March 2026 arXiv papers — page 119

Navid Mojahed, Mahdis Rabbani, Shima Nazari

We propose a data-driven linear modeling framework for controlled nonlinear hereditary systems that combines Koopman lifting with a truncated Grunwald-Letnikov memory term. The key idea is to model nonlinear state dependence through a lifted observable representation while imposing history dependence directly in the lifted coordinates through fixed fractiona

Xavier Gonzalez

Massively parallel hardware (GPUs) and long sequence data have made parallel algorithms essential for machine learning at scale. Yet dynamical systems, like recurrent neural networks and Markov chain Monte Carlo, were thought to suffer from sequential bottlenecks. Recent work showed that dynamical systems can in fact be parallelized across the sequence lengt

Mattia Rigotti, Nicholas Thumiger, Thomas Frick

Neural operators on irregular meshes face a fundamental tension. Spectral positional encodings, the natural choice for capturing geometry, require cubic-complexity eigendecomposition and inadvertently break gauge invariance through numerical solver artifacts; existing efficient approximations sacrifice gauge symmetry by design. Both failure modes break discr

Kshitijkumar A. Surjuse, Zhihao Deng, Andrey Asadchev, Edward F. Valeev

In J. Chem. Phys. 152, 144105 (2020) Lehtola et al introduced the efficient Gaussian-basis representation of Superposition of Atomic Potentials (SAP) which "can be easily implemented in any Gaussian-basis quantum chemistry code in terms of two-electron integrals". Here we demonstrate that it is possible to evaluate Gaussian AO representation of SAP by nearly

Shachar Don-Yehiya, Asaf Yehudai, Leshem Choshen, Omri Abend

The ``LLM-as-a-judge'' paradigm has become a standard method for evaluating open-ended generation. To address the quadratic scalability costs of pairwise comparisons, popular benchmarks like Arena-Hard and AlpacaEval compare all models against a single anchor. However, despite its widespread use, the impact of anchor selection on the reliability of t

M. Cristina Câmara, Gabriel Lopes Cardoso

By viewing Einstein's field equations -- reduced to two dimensions -- as an integrable system, one can simultaneously obtain exact solutions to both the equations themselves and their associated Lax pair via a canonical Wiener-Hopf factorisation of a so-called monodromy matrix. In this article, we review this remarkable interplay between gravitational field

Reek Das, Biplab Kanti Sen

Federated Learning (FL) is increasingly applied in sectors like healthcare, finance, and IoT, enabling collaborative model training while safeguarding user privacy. However, FL systems are susceptible to Byzantine adversaries that inject malicious updates, which can severely compromise global model performance. Existing defenses tend to focus on specific att

Chi-Fang Chen, András Gilyén

We present a general protocol for estimating $M$ observables from only $\mathcal{O}(\log (M)/\varepsilon^2)$ copies of a Gibbs state whose Hamiltonian is accessible. The protocol uses single-copy, nonadaptive measurements and uses a total Hamiltonian simulation time of $\widetilde{\mathcal{O}}(\beta M/\varepsilon^2)$; we show that the sample complexity is op

Kerui Ren, Guanghao Li, Changjian Jiang, Yingxiang Xu

Streaming reconstruction from uncalibrated monocular video remains challenging, as it requires both high-precision pose estimation and computationally efficient online refinement in dynamic environments. While coupling 3D foundation models with SLAM frameworks is a promising paradigm, a critical bottleneck persists: most multi-view foundation models estimate

Rui Ge, Yichao Fu, Yuyang Qian, Junda Su

Large language models are increasingly deployed as autonomous agents that must plan, act, and recover from mistakes through long-horizon interaction with environments that provide rich feedback. However, prevailing outcome-driven post-training methods (e.g., RL with verifiable rewards) primarily optimize final success signals, leaving rich environment feedba

Moritz Pawlowsky, Antonis Vamvakeros, Alexander Weiss, Anja Bielefeld

Vision transformers (ViTs) - especially feature foundation models like DINOv2 - learn rich representations useful for many downstream tasks. However, architectural choices (such as positional encoding) can lead to these models displaying positional biases and artefacts independent of semantic content. This makes zero-shot adaption difficult in fields like ma

Karthik Ragunath Ananda Kumar, Subrahmanyam Arunachalam

Automated presentation generation remains a challenging task requiring coherent content creation, visual design, and audience-aware communication. This work proposes an OpenEnv-compatible reinforcement learning environment where LLM agents learn to research topics, plan content, and generate professional HTML slide presentations through tool use. We introduc

