April 2026 arXiv papers — page 36

Wei-Jia Huang, Christophe Chareton, Yu-Fang Chen, Kai-Min Chung

Equivalence checking of quantum circuits is a central verification task in quantum computing, ensuring the correctness of circuit optimizations, hardware mappings, and compilation pipelines. Among the primary symbolic methods for this purpose, the path-sum formalism provides a compact representation with powerful reduction rules that yield a canonical form f

Wang Fan, Wei Cao, Xi Zha, Kedi Ma

Long contexts improve capabilities of large language models but pose serious hardware challenges: compute and memory footprints grow linearly with sequence length. Particularly, the decoding phase continuously accesses massive KV cache, dramatically increasing bandwidth and computing pressure. Existing accelerators are primarily designed and evaluated for sh

Hisashi Hayakawa, Mitsuru Sôma, Noortje Peek, Jean-Pierre Rozelot

Total solar eclipses (TSEs) offer a unique opportunity to observe the solar atmosphere, detect limb phenomena, and accurately measure the solar radius. Following the TSE in 1733, Wassenius first reported the existence of prominences to the scientific community. Wassenius' original manuscript is held in the Royal Academy Archives of Sweden; this study transla

Jialin Lei, Teng Zhang

The famous Stallings equalizer conjecture has remained open for more than 40 years, which states that, for any free group \(F_n\) of rank \(n\ge 2\), any free group \(F\), and any two monomorphisms $g,h:F_n\to F,$ the equalizer $\Eq(g,h)=\{w\in F_n\mid g(w)=h(w)\}$ satisfies $\rk \Eq(g,h)\le n.$ The only known case is $n=2$, due to A. D. Logan in 2022. By in

Chenkai Pan, Xinglong Xu, Yuhang Xu, Yujun Wu

Reliably transferring specialized human knowledge from text into large language models remains a fundamental challenge in artificial intelligence. Fine-tuning on domain corpora has enabled substantial capability gains, but the process operates without feedback: when a model fails on a domain task, there is no method to diagnose what is deficient in the train

Peihao Yan, Yun Chen, Jie Lu, Qijun Wang

Emerging delay-critical edge AI applications, such as VR perception and real-time video analytics, impose stringent latency and reliability requirements on 5G networks. However, existing mobility management mechanisms are largely reactive and fail to adapt to dynamic network conditions, resulting in suboptimal handover decisions and degraded performance. In

Rafael Holanda, Cleto B. Miranda-Neto

This paper studies finite projective dimension of finitely generated modules over a Noetherian local ring, by means of spectral sequence methods related to generalized local cohomology. Our main goal is to address a question raised by D. Jorgensen over fifteen years ago, concerning a prescribed bound (via Ext vanishing) for projective dimension over a comple

Mattia Carrino, Stefan Hohenegger

Information theory is a powerful framework to capture aspects of dynamical systems with multiple degrees of freedom. Mathematically, the dynamics can be represented as a continuous curve $\mathcal{C}$ on a suitable hyperplane in flat space and the Fisher information provides the norm of an infinitesimal displacement along this curve. In many applications, ho

Esteban Rodríguez-Betancourt, Edgar Casasola-Murillo

Modern self-supervised representation learning methods often relies on empirical heuristics that are not theoretically grounded. In this study we propose HyDeS, a theoretically grounded method based on multi-view mutual information maximization within an hyperspherical space using Shannon differential entropy with a non-parametric von Mises-Fisher density es

Takato Yasuno

Bayesian finite mixture models can identify discrete risk clusters (low-risk vs. high-risk equipment), but face three critical bottlenecks: (1) insufficient degradation signals from coarse state discretization, (2) unstable cluster identification when data inherently supports fewer clusters than explored, and (3) computational infeasibility of Markov Chain M

Yasuhito Nakajima, Kentaro Yamaguchi

We investigate the necessary and sufficient condition for the existence of good involutions of symplectic quandles, which are defined on free $R$-modules with an antisymmetric bilinear form. In particular, we discuss the nonexistence of good involutions of symplectic quandles.

