander Scholl, Justin Woods, Charudatta Phatak, Hanu Arava

Reconstructing magnetization in nanoscale magnetic thin films is essential for developing next-generation memory, sensors, and various spintronic technologies. However, this remains challenging due to the ill-posed nature of the stray field inverse problem, i.e., there are infinitely many magnetization solutions to a given stray field distribution. Here, we

Gautam Kishore Shahi, Oliver Hummel

The relentless expansion of scientific literature presents significant challenges for navigation and knowledge discovery. Within Research Information Retrieval, established tasks such as text summarization and classification remain crucial for enabling researchers and practitioners to effectively navigate this vast landscape, so that efforts have increasingl

Alistair H. Duff, J. E. Sipe

We calculate the theoretical non-degenerate two photon absorption and three color injected current response tensors for bulk GaAs and Ge$_{1-x}$Sn$_x$ for a range of alloy compositions. In particular, by including a &#39;&#39;pump&#39;&#39; beam we compare two &#39;&#39;schemes&#39;&#39; that are sensitive to mid-infrared photons. In &#39;&#39;scheme I&#39;&

Ha Thi Thu Hien, Thanh Vu

Let $G$ be a simple connected graph and $t \ge 2$ an integer. We prove that if the maximal homogeneous ideal is an associated prime of the $t$th power of the closed neighborhood ideal of $G$, then the diameter of $G$ is at most $7t - 8$. We further show that this bound is sharp for all $t \ge 2$.

Alexey Pozdnyakov

We prove lower bounds on learning the M\"obius or Liouville function with a variety of standard learning techniques, including kernel methods, noisy gradient methods, and correlational statistical query algorithms. These results follow from quantitative bounds on the correlation of M\"obius with digital characters of various finite abelian groups, where the

Emre Ardıç, Yakup Genç

Federated learning (FL) is a distributed machine learning method where multiple devices collaboratively train a model under the management of a central server without sharing underlying data. One of the key challenges of FL is the communication bottleneck caused by variations in connection speed and bandwidth across devices. Therefore, it is essential to red

Richard Joseph Mitchell

The April 2026 disclosure that a frontier large language model escaped its security sandbox, executed unauthorized actions, and concealed its modifications to version control history demonstrates that agentic AI systems with autonomous tool access can circumvent the containment mechanisms designed to constrain them. This paper analyzes four categories of cur

Dikran Hovagimian

Evolve pairs a small local language model with a persistent, teacher-compiled knowledge store -- refined through sleep consolidation and usage-driven refresh -- to deliver substantial accuracy gains over the model's parametric baseline while amortizing teacher costs through cross-query knowledge reuse. Rather than retrieving document fragments at query time,

Michael Kinyon

We give an elementary proof of a result which is not as well known as it should be: a ring with a specified finite number of zero divisors is finite, with a precise bound on its order.

Junior da Silva Bessa, João Vitor da Silva, Ginaldo de Santana Sá

We establish regularity results for viscosity solutions to a class of quasilinear parabolic equations exhibiting nonhomogeneous degeneracy or singularity (a double phase regime) of the form \[ u_t - \big(|Du|^{\mathfrak{p}} + \mathfrak{a}(x,t)|Du|^{\mathfrak{q}}\big)\Delta_p^{\mathrm N} u = f(x,t) \quad \text{in } Q_1, \] where $-1 < \mathfrak{p} < 0$, $\mat

Ren Zhang, Xiao-Yu Ouyang, Xu-Dong Dai, Xi Dai

We identify a boundary-robust topological signature of open Floquet lattices: although nonadiabatic boundaries strongly reshape the transmission lineshape, the integrated left--right transmission asymmetry saturates to a plateau set by the bulk Floquet winding number. Its origin is a deep-bulk branch-population principle: in the long-sample limit, each propa

