April 2026 arXiv papers — page 151

Shunta Ide, Shu Tanaka

Quantum annealing (QA) is a promising approach for solving combinatorial optimization problems; however, it is known to exhibit unfair sampling, in which degenerate ground states are not sampled with equal probability even for sufficiently long annealing times. Fair sampling is important in applications such as solution diversity assessment and combinatorial

Vicente Vergara

We study the $p$-variational capacity of interior condensers in a bounded open set $\Omega\subset\mathbb R^n$ when both plates are determined by a single phase $\theta:\Omega\to\mathbb R$ in $W^{1,\infty}(\Omega)$ through sublevel and superlevel sets. By restricting the admissible class to potentials of the form $u=v\circ\theta$ and applying the coarea formu

Wenqi Cai, John Abanes, Nikolaos Evangeliou, Anthony Tzes

Ensuring operational safety is critical for human-to-humanoid motion imitation. This paper presents a vision-based framework that enables a humanoid robot to imitate human movements while avoiding collisions. Human skeletal keypoints are captured by a single camera and converted into joint angles for motion retargeting. Safety is enforced through a Control B

Zhipeng Chen, Tao Qian, Wayne Xin Zhao, Ji-Rong Wen

Recently, scaling reinforcement learning with verifiable rewards (RLVR) for large language models (LLMs) has emerged as an effective training paradigm for significantly improving model capabilities, which requires guiding the model to perform extensive exploration and learning, leading to substantial computational overhead and becoming a key challenge. To re

Bas Cornelissen, Willem Zuidema, John Ashley Burgoyne, Henkjan Honing

How to describe the shape of a melodic phrase? Scholars have often relied on typologies with a small set of contour types. We question their adequacy: we find no evidence that phrase contours cluster into discrete types, neither in German or Chinese folksongs, nor in Gregorian chant. The test for clustering we propose applies the dist-dip test of multimodali

Yongxiang Liu, Jie Zhou, Yafei Song, Tianpeng Liu

Synthetic Aperture Radar (SAR) imagery generation is essential for deepening the study of scattering mechanisms, establishing trustworthy electromagnetic scene models, and fundamentally alleviating the data scarcity bottleneck that constrains development in this field. However, existing methods find it difficult to simultaneously ensure high fidelity in both

Zhishuai Liu, Guoxin Wei

In this paper, we study the area-preserving and length-preserving $\kappa^\alpha$-type curvature flows of smooth, closed, convex curves in the two-dimensional hyperbolic plane $\mathbb H^2$ for $\alpha<0$ and prove that convexity is preserved along the flows. Assuming that the flows exist for all time, we show that the evolving curves converge smoothly to ge

Noureldin Mohamed, Saif Al-Kuwari

Device-independent quantum key distribution (DI-QKD) provides the highest level of cryptographic security by certifying secrecy through observed Bell inequality violations, independent of the internal device physics. However, the transition from theory to practice is obstructed by the dual challenge of closing the detection loophole and achieving viable key

Rajeshree Chakraborty, Ramandeep S. Johal

We analyze the steady-state formalism for optimizing the cooling power of a thermoelectric refrigerator (TER), unifying the endoreversible and exoreversible approximations within one framework. Although the cooling power is non-optimizable within the endoreversible model based on Newtonian heat-transfer law, we show that the issue can be circumvented in the

Qiang Wan, Ze Yang, Dawei Yang, Ying Fan

Generative Recommendation (GR) has gained traction for its merits of superior performance and cold-start capability. As the vital role in GR, Semantic Identifiers (SIDs) represent item semantics through discrete tokens. However, current techniques for SID generation based on vector quantization face two main challenges: (i) training instability, stemming fro

M. De Simone, L. Cacciapuoti, D. Capela, E. Macias

Protostellar streamers are elongated structures that channel material from larger scale onto disks, influencing their physical and chemical evolution. The M512 protostar in Orion/Lynds 1641 hosts one of the most massive and extended streamer discovered so far, offering a unique opportunity to study these processes. We investigate the morphology, chemistry, a

R. Lopes Seeger, G. Philippe, A. Jenkins, L. C. Benetti

We report the experimental control of Floquet magnons in a magnetic vortex. Using microwave spectroscopy of vortex state magnetic tunnel junctions (MTJs), we find that self-induced Floquet sidebands form frequency combs whose existence depend on the vortex core orbit. By shifting the vortex core with an applied magnetic field, we switch the system between re