Arijit Dutta, Marko Toroš, Sougato Bose, Anupam Mazumdar

The paper presents a scheme to detect entanglement arising from the quantum nature of gravity between a spin qubit and photons, using Stokes parameters. One of the crucial tests of the general theory of relativity is the bending of light due to the curvature. Recently, a quantum counterpart of this experiment to test the quantum nature of the gravitational i

Yutaro Naito

In this paper, we study complete simplicial toric varieties admitting faithful actions of large symmetric groups. First, we correct a recent classification result by Esser, Ji, and Moraga concerning $4$-dimensional toric varieties with $S_6$-actions over the complex numbers $\mathbb{C}$, providing the complete list of such varieties. Second, we extend the st

Md Shohel Rana, Tanoy Debnath

Face swapping aims to optimize realistic facial image generation by leveraging the identity of a source face onto a target face while preserving pose, expression, and context. However, existing methods, especially GAN-based methods, often struggle to balance identity preservation and visual realism due to limited controllability and mode collapse. In this pa

Parampuneet Kaur Thind, Vaibhav Katturu, Giacomo Zema, Roberto Del Prete

Designing deep networks that meet strict latency and accuracy constraints on edge accelerators increasingly relies on hardware-aware optimization, including neural architecture search (NAS) guided by device-level metrics. Yet most hardware-aware NAS pipelines still optimize architectures under full-precision assumptions and apply low-precision adaptation onl

Paul Roux, Sylvain Ribault, Jesper Lykke Jacobsen

We show that in critical loop models, torus 1-point functions can be expressed in terms of sphere 4-point functions at a different central charge. Unlike in the Moore--Seiberg formalism, crossing symmetry on the sphere therefore implies modular covariance on the torus. We systematically compute torus 1-point functions in critical loop models, using a numeric

Rafael Bassi Stern, Ruobin Gong, Joseph B. Kadane, Mark J. Schervish

Measures of association in contingency tables, such as odds ratios and their generalizations, are often studied under different sampling schemes that either fix or leave random the margins of the table. While classical results show that certain odds ratios are unaffected by constraining the margins, it is less clear when this invariance holds more generally.

Eric Hillebrand

Heinsohn and Steiger's "Eigentum, Zins und Geld" (1996) proposes the property premium as the foundational determinant of interest, replacing time preference. This paper examines whether the replacement succeeds. It does not. The two arguments against time preference, the savings-inelasticity claim after Hahn and the portfolio-shift claim after Keynes, both f

Yuexin Su, Chenyi Zhang, Peiyuan Huang, Tongyang Li

Computing approximate Karush--Kuhn--Tucker (KKT) points for constrained nonconvex programs is a fundamental problem in mathematical programming. Interior-point trust-region (IPTR) methods are particularly attractive for such problems because they maintain strictly feasible iterates throughout the iterative process and converge to a first-order and second-ord

Zirui Chen, Shiliang Guo, Shiyu Zhao

Guiding Vector Fields (GVFs) are a powerful tool for robotic path following. However, classical methods assume smooth, ordered curves and fail when paths are unordered, multi-branch, or generated by probabilistic models. We propose a unified framework, termed the Score-Induced Guiding Vector Field (SGVF), which leverages score-based generative modeling to co

Samuel Ozechi, Banjo Francis, Wisdom Yakanu, Joe Wayne Byers

This study proposes a portfolio optimization framework that integrates advanced deep learning architectures with traditional financial models to enhance risk-adjusted performance. Using historical data from 2015-2023 across equities, ETFs, and bonds, the research evaluates the predictive power of Graph Neural Networks (GNNs), Deep Reinforcement Learning (DRL

Jonas Knoerr

Integral representations for continuous polynomial local functionals on convex functions are established in terms of a finite family of polynomials. This result is obtained by approximation from a classification of the dense subspace of smooth polynomial local functionals, which is based on a Paley--Wiener--Schwartz-type classification of the Goodey--Weil di

Kjell S. Heinrich, Simen Å. Ellingsen

We derive the formal solution to the dispersion relation for linear surface waves on a horizontal mean current with arbitrary vertical dependence. The problem is cast in a Green's function framework for the Rayleigh equation, neglecting viscosity but making no further approximations about the mean velocity profile. The solution is the dispersion relation in

Arnovi Moinuddin, El Mehdi Er Raqabi, Pascal Van Hentenryck

Coordinating production and material transfers is increasingly important in modern manufacturing systems equipped with mobile transfer robots, known as transbots. This study considers a flexible job shop environment in which heterogeneous transbots transport parts between machines. The shop floor is partitioned into zones, with each transbot assigned to a sp

Ryuto Tomihari, Taiki Kinoshita, Yosuke Oba, Shota Yamanaka

In projectors and head-mounted displays, an out-of-focus image appears blurred. Even when a display itself is in focus, computer operation may be hindered if the display is far from the user or if a user has poor visual acuity, because the user cannot see the screen clearly. In this study, we conducted an experiment in which participants performed a pointing