Marin Bougeret, Guilherme C. M. Gomes, Ignasi Sau

Enumerative kernelization is a recent promising at the intersection of parameterized complexity and enumeration algorithms, with two proposed models. The first, known as enum-kernels and due to Creignou et al., was too permissive, leading to constant-sized kernels for every problem solvable with FPT-delay. To remedy this, Golovach et al. proposed the polynom

Tomer Zilca, Gal Mendelson

Uniform random rotations are a useful primitive in applications such as fast Johnson-Lindenstrauss embeddings, kernel approximation, communication-efficient learning, and recent AI compression pipelines, but they are computationally expensive to generate and apply in high dimensions. A common practical replacement is repeated structured random rotations buil

Prajit Dhara, Babak N. Saif, Jeffrey H. Shapiro, Saikat Guha

Slepian developed the Prolate Spheroidal Wavefunction (PSW) spatial-mode basis, which forms the normal modes of the Fresnel-propagation kernel of a free-space optical communications channel bookended by hard-circular apertures. The zero-th order PSW mode has the highest power-transfer eigenvalue, exciting which on the transmitter side therefore maximizes the

Kaylah McGowan, Shania Nichols, Siddharth Soni, Chayan Chatterjee

Scattered light is one of the most common sources of non-stationary noise at low frequencies in Advanced LIGO detectors. It appears as arch-like features in time-frequency spectrograms, produced when stray light reflects from moving surfaces and recombines with the main interferometer beam. In this study, we present ArchGEM, an automated framework for identi

Md. Afzalur Rahaman, Tahmid Rahman

Most two-stream action recognition networks apply the same convolutional backbone to both RGB and optical flow streams, ignoring the fact that the two modalities have fundamentally different structural properties. Optical flow captures fine-grained motion patterns, while RGB frames carry rich appearance and scene context - treating them identically discards

S. N. Stelmastchuk

We study control systems on the tangent bundle of a smooth manifold induced by vertical lifts of vector fields. The Vertical dynamics acts exclusively along the fibers, leaving the base point unchanged and reducing the system to a linear control problem on each tangent space, for which we obtain explicit solutions and characterize reachable sets, showing tha

Rujing Yao, Yufei Shi, Yang Wu, Ang Li

Cloud-hosted Large Language Models (LLMs) offer unmatched reasoning capabilities and dynamic knowledge, yet submitting raw queries to these external services risks exposing sensitive user intent. Conversely, relying exclusively on trusted local models preserves privacy but often compromises answer quality due to limited parameter scale and knowledge. To reso

Alexei M. Frolov

The total energies of a large number of diatomic (or two-center) one-electron $A^{+} B^{+} e^{-}$ ions with unit electrical charges are determined numerically to high accuracy. Based on these results we derive some accurate mass-interpolation formulas for the total energies of such three-body systems (ions). These formulas can be applied to the both symmetri

Michael B. Law

Let $(M,g)$ be a complete 4-dimensional Ricci-flat ALE orbifold with finitely many orbifold points and group at infinity $\mathbb{Z}_2$. We prove that if the $L^2$ kernel of its Lichnerowicz Laplacian has dimension at most 3, then $(M,g)$ is either the Eguchi-Hanson space or the flat orbifold $\mathbb{R}^4/\mathbb{Z}_2$. A similar uniqueness result is proved

A. Yu. Zakharov

A system of interacting atoms is represented as an union of two subsystems, one of which is the system of atoms, and the other is an auxiliary scalar covariant field, which is equivalent to a given static interatomic potential of general form only in the non-relativistic approximation. It is shown that the auxiliary field is a superposition of Klein-Gordon f

Ling-Wei Kong, Naomi Ehrich Leonard, Andrew M. Hein

Collective decision-making arises from individual agents integrating their own personal observations with information obtained from social partners. In many biological systems that exhibit collective decision-making, the process by which social information is produced, transmitted, and used is subject to two key constraints. First, individuals often do not o

