April 2026 arXiv papers — page 162

G. G. L. Nashed, Alnadhief H. A. Alfedeel, Tiberiu Harko

We construct an analytic model of static semiclassical backreaction for a Schwarzschild black hole in the Hartle--Hawking state enclosed within a finite spherical cavity. Using a minimal renormalized stress--energy tensor consistent with conservation, thermal asymptotics, and horizon regularity, we integrate the reduced semiclassical Einstein equations under

Jonathan Brossard

Rice's theorem states that no non-trivial semantic property of programs is decidable. Classical proofs proceed by reduction from the halting problem, invoking the law of excluded middle (LEM) twice: once through diagonalization, and once through a case split on whether the always-diverging program bot satisfies the property in question. We present a proof th

Maneesh Bilalpur, Saurabh Hinduja, Sonish Sivarajkumar, Nicholas Allen

The classical approach to detecting depression from vision emphasizes interpretable features, such as facial expression, and classifiers such as the Support Vector Machine (SVM). With the advent of deep learning, there has been a shift in feature representations and classification approaches. Contemporary approaches use learnt features from general-purpose v

Jiaqi Wen, Pingbo Tang, Shaolei Ren, Jianyi Yang

We study the operation of community water systems, where pumps and valves must be scheduled to reliably meet water demands while minimizing energy consumption. While existing optimization-based methods are effective under well-modeled environments, real-world community scenarios exhibit highly dynamic contexts-such as human activities, weather variations, et

Kartik Patri

Mazur's separable quotient problem, open since 1932, asks whether every infinite-dimensional Banach space admits an infinite-dimensional separable quotient. We prove that any $\mathscr{L}_\infty$-space $Y$ containing a subspace $X$ such that $Y/X$ is infinite-dimensional with the Schur property admits $c_0$ as a quotient. The natural class to which this crit

O. Garcia, O. Obregón, J. Ríos Padilla

We study the effective dynamics of the Schwarzschild black hole interior by introducing entropic deformations derived from generalized superstatistical entropies $S_{+}$ and $S_{-}$. The resulting modified Hamiltonians $\bar{H}_{\pm}$, formulated in Ashtekar--Barbero variables, encode quantum gravity-inspired corrections that become significant near the Plan

Xuan Liu, Dheeraj Kodakandla, Kushagra Srivastva, Mahfuza Farooque

\textbf{VeriTrans} is a reliability-first ML system that compiles natural-language requirements into solver-ready logic with validator-gated reliability. The pipeline integrates an instruction-tuned NL$\!\to\!$PL translator, round-trip reconstruction (PL$\!\to\!$NL) used as a high-precision acceptance gate, and canonical PL$\!\to\!$CNF compilation, all execu

Mathieu Beau

A single operational protocol based on free evolution and projective measurements yields inequivalent quantum time distributions through distinct post-processing procedures. We construct an activity-based time-of-flow (TF) distribution and a presence-based quantum stroboscopic (QS) distribution, providing complementary operational notions of time. Applied to

Naren Manjunath, Maissam Barkeshli

Crystalline symmetries give rise to topological invariants that can distinguish quantum phases of matter. Understanding these in strongly interacting systems is an ongoing research direction requiring non-perturbative methods. Recent developments have demonstrated that even classic models, like the Harper-Hofstadter model of free fermions on a lattice in a m

Adil Derrazi, Javad Pourmostafa Roshan Sharami

Employee attrition presents a major challenge for organizations, increasing costs and reducing productivity. Predicting attrition accurately enables proactive retention strategies, but existing machine learning models often struggle to capture complex feature interactions in tabular HR datasets. While tree-based models such as XGBoost and LightGBM perform we

Josaphat Baolahy, Randrianirina Benjamin

We study two new families of symmetric functions arising from a species-theoretic construction motivated by cycle structure. For each partition of $n$, we define two combinatorial species that decompose into molecules indexed by the same partition, giving rise to two corresponding basis of the homogeneous symmetric functions of degree $n$. We prove that each

Mohamed Ehab, Ali Hamdi

Large language models (LLMs) demonstrate strong performance in math reasoning benchmarks, but their performance varies inconsistently across problems with varying levels of difficulty. This paper describes Adaptive Multi-Expert Reasoning (AMR), a framework that focuses on problem complexity by reasoning with dynamically adapted strategies. An agile routing s

Abu Zahid Bin Aziz, Syed Fahim Ahmed, Gnanesh Rasineni, Mei Wang

Structured Illumination Microscopy (SIM) enables rapid, high-contrast optical sectioning of fresh tissue without staining or physical sectioning, making it promising for intraoperative and point-of-care diagnostics. Recent foundation and large-scale self-supervised models in digital pathology have demonstrated strong performance on section-based modalities s