Christian Weiß

For $s \geq 0$ and a parameter $0 < \beta < 1$, the weak pair correlation function $f_{N,\beta}(s)$ for the first $N \in \mathbb{N}$ elements of a sequence $(x_n)_{n \in \mathbb{N}} \subset[0,1]$ is evidently non-decreasing in $s$. Moreover, it satisfies $\lim_{N \to \infty} f_{N,\beta}(0) = 0$ if the elements of $(x_n)_{n \in \mathbb{N}}$ are distinct. Beyo

Mohammadmehdi Ataei, Farzaneh Askari, Kamal Rahimi Malekshan, Pradeep Kumar Jayaraman

Computer-Aided Design (CAD) models are defined by their construction history: a parametric recipe that encodes design intent. However, existing large-scale 3D datasets predominantly consist of boundary representations (B-Reps) or meshes, stripping away this critical procedural information. To address this scarcity, we introduce Zero-to-CAD, a scalable framew

Boniface Bahati Tadjuidje, Jin L. C. Guo, Jinghui Cheng

Open-source software (OSS) developers often struggle to understand and respond to user context, while existing tools, such as issue trackers (for handling bugs, requests, and feedback), largely focus on technical discussion. Although personas could help, limited resources and UX expertise make them hard to scale. We present PersonaFlow, a tool that generates

Pablo Mateo-Torrejón, Alfonso Sánchez-Macián

The rapid integration of Large Language Models (LLMs) into Multi-Agent Systems (MAS) has significantly enhanced their collaborative problem-solving capabilities, but it has also expanded their attack surfaces, exposing them to vulnerabilities such as prompt infection and compromised inter-agent communication. While emerging graph-based anomaly detection meth

Andriy Miranskyy, Adam Sorrenti, Jasmine Thind, Claude Gravel

Zero-noise extrapolation (ZNE) mitigates errors in near-term quantum devices by extrapolating measurements obtained at amplified noise levels to estimate noise-free expectation values. In practice, commonly used extrapolation models are fitted without enforcing physical constraints, which can yield predictions outside the valid range of quantum observables.

Johannes Moll, Jannik Lübberstedt, Christoph Nuernbergk, Jacob Stroh

Multiple myeloma is managed through sequential lines of therapy over years to decades, with each decision depending on cumulative disease history distributed across dozens to hundreds of heterogeneous clinical documents. Whether LLM-based systems can synthesise this evidence at a level approaching expert agreement has not been established. A retrospective ev

Wenxuan Yang, Xiaoyang Xu, Hanyu Zhang, Zhexuan Xu

Multi-behavior recommendation aims to predict user conversions by modeling various interaction types that carry distinct intent signals. Recently, generative sequence modeling methods have emerged as an important paradigm for multi-behavior recommendation by achieving flexible sequence generation. However, existing generative methods typically treat behavior

Dobrycheva D. V., Vavilova I. B., Kompaniiets O. V., Khramtsov V.

Context. Convolutional neural networks (CNNs) are widely used for automated galaxy morphological classification in large surveys. However, projection effects, image artefacts, and intrinsic degeneracies limit reliable identification of detailed features, requiring large-scale visual validation. Aims. To visually inspect SDSS galaxies at 0.02 < z < 0.1 classi

Riley Grossman, Yi Chen

Unsupervised automatic readability assessment (ARA) methods have important practical and research applications (e.g., ensuring medical or educational materials are suitable for their target audiences). In this paper, we propose a new zero-shot prompting methodology for ARA and present the first comprehensive evaluation of using large language models (LLMs) a

Esteban Rodríguez-Betancourt, Edgar Casasola-Murillo

Content-based image retrieval (CBIR) systems enable users to search images based on visual content instead of relying on metadata. The text domain has benefited from vector search of representations created with unsupervised methods such as BERT. However, modern self-supervised learning methods for vision are mostly not reported in CBIR-related literature, i

Zihan Liu, Yizhen Wang, Rui Wang, Xiu Tang

Fine-tuning unlocks large language models (LLMs) for specialized applications, but its high computational cost often puts it out of reach for resource-constrained organizations. While cloud platforms could provide the needed resources, data privacy concerns make sharing sensitive information with third parties risky. A promising solution is split learning fo

Zeki Zeybek, Rick Mukherjee, Peter Schmelcher

Quantum optimal control (QOC) provides a systematic framework for achieving high-fidelity operations in quantum systems and plays a central role in tasks such as gate synthesis, state transfer, and pulse design. Existing QOC methods broadly fall into two categories: gradient-based and gradient-free algorithms. The associated optimization landscape is often h

Thomas Marquart, Alexis Lavail

The high-resolution near-infrared spectrograph CRIRES+ at ESO VLT covers the Y, J, H, K, L and M bands. The U-Ne and Fabry-Perot calibration light sources, however, only work up to the K-band, leaving the bands L and M without wavelength calibration, and without a way to measure the inclination of the long slit relative to the detector frame. To remedy this,