Pedro Fittipaldi de Castro, Yifan Wang, Wladimir A. Benalcazar

The extension of topological band theory to non-Hermitian Hamiltonians with line energy gaps remains largely unexplored, despite early indications of rich underlying physics. In these systems, Wilson loops, the objects characterizing polarization, become nonunitary. Yet, the physical consequences of this nonunitarity have remained unclear. Using biorthonorma

Lila Perkins, Baosen Zhang

In electricity markets, customers are increasingly constrained by their budgets. A budget constraint for a user is an upper bound on the price multiplied by the quantity. However, since prices are determined by the market equilibrium, the budget constrained welfare maximization problem is difficult to define rigorously and to work with. In this letter, we sh

Kiran Kumar, Mostafa Sabri

We undertake a detailed analysis of ergodicity for homogeneous discrete-time quantum walks on the integer lattice. The most significant result of our paper holds in dimension one, and gives a complete equivalence between the absolutely continuous spectrum of the unitary operator encoding the walk, and the equidistribution of its dynamics in position space, w

Tomer Milo, Guy Moshkovitz

We prove a nearly polynomial inverse theorem for the Gowers $U^d$ norm, over finite fields of non-small characteristic, for polynomials of degree $d+1$. The case of degree $d$ was very recently settled by Mili\'{c}evi\'{c} and Randelovi\'{c} with a fully polynomial bound. We moreover provide a nearly polynomial inverse theorem for homogeneous polynomials of

Felix Kröber, Genc Hoxha, Ribana Roscher

Label noise in the sense of incorrect labels is present in many real-world data sets and is known to severely limit the generalizability of deep learning models. In the field of remote sensing, however, automated treatment of label noise in data sets has received little attention to date. In particular, there is a lack of systematic analysis of the performan

Vasudevarao Allu, Raju Biswas, Rajib Mandal

The primary objective of this paper is to establish several sharp results concerning the Bohr inequality, the refined Bohr inequality, and the improved Bohr inequality for the classes of analytic functions and harmonic mappings defined on the shifted disks \[ \Omega_{\gamma}=\left\{z\in\mathbb{C}:\left|z+\frac{\gamma}{1-\gamma}\right|<\frac{1}{1-\gamma}\righ

Atharv Naphade, Samarth Bhargav, Sean Lim, Mcnair Shah

A hallmark of human intelligence is Introspection-the ability to assess and reason about one's own cognitive processes. Introspection has emerged as a promising but contested capability in large language models (LLMs). However, current evaluations often fail to distinguish genuine meta-cognition from the mere application of general world knowledge or text-ba

Yuanche Liu, Antonela Matijašić, Tiziano Peraro, Yingxuan Xu

In this work, we calculate two-loop six-point planar massless Feynman integrals at higher orders in the dimensional regulator $\epsilon$, corresponding to higher transcendental weights. In previous works, these integrals were calculated up to weight four for the purpose of two-loop gauge theory amplitudes. Using modern rational reconstruction methods, we ide

Steve Shkoller

We introduce a postprocessing procedure that recovers sub-cell wave geometry from a standard one-dimensional Euler shock-capturing computation using differentiated Riemann variables (DRVs) -- characteristic derivatives that separate the three wave families into distinct localized spikes. Filtered DRV surrogates detect the waves, plateau sampling extracts the

Saksham Jain, Alex Luedtke

Beyond conditional average treatment effects, treatments may impact the entire outcome distribution in covariate-dependent ways, for example, by altering the variance or tail risks for specific subpopulations. We propose a novel estimand to capture such conditional distributional treatment effects, and develop a doubly robust estimator that is minimax optima

Chiho Yoon, Fan Zhang

Recent experiments in rhombohedral graphene report an unusual superconducting phase emerging from a spin- and valley-polarized quarter-metal state. The prevailing interpretation invokes chiral topological superconductivity, but the role of the `Fulde-Ferrell' phase factor due to intra-valley pairing has remained largely unexplored. Here we show, via a gauge

Tanausú Aguilar-Hernández, Petros Galanopoulos, Elena de la Rosa

We study for the first time the action of the Hilbert matrix $$\mathcal H=(c_{n,k})_{n,k\geq 0}, \quad c_{n,k}=\frac{1}{n+k+1}$$ on the analytic tent spaces $AT^q_p, 1<p,q <\infty,$ of the unit disc $\mathbb D$ of the complex plane. They were proposed by Triebel as the natural analytic version of the tent spaces of measurable functions defined by Coifman, Me