Joubine Aghili, Rémi Imbach, Anne Pallarès, Philippe Schmitt

Time-Resolved Spectroscopy (TRS) is a powerful modality for non-invasive characterization of turbid media. However, extracting optical properties, absorption $\mu_a$ and reduced scattering $\mu_s'$, from 3D stochastic measurements remains computationally expensive for real-time applications. In this paper, we propose a data-efficient, physics-informed transf

Argyrios Papoudakis, Mirella Lapata, Frank Keller

Character description generation is an important capability for narrative-focused applications such as summarization, story analysis, and character-driven simulations. However, generating accurate character descriptions from long-form narratives (e.g., novels) is challenging: models must track evolving attributes (e.g., relationships and events), integrate e

Méziane Ait Ziane, Michel Zasadzinski, Cédric Join, Michel Fliess

Our objective is to study the performance and robustness of the model-free strategy for controlling the oxygen stoichiometry of a fuel cell air supply system with a proton exchange membrane. After reviewing the literature on modeling and control of this process, the model-free approach appears to be a good candidate because, on the one hand, it allows straig

Lorenzo Piarulli, Marco Faltelli, Dirk Pleiter, Karthee Sivalingam

High-performance computing (HPC) systems increasingly support both scalable AI training and large-scale simulation workloads. Both typically rely heavily on collective communication operations. On modern supercomputers, however, network congestion has emerged as a major limitation, driven by heterogeneous traffic patterns resulting from diverse workload mixe

Tatsuya Matsuki, Shun Furusawa, Kohsuke Sumiyoshi, Hong Shen

In this study, we investigate the impact of effective nucleon mass and the existence of the dineutron $(\mathrm{^{2}n})$ and the tetraneutron $(\mathrm{^{4}n})$ on the thermodynamic properties and nuclear compositions by constructing new equations of state. Our results indicate that the model with a larger effective nucleon mass slightly alters the nuclear c

Mir Mehedi A. Pritom, Seyed Mohammad Sanjari, Maraz Mia, Ashfak Md Shibli

SMS Phishing (also known as 'smishing') is a growing deceptive social engineering (SE) attack that leverages mobile SMS to conduct cybercrimes such as stealing sensitive information or spreading malware by tricking users into interacting with attackers' messages (e.g., responding to or clicking URLs). This threat has increased rapidly in recent years, causin

Linfeng Xie, Xiaogang Liu

Let $tK_4$ denote the family of all graphs consisting of $t$ copies of $K_4$ that are allowed to share vertices and $t\mathcal{K}_{4}^{-}$ be the set of all unbalanced signed graphs whose underlying graphs are elements of $tK_4$. In this paper, we characterize the extremal graphs that achieve the maximum index and spectral radius among all $t\mathcal{K}_{4}^

Haofu Yang, Jiaji Liu, Chen Huang, Faguo Wu

Developing non-collaborative dialogue agents traditionally requires the manual, unscalable codification of expert strategies. We propose \ours, a method that leverages large language models to autonomously induce both strategy actions and planning logic directly from raw transcripts. METRO formalizes expert knowledge into a Strategy Forest, a hierarchical st

Steven Rivetti, Gabor Fodor, Emil Bjornson, Mikael Skoglund

Integrated sensing and communication (ISAC) systems rely on communication waveforms to perform sensing tasks, thus making their sensing performance strongly dependent on the level of communication symbol knowledge available to the sensing receivers. However, the existing literature fails to capture this dependency, often relying on assumptions of full symbol

Frederic Rendell-Bhatti, Vinzent G. Hana, Csongor Joba, David Boldrin

Sustainable solid-state refrigerants based on barocaloric materials are often limited by thermal hysteresis associated with supercooling effects. Here, we present imaging methods to investigate and compare thermal behaviour and transition kinetics of the barocaloric molecular crystal neopentyl glycol (NPG) with those of a lightly doped derivative, NPG$_{0.99

Nassir Navab, Zhongliang Jiang

For the past decades medical robotic solutions were mostly based on the concept of tele-manipulation. While their design was extremely intelligent, allowing for better access, improved dexterity, reduced tremor, and improved imaging, their intelligence was limited. They therefore left cognition and decision making to the surgeon. As medical robotics advances

Filippo Quarenghi, Ryan Cotsakis, Tom Beucler

The ``differentiability gap'' presents a primary bottleneck in Earth system deep learning: since models cannot be trained directly on non-differentiable scientific metrics and must rely on smooth proxies (e.g., MSE), they often fail to capture high-frequency details, yielding ``blurry'' outputs. We develop a framework that bridges this gap using two differen