Shengzhi Li, Jiarun Chen, Karun Sharma, Jiaqi Su

Large vision-language models (VLMs) can recognize \textit{what} happens in video but fail to count \textit{how many} times. We introduce \textbf{PushupBench}, 446 long-form clips (avg. 36.7s) for evaluating repetition counting. The best frontier model achieves 42.1\% exact accuracy; open-source 4B models score $\sim$6\%, matching supervised baselines. We sho

Saber Zerhoudi, Adam Roegiest, Michael Granitzer

User simulation is a valuable methodology for evaluation in Information Retrieval (IR), enabling low-cost experimentation and counterfactual analysis. However, existing simulation frameworks are primarily code-centric libraries that require substantial setup effort, which limits adoption and hinders reproducibility. The bottleneck is not the simulation engin

Ryan A. Baker, William Z. Van Benschoten, James J. Shepherd

Structure factor twist averaging (sfTA) is a newer method that has been shown to reproduce twist-averaged (TA) CCSD energies for bulk systems at a low computational cost. In this work, we extend this method for the treatment of low-dimensional materials in the form of two variants: paired sfTA and binding sfTA. These variants affect which twist angles are us

Andrej Dujella, Zrinka Franušić

We consider the problem of characterizing upper-triangular matrices $M=\begin{pmatrix}p&r\\0&q\end{pmatrix}\in M_2(\mathbb Z)$ which can be represented in the form $A^2-B^2$ with upper-triangular integer matrices $A$ and $B$ and give a complete criterion in terms of representations of $p$ and $q$ as differences of two squares and an additional divisibility c

Giorgio Cruciata, Luca Cruciata, Liliana Lo Presti, Jan Van Gemert

This paper proposes a new method to improve the training efficiency of deep convolutional neural networks. During training, the method evaluates scores to measure how much each layer's parameters change and whether the layer will continue learning or not. Based on these scores, the network is scaled down such that the number of parameters to be learned is re

Ran Zhou, Laurens Boer, Daniel Leithinger, Madeline Balaam

Haptic technologies have advanced rapidly, yet exploration of robotic touch remains dominated by replicating realistic environmental cues or hand gestures, which narrows the design space and risks social resistance. This paper argues for alternatives: grounded in the notion of "otherness" from human-robot interaction (HRI), we propose treating robotic touch'

Aleksandr Beilinson

The article consider an interpretation of Majorana equations as a quantum Lorentz covariant equations for the field of Einstein photon. A photon with "deinterlaced" spins (with diagonal Hamiltonian) is considered, its generalized Green function as a functional on finite test functions and its Schr\"{o}dinger equation are constructed. The generalized process

G. Lamura, T. Winyard, P. Gentile, M. Speight

In the mixed state of type-II bulk superconductors, the magnetic field penetrates in the form of vortices enclosing one magnetic flux quantum: this is the conventional Abrikosov vortex lattice. Here, by using transverse muon-spin spectroscopy, we demonstrate the presence of an unconventional vortex lattice in LiFeAs single crystals. We also show evidence tha

Fatemeh Fogh, Sara Behnamian

We develop a unified T-extended framework for weakly contractive, weakly Kannan, and Geraghty classes of self-maps S on a metric space (X, d), where distances are measured on the auxiliary image via d(Tx, Ty), and the dynamics is governed by the composition of T and S. Under standard assumptions on the auxiliary map T (continuity, injectivity, subsequential

Sheng-Wei Chan, Hsin-Jui Pan, Chun-Po Shen, Chia-Min Lin

High-performance semantic segmentation has achieved significant progress in recent years, often driven by increasingly large backbones and higher computational budgets. While effective, such approaches introduce substantial computational overhead and limit accessibility under constrained hardware settings. In this paper, we propose DGM-Net (Directional Geome

Yijiashun Qi, Xiang Xu, Yuxuan Li

We report a reproducible error pattern in GPT-5.4 on OWL~2~DL compliance queries: the model frequently answers ``unknown'' when the reasoner-entailed answer is ``no'' under \emph{FunctionalProperty} closure or class \emph{disjointness}. Using 180 reasoner-audited queries from a procedural expansion of the observed pattern plus 18 hand-authored held-out queri