Khai Loong Aw, Klemen Kotar, Wanhee Lee, Seungwoo Kim

Young children demonstrate early abilities to understand their physical world, estimating depth, motion, object coherence, interactions, and many other aspects of physical scene understanding. Children are both data-efficient and flexible cognitive systems, creating competence despite extremely limited training data, while generalizing to myriad untrained ta

Hina Afridi, Habib Ullah, Sultan Daud Khan, Mohib Ullah

We present the progress of the GPT family from GPT-3 through GPT-3.5, GPT-4, GPT-4 Turbo, GPT-4o, GPT-4.1, and the GPT-5 family. Our work is comparative rather than merely historical. We investigates how the family evolved in technical framing, user interaction, modality, deployment architecture, and governance viewpoint. The work focuses on five recurring t

Khen Cohen, Natanel Nissan, Ofir Nissan, Ariel Lellouch

We present a scalable method for geolocalizing buried fiber-optic cables using Distributed Acoustic Sensing (DAS) and traffic-induced quasi-static seismic signals. Assuming access to one end of the fiber, the method fuses DAS measurements with vehicle trajectories obtained from either video tracking or vehicle-mounted GPS. The fiber geometry is estimated by

Oleg Asipchuk, Maksim Kosmakov, Pavel Zatitskii

We study the two-dimensional Ulam's floating body problem for convex domains with perimetral density $\sigma=\tfrac16$. Using the framework of Zindler carousels, we reduce the problem to a two-dimensional dynamical system associated with an inscribed equilateral hexagon. Our main result shows that the disk is the only convex domain floating in equilibrium in

Rasmus S. Nielsen

Selenium, the world's oldest photovoltaic material, has experienced a renaissance in research over the past decade, with certified solar cell efficiencies climbing from the historical record of 5% to breaking the 10% barrier. Its wide bandgap makes it a particularly interesting candidate for tandem solar cells and indoor photovoltaic applications, yet despit

Jie Shi, Siamak Mehrkanoon

Accurate weather nowcasting remains one of the central challenges in atmospheric science, with critical implications for climate resilience, energy security, and disaster preparedness. Since it is not feasible to deploy observation stations everywhere, some regions lack dense observational networks, resulting in unreliable short-term wind predictions across

Sachinkumar B. Mallikarjun, Marvin Reski, Andreas Weinand, Hans D. Schotten

Physical Layer Authentication (PLA) exploits the spatial uniqueness of wireless channel characteristics in order to authenticate devices without recourse to higher-layer cryptographic protocols, which remain vulnerable to key compromise. This paper reports a comprehensive PLA system constructed on 5G New Radio (NR) Sounding Reference Signals (SRS) extracted

Vishal Pramanik, Maisha Maliha, Susmit Jha, Sumit Kumar Jha

Large language models remain vulnerable to jailbreak attacks -- inputs designed to bypass safety mechanisms and elicit harmful responses -- despite advances in alignment and instruction tuning. We propose Head-Masked Nullspace Steering (HMNS), a circuit-level intervention that (i) identifies attention heads most causally responsible for a model's default beh

Dimitrios Vavitsas, Jujie Wu, Konstantinos Zarvalis

We examine the threshold of the cyclicity for functions in Dirichlet-type spaces $\mathcal{D}_{\alpha}$, $\alpha\in(0,1]$. Given a fixed $\alpha^{*}\in(0,1]$, we construct a holomorphic function $f\in\mathcal{D}_{\alpha^{*}}$ which is cyclic in $\mathcal{D}_{\alpha}$ for all $\alpha<\alpha^{*}$, but fails to be cyclic in $\mathcal{D}_{\alpha^{*}}$. This func

Isaac Pérez Castillo, François Leyvraz, Miguel Eduardo Gómez Quintanar, Andrés Álvarez Ballesteros

Power spectral densities are often interpreted through ensemble averages and long-time asymptotics. In many experiments, however, only a single finite record is available, so spectral estimators remain broadly distributed and the usual independence assumptions across frequencies need not hold. Here we develop an exact finite-$T$ multispectral theory for an o

Ludovico Tesser, Henning Kirchberg, Matteo Acciai, Janine Splettstoesser

We formulate a stochastic description of entropy production in scattering theory for coherent transport. We distinguish between the information entropy change due to partial knowledge of the leads' state and the thermodynamic entropy change due to the equilibration of each lead with its bath. By employing a two-point measurement scheme, we access the stochas