Kanishka Mitra, Satyam Kumar, Frigyes Samuel Racz, Deland Liu

Robot-assisted therapy can deliver high-dose, task-specific training after neurologic injury, but most systems act primarily at the limb level-engaging the impaired neural circuits only indirectly-which remains a key barrier to truly contingent, neuroplasticity-targeted rehabilitation. We address this gap by implementing online, dual-state motor imagery cont

Lukas Lüchtrath, Christian Mönch

We introduce a model for directed spatial networks. Starting from an age-based preferential attachment model in which all arcs point from younger to older vertices, we add \emph{reciprocal} connections whose probabilities depend on the age difference between their end-vertices. This yields a directed graph with reciprocal correlations, a power-law indegree d

Sourya Saha, Saptarshi Debroy

Immersive extended reality (XR) applications introduce latency-critical workloads that must satisfy stringent real-time responsiveness while operating on energy- and battery-constrained devices, making execution placement between end devices and nearby edge servers a fundamental systems challenge. Existing approaches to adaptive execution and computation off

Zhitao Zeng, Mengya Xu, Jian Jiang, Pengfei Guo

Surgical intelligence has the potential to improve the safety and consistency of surgical care, yet most existing surgical AI frameworks remain task-specific and struggle to generalize across procedures and institutions. Although multimodal foundation models, particularly multimodal large language models, have demonstrated strong cross-task capabilities acro

Kosei Hatakeyama, Ryotaro Fukuzumi, Akira Matsumura, Daisuke Miki

We analytically investigate a quantum estimation method for a mechanical oscillator in a detuned cavity system based solely on homodyne measurement records, building on the framework developed by C.Meng et al. (Science Advances 8, 7585 (2022)). Estimation based only on measurement records is important because it enables state verification without assuming kn

Julien Bauland, Tim J. Wooster, Peter Fischer, Jan Vermant

Fluid gels are soft materials formed by shearing biopolymer solutions during the sol-gel transition. Their ability to yield and flow beyond a critical stress makes them attractive for designing versatile, biocompatible materials in food, health care and medical applications. Although it is well established that both microstructure and mechanical properties d

Nicolas Monod

The automorphism group of a regular locally finite tree is shown to admit irreducible Banach representations that are not admissible. The dense subspace of smooth vectors contains no algebraically irreducible component.

Xiaoyu Chu, Shashikant Ilager, Yizhen Zang, Sacheendra Talluri

Incident management is essential to maintain the reliability and availability of cloud computing services. Cloud vendors typically disclose incident reports to the public, summarizing the failures and recovery process to help minimize their impact. However, such reports are often lengthy and unstructured, making them difficult to understand, analyze, and use

Yi Chen, Daiwei Chen, Sukrut Madhav Chikodikar, Caitlyn Heqi Yin

Large language models (LLMs) frequently hallucinate, limiting their reliability in knowledge-intensive applications. Retrieval-augmented generation (RAG) and conformal factuality have emerged as potential ways to address this limitation. While RAG aims to ground responses in retrieved evidence, it provides no statistical guarantee that the final output is co

Muhammad Aamir, Naoya Muramatsu, Sangyun Shin, Matthew Wijers

Depth estimation and 3D reconstruction have been extensively studied as core topics in computer vision. Starting from rigid objects with relatively simple geometric shapes, such as vehicles, the research has expanded to address general objects, including challenging deformable objects, such as humans and animals. However, for the animal, in particular, the m

Swata Marik, Swayamjit Saha, Garga Chatterjee

This study develops a digitalized forecasting-inventory optimization pipeline integrating traditional forecasting models, machine learning regressors, and deep sequence models within a unified inventory simulation framework. Using the M5 Walmart dataset, we evaluate seven forecasting approaches and assess their operational impact under single- and two-echelo

Henrique Souza, Pavel Zalesskii

We establish new examples and non-examples of pro-p limit groups among the class of Demushkin groups, that is, pro-p Poincar\'e duality groups of dimension 2.