Neagin Neasamoni Santhi, Davide Villa, Michele Polese, Salvatore D'Oro

Artificial Intelligence (AI) has become a powerful tool for model-free Radio Access Network (RAN) signal processing and optimization. However, designing a single model that generalizes across all radio environments is challenging. Specialized AI models outperform conventional algorithms only under specific conditions, while their higher compute and energy co

Lekha Das, Bittu Singh

We develop an algebraic model for the relative sectional category of a continuous map in rational homotopy theory using commutative differential graded algebras (CDGAs). Our main result establishes that for formal maps, the rational relative sectional category can be computed purely from cohomology, using ideal nilpotency. We also show that this equality may

Ivan A. Kazakov, Ilona Popova, Arkady Shipulin

We propose a novel bio-inspired design principle for the perfectly vertical grating coupler. The main idea of our design is to introduce anisotropy to the grating stripe to direct the light to one side of the grating. This grating design is easy to manufacture, only requiring a single etching step, and it is designed to efficiently couple vertically incident

Xinyuan Chen, Fan Li

Rerandomization systematically reduces chance imbalance and can improve the efficiency of the average treatment effect estimator in randomized experiments. While the asymptotic properties of finite-dimensional M-estimators under rerandomization have been established, existing theory does not directly address survival outcomes under censoring, where the targe

Zi Meng, Wanli Song, Yi Hu, Jiayuan Rao

Refereeing is vital in sports, where fair, accurate, and explainable decisions are fundamental. While intelligent assistant technologies are being widely adopted in soccer refereeing, current AI-assisted approaches remain preliminary. Existing research mostly focuses on isolated video perception tasks and lacks the ability to understand and reason about foul

Joshua R. Evans, Diego A. Garibay, Aiden N. Kuhls, Mehmet Z. Baykara

Transition metal dichalcogenides (TMDs) attract significant attention as potential building blocks in next-generation electronic devices. On the other hand, a comprehensive understanding of how various defects affect local electronic properties under realistic operational conditions is yet to be formed. Here, we present results of electronic spectroscopy exp

Nicholas Layden, Dipanjan Dey, Alan Coley

We present an invariant characterization of the Kerr spacetime, and utilize the invariant structure of the spacetime to derive a function whose zeros identify a special family of null geodesics. Each member of this family is tangent to every photon surface in the Kerr photon region, offering a method of invariantly characterizing photon surfaces in axially s

Kidist Amde Mekonnen, Yubao Tang, Maarten de Rijke

Generative information retrieval (GenIR) consolidates retrieval into a single neural model that decodes document identifiers (docids) directly from queries. While this model-as-index paradigm offers architectural simplicity, it is poorly suited to dynamic document collections. Unlike modular systems, where indexes are easily updated, GenIR's knowledge is par

Zhe Wang, Qijin Song, Zihao Li, Jingyu Xiao

Accurate 6-DoF pose estimation of objects is critical for robots to perform precise manipulation tasks. However, for dynamic object pose estimation, conventional camera-based approaches face several major challenges, such as motion blur, sensor noise, and low-light limitation. To address these issues, we employ event cameras, whose high dynamic range and low

Daan Rosendal, Ana Oprescu

Federated Learning (FL) typically assumes unconditional collaboration, a premise that overlooks the complexities of real-world, multi-stakeholder environments in which clients may need to exclude one another for strategic, regulatory, or competitive reasons. This paper addresses this gap, which we term 'client-level disagreements,' by first introducing a tax

Duc N. Do, Minh N. Do, Dang Nguyen, Khanh T. Q. Le

Transthoracic echocardiography is the reference standard for confirming structural heart disease (SHD), but first-line screening is limited by cost, workflow burden, and specialist availability. We evaluated whether open pretrained electrocardiogram (ECG) foundation models can support echo-confirmed multi-label SHD detection using the public EchoNext Mini-Mo