Bendegúz Györök, Roel Drenth, Chris Verhoek, Tamás Péni

The integration of first-principles models with learning-based components, i.e., model augmentation, has gained increasing attention, as it offers higher model accuracy and faster convergence properties compared to black-box approaches, while generating physically interpretable models. Recently, a unified formulation has been proposed that generalizes existi

Dzenan Hamzic, Florian Skopik, Max Landauer, Markus Wurzenberger

Cyber threat intelligence (CTI) analysts must answer complex questions over large collections of narrative security reports. Retrieval-augmented generation (RAG) systems help language models access external knowledge, but traditional vector retrieval often struggles with queries that require reasoning over relationships between entities such as threat actors

Xiangyu Liang, Sicheng Zhang

In this paper we prove that if a closed set in R^N is close to a cone over a simplicial complex at each point and at each scale, then it is locally bi-H\"older equivalent to such a cone. This generalizes Reifenberg's Topological Disk Theorem in 1960 and G. David, T. De Pauw and T. Toro's result in 2008.

Ajinkya Mohgaonkar, Lukas Gosch, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar

Label-flipping attacks, which corrupt training labels to induce misclassifications at inference, remain a major threat to supervised learning models. This drives the need for robustness certificates that provide formal guarantees about a model's robustness under adversarially corrupted labels. Existing certification frameworks rely on ensemble techniques suc

Zhenghao Xie, Jing Xiao, Zhenqi Wang, Kexin Ma

Remote sensing understanding inherently requires multi-resolution observation, since different targets and application tasks demand different levels of spatial detail. While low-resolution (LR) imagery enables efficient global observation, high-resolution (HR) imagery provides critical local details at a much higher acquisition cost and with limited coverage

Youtian Su, Wei-Xi Huang, Chunxiao Xu

Direct numerical simulations are performed to investigate the combined effects of surface roughness and wall heat transfer on spatially developing compressible turbulent boundary layers at $Ma=2.5$. The roughness consists of transverse square bars with $\lambda_x/k=8$ and $k^+ \approx 35$, under adiabatic and wall-cooling ($T_w/T_r = 0.5$) conditions. Dynami

Marcus Binder Nilsen, Teodor Olof Benedict Åstrand, Tuhfe Göçmen, Pierre-Elouan Réthoré

Wind farm wake steering optimization is challenging due to complex flow physics and changing conditions. This paper presents a hierarchical framework that combines reinforcement learning with model predictive control, where an RL agent learns compensatory state estimates for an MPC controller, rather than directly controlling turbines. Evaluated on a three-t

Vygintas Gontis, Lesya Kolinets

The long-run convergence of developing economies toward advanced countries exhibits robust empirical regularities, yet the mechanisms underlying technological diffusion remain insufficiently specified in standard growth models. In this paper, we extend the neoclassical framework by introducing a micro-founded mechanism of technological transfer as a driver o

Zhaoyang Qiu, Daiwen Huang, Bixiang Wang

In this paper, we investigate the limiting dynamics of invariant measures of the stochastic Landau-Lifshitz-Bloch equation driven by the Stratonovich noise defined on the entire space $\R^2$. We first prove the set of all invariant measures of the stochastic equation for small noise is tight in $H^1(\R^2)$, and then prove every limit of a sequence of invaria

Hanuman Verma, Akshansh Gupta, Pranabesh Maji, Saurav Mandal

Accurate brain image segmentation, particularly for distinguishing various tissues from magnetic resonance imaging (MRI) images, plays a pivotal role in finding the neurological dis ease and medical image computing. In deep learning approaches, loss functions are very crucial for optimizing the model. In this study, we introduce a novel loss function integra

Jinyuan Liu, Yang Wang, Zeyu Zhao, Weixin Li

Reasoning video object segmentation predicts pixel-level masks in videos from natural-language queries that may involve implicit and temporally grounded references. However, existing methods are developed and evaluated in an offline regime, where the entire video is available at inference time and future frames can be exploited for retrospective disambiguati

Frédéric Noé, Betty Hachard, Hadrien Ceyte, Noëlle Bru

This study investigated the effects of mental fatigue (MF) induced by a 90-min AX-continuous performance test (AX-CPT) on balance control by addressing the issue of the heterogeneity of individuals' responses. Twenty healthy young active participants were recruited. They had to carry out two balance tasks (sway as little as possible on a stable support with

Axel Andersson, György Dán

We present a framework for bridging the gap between sensor attack detection and recovery in cyber-physical systems. The proposed framework models modern-day, complex perception pipelines as bipartite graphs, which combined with anomaly detector alerts defines a Bayesian network for inferring compromised sensors. An active probing strategy exploits system non