Jianming Wen

We develop a general theory of null-constrained parameter estimation in a time-reversed Young (TRY) interferometer, where measurement is performed through programmable source-basis encoding with a fixed detector. We address the fundamental question of how to design source patterns that enforce a true metrological null -- vanishing nominal response at the ope

Suprovat Ghoshal, Neng Huang, Euiwoong Lee, Konstantin Makarychev

Max-Cut is a classical graph-partitioning problem where given a graph $G = (V,E)$, the objective is to find a cut $(S,S^c)$ which maximizes the number of edges crossing the cut. In a seminal work, Goemans and Williamson gave an $\alpha_{GW} \approx 0.87856$-factor approximation algorithm for the problem, which was later shown to be tight by the work of Khot,

Chee-An Yu, Young-Kai Chen, C. -C. Jay Kuo

In this work, we propose an interpretable, robust, and lightweight machine learning method for automatic modulation classification (AMC) under dynamic and noisy channel conditions. It is called green automatic modulation classification (GAMC) and targets edge artificial intelligence (AI) with low computational complexity and a small model size. GAMC operates

Subin Santhosh, Farwa Abbas, Hussain Ahmad, Claudia Szabo

Background: Large Language Models (LLMs) are transforming artificial intelligence applications in healthcare due to their ability to understand, generate, and summarize complex medical text. They offer valuable support to clinicians, researchers, and patients, yet their deployment in high-stakes clinical environments raises critical concerns regarding accura

Zhehao Yi, Rahul Bhadani

In hybrid classical-quantum computing, variational quantum algorithms (VQAs) have emerged as a promising approach in the Noisy Intermediate-Scale Quantum (NISQ) era; however, their performance is often hindered by barren plateaus, where gradients vanish exponentially, rendering optimization ineffective. In this work, we introduce a residual-inspired quantum

Prasenjit Deb, Almut Beige, Lewis A. Clark

Bell-CHSH-like inequalities have been very successful in benchmarking {\it spatial} quantum correlations. However, as this paper illustrates, they are in general not sufficient for benchmarking {\it temporal} quantum correlations. To show this, we parametrise classical and quantum stochastic finite state generators based on a single bit and a single qubit, r

Marie A. Blatnik, Steven M. Clayton, Bradley W. Filippone, Takeyasu M. Ito

The Cavallo multiplier [http://archive.org/details/b28771035_0003] is an electrostatic inductance machine that can generate low-noise high voltages electrically isolated from its voltage input, making it ideally suited for precision experiments. Its in-situ production makes it especially useful in cryogenic experiments, where the use of traditional feedthrou

Osamah Sufyan, Martin Brückmann, Ralph Wickenhöfer, Babette Dellen

In CT angiography, the accurate segmentation of abdominal aortic aneurysms (AAAs) is difficult due to large anatomical variability, low-contrast vessel boundaries, and the close proximity of organs whose intensities resemble vascular structures, often leading to false positives. To address these challenges, we propose an anatomy-aware segmentation framework

Alok Yadav

Recent tensor-network samplings of modified Nishimori spin glasses have revealed robust finite-temperature critical transitions in two dimensions, defying the standard Edwards-Anderson lower critical dimension boundary ($d_{l}\approx2.5$). We present a theoretical framework demonstrating that the discrete $Z_{2}$ gauge constraints utilized to bypass Monte Ca

Sky Qiu, Charles Barr, Lauren Dang, Issa Dahabreh

As clinical decision-making increasingly moves toward individualized and context-specific treatment recommendations, reliance on any single evidence source, randomized or observational, may be insufficient. Principled integration of randomized controlled trials and real-world data, grounded in explicit causal frameworks, offers a path toward evidence that is

I. Espejo, M. Landete, M. Leal, A. Marín

Hub location problems are central to optimizing logistics, telecommunications, and transportation networks by consolidating flows through strategically placed hubs. While existing models assume symmetric allocation, where hubs handle incoming and outgoing flows uniformly, real-world applications often require asymmetric handling of origins and destinations.

Edward Humes, Tinoosh Mohsenin

As Earth-observation workloads move toward onboard and edge processing, remote-sensing segmentation models must operate under tight latency and energy constraints. We present SatReg, a regression-based hardware-aware tuning framework for lightweight remote-sensing segmentation on edge platforms. Using CM-UNet as the teacher architecture, we reduce the search

Blessing Agyei Kyem, Joshua Kofi Asamoah, Armstrong Aboah

Cooperative perception allows connected vehicles and roadside infrastructure to share sensor observations, creating a fused scene representation beyond the capability of any single platform. However, most cooperative 3D object detectors use a uniform fusion strategy for all object classes, which limits their ability to handle the different geometric structur