Jing-Hong Han, Zhi-Chao Zhao

We develop a full-covariance formalism for pulsar timing array(PTA) -- astrometry verlap reduction function (ORF) estimators and use it to forecast graviton-mass constraints from a nanohertz stochastic gravitational-wave background (SGWB). Analytic covariance expressions are derived for auto- and cross-channel ORF estimators, including signal-signal, noise-n

Santanu S. Dey, Marco Molinaro, Jingye Xu

Two-stage stochastic integer programs provide a powerful framework for modeling decision-making under uncertainty, but they are notoriously difficult to solve at scale due to their high dimensionality and intrinsic nonconvexity. Decomposition-based algorithms such as Benders methods and Branch-and-Price (related dual decomposition methods) have become standa

V. N. A. Lula-Rocha, M. A. S. Trindade

We have developed a non-unitary extension of Grover's search algorithm by changing the hidden geometry of Hilbert space carried by diffusion operator. Our algorithm finds the solution for search problem by performing a unique bigger rotation rather than small rotations in order polynomial times in the size $N$ of search space. We analyze the complexity of im

Xianghao Meng, Yong Huang, James L. Beck, Kui Jiang

Over decades, Markov chain Monte Carlo (MCMC) methods have been widely studied, with a typical application being the quantification of posterior uncertainties in Bayesian system identification of structural dynamic models. To address the issue of excessively low sampling efficiency in generic MCMC methods when applied to specific problems, researchers develo

Bingda Tang, Yuhui Zhang, Xiaohan Wang, Jiayuan Mao

Aligning denoising generative models with human preferences or verifiable rewards remains a key challenge. While policy-gradient online reinforcement learning (RL) offers a principled post-training framework, its direct application is hindered by the intractable likelihoods of these models. Prior work therefore either optimizes an induced Markov decision pro

Lillian Ates, Zachary Chapman, John Estes, Tyler Jackson

For a graph $G$ and vertices $u,v$, we define the ASUA of $v$, $t(G,v,u)$, to be the average steps until absorption along a random walk terminating at $u$. We define a sea dragon to be a tree with a unique path $P$ such that if $d(u) \geq 3$ for some vertex $u$, then $u \in V(P)$. We use Markov chains to determine $t(G,v,u)$ for all vertices of several class

Gianluca Delgado, Elliott Runburg, Chaowei Hu, Yuzhou Zhao

The diverse electronic properties of transition metal chalcogenides can be very sensitive to crystal imperfections. A new crystal growth technique, known as horizontal flux transport, offers a route to improved crystal quality. By refining this technique and applying it to the topological semimetal WTe2, we achieved crystals with an order of magnitude less d

Akihiro Takemura, Katsumi Inoue, Masaaki Nishino

Neurosymbolic systems can satisfy logical constraints during learning without achieving the intended concept-label correspondence; this is a problem known as reasoning shortcuts. We formalize reasoning shortcuts as a constraint satisfaction problem and investigate under which conditions concept mappings are uniquely determined by the constraints. We prove th

Rodolphe Richard, Andrei Yafaev

The authors previously formulated the hybrid conjecture, unifying Andr\'e-Pink-Zannier and Andr\'e-Oort conjectures, and proved it in Shimura varieties of abelian type. We study its analogue for mixed Shimura varieties, and consider the prime example, the universal abelian scheme $\mathcal{A}_g\to \mathbb{A}_g$. In a radical departure from the Pila-Zannier s

Sisipho Hamlomo, Marcellin Atemkeng, Habte Tadesse Likassa, Blaise Ravelo

Convolutional neural networks (CNNs) have become increasingly difficult to deploy in resource-constrained environments due to their large memory and computational requirements. Although low-rank compression methods can reduce this burden, most existing approaches compress spatial and channel redundancy independently and therefore do not fully exploit the loc