Boshuai Ye, Arif Ali Khan, Peng Liang

The efficient execution of fault-tolerant quantum algorithms is fundamentally limited by the production rate of magic states required for non-Clifford operations. While circuit optimization typically targets T-depth, static T-depth does not reliably predict executable performance under bounded T-state delivery. We introduce a model that captures demand-suppl

Yoshihide Yamato, Yuri Aikawa, Kenji Furuya, Charles J. Law

The sulfur chemistry in protoplanetary disks directly affects the composition and potential habitability of nascent planets, but its volatile inventory remains highly uncertain. Here, we present deep Atacama Large Millimeter/submillimeter Array (ALMA) observations of hydrogen sulfide (H$_2$S) along with SO and SO$_2$ in the disk around HD 163296 at an angula

Bo Li, Mingda Wang, Gexiang Fang, Shikun Zhang

We revisit retrieval-augmented generation (RAG) by embedding retrieval control directly into generation. Instead of treating retrieval as an external intervention, we express retrieval decisions within token-level decoding, enabling end-to-end coordination without additional controllers or classifiers. Under the paradigm of Retrieval as Generation, we propos

Pranav Kedia, Rajini Makam, Heiko Hamann, Suresh Sundaram

Accurate and continuous localization of Autonomous Underwater Vehicles (AUVs) in GPS-denied environments is a persistent challenge in marine robotics. In the absence of external position fixes, AUVs rely on inertial dead-reckoning, which accumulates unbounded drift due to sensor bias and noise. This paper presents BIND-USBL, a cooperative localization framew

Alex Shvets

We give an independent eta-product derivation of the level-8 Apery limit lim B_n^{(8)}/s_n = (7/32) zeta(3), where s_n = sum_{k=0}^n C(n,k)^2 C(2k,n)^2 and B_n^{(8)} is the rational companion sequence satisfying the same cubic recurrence with initial values B_0^{(8)}=0, B_1^{(8)}=1. This value was identified numerically by Almkvist-van Straten-Zudilin and wa

Jose Gonzalez-Belmonte, Jaerock Kwon

One of the concerns with autonomous vehicles is their ability to communicate their intent to other road users, specially pedestrians, in order to prevent accidents. External Human-Machine Interfaces (eHMIs) are the proposed solution to this issue, through the introduction of electronic devices on the exterior of a vehicle that communicate when the vehicle is

Klaus Mezger, Jonas Pape, Aryavart Anand, Pascal M. Kruttasch

Chemical and chronological information preserved in meteorites permits the reconstruction of events and processes in the solar nebula from the formation of the first solids to the accretion of planetary bodies and their subsequent differentiation. The path from a gas-dust cloud to differentiated planets includes intervals of steady evolution interrupted by s

Abdul Rahman

We present \emph{GlobalCY}, a JAX-based framework for globally defined and symmetry-aware neural K\"ahler-potential models on projective hypersurface Calabi--Yau geometries. The central problem is that local-input neural K\"ahler-potential models can train successfully while still failing the geometry-sensitive diagnostics that matter in hard quartic regimes

Mario Lino, Nils Thuerey

Analyzing unsteady fluid flows often requires access to the full distribution of possible temporal states, yet conventional PDE solvers are computationally prohibitive and learned time-stepping surrogates quickly accumulate error over long rollouts. Generative models avoid compounding error by sampling states independently, but diffusion and flow-matching me

Qilin Zhang, Jinyu Zhu, Olaf Wysocki, Benjamin Busam

Recent semantic 3D Gaussian Splatting (3DGS) methods primarily rely on 2D foundation models, often yielding ambiguous boundaries and limited support for structured urban semantics. While city models such as CityGML encode hierarchically organized semantics together with building geometry, these labels cannot be directly mapped to Gaussian primitives. We pres

Zakhar Yagudin, Murad Mebrahtu, Ren Jin, Jiaqi Huang

High-speed autonomous racing presents extreme perception challenges, including large relative velocities and substantial domain shifts from conventional urban-driving datasets. Existing benchmarks do not adequately capture these high-dynamic conditions. We introduce EagleVision, a unified LiDAR-based multi-task benchmark for 3D detection and trajectory predi

Alexandre Muzy

Cyber-Physical Systems (CPS) produce behavior through execution on substrates coupling computation with physical processes. However, usual engineering approaches do not treat execution semantics as first-class engineering entities. Formal verification reasons about model behaviors under fixed semantic assumptions that are not revisable and do not account for