Michael A. Laurenzano, Shihan Cheng, David A. B. Hyde

We present Incisor, a cloud HPC job submission system for the ex ante instance selection problem: choosing suitable hardware in the challenging but common setting where only the executable, inputs, and invocation commands are available at submission time. In practice, this task is manual and expertise-intensive, requiring users to combine incomplete knowledg

Dmitry Pasechnyuk-Vilensky, Martin Takáč

We study adaptive aggregation for heterogeneous local SGD in convex finite-sum optimization, allowing heterogeneous local horizons, minibatch sizes, gradient noise, and participation. We introduce HEW-Local SGD, a corrected local-SGD method that chooses nodewise server weights by minimizing an explicit one-round upper bound on the next objective value. This

David Hume

We deduce from a theorem of Dvorak--Norin that the separation and treewidth profiles of graphs are asymptotically equivalent, resolving a question of Huang--Hume--Kelly--Lam. As an application, we calculate the separation profiles of Cayley graphs of tree-graded graphs in terms of their pieces. Examples of tree-graded graphs include Cayley graphs of free pro

Christiane Attig, Christiane Wiebel-Herboth, Patricia Wollstadt, Tim Schrills

As human-AI cooperation becomes increasingly prevalent, reliable instruments for assessing the subjective quality of cooperative human-AI interaction are needed. We introduce two theoretically grounded scales: the Perceived Cooperativity Scale (PCS), grounded in joint activity theory, and the Teaming Perception Scale (TPS), grounded in evolutionary cooperati

Tobias C. Sutter, Christopher Popp, Beatrix C. Hiesmayr

Entanglement distillation is the process of converting noisy entangled states into maximally entangled pure states via local operations and classical communication. A long-standing, unresolved question is which entangled states are amenable to distillation, known as the distillability problem. We solve this for Bell-diagonal qutrits with Weyl structure, and

Dongxing Mao, Yilin Wang, Linjie Li, Zhengyuan Yang

Despite recent advances in text-to-image generation, models still struggle to accurately render prompt-specified text with correct spatial layout -- especially in multi-span, structured settings. This challenge is driven not only by the lack of datasets that align prompts with the exact text and layout expected in the image, but also by the absence of effect

Mesfin Taye

Warm-blooded vertebrates accumulate approximately $\Nstar \approx 10^9$ cardiac cycles over a natural lifetime, a striking empirical regularity first quantified by Lindstedt and Calder yet lacking a physical interpretation. We propose that this invariance is consistent with a conserved thermodynamic budget, formulated here as the Principle of Biological Time

Sergio D. Sierra M., Monica Sinha, Marcela Múnera, Carlos A. Cifuentes

Falls among older adults are a significant public health concern, leading to severe injuries, loss of independence, and increased healthcare costs. This study evaluates the effectiveness of various models, including a Geometric approach, XGBoost, SVM, and several deep learning architectures, in classifying walker usage, standing vs. sitting, and posture for

O. Shebeko, A. Arslanaliev, Y. Kostylenko, V. Chahar

We present a field-theoretical description of the deuteron electrodisintegration reaction d(e,e'p)n induced by unpolarized and polarized electrons. The approach combines the Lehmann-Symanzik-Zimmermann in(out) formalism with the clothed particle representation in the instant form of relativistic dynamics, providing a fully relativistic and gauge-independent

Wenbo Wu, Yuqin Chen, Jianhui Lian, Martín López-Corredoira

This study explores the density profile of the stellar disk, radially and azimuthally, based on approximately 8.4 million red clump stars selected from Gaia Bp/Rp spectra. After correcting for selection effects and distance uncertainties, we fit the vertical stellar density profile of the Galactic disk with a two-component model consisting of geometrically t

Anthony Faure-Gignoux, Kevin Delmas, Adrien Gauffriau, Claire Pagetti

Deploying complex Convolutional Neural Networks (CNNs) on FPGA-based accelerators is a promising way forward for safety-critical domains such as aeronautics. In a previous work, we have explored the Versatile Tensor Accelerator (VTA) and showed its suitability for avionic applications. For that, we developed an initial stand-alone compiler designed with cert

Young-Hun Kim

Genomic Schur functions were introduced by Pechenik and Yong in connection with the $K$-theory of Grassmannians. Pechenik proved that genomic Schur functions admit a positive expansion in the basis of fundamental quasisymmetric functions and, for partitions with two parts, a positive expansion in the Schur basis. Later, Kim and Yoo constructed $0$-Hecke modu