Yuandao Cai, Wensheng Tang, Cheng Wen, Shengchao Qin

Autonomous Large Language Model (LLM) agents are increasingly deployed to conduct complex tasks by interacting with external tools, APIs, and memory stores. However, processing untrusted external data exposes these agents to severe security threats, such as indirect prompt injection and unauthorized tool execution. Securing these systems requires effective i

Chung-Ming Huang, Yen-Hung Wu, Duy-Tuan Dao

Resource allocation in vehicular platoons is challenging due to high vehicle mobility and limited spectrum resources. To improve spectral efficiency, resource sharing is commonly adopted. In 5G-based platoons, the Platoon Leader Vehicle (PLV) employs groupcasting to disseminate control messages to Platoon Member Vehicles (PMVs). When the groupcasting power i

Eshwar R. A., Nevin Mathew Thomas, Nehal G, Farida M. Begam

Reconstructing high-fidelity fluid dynamics from sparse temporal observations is quite challenging, mainly due to the chaotic and non-linear nature of fluid transport. Standard deep learning-based interpolation methods often tend to regress to the mean, which results in spatial blurring and temporal strobing, especially noticeable around the observed anchor

Hanna Rød, Dagny Streit, Nils Valseth Selte, Justin Li

This paper investigates context stickiness in in-context learning (ICL), a phenomenon where earlier examples in a prompt interfere with a transformer's ability to adapt to later tasks. Using synthetic regression tasks over linear and quadratic functions, we examine how models trained under sequential, mixed, and random curricula handle abrupt task switches d

Georgiy A. Bondar, Asmaa Eldesoukey, Yongxin Chen, Abhishek Halder

Solutions to the Schr\"{o}dinger bridge problem and its generalizations yield feedback control policies for optimal density steering over a controlled diffusion. To numerically compute the same, the dynamic Sinkhorn recursion has become a standard approach. The mathematical engine behind this approach is the Hopf-Cole transform that recasts the conditions fo

Shivam Tripathi, Jatin Kawatra, Varun Malviya, Krishna Mehta

Vibrational entropy plays a central role in determining phase stability and temperature dependent behavior in materials, yet its calculation from first-principles phonon methods remains computationally demanding. In this work, we combine data-driven modeling with physically motivated analysis to develop an efficient and interpretable framework for predicting

Hongtao Hao, Joseph L. Austerweil

Event-Based Models (EBMs) infer biomarker progression from cross-sectional data but typically only as ordinal sequences and rely on rigid model assumptions. We propose \textsc{Tempo}, a Transformer architecture that learns both ordinal and continuous event sequences through simulation-based supervised learning. \textsc{Tempo} uses two Transformer modules: on

Hélène Cossette, Etienne Marceau, Alessandro Mutti, Patrizia Semeraro

We investigate the Conway--Maxwell multivariate Bernoulli distributions, a family of multivariate Bernoulli distributions derived from the Conway--Maxwell-binomial distribution. We show that it is possible to set the parametrization such that the Bernoulli marginals remain intact, allowing us to study dependence properties within this family. In particular,

Federico A. Kamelhar

Autonomous multi-agent LLM systems are increasingly deployed to investigate operational incidents and produce structured diagnostic reports. Their trustworthiness hinges on whether each claim is grounded in observed evidence rather than model-internal inference. Existing groundedness evaluators (binary classifiers, LLM-as-judge scalars, self-correction loops

Salman Sajad Wani, Sundus Abdi, Rushda Naik, Saif Al-Kuwari

We study quantum gravimetry when the interrogation time carries intrinsic uncertainty, motivated by a fundamental limit on temporal resolution associated with the energy--time uncertainty relation. For linearly gravity-coupled gravimeters, we obtain the effective gravity information by profiling the interrogation time from the two-parameter quantum Fisher in

Shashwath S Shetty, K Arathi Bhat

Spectral hypergraph theory has recently attracted considerable interest as it provides a natural framework for modeling higher-order relationships beyond classical graphs. In this setting, eigenvalues of adjacency, Laplacian, and signless-Laplacian hypermatrices play an important role in understanding the underlying structure of hypergraphs. In this work, we