Zihang Fu, Haonan Wang, Jian Kang, Kenji Kawaguchi

Multimodal adaptation can erode temporal reasoning (TR) in video-language models (VLMs), leaving models able to perceive salient events yet unable to infer their temporal and causal structure. We introduce MERIT, a gradient-free framework that repairs this capability through layer-selective model merging. MERIT assigns each self-attention layer a VLM-dominan

Felix Frankus, Sina Borgi, Albert Zelenika, Basit Ali

During plastic deformation, metals change shape while continuously becoming stronger. The microscopic origin of these processes lies in the proliferation and movement of line defects, dislocations, and the subsequent self-organisation and pinning of dislocations on lattice imperfections, including other dislocations. The nature of these multiscale processes

Yi-Bo Liang, Hong-Rong Li

We reveal that a fundamental minimal length naturally replaces the Schwarzschild singularity with future infinity, formalizing the ``asymptotic throat'' as a geometric inevitability. This scheme avoids the topology changes, multiple horizons, and universe towers characteristic of existing regular black hole models. We establish a general regularization frame

Gan Pei, Junhao Ning, Boqiu Shen, Yan Zhu

Remote photoplethysmography (rPPG) enables non-contact heart rate measurement from facial videos, but its performance is significantly degraded by facial motions such as speaking and head shaking. To address this issue, we propose two plug-and-play modules. The Angle-guided ROI Adaptive Optimization module quantifies ROI-Camera angles to refine motion-affect

Asma Al-Dahmani, Abdulla Bin Safwan, Mohammad Obeidat, Belal Alsinglawi

Security operations in smart cities demand detection systems that balance accuracy with response time. While ensemble methods like Random Forest achieve high accuracy, their computational overhead impedes real-time forensic triage. We present the first systematic evaluation of TabPFNv2.5, a transformer-based foundation model, against traditional ensemble cla

Lucas Girard, Elia Lapenta

We propose a novel procedure for estimating and conducting inference on average marginal effects in partially linear instrumental regressions using Reproducing Kernel Hilbert Space methods. Our procedure relies on a single regularization parameter. We obtain the consistency and asymptotic normality of our estimator. Since the variance of the limiting distrib

Peter Reimann, Christian Eidecker-Dunkel

Absence of thermalization after a global quantum quench is a well-established numerical observation in integrable many-body systems, and can be empirically related to a violation of the eigenstate thermalization hypothesis (ETH) in such models. Still, in many of those examples a weaker version of the conventional ETH (wETH) has been numerically reported or e

Jijun Xiang, Tao Wang, Jiayi Wang, Pengxiang Wang

While Hyperspectral Anomaly Detection (HAD) excels at identifying sparse targets in complex scenes, existing models remain trapped in a scalar "reconstruction-as-endpoint" paradigm. This reliance on ambiguous scalar residuals consistently triggers sub-pixel anomaly vanishing during spatial downsampling, alongside severe confirmation bias when unpurified anom

Saswati Roy, Shubham Kala, Sayanika Modak, Hemwati Nandan

We investigate the gravitational lensing of massless particles around a Kerr-Sen black hole immersed in a magnetized, cold, pressureless plasma medium. Both homogeneous and inhomogeneous plasma distributions are considered in this study to mimic realistic astrophysical environments. The light deflection angle is computed, and the effects of the black hole's

Nafiseh Ghaffar Nia, Vinesh Appadurai, Suchithra V., Chinmay Rane

Reliable recognition of standard cine cardiac MRI views is essential because each view determines which cardiac anatomy is visualized and which quantitative analyses can be performed. Incorrect view identification, whether by a human reader or an automated deep learning system, can propagate errors into segmentation, volumetric assessment, strain analysis, a

Yiran Qin, Jiahua Ma, Li Kang, Wenzhan Li

Recent advancements in foundational models, such as large language models and world models, have greatly enhanced the capabilities of robotics, enabling robots to autonomously perform complex tasks. However, acquiring large-scale, high-quality training data for robotics remains a challenge, as it often requires substantial manual effort and is limited in its

Jules Grass

This paper builds upon the methods developed in [22] and [15] to investigate the large population behavior of non exchangeable systems of N diffusive particles when the interaction matrix converges (in some sense) to a graphon. We first prove that the particle system is well approximated in Fisher information by the so-called independent projection system by

Rok Spruk

This paper develops a political-economy theory of statehood without capacity. I argue that under specific institutional and geopolitical conditions, a polity can become trapped in an equilibrium of nominal statehood: a state in which claims to sovereignty, external recognition, and symbolic legitimacy persist or even strengthen while the coercive, fiscal, ad