Bruno de Mendonça Braga, Alcides Buss, Ruy Exel

We investigate the large scale geometry of certain metric spaces through the lens of dynamics. Our approach establishes a close connection between large scale dynamical phenomena and operator algebras by characterizing various large scale dynamic behaviors in terms of GNS representations of the uniform Roe algebras arising from natural canonical states. Our

Xingyu Zhou, Malcolm Macdonald, Roberto Armellin, Dong Qiao

This paper develops a robust angles-only IROD method based on polynomial optimization for arbitrary nonlinear dynamics. First, the relative motion is approximated by high-order Taylor polynomials within the differential algebra framework, and the resulting cross-product-residual minimization problem is solved through a recursive polynomial optimization proce

Syeda Kaneez Fatima, Yousuf Abrar, Abdul Rehman Tahir, Amelia Nawaz

Autonomous coding agents are reshaping software development by creating pull requests (PRs) on GitHub, referred to as agentic PRs. In parallel, the review process is also becoming autonomous, thereby making reviewer bots key actors in the assessment of these agentic PRs. However, their influence on PR acceptance and resolution remains unclear. This study emp

Bongshin Lee, Seongjae Bae, Mengying Li, Eun Kyoung Choe

Mobile health (mHealth) applications support health management through the collection and visualization of rich data, yet the quality of the visualizations varies widely. A key limitation lies in the challenge of effectively visualizing temporally dense, irregular, and context-dependent health data within the constrained mobile interfaces. We argue that this

Kaijun Zhou, Qiwei Chen, Da Peng, Zhiyang Li

Vision-Language-Action (VLA) models are promising for generalist robot control, but on-robot deployment is bottlenecked by real-time inference under tight cost and energy budgets. Most prior evaluations rely on desktop-grade GPUs, obscuring the trade-offs and opportunities offered by heterogeneous edge accelerators (GPUs/XPUs/NPUs). We present a systematic a

Károly Seller, Günter Sigl

We study the magnetic fields produced in the early Universe during the electroweak symmetry breaking by considering random configurations of an inhomogeneous Higgs field. By exploiting the inherent randomness of the initial configurations the spectrum of the produced magnetic field is essentially analytic, which bypasses the need for costly lattice simulatio

Kenta Watanabe

For a smooth irreducible curve $C$, its second gonality $d_2$ is defined to be the minimum integer $d$ such that $C$ admits a linear series $g_d^2$. In this paper, we compute the second gonality of a smooth aCM curve $C$ lying on a smooth quartic surface in $\mathbb{P}^3$, whose Clifford index is computed by a net on $C$.

Connor Baumler, Calvin Bao, Huy Nghiem, Xinchen Yang

Despite the growing use of large language models (LLMs) for writing tasks, users may hesitate to rely on LLMs when personal style is important. Post-editing LLM-generated drafts or translations is a common collaborative writing strategy, but it remains unclear whether users can effectively reshape LLM-generated text to reflect their personal style. We conduc

Sinin Zhang, Yunfei Xie, Yuxuan Cheng, Haoyu Zhang

Vision-Language Models (VLMs) have demonstrated strong performance on textbook-style physics problems, yet they frequently fail when confronted with dynamic real-world scenarios that require temporal consistency and causal reasoning across frames. We identify two fundamental challenges underlying these failures: (1) spatio-temporal identity drift, where obje

Hongxin Li, Xiping Wang, Jingran Su, Zheng Ju

Autonomous agents capable of navigating Graphical User Interfaces (GUIs) hold the potential to revolutionize digital productivity. However, achieving true digital autonomy extends beyond reactive element matching; it necessitates a predictive mental model of interface dynamics and the ability to foresee the "digital world state" resulting from interactions.

Pengfei Suo, Wudi Cao, Xingxing Wu, Wenjie Zhang

Material properties are fundamentally dictated by multiscale phenomena, which often reach mesoscale in size. The {\mu}m mesoscale is also the size which can be observed directly under an optical microscope, bridging the atomistic microscopic description with the continuous model macroscopic world. In this work, we report an unprecedented molecular dynamics (

Joanna Delicaris, Erika Ábrahám, Anne Remke

Control applications for cyber-physical systems must make reliably safe control decisions in the presence of continuous dynamics as well as stochastic uncertainty. Providing safety guarantees for such systems requires formal modeling and analysis techniques that capture these aspects. For modeling, in this paper we consider rectangular automata with random c

E. Farah, A. Ouahsine, P. G. Verdin, B. Kaoui

The direct-forcing immersed boundary method (DF-IBM) algorithm previously developed by the authors is extended by coupling the Navier-Stokes equations with the Newton-Euler equations for rigid body dynamics within the DF-IBM framework. This coupling broadens the applicability of the previous development, from stationary or prescribed motion to flow-induced (