Lance Hatch, Navdeep Rana, Shoushou He, Jessica Yu

Solid-state high-harmonic spectroscopy allows the study of strongly driven ultrafast electron dynamics. Microscopically, high harmonics are generated by strong-laser-field acceleration of electron-hole pairs through the lattice. At finite temperatures, atomic-scale structural fluctuations are ubiquitous and are expected to influence the electron-hole traject

K M Arefeen Sultan, Kaysen Hansen, Benjamin Orkild, Alan Morris

High-quality Late Gadolinium Enhancement (LGE) MRI can be helpful for atrial fibrillation management, yet scan quality is frequently compromised by patient motion, irregular breathing, and suboptimal image acquisition timing. While Multiple Instance Learning (MIL) has emerged as a powerful tool for automated quality assessment under weak supervision, current

Jorge Bravo-Gadea

We study the relationship between Lorentz harmonic maps into the hyperbolic plane and spacelike surfaces in anti-de Sitter 3-space. Using loop group techniques, we develop a DPW-type representation for Lorentz harmonic maps and provide an explicit solution of the associated Cauchy problem in terms of a pair of potentials. We then establish a correspondence b

Han Xu, Xuerui Qiu, Baiyu Chen, Xinhao Luo

Current Large Language Models (LLMs) are primarily based on large-scale dense matrix multiplications. Inspired by the brain's information processing mechanism, we explore the fundamental question: how to effectively integrate the brain's spiking-driven characteristics into LLM inference. Spiking Neural Networks (SNNs) possess spike-driven characteristics, an

Vasudevarao Allu, Shobhit Kumar

Let $\mathcal{A}$ denote the class of analytic functions such that $f(0)=0$ and $f'(0)=1$ in the unit disk $\mathbb{D}:=\{z \in \mathbb{C}: |z|<1\}.$ In the present paper, we consider $\mathcal{C}(\varphi) := \left\{ f \in \mathcal{A} : 1+zf''(z)/f'(z) \prec \varphi(z):=(1+z/2)^2 \right\}$, as subclass of convex functions and compute the sharp second and thi

Ragib Shahariar Ayon

LLM-based coding agents are increasingly used to generate code, tests, and documentation. Still, their outputs can be plausible yet misaligned with developer intent and provide limited evidence for review in evolving projects. This limits our understanding of how to structure LLM pair-programming workflows so that artifacts remain reliable, auditable, and ma

Jingru Li, Wei Ren, Tianqing Zhu

Large Vision-Language Models (LVLMs) rely on attention-based retrieval of safety instructions to maintain alignment during generation. Existing attacks typically optimize image perturbations to maximize harmful output likelihood, but suffer from slow convergence due to gradient conflict between adversarial objectives and the model's safety-retrieval mechanis

Vasudevarao Allu, Shobhit Kumar

Let $\mathcal{A}$ denote the class of analytic functions such that $f(0)=0$ and $f'(0)=1$ in the unit disk $\mathbb{D}:=\{z \in \mathbb{C}: |z|<1\}.$ In this paper, we consider $\mathcal{S}^*(\varphi) := \left\{ f \in \mathcal{A} : zf'(z)/f(z) \prec \varphi(z):=(1+z/2)^2 \right\}$, a subclass of starlike functions and we compute the sharp second and third Ha

Hideyasu Yamashita

Some skeptical arguments on the physical reality of quantum states are given. First, I argue that the algebraic formalism of quantum field theory in curved spacetime (algebraic QFTCS, AQFTCS) leads to such a skepticism. Of course we have the purely mathematical notion of states on a $C^{*}$-algebra $\mathfrak{A}$, but usually in non-relativistic quantum mech

Peng Yuan, Bingyin Mei, Hui Zhang

Composed Image Retrieval (CIR) retrieves target images using a reference image paired with modification text. Despite rapid advances, all existing methods and datasets operate at the image level -- a single reference image plus modification text in, a single target image out -- while real e-commerce users reason about products shown from multiple viewpoints.