E. Fogh, N. L. Amin, G. S. Tucker, M. Aouane

Many of the most interesting scientific subjects are also the hardest to study with neutrons. Metal-organic frameworks, organic superconductors, quantum magnets, pressure-tuned materials, are systems where the relevant signals are weak, the samples are tiny, or the experiments need extreme sample environments such as pressure cells and high-field cryomagnets

Zakaria Bensaid, Roxana Dumitrescu, Anis Matoussi, Wissal Sabbagh

We provide a new characterization of law-invariant backward stochastic differential equations (i.e. BSDEs) with quadratic growth. This answers the open question raised in Xu--Xu--Zhou (2022) on necessary conditions for law-invariance of g-expectations, and extends the analysis to general (possibly non-deterministic) generators. We also introduce and compare

Emanuele Costa, Javier Menendez

Quantum computing is increasingly offering concrete solutions toward the simulation of nuclear structure, with the potential to overcome the exponential scaling that limits classical diagonalization methods in large spaces. A particularly efficient encoding scheme, based on collective like-nucleon pairing modes, reduces the qubit requirements by half and avo

Toru Seo

Optimization using network traffic models requires computing gradients of objective functions with respect to model parameters. However, derivation of such gradients has often been considered difficult or impractical due to their complexity and size. Conventional approaches rely on numerical differentiation or derivative-free methods that do not scale well w

Ling Qiao, Fumin Luo, Qinglang Guo

Superconducting quantum computing is advancing toward the thousand- and even million-qubit regime, making wafer-scale fabrication an essential pathway for achieving large-scale, cost-effective quantum processors. This manufacturing paradigm imposes new requirements on quantum-chip electronic design automation (Q-EDA): design tools must not only generate layo

Hu Wei

The dominant paradigm for building LLM based agents is the Agent Loop, an iterative cycle where a single language model decides what to do next by reading an ever growing context window. This paradigm has three structural weaknesses: implicit dependencies between steps, unbounded recovery loops, and mutable execution history that complicates debugging. We ch

Yuliya Bilinskaya, Sreenath K. Manikandan

We present an analytically tractable model of a driven quantum harmonic emitter, such as an oscillating charged dipole, emitting radiation via resonance fluorescence. With this model we are able to characterize the quantum mechanical correlations that are built up at early times between the drive, the resonant emitter, and its fluorescence. We describe detec

M. Aguiar Ferreira, B. Ganapathisubramani

Uncertainty in estimating the log-law parameters is arguably the greatest obstacle to establishing definitive conclusions regarding their numerical values and universality. This challenge is exacerbated by the limited number of studies that provide thorough uncertainty analyses of experimental data and fitting procedures, and those that do often adopt differ

Adrienne Kline, Abhijit Gaonkar, Daniel Pittman, Chris Kuehn

Removing patient-specific information from medical images is crucial to enable sharing and open science without compromising patient identities. However, many methods currently used for deidentification have negative effects on downstream image analysis tasks because of removal of relevant but non-identifiable information. This work presents an end-to-end de

Haibo Liu, Guang Lin

Diffusion models have emerged as powerful generative priors for solving PDE-constrained inverse problems. Compared to end-to-end approaches relying on massive paired datasets, explicitly decoupling the prior distribution of physical parameters from the forward physical model, a paradigm often formalized as Plug-and-Play (PnP) priors, offers enhanced flexibil

Koki Ryu, Hitomi Yanaka

Personalized image aesthetics assessment (PIAA) is an important research problem with practical real-world applications. While methods based on vision-language models (VLMs) are promising candidates for PIAA, it remains unclear whether they internally encode rich, multi-level aesthetic attributes required for effective personalization. In this paper, we firs

Zhegong Shangguan, Alessandro Di Nuovo, Angelo Cangelosi

Robots are increasingly entering human-interactive scenarios that require understanding of quantity. How intelligent systems acquire abstract numerical concepts from sensorimotor experience remains a fundamental challenge in cognitive science and artificial intelligence. Here we investigate embodied numerical learning using a neural network model trained to

Hyoseok Ju, Giseop Kim

Crowd-sourced cooperative mapping from monocular cameras promises scalable 3D reconstruction without specialized sensors, yet remains hindered by two scale-specific failure modes: abrupt scale collapse from false-positive loop closures in repetitive environments, and gradual scale drift over long trajectories and per-robot scale ambiguity that prevent direct