Jiabo Huang, Zhizhong Li, Sina Sajadmanesh, Weiming Zhuang

Deploying vision-language models (VLMs) in resource-constrained settings demands low latency and high throughput, yet existing compact VLMs often fall short of the inference speedups their smaller parameter counts suggest. To explain this discrepancy, we conduct an empirical end-to-end efficiency analysis and systematically profile inference to identify the

Shuo Liu, John Mott

This paper investigates the evolving causal mechanisms of flight delays in the U.S. domestic aviation network from 2010-2024. Utilizing a three-level hierarchical Bayesian model on Bureau of Transportation Statistics (BTS) on-time performance data, we decouple the marginal contribution factors of weather, national aviation system (NAS), security delays, and

Nij Dorairaj, Debabrata Chatterjee, Hong Wang, Hong Jiang

Integration of CPU and GPU technologies is a key enabler for modern AI and graphics workloads, combining control-oriented processing with massive parallel compute capability. As systems evolve toward chiplet-based architectures, pre-silicon validation of tightly coupled CPU-GPU subsystems becomes increasingly challenging due to complex validation framework s

Martin Buck, Christina Frederick, Kasso Okoudjou, Alexander Stangl

We consider the Gabor system $\mathcal{G}(g,a\mathbb{Z}\times b\mathbb{Z})$ generated by a continuous, compactly supported function $g$ over the time-frequency lattice generated by the parameters $a$ and $b$. We show that, under an appropriate ordering of the Gabor elements, certain submatrices of the Gram matrix of $\mathcal{G}(g,a\mathbb{Z}\times b\mathbb{

V. A. Bogachev, I. V. Kolokolov, V. V. Lebedev, A. V. Nemtseva

We investigate analytically and numerically correlation functions of the phase of light waves that propagate through turbulent media. We examine the case of strong scintillations that occur at large values of the Rytov dispersion, $\sigma^2_R$. Then, it is possible to relate the pair correlation function of phase gradients to the known pair correlation funct

Timothy Douglas, Roben Delos Reyes, Asanobu Kitamoto

Social media has become a critical source of situational awareness during disasters, providing real-time insights into evolving impacts and emerging needs. To support crisis response at scale, recent work has increasingly leveraged large language models (LLMs) to automatically classify and summarize situational information from social media streams. However,

Yuliang Wu, Yanhan Lin, WengKit Lao, Yuhao Lin

To meet the demands of increasingly diverse dexterous hand hardware, it is crucial to develop a policy that enables zero-shot cross-embodiment grasping without redundant re-learning. Cross-embodiment alignment is challenging due to heterogeneous hand kinematics and physical constraints. Existing approaches typically predict intermediate motion targets and re

Julien Bauland, Stéphane G. Roux, Stefan Gstöhl, Christian M. Schlepütz

Suspensions of attractive particles form space-spanning networks that endow the suspension with solid-like behavior at rest. The microstructure of these colloidal gels depends sensitively on the shear history and on the path followed across the sol-gel transition, resulting in viscoelastic properties that can be tuned by shear. Here, we report in situ X-ray

Krishamsu Subedi Chhetri, Aryan Mayor, Elise Corbin, Logan Walker

Soft robotics leverages deformable materials to develop robots capable of navigating unstructured and dynamic environments. Silicone Voxel-Based Soft Robots (Silibots) are a type of pneumatically actuated soft robots that rely on the inflation and deflation of their voxels for shape-shifting behaviors. However, traditional pneumatic actuation methods (high p

Vasco Brattka, Christopher Sorg

Computational properties of the Hahn-Banach theorem have been studied in computable, constructive and reverse mathematics and in all these approaches the theorem is equivalent to weak K\H{o}nig's lemma. Gherardi and Marcone proved that this is also true in the uniform sense of Weihrauch complexity. However, their result requires the underlying space to be va

Yixuan Huang, Jiawei Chen, Shengfan Zhang, Zongsheng Cao

Collaborative filtering (CF) recommendation has been significantly advanced by integrating Graph Neural Networks (GNNs) and Graph Contrastive Learning (GCL). However, (i) random edge perturbations often distort critical structural signals and degrade semantic consistency across augmented views, and (ii) data sparsity hampers the propagation of collaborative

Lucas C. P. A. M. Müssnich, Renato Vasconcellos Vieira

The tenfold way is a classification scheme for the building blocks of free fermion systems. More precisely, it classifies the isomorphism classes of spaces of equivariant free Hamiltonians in irreducible fermion systems with symmetries. This classification scheme naturally leads to the K-theoretical classification of topological phases of matter, known as th

Christian Belardi, Justin Lovelace, Kilian Q. Weinberger, Carla P. Gomes

Guided diffusion sampling relies on approximating often intractable likelihood scores, which introduces significant noise into the sampling dynamics. We propose using adaptive moment estimation to stabilize these noisy likelihood scores during sampling. Despite its simplicity, our approach achieves state-of-the-art results on image restoration and class-cond