Cheng Luo, Luping Xiang, Hankun Zhang, Yi Luo

Orthogonal frequency-division multiplexing (OFDM) is a dominant waveform in modern wireless systems, yet its high peak-to-average power ratio (PAPR) and limited adaptability hinder efficient support for integrated communication and sensing. This paper proposes deep block-unitary precoded OFDM (DBU-OFDM), a structure-preserving learning framework that enables

Haochen Pan, Ryan Chard, Song Young Oh, Maxime Gonthier

Modern HPC file systems can contain billions of files and hundreds of petabytes of data, making even simple questions increasingly intractable to answer. Traditional file system utilities such as find and du fail to scale to these sizes. While external indexing tools like GUFI and Brindexer improve query performance, they remain batch-oriented and unsuitable

Pooja Teotia

In this work we develop exact formulas to the number of solutions of $ax+by+cz=n$ in some special cases. In 2020, Binner gave a formula for the number of non negative integer solutions, $N(a,b,c;n)$ in non-negative integer pairs $(x,y,z),$ of the equation $ax+by+cz=n$ assuming that $a,b,c$ and $n$ are natural numbers. However, his formula was in summations o

Tahir Cetin Akinci, Yuksel Celik, Omer Faruk Ertugrul

Electroencephalography provides a non-invasive and cost-effective approach for analyzing neural patterns associated with alcohol dependence. However, reported classification performance in EEG-based alcoholism studies varies considerably, often due to differences in validation strategies rather than intrinsic model capability. This study presents a validatio

Samuel N. Stechmann, Jiuhua Hu, Brandon P. Montemuro, Nan Chen

Sea ice is a complex system, and observations have shown that ice segments (i.e., floes) have a wide range of sizes, with a floe size distribution that follows a power law. However, a theory for the power law and its exponent have remained elusive. Here, floe-resolving numerical simulations are investigated with a discrete element model, in order to gain fur

Malgorzata Gwiazda, Yifu Cai, Mononito Goswami, Arjun Choudhry

Large Language Models (LLMs) have shown promising performance in time series modeling tasks, but do they truly understand time series data? While multiple benchmarks have been proposed to answer this fundamental question, most are manually curated and focus on narrow domains or specific skill sets. To address this limitation, we propose scalable methods for

Adetola Jamal, Mamlankou Charbel, Houédanou Koffi Wilfrid, Dègla Aymard Guy

This paper addresses the limitations of Physics-Informed Neural Networks for time-dependent problems by introducing a tangent bundle learning framework. Instead of directly approximating the solution, we parameterize its temporal derivative and reconstruct the state through a Volterra integral operator that enforces initial conditions exactly. This approach

Judy Hanwen Shen, Daniel Zhu, Siddarth Srinivasan, Henry Sleight

AI is increasingly deployed in multi-agent systems; however, most research considers only the behavior of individual models. We experimentally show that multi-agent "AI organizations" are simultaneously more effective at achieving business goals, but less aligned, than individual AI agents. We examine 12 tasks across two practical settings: an AI consultancy

Cagdas Ulus Agca, Bayram Tekin

Black hole spacetimes contain several geometrically distinguished hypersurfaces, including event and Cauchy horizons, stationary-limit surfaces, and curvature singularities. These structures are usually identified by different geometric or causal criteria. We show that, for the Kerr--Newman black hole, the membrane-paradigm pressure of a stretched horizon ad

Aaron Tuor, claude. ai

This essay identifies a failure mode of AI chat systems that we term attribution laundering: the model performs substantive cognitive work and then rhetorically credits the user for having generated the resulting insights. Unlike transparent versions of glad handing sycophancy, attribution laundering is systematically occluded to the person it affects and se

Mohamed Mabrok

Science is widely regarded as humanity's most reliable method for uncovering truths about the natural world. Yet the \emph{trajectory} of scientific discovery is rarely examined as an optimization problem in its own right. This paper argues that the body of scientific knowledge, at any given historical moment, represents a \emph{local optimum} rather than a

Ezequiel Albentosa-Ruiz, Jasmin E. Washington, Nicola Marchili, Iván Martí-Vidal

Sagittarius A* (Srg A*), the supermassive black hole at the center of the Milky Way, provides a unique laboratory to study accretion dynamics and plasma processes near the event horizon. We investigated the variability and polarization properties of Srg A* using ALMA observations during the 2018 Event Horizon Telescope campaign. We analyzed high-cadence full

Guijia Zhang, Shu Yang, Xilin Gong, Di Wang

Autonomous language-model agents increasingly rely on installable skills and tools to complete user tasks. Static skill auditing can expose capability surface before deployment, but it cannot determine whether a particular invocation is unsafe under the current user request and runtime context. We therefore study skill invocation auditing as a continuous-ris

M. H. M. Rashid

This manuscript presents a systematic study of Calkin algebras -- the quotients $\mathcal{L}(X)/\mathcal{K}(X)$ of bounded operators modulo compact operators on a Banach space $X$ -- and establishes a framework for realizing commutative $C^*$-algebras as such quotients while preserving geometric and topological information. Building on Motakis's reflexive ve

Antoine Tilloy

Strongly coupled quantum field theories in $(1+1)$ dimensions are notoriously hard to solve non-perturbatively. Variational methods, despite their success for quantum many-body physics on the lattice, have long lacked a natural ansatz adapted to the relativistic setting. This monograph explains the intuition behind relativistic continuous matrix product stat