Qing Guo, Qihan He, Wei Shuai, Xuexiu Zhong

The aim of this paper is to establish multiple positive normalized solutions $(u,v,\lambda_1,\lambda_2)\in H^1(\mathbb{R}^N,\mathbb{R}^2)\times \mathbb{R}^2$ to the following coupled Schr\"odinger system involving Sobolev critical exponent: $$ \begin{cases} -\Delta u+\lambda_1 u=\mu_1|u|^{p-2}u+\nu\alpha|u|^{\alpha-2}u|v|^\beta, x\in \mathbb{R}^N,\\ -\Delta

S Porter, D Deidda, D R McGowan, J Anton-Rodriguez

Quantitative post-treatment activity imaging is essential for personalised dosimetry after Yttrium-90 selective internal radiation therapy (SIRT). Yttrium-90 PET offers high spatial resolution but is extremely low-count, whereas bremsstrahlung SPECT has higher count statistics but is degraded by blur, scatter, and septal penetration. Since both modalities im

ATLAS Collaboration

In ultra-relativistic heavy ion collisions at the LHC, each nucleus acts as a source of high-energy quasi-real photons that can participate in scattering processes without causing either participating nucleus to break up and emit forward neutrons. This paper extends recent measurements of $\gamma+A\rightarrow\mathrm{jets}$ production in ultra-peripheral Pb+P

Dong Xiao, Jian Wang

Wideband spectrum sensing motivates sub-Nyquist sampling architectures that exploit spectral sparsity, yet in blind scenarios where subband locations are unknown, existing schemes require sampling rates at least twice the theoretical minimum. To this end, we propose a dual-frequency aliasing wideband converter (DAWC), which partitions the multiband spectrum

Matteo Amestoy, R. Vermeulen, Mark A. van de Wiel, Wessel N. van Wieringen

A novel data-driven methodology is presented for the joint selection of prior parameters for both fixed and random effects in Linear Mixed Models (LMMs). This approach facilitates the estimation of complex random-effects structures, as well as potentially high-dimensional data. Although Bayesian frameworks require the specification of informative prior param

Lisa Korver, Mohamed Mostagir, Sherief Reda

As the application of Large Language Models (LLMs) spreads across various industries, there are increasing concerns about the potential for their misuse, especially in sensitive areas such as political discourse. Deliberately aligning LLMs with specific political ideologies, through prompt engineering or fine-tuning techniques, can be advantageous in use cas

Laïla Elkoussy, Julien Perez

In this paper, we introduce SWE-QA, a text and code corpus aimed at benchmarking multi-hop code comprehension, addressing the gap between simplified evaluation tasks and the complex reasoning required in real-world software development. While existing code understanding benchmarks focus on isolated snippets, developers must routinely connect information acro

Phat Lam

Electroencephalography (EEG) is highly susceptible to artifact contamination, such as electrooculographic (EOG) and electromyographic (EMG) interference, which severely degrades signal quality and hinders reliable interpretation in applications including neurological diagnosis, brain-computer interfaces (BCIs), etc. Effective EEG denoising remains challengin

Md Shohel Rana, Andrew H. Sung

AI-generated media are advancing rapidly, raising pressing concerns for content authenticity and digital trust. We introduce DYMAPIA, a multi-domain Deepfake detection framework that fuses spatial, spectral, and temporal cues to capture subtle traces of manipulation in visual data. The system builds dynamic anomaly masks by combining evidence from Fourier sp

Romain Lloria, Boris Kolev

We reformulate the Elasticity complex and Saint-Venant's compatibility condition using the generalized differential complex of Dubois-Violette-Henneaux. This is just a slight and natural modification of the de Rham complex to take account of the index symmetry of the tensors involved. An integrating formula to recover the displacement from the strain and sim

Ryosuke Nogami, Jaeha Lee

We analyze device-dependent correlation sets generated by fixed local dichotomic measurements for two-qubit systems in the $(2,m,2)$ Bell scenario. We consider three fundamental state spaces for the composite system: the separable state space, the standard quantum state space, and the maximal tensor-product state space, which contains beyond-quantum states c

Debarthi Pal, Ritajit Majumdar, Padmanabha Venkatagiri Seshadri, Anupama Ray

Noise in contemporary quantum hardware is highly non-uniform across qubits and couplers, giving rise to localized low-noise "islands" within otherwise noisy device topologies. As quantum workloads scale, executions are increasingly forced to traverse high-noise regions, degrading algorithmic fidelity. Circuit cutting provides a route to circumvent such regio