Minchul Kam, Jongho Park, Sascha Trippe, Do-Young Byun

We present multi-frequency polarimetric observations of 3C 84 obtained with the Korean VLBI Network at 43-141 GHz, the Very Long Baseline Array at 43 GHz, and the High Sensitivity Array at 8 GHz from 2015 to 2024. We find that the Faraday rotation measure (RM) decreases systematically with distance from the black hole over 1-8 pc, following a single power-la

Aqil Sajjad, Isack Padilla, Saikat Guha

Any quantum state of the radiation field, sliced in small non-overlapping space-time bins is a collection of single-rail qubits, each spanning the vacuum and single-photon Fock state of a mode. Quantum logic on these qubits would enable arbitrary measurements on information-bearing light, but is hard due to the lack of strong nonlinearities. With unentangled

Hugues Moyart

In his approach to Jones theorem on the interpolation of Hardy spaces on the torus, Pisier introduced an original method allowing the computation of complex interpolation spaces by means of real interpolation techniques. This approach has been successfully extended to noncommutative analytic Hardy spaces arising from subdiagonal algebras. In this paper, we f

Han Lin, Xichen Pan, Zun Wang, Yue Zhang

Pixel-space diffusion has recently re-emerged as a strong alternative to latent diffusion, enabling high-quality generation without pretrained autoencoders. However, standard pixel-space diffusion models receive relatively weak semantic supervision and are not explicitly designed to capture high-level visual structure. Recent representation-alignment methods

Subarna Saha, Alif Al Hasan, Fariha Tanjim Shifat, Mia Mohammad Imran

Novice programmers often struggle to comprehend code due to vague naming, deep nesting, and poor structural organization. While explanations may offer partial support, they typically do not restructure the code itself. We propose code refactoring as cognitive scaffolding, where cognitively guided refactoring automatically restructures code to improve clarity

Jian Yang, Wei Zhang, Jiajun Wu, Junhang Cheng

Recent code large language models have achieved remarkable progress on general programming tasks. Nevertheless, their performance degrades significantly in industrial scenarios that require reasoning about hardware semantics, specialized language constructs, and strict resource constraints. To address these challenges, we introduce InCoder-32B (Industrial-Co

Nora Schneider, Georg Manten, Niki Kilbertus

We develop a conservative continuous-time stochastic control framework for treatment optimization from irregularly sampled patient trajectories. The unknown patient dynamics are modeled as a controlled stochastic differential equation with treatment as a continuous-time control. Naive model-based optimization can exploit model errors and propose out-of-suppo

Justin McMillen, Robert Van Alphen, Taha Sadeghi Chorsi, Jason Shabaga

Forecasting permafrost thaw from aerial lidar requires projecting 3D point cloud features onto 2D prediction grids, yet naive aggregation methods destroy the vertical structure critical in forest environments where ground, understory, and canopy carry distinct information about subsurface conditions. We propose a projection decoder with learned height embedd

Marco Morandotti, Piotr Rybka, Glen Wheeler

We show stabilisation of solutions to one-dimensional advective Cahn-Hilliard equation modeling the Langmuir-Blodgett thin films. This problem has the structure of a gradient flow perturbed by a linear term $\beta u_x$. Through application of an abstract result by Carvalho-Langa-Robinson, we show that for small $\beta$ the equation has the structure of gradi

Younes Ben Mazziane, Vinay Kumar B. R., Othmane Marfoq

Elastic-Sketch is a hash-based data structure for counting item's appearances in a data stream, and it has been empirically shown to achieve a better memory-accuracy trade-off compared to classical methods. This algorithm combines a heavy block, which aims to maintain exact counts for a small set of dynamically elected items, with a light block that implemen

Joshua Roy Palathinkal, Hardani Ismu Nabil, Muhammad Iqbal Rochman, Hossein Nasiri

The recently deployed 6 GHz spectrum in the U.S. utilizes distinct power categories, with the latest proposed "Geofenced Variable Power" (GVP) category permitting indoor and outdoor operations without continuous Automated Frequency Coordination (AFC) by relying instead on local databases of exclusion zones. Consequently, the safe operation of GVP devices dep

Chunhao Cai, Yiwu Shang, Cong Zhang

This paper consider the LAN property for the mixed O-U process under high-frequency observation when H>3/4. As considered in mixed fractional Brownian motion, we will also use the projection step to get the non-diagonal rate matrix.