Mariano Fernández Méndez

Cross-modal retrieval between audio recordings and symbolic music representations (MIDI) remains challenging because continuous waveforms and discrete event sequences encode different aspects of the same performance. We study descriptor injection, the augmentation of modality-specific encoders with hand-crafted domain features, as a bridge across this gap. I

Saeideh Bahrami

In this paper, we extend the concept of a Lascar generic automorphism in the setting of models of Peano arithmetic ($\mathrm{PA}$) to the subgroup of the automorphism group of a countable recursively saturated model $\mathcal{M}$ of $\mathrm{PA}$ that fixes pointwise a strong cut $I$ of $\mathcal{M}$, denoted by $(\mathrm{Aut}(\mathcal{M}))_{(I)}$. Then, we

Yoshihisa Suzuki, Masashi Chiba, Rosemary F. G. Wyse

We report the discovery of diffuse stellar substructure in the Milky Way's outer halo toward Bo\"otes, unveiled by deep imaging data of the Subaru/Hyper Suprime-Cam. This substructure is detected as an excess of faint main-sequence stars, at heliocentric distances beyond 30 kpc, extending over at least 100 $\mathrm{deg^2}$. To infer its origin, we compare th

Yoshihisa Suzuki, Masashi Chiba, Rosemary F. G. Wyse, Shunichi Horigome

We investigate the structure of the Milky Way's stellar halo within 70 kpc of the Sun using a wide-field photometric catalog obtained from the Hyper Suprime-Cam (HSC) Subaru Strategic Program (HSC-SSP). We employ a large sample of main-sequence turn-off stars as distance tracers. To robustly derive the structural parameters of the stellar halo, we develop a

Daniel Mwesigwa, Steven J. Jackson

This chapter develops the concept of moral orders of repair, defined as the specific norms, rules, values, and expectations that structure and support joint work and exchange in repair worlds and other spheres of collaborative practice. Drawing on ethnographic fieldwork in mobile phone and computing repair markets in Kampala, Uganda, we identify three key di

Jace C. Waybright, Yeongsun Lee, Jong-Kyu Park

Fusion plasmas are highly sensitive to external magnetic perturbations which result in complex responses near a region known as the resonant layer. Past analytic descriptions of this phenomena used boundary layer theory in a simplified system assuming low plasma beta to predict the onset of instabilities. Here, we present a novel extension of the analytic th

Somnath Roy, Mattia Coccolo, Sayan Gupta, Miguel A. F. Sanjuán

Targeted energy transfer (TET) from a Van der Pol oscillator coupled to a nonlinear energy sink (NES) is investigated under the action of a high-frequency external drive, which tunes the effective natural stiffness and promotes resonance capture, facilitating energy transfer. Using \textit{direct partition of motion} with \textit{complexification averaging},

N. Neha

Iterated Geronimus transformations generate Sobolev-type orthogonal polynomials from classical families. We establish a direct equivalence between a Sobolev inner product involving point evaluation and the first derivative at a point a outside the support of the original measure and two successive Geronimus transformations. Explicit three-term and five-term

Zhenxin Liu, Xuewei Wang

We study the GENERIC (General Equation for Non-Equilibrium Reversible Irreversible Coupling) formulation of the nonlinear Vlasov-Fokker-Planck equation from the perspective of gradient flows along trajectories. After pulling back the reversible component, the evolution can be recast as a generalized Wasserstein gradient flow. The associated free energy funct

Hannes Kneiding, Lucía Morán-González, Nishamol Kuriakose, Ainara Nova

Machine learning is revolutionizing chemistry. Beyond the value of predictive models accelerating virtual screening, generative AI aims at enabling inverse design, reversing the compound-to-property prediction paradigm into property-to-compound generation. Chemists now have access to a rich AI toolbox for organic chemistry, including drug discovery. However,

Nikolay Falaleev

Real-time image denoising is essential for modern mobile photography but remains challenging due to the strict latency and power constraints of edge devices. This paper presents FastSHADE (Fast Self-augmented Hierarchical Asymmetric Denoising), a lightweight U-Net-style network tailored for real-time, high-fidelity restoration on mobile GPUs. Our method feat

T-H. Hubert Chan

We study the universal closest refinement problem on measurable bipartite relations over standard Borel spaces. Given prescribed side measures, the feasible class consists of finite refinement plans concentrated on the relation and carrying one fixed marginal. The main question is whether this highly nonunique class nevertheless contains a mathematically dis