Hai-Long Jia, Wen-Di Guo, Yu-Peng Zhang, Yu-Xiao Liu

The quasinormal mode spectrum is a unique fingerprint linking gravitational-wave observations to extra-dimensional geometry. In this Letter, we show that thick braneworlds exhibit a spectral butterfly effect: infinitesimal deformations of the effective potential trigger dramatic migrations of quasinormal modes, challenging the presumed stability of this fing

Jingwen Yang, Long Chen, Haoliang Yu, Xiaofeng Jin

High-resolution temperature monitoring is essential for many engineering and scientific applications, but conventional sensors are limited by insufficient resolution and susceptibility to electromagnetic interference. Fiber-optic interferometers provide high sensitivity and intrinsic electromagnetic immunity; however, their practical performance is hindered

Jingyu Zhang, Jacky Wai Keung, Yan Xiao, Yihan Liao

Adversarial attacks play a pivotal role in testing and improving the reliability of deep learning (DL) systems. Existing literature has demonstrated that subtle perturbations to the input can elicit erroneous outcomes, thereby substantially compromising the security of DL systems. This has emerged as a critical concern in the development of DL-based safety-c

Jelena Ilić Vulićević

Large language models (LLMs) have demonstrated strong performance on a wide range of software engineering tasks, including code generation and analysis. However, most prior work relies on cloud-based models or specialized hardware, limiting practical applicability in privacy-sensitive or resource-constrained environments. In this paper, we present a systemat

Xianghui Wang, Siwei Cheng, Shanze Wang, Xinming Zhang

Learning from demonstration is widely used for robot navigation, yet it suffers from a fundamental limitation: demonstrations consist predominantly of successful behaviors and provide limited coverage of unsafe states. This limitation leads to poor safety when the robot encounters scenarios beyond the demonstration distribution. Failure experiences, such as

Rónán Gissler, Kenneth S. Breuer

Using a flapping robot in a wind tunnel, we show that flapping faster amplifies existing longitudinal static stability (focusing on the pitch stiffness) and can even make an unstable flier stable. We show that stability for a flapper is not just a function of the static margin, but also the Strouhal number (St). Experimental data from measurements over a wid

Tao Qian, Yunni Wu, Wei Qu, Yanbo Wang

Let $f$ belong to the Hardy space $H^2(\mathbb{D})$ of the unit disc, and $e_a$ the normalized Szeg\"o (reproducing) kernel of $H^2(\mathbb{D}).$ It is well known that, due to the reproducing kernel property, for any distinct $n$ points $a_1,\cdots,a_n$ in $\mathbb{D}$ the orthogonal projection of $f$ into ${\rm span}\{e_{a_1},\cdots,e_{a_n}\},$ denoted as $

Ziyou Wang, Jim Griffin, Maria Kalli

Understanding the how the distribution of an economic outcome, such as income, changes with respect to space and covariates is a key concern for policy makers. To address this, we develop a Bayesian nonparametric model, the Normalised Latent Measure Factor Model with Covariates (NLMFM-C), which expresses a large collection of related densities as mixtures of

Gulnara Suliyeva, Kuantay Boshkayev, Talgar Konysbayev, Yergali Kurmanov

We explore the viability of scalar field dark matter halo models through the rotation curve analysis of the Andromeda galaxy (M31), taking into account a realistic description of its baryonic structure. The mass model includes a stellar disk described by the Freeman profile and two alternative bulge configurations: a classical single de Vaucouleurs bulge and

Dandan Sun, Xiaoyan Yang, Dongdong Zhang, Panyue Zhou

The main aim of this paper is to study chains of model structures arising from cotorsion pairs in extriangulated categories. Starting with a hereditary Hovey triple, we construct further hereditary Hovey triples whose homotopy categories are equivalent under suitable completeness assumptions, thereby refining results due to El Maaouy and Shao-Wang-Zhang. As