Emine Yildirim

In this paper, we present the production of double SM-like Higgs $ (h) $ at the future electron positron colliders within the context of $ S_{3} $ model with three Higgs doublets (S3-3H) and no CP violation to describe beyond the Standard Model Higgs Physics. We focus first on the numerically allowed parameter space of the model, taking into account theoreti

Matthew Teasdale, Dimitris Stamatellos

Over 50 circumbinary exoplanets have been discovered in recent years, with several of them being gas giants on wide orbits ($>10$AU). The aim of this work is to investigate whether these planets can form through circumbinary disc fragmentation due to gravitational instability. We perform hydrodynamic simulations of marginally unstable (i) circumstellar discs

Akash Sharma, Chinmay Mhatre, Sankalp Gawali, Ruthvik Bokkasam

Robust vehicle detection from fixed CCTV cameras is critical for Intelligent Transportation Systems. Yet existing benchmarks predominantly feature relatively homogeneous, highly organized traffic patterns captured from ego-centric driving perspectives or controlled aerial views. This regional and sensor view bias creates a significant gap. Models trained on

Targyn A. Nauryz

We study the nonlinear generalized heat equation $C(u)u_t=\frac{1}{z^{\nu}}\left(K(u)z^{\nu}u_z\right)_z$, where $C(u)$ and $K(u)$ are temperature-dependent thermal coefficients and $\nu>0$ is a geometric parameter describing the varying cross-section geometry. By applying the classical Lie symmetry method, we derive the determining equations and perform a c

Leonida A. Gizzi, Damiano Del Sarto, Federico Avella, Gabriele Bandini

Radiotherapy with Very High Energy Electron (VHEE) beams is being extensively investigated for the treatment of deep-seated tumours, even in view of novel protocols based on the so-called FLASH effect. Laser WakeField Acceleration (LWFA) provides a compact and affordable accelerator technology for VHEE electron beams, featuring ultra-high instantaneous dose

Jason Ramapuram, Eeshan Gunesh Dhekane, Amitis Shidani, Dan Busbridge

Speech-only spoken language models (SLMs) lag behind text and text-speech models in performance, with recent discrete autoregressive (AR) SLMs indicating significant computational and data demands to match text models. Since discretizing continuous speech for AR creates bottlenecks, we explore whether continuous diffusion (CD) SLM is more viable. To quantify

Hiromitsu Goto, Tao Tao, Zheng-Lin Chia

Quantitative analysis of the kinematic chain in sports motion is essential for performance evaluation and injury prevention. Conventional methods such as the kinematic-sequence (KS) and continuous relative phase (CRP) are confined to adjacent joint pairs and lack a unified framework for whole-body coordination, while segmental power-flow analysis requires fo

Yuan Tian, Hao Ge, Jiangpo Zheng, Xiujuan Zhang

Encoding more information into wave fields is a central goal in imaging, communication, and wave control. Optical holography benefits from polarization multiplexing, but acoustic holography remains largely limited to pressure-only encoding because sound in fluids lacks naturally independent vector channels. Here, we show that particle velocity can serve as a

Kun Meng, Shao-Jun Zhang, Nan Yang

Extreme Mass Ratio Inspirals (EMRIs) are among the key targe sources for the space-based gravitational wave (GW) detectors. The waveforms of the EMRIs are highly sensitive to the types of the central supermassive black hole (SBH) and can serve as a novel sensitive tool to probe the background spacetime. In this work, we compute GWs radiated from EMRIs in the

Dömötör Pálvölgyi

We extend a recent construction concerning polychromatic colorings of hereditary hypergraph families. For every integer $h\ge 4$ we construct a $(2h-1)$-uniform hypergraph which has no polychromatic $3$-coloring, but all of whose $h$-heavy restricted subhypergraphs are $2$-colorable. Together with the previously known case $h=3$, this gives examples with uni

Ida Juliane Bundgaard, Catarina G. Ferreira, Yonas Lebsir, Christos Tserkezis

We introduce transition metal-dichalcogenide (TMD) nanostructures as a promising platform for the realisation of structural colours. Processing of semianalytically calculated reflectance spectra of TMD nanosphere arrays shows a wide range of colours, which are obtained simply through tailoring the radius and separation of spheres in the array, with the size-

Fei Gao, Yi Lu, Minghui Ding, Xinyang Wang

Through analyzing the quark propagator under the magnetic field, we establish that the axial anomaly originates from an additional Dirac structure in quark propagator induced by the magnetic field. This Dirac structure also allows one to connect the axial anomaly with the topological properties of the system by checking the axial vector Ward identity. For th