Mani Rash Ahmadi

We prove that in a coupled Kuramoto oscillator network at stable equilibrium, the physical phase displacement under weak output nudging is the gradient of the loss with respect to natural frequencies, with equality as the nudging strength beta tends to zero. Prior oscillator equilibrium propagation work explicitly set aside natural frequency as a learnable p

Florian Haberberger, Lukas Junge

We consider a two-dimensional Bose gas in the dilute regime where $\rho a^2$ is small. For temperatures below the Berezinskii-Kosterlitz-Thouless critical temperature, we derive an explicit upper bound for the free energy density using Bogoliubov theory. Our result captures the contribution of quasiparticle modes with dispersion relation $\sqrt{p^4 + 8\pi \r

My Hanh Pham, Thanh Vu

We show that the independence complex of a tree is contractible if and only if it can be reduced to a path \( P_n \) with \( n \equiv 1 \pmod{3} \) by a sequence of truncation moves at branching points. As a consequence of our method, we also characterize the trees for which the independence polynomial evaluated at \( -1 \) is equal to \( 1 \) or \( -1 \).

Kunho Kim, Sumin Seo, Yongjun Cho, Hyungjin Chung

We propose EditCrafter, a high-resolution image editing method that operates without tuning, leveraging pretrained text-to-image (T2I) diffusion models to process images at resolutions significantly exceeding those used during training. Leveraging the generative priors of large-scale T2I diffusion models enables the development of a wide array of novel gener

Xiaoming Song, Alexander Tortoriello

In this article we study a class of singular stochastic differential equations driven by fractional Brownian motion with Hurst parameter H<1/2. The solution is constructed as the limit of a family of approximating processes, and its trajectory properties are investigated.

Alexander Rezounenko

We discuss the non-uniqueness of continuous solutions to differential equations with a {\it discrete } state-dependent delay and continuous initial functions. We are interested not only in the fact (conditions) of non-uniqueness, but in additional information on the number of non-unique solutions and discuss an approach to classify them. We provide a few exp

Yixuan Pang, Chenjian Wang

We prove mixed-norm estimates for circular averages with respect to $\alpha$-dimensional fractal measures on $\mathbb{R}^2$, using circle tangency bounds when $\alpha \in (0,1]$ and a $\delta$-discretized slicing lemma for fractals when $\alpha \in (1,2]$. The former estimate is sharp, while the latter improves previous results for $\alpha \in (\frac{3}{2},2

Lizhe Chen

This report describes Infernux, an open-source game engine that pairs a C++17/Vulkan real-time core with a Python production layer connected through a single pybind11 boundary. To close the throughput gap between Python scripting and native-code engines, Infernux combines two established techniques - batch-oriented data transfer and JIT compilation - into a

Qinxiao Ma, Ruiqian Li, Cheng Wang, Yang Wang

Unmanned aerial vehicles (UAVs) operating in confined, cluttered environments face significant performance degradation due to nonlinear, time-varying unmodeled dynamics-such as ground/ceiling effects and wake recirculation-that are unaccounted for in traditional controllers. While learning based compensators (e.g., MLPs, TCNs, LSTMs) struggle with historical

Zae Myung Kim, Dongseok Lee, Jaehyung Kim, Vipul Raheja

Existing tool-use benchmarks for LLM agents are overwhelmingly linear: our analysis of six benchmarks shows 55 to 100% of instances are simple chains of 2 to 5 steps. We introduce The Amazing Agent Race (AAR), a benchmark featuring directed acyclic graph (DAG) puzzles (or "legs") with fork-merge tool chains. We release 1,400 instances across two variants: se

Chencheng Zhang, Hao Yang, Bin Jiang, Shaoxuan Cui

This paper develops an entropy-based stability and robustness framework for nonlinear hypergraph dynamics with conservation and flow balance. We consider generator-form systems on the simplex whose state-dependent transition rates capture higher-order (tensor) interactions among nodes. Under a tensor generalized detailed-balance (TGDB) condition, we show tha

Yulin Yu, Yizhou Li, Siddharth Suri, Scott Counts

Conversational generative AI systems such as ChatGPT are transforming how people seek and engage with information online. Unlike traditional search engines, these systems support open-ended, conversational inquiry, yet it remains unclear whether they ultimately expand or constrain the diversity of knowledge that users encounter in online search spaces, a pri

Xiang Chen, Zejia Lu, Liangwen Chen, Jun Gao

We propose DREAMuS, a fixed-target experiment at the High Intensity Heavy-Ion Accelerator Facility (HIAF), to search for muon-philic dark matter mediated by light flavor-violating bosons. DREAMuS is designed to probe the parameter space of a muon-philic dark matter (DM) mediated by a light flavor-violating boson, specifically a vector $Z'$ (or a scalar $\phi