Yuxin Zhao, Han Huang, Fangji Fu, Zhifeng Hao

As the cornerstone of modern power systems, the Unit Commitment Problem (UC) is critical for ensuring operational security and economic efficiency in the ongoing global energy transition. However, existing UC studies typically propose specialized algorithms for specific variants and operational requirements, tightly coupling the algorithms to their target mo

Jingpeng Wu

This article focuses on the Vlasov-Poisson system with point charges in bounded convex domains, accounting the interactions of point charges with the self-consistent electric field and the boundary, which were not addressed in the previous work \cite{Wu24}. We provide a rigorous characterization of the charge-boundary effect and establish the global well-pos

Nensi Toncich, Ankit Yadav, Jan Fikar, Ralph Spolenak

Reactive multilayer thin films, a class of energetic materials, are increasingly recognized for their potential in joining applications, utilizing the chemical energy released as heat during exothermic reactions. These materials hold also promise for additional diverse technological applications, which require precise control over heat release rates and reac

Hünkar Can Tun, Yifan Dong, Andreas Pavlogiannis

Atomicity is a fundamental abstraction in concurrency, specifying that program behavior can be understood by considering specific code blocks executing atomically. However, atomicity invariants are tricky to maintain while also optimizing for code efficiency, and atomicity violations are a common root cause of many concurrency bugs. To address this problem,

Sung Kei Li, Thomas Broadhurst, Jose M. Diego, Jeremy Lim

Stellar microlensing is a powerful method to constrain compact dark matter models, uncover binary stars, and exoplanets during caustic crossing events. At cosmological distances, {\it James-Webb Space Telescope} ({\it JWST}) is routinely detecting microlensed giant stars in highly magnified galaxies behind massive lensing clusters. Here, we explore for the f

Chris Sainsbury, Feng Dong, Andreas Karwath

Sparse autoencoders (SAEs) have been applied to large language models and protein language models, but not systematically to electronic health record (EHR) foundation models. We train TopK SAEs on FlatASCEND, a 14.5-million-parameter autoregressive clinical sequence model, at all 10 residual stream extraction points on INSPECT (outpatient) and MIMIC-IV (ICU)

M. C. Santos, E. Lora da Silva, D. S. Baptista, T. Santos

We present a comprehensive set of calculations to investigate the effect of strain-induced x-y topological perturbation in the monolayer graphene sheet. We show that the induced curvature with the defined strain constraint, energetically stabilizes the systems. The electronic properties are modified when the amplitude of the curvature of the sheet increases,

Xiamiao Zhao, Xin Cheng, Cheng Chi, Ervin Győri

Let $C_k$ denote the cycle of length $k$, $S_t$ be a star with $t$ edges. And let $B_t$ be the graph consisting of $t$ copies of $C_4$ sharing one fixed edge. Equivalently, $B_t=K_2 \mathbin{\square} S_t$, which is the Cartesian product of a star with $t$ edges and an edge. Recently, Gao, Janzer, Liu and Xu [\textit{Israel J. Math. 269(2025)}] proved that th

Peiyang Liu, Zhirui Chen, Xi Wang, Di Liang

Monte Carlo Tree Search (MCTS) has been widely used for automated reasoning data exploration, but current supervision extraction methods remain inefficient. Standard approaches retain only the single highest-reward trajectory, discarding the comparative signals present in the many explored paths. Here we introduce \textbf{Contrastive Reasoning Path Synthesis

Nathan A. Judd, Dario Spanò

A new class of time-dependent Dirichlet priors is introduced as a generalisation of the Wright-Fisher diffusion, allowing discontinuities in the trajectories, as well as non-Markovian memory. This class is obtained as a simple stochastic time-change (subordination), interpreted as a hyper-prior assigned to the operational time-clock of a Wright-Fisher diffus

Luca Mariot, Federico Mazzone, Luca Manzoni, Alberto Leporati

We consider threshold secret sharing schemes based on cellular automata (CA) that allows for anonymous reconstruction, meaning that the secret can be recovered only as a function of the shares, without knowing the participants' identities. To this end, we revisit the basic characterization of $(2,n)$ threshold schemes based on CA in terms of Mutually Orthogo

Ruikai Wu, Deepika Gill, Sangeeta Sharma, Sam Shallcross

Two-dimensional materials that exhibit optically active spin and valley degrees of freedom represent one of the most fascinating -- and potentially most technologically useful -- platforms for the ultrafast interaction of light and matter. Here we show, via the example of Cr$_2$SO, that two dimensional altermagnets host valley states controllable by femtosec