Sebastián V. Romero, Xi Chen, Yue Ban

Quantum batteries (QBs) have emerged as a promising route for fast energy storage and on-chip power supply in quantum devices. Given the limited analytical understanding of open Floquet QBs, we employ the kicked-Ising model as a tractable platform to systematically study its performance under realistic conditions, including finite temperature effects and env

Ikhlas Enaieh, Olivier Fercoq, García Ángel

We investigate the explanability properties of the recently proposed linear-min-max neural networks. At initialization, they can be interpreted as k-medoids with the infinity norm as a distance. Then, they are trained using subgradient descent to better fit the data. The model has been shown to be a universal approximator. Yet, we can trace the decision proc

Maike Reimer, Bianca Burkert, Theresa Goerg, Isabell M. Welpe

Conflict in research teams was a near-ubiquitous phenomenon, with the three top issues being lack of respect or overconfidence, non-collegial behaviour and authorship. Most frequently involved (and perceived as most helpful) were informal sources of support, such as colleagues at the same institution and private contacts. Official institutional bodies were l

Rameshwar Mishra, A V Subramanyam

The recent surge in content consumption through streaming services has driven a growing demand for personalized content. Personalized advertisements (ads) play a crucial role in enhancing both user engagement and ad effectiveness. A key aspect of ad personalization involves replacing existing regions in a frame with custom, Photoshop-generated banners. Howev

Juhee Lee, Seong-Gyu Yang, Ludvig Lizana

We study how stochastic resetting affects first-passage processes in systems of many interacting particles. While resetting is well understood for single-particle dynamics, its consequences for collective behavior remain less clear. We consider a protocol in which all surviving particles are reset to the position of the most extreme one, motivated by problem

Yufan Zhou, Yirui Huang, Zhao Wang, Yucheng Jin

Diversity is an important evaluation criterion for recommender systems beyond accuracy, yet users differ in their willingness to engage with novel and diverse content. In this work, we investigate how a Large Language Model (LLM)-based multi-agent system supports users' exploration of diverse recommendations, and how individual characteristics shape user exp

Abdallah Abou Hasna, Nada Chendeb, Ammar El Falou

Public warning systems (PWS) in cellular networks enable authorities to broadcast emergency alerts to all mobile phones in a geographic region in the event of threats such as earthquakes or severe weather. If an attacker can imitate these alerts and transmit a forged warning containing fake news or phishing links, the impact could range from public panic to

Seyyid Osman Sevgili, Atahan Cilan, Mahir Demir, Özgün Can Yürütken

This article evaluates an artificial intelligence (AI)-based Automatic Ground Collision Avoidance System (AGCAS) designed for advanced jet trainers to enhance operational effectiveness. In the continuously evolving field of aerospace engineering, the integration of AI is crucial for advancing operations with improved timing constraints and efficiency. Our st

Carlos Contreras Peña, Jeong-Eun Lee, Philip W. Lucas, Gregory Herczeg

FU Ori outbursts are thought to play a key role in stellar mass assembly and in the chemistry of protoplanetary disks during the early formation of stars. However, uncertainties remain regarding the universality of these events and the physical mechanism driving the high-amplitude variability. In this work, we present an analysis of optical, near- and mid-IR

Leonardo Haw-Yang Foo, Chih-Kai Yang, Chen-An Li, Ke-Han Lu

Large Audio-Language Models show consistent performance gains across speech and audio benchmarks, yet high scores may not reflect true auditory perception. If a model can answer questions without processing the acoustic signal, the benchmark fails as a measure of auditory understanding. We present a diagnostic framework using two axes: text prior, which meas

Jun Sasaki

In this paper, we study the moduli space of Higgs pairs, which can be considered as a generalization of holomorphic pairs. Higgs pairs are an example of quiver bundles. We introduce the notion of $\tau$-stability of Higgs pairs for $\tau\in\mathbb{R}$ and establish the Kobayashi-Hitchin correspondence for Higgs pairs. The differential-geometric objects corre

Senan Sekhon

A well-known result from the 1960s characterizes all Euclidean domains in which division is guaranteed to produce a unique quotient and remainder. As this relies on the historical (and more restrictive) definition of a Euclidean domain, the question of whether the result still holds under the modern definition was left open. In this paper, we prove the answe

Sicong Cao, Jinxuan Xu, Le Yu, Jing Yang

Accurate vulnerability-inducing commit identification serves as a foundation for a series of software security tasks, such as vulnerability detection and affected version analysis. A straightforward solution is the SZZ algorithm, which traces back through the code history to identify the earliest commit that modify the vulnerable code. Unfortunately, neither