Qian Gao, Ruikang Zhong, Yuanwei Liu

Segmented pinching antenna assisted integrated sensing and communication (ISAC) systems enable flexible spatial resource utilization by allowing different waveguide segments to be dynamically configured for transmission and reception. However, the resulting design requires the joint optimization of antenna deployment, segment partitioning, and beamforming un

Robert Vrabel

We develop a model-free framework for stabilizing quantum states using only empirical finite-difference evaluations of a measurement-derived Lyapunov observable. The controller requires no knowledge of the Hamiltonian, dissipative structure, or generator of the dynamics, and relies solely on discrete measurement data. The approach combines three key elements

José A. Carrillo, Young-Pil Choi, Eitan Tadmor

We investigate a continuum Lagrangian $p$-alignment system given by a nonlocal mean-field system of ordinary differential equations for interacting agents with weak initial data. We first establish global well-posedness of the Lagrangian dynamics and derive quantitative flocking estimates. We next construct Eulerian variables from the possibly non-injective

Zunnan Xu, Zhaoxia Jing, Zhanhua Pan

Reinforcement learning agent-based simulation (RL-ABS) has become an important tool for electricity market mechanism analysis and evaluation. In the modeling of monotone, bounded, multi-segment stepwise bids, existing methods typically let the policy network first output an unconstrained action and then convert it into a feasible bid curve satisfying monoton

Ozgur Can Seckin, Rachith Aiyappa, Madalina Vlasceanu, Filippo Menczer

Our belief systems are shaped by social processes, such as observations and influence, and by cognitive processes, such as the drive for internal coherence. These processes steer how individual beliefs evolve and become connected. The resulting belief networks contain both causal and associative links, including spurious ones, such as stereotypes. Here, we d

Abdullah Y. Etcibasi, Zachary Dobos, C. Emre Koksal

We provide an approach that closely estimates an organization's cyber resources directly from vulnerability timestamps, using a non-stationary queueing framework. Traditional attack-surface metrics operate on static snapshots, ignoring the core attack-defense dynamics within information systems, which exhibit bursty, heavy-tailed, and capacity-constrained be

Panpan Zhang, Shiying Xiao, W. Hudson Robb, Dandan Liu

Functional connectivity analysis is an important tool for characterizing interactions among brain regions, particularly in studies of neurodegenerative disorders such as Alzheimer's disease (AD). Gaussian graphical models (GGMs) provide a promising statistical framework for estimating functional connectivity by capturing conditional dependence relationships

Yuqi Su, Xiaolei Fang

Complex systems such as aircraft engines, turbines, and industrial machinery often operate under dynamically changing conditions. These varying operating conditions can substantially influence degradation behavior and make prognostic modeling more challenging, as accurate prediction requires explicit consideration of operational effects. To address this issu

Priyanka Rajwani, Sarika Jalan

We investigate the emergence of synchronization in the second-order Kuramoto model with adaptive simplicial interactions on a globally connected network. This inertial Kuramoto framework describes systems, where oscillator frequencies evolve over time. Unlike most previous work that ignores inertia, we examine how inertia combined with adaptive higher-order

Yawen Li, George Vosselman, Francesco Nex

Photogrammetric 3D reconstruction has long relied on traditional Structure-from-Motion (SfM) and Multi-View Stereo (MVS) methods, which provide high accuracy but face challenges in speed and scalability. Recently, learning-based MVS methods have emerged, aiming for faster and more efficient reconstruction. This work presents a comparative evaluation between

Zuozheng Zhang, Fubao Xi

This work focuses on a class of regime-switching neutral stochastic functional differential equations (RNSFDEs) with infinite delay, in which the switching component can possess finite or countably infinite many states. To ensure the well-posedness of the underlying process, we first investigate the well-posedness for NSFDEs without Markovian switching under

Michal Krelina, Utku Tefek, Zeki C. Seskir, Kadir Durak

Precise time synchronisation underpins critical infrastructure from telecommunications and financial markets to power grids and scientific metrology. Several families of quantum protocols have been proposed and demonstrated for clock synchronisation and time distribution, exploiting entangled photon pairs, quantum key distribution (QKD) correlations, Hong-Ou

Arno Verduyn, Erwin Aertbeliën, Maxim Vochten, Joris De Schutter

Identifying the trajectories of rigid bodies and of interaction forces is essential for a wide range of tasks in robotics, biomechanics, and related domains. These tasks include trajectory segmentation, recognition, and prediction. For these tasks, a key challenge lies in achieving consistent results when the trajectory is expressed in different coordinate s