Zhigang Song, Peng Xu, Kai Chang

Solid-state platforms are particularly attractive for quantum optics because they facilitate on-chip integration and are compatible with established semiconductor and photonic technologies. However, a major challenge in solid-state quantum optics is the fabrication of arrays of identical emitters, such as quantum dots. In this work, we propose moire superlat

Zehao Qin, Xiaojian Lin, Ping Zhang, Hongliang Wu

Accurate interpretation of electrocardiogram (ECG) remains challenging due to the scarcity of labeled data and the high cost of expert annotation. Self-supervised learning (SSL) offers a promising solution by enabling models to learn expressive representations from unlabeled signals. Existing ECG SSL methods typically rely on either contrastive learning or r

Pietro Klausner, Marco Antonelli, Gianluca Colò, Francesca Gulminelli

Nuclear density functional theory provides a unified description of finite nuclei and bulk nuclear matter, and is widely used to model the neutron star equation of state. However, extrapolations to supra-saturation densities require a quantified treatment of uncertainties arising from parameter estimation and functional choices. We present an updated Bayesia

Faizuddin Ahmed, Mohsen Fathi, Edilberto O. Silva

In this work, we systematically investigate the null geodesics of electrically charged black holes in a gravitational framework that incorporates Lorentz violation induced by a background Kalb-Ramond (KR) field, in the presence of perfect-fluid dark matter. The properties of the photon sphere, black hole shadow, and photon trajectories are analyzed in detail

Thomas Apel, Katharina Lorenz, Johannes Pfefferer

The Stokes problem with non-homogeneous Dirichlet boundary condition is solved numerically using conforming discretizations and an approximation of the boundary datum in the corresponding trace space. Optimal discretization error estimates in various norms are derived. In the case of a homogeneous differential equation, all estimates of the velocity error ar

Jianshi Wu, Minghang Zhu, Dunqiang Liu, Wen Li

LiDAR relocalization has attracted increasing attention as it can deliver accurate 6-DoF pose estimation in complex 3D environments. Recent learning-based regression methods offer efficient solutions by directly predicting global poses without the need for explicit map storage. However, these methods often struggle in challenging scenes due to their equal tr

Jiapeng Yu, Anzhi Sheng, Long Wang

Cooperation is a key driver of human social progress. Studies of the evolution of cooperation typically assume a deterministic outcome for social interactions. But in real-world social interactions, interaction outcomes are often subject to stochastic perturbations arising from open environments. Individuals may show different attitudes towards such uncertai

Beniamino Di Lorenzo, Gian Carlo Maffettone, Mario di Bernardo

We address density control problems for large-scale multi-agent systems in leader-follower settings, where a group of controllable leaders must steer a population of followers toward a desired spatial distribution. Unlike prior work, we explicitly account for follower-follower interactions, capturing realistic behaviors such as flocking and collision avoidan

Anton Pakhunov

We present an analytical theory of greedy peeling decoding for bivariate bicycle (BB) codes under circuit-level noise. The deferred greedy decoder achieves 330x latency reduction over belief propagation (BP) at p = 10^{-3} while maintaining identical logical error rate. Our main theoretical contribution is a closed-form collision resolution factor A_0 = |tru

Anlan Yu, Zaishu Chen, Peili Song, Zhiqing Hong

Imitation learning is a powerful paradigm for training robotic policies, yet its performance is limited by compounding errors: minor policy inaccuracies could drive robots into unseen out-of-distribution (OOD) states in the training set, where the policy could generate even bigger errors, leading to eventual failures. While the Data Aggregation (DAgger) fram

Qi Liu, Xuefei Wu, Yingchun Cheng, Haiyan Zhou

In their seminal 1989 work (IEEE Trans. Inf. Theory 35(3):655-657), Roth and Lempel constructed a well-known family of non-Reed-Solomon maximum distance separable (MDS) codes. For decades, this family of codes has attracted extensive research attention due to its algebraic structure, low-complexity decoding, and broad applications in cryptography and data st

Thomas Gossard

Accurate dynamic models for racket-ball bounces are essential for reliable control in robotic table tennis. Existing models typically assume simple linear models and are restricted to inverted rubbers, limiting their ability to generalize across the wide variety of rackets encountered in practice. In this work, we present a unified framework for modeling bal

Xin Wang, Yuan Gao, George Yiasemis, Antonio Portaluri

Efficient and explainable breast cancer (BC) risk prediction is critical for large-scale population-based screening. Breast MRI provides functional information for personalized risk assessment. Yet effective modeling remains challenging as fully 3D CNNs capture volumetric context at high computational cost, whereas lightweight 2D CNNs fail to model inter-sli