Naoya Egawa, Kaoru Mizuta, Joji Nasu

Quantum signal processing (QSP), originally developed for composite pulse sequences in nuclear magnetic resonance systems, has recently attracted attention as a unified framework for quantum algorithms. A pioneering study applied QSP to nonequilibrium control in integrable many-body systems, enabling the realization of nonequilibrium dynamics with greater fl

Jonggeun Lee, Junseong Pyo, Jeongmin Park, Yohan Jo

Robust voice agents require exposure to the full diversity of how people interact through speech. However, obtaining enough spoken interactions is prohibitively expensive. Building spoken user simulators that address this requires large-scale spoken task-oriented dialogue (TOD) data encompassing spoken user behaviors, yet existing datasets are limited in sca

Malen Etxeberria-Etxaniz, Andrés Arnau, Asier Eiguren

We investigate the response of the Rashba spin-orbit system to a time-periodic scalar potential, in order to determine whether an induced magnetization exists. We approach this by employing the Floquet-Green function method within the Keldysh formalism, computing the non-equilibrium steady state of the system. We find that, even in the absence of an external

Huimin Xiong, Zijie Meng, Tianxiang Hu, Chenyi Zhou

3D intraoral scans (IOS) are increasingly adopted in routine dentistry due to abundant geometric evidence, and unified multi-disease diagnosis is desirable for clinical documentation and communication. While recent works introduce dental vision-language models (VLMs) to enable unified diagnosis and report generation on 2D images or multi-view images rendered

Yuji Cao, Tongxin Li, Yue Chen

Quantum computing has been regarded as a promising approach to accelerate power system optimization. However, challenges such as limited qubits and inherent noise hinder their widespread adoption in power systems. In this paper, we propose a qubit-efficient framework for solving a crucial power system optimization problem, the probabilistic optimal power flo

Valerii Beloshapka

A procedure for the algebraization of a $CR$-manifold and its holomorphic automorphisms is described. Examples of the application of algebraization are considered. Questions arising in connection with the algebraization of a $CR$-manifold are formulated. The possibilities of extending this procedure to other branches of geometry and analysis are discussed.

Luke K. Davis

The realization of efficient micro-machines built from active matter requires precise thermodynamic control far from equilibrium. Despite theoretical progress, the focus on single-parameter driving, coupled with strict theoretical assumptions, limits efforts to capture modern multi-parameter control experiments. Here, guided by careful theoretical considerat

Yongyuan Liang, Shijie Zhou, Yu Gu, Hao Tan

Recent advances in multimodal agents have improved computer-use interaction and tool-usage, yet most existing systems remain reactive, optimizing actions in isolation without reasoning about future states or long-term goals. This limits planning coherence and prevents agents from reliably solving high-level, multi-step tasks. We introduce TraceR1, a two-stag

Tsutomu T. Takeuchi

The cosmic dipole in galaxy number counts is traditionally described by the Ellis--Baldwin (EB) formula under simplifying assumptions of power-law source counts and flux-limited selection. We reformulate the EB dipole as a symmetry response of observed counts to a Lorentz boost, leading to the general expression $D=\beta R$, where $R=\partial\ln N/\partial\l

Jonas Ziebarth, Thomas Fuhrmann-Lieker

Many diatoms follow a size diminuation - size restoration cycle in their vegetative phase, leading to daughter cells that differ in size. For the diatom Seminavis robusta, we investigated by cell tracking over several generations whether the size difference reflects also in different intermitotic times or in the mobility of the cells. A tracking setup and ma

Stefan Aimet, Philipp Schmoll, Jens Eisert, Jörg Schmiedmayer

Zero modes, understood here as degrees of freedom with vanishing confining frequency, play a central role in the nonequilibrium dynamics of bosonic systems. In Gaussian models, however, they lead to an unbounded, logarithmic growth of entanglement entropy. We show that this divergence is not an intrinsic property of zero modes themselves, but arises specific

Jeremy Brazas, Gregory R. Conner, Paul Fabel, Curtis Kent

The notions of tree-like loop and Lipschitz tree-like loop were introduced by Hambly and Lyons in their 2010 Annals of Mathematics paper. They showed that the Lipschitz tree-like property determines an equivalence relation on the set of paths of bounded variation in a given metric space and then asked if this notion could be extended to paths without the Lip

Francisco Antonio Bezerra Coutinho, Marcos Amaku, Esper Georges Kallas, Eduardo Massad

OBJECTIVE: To propose a new approach to analyze the age-distribution of reported cases for vector-transmitted infections. METHODS: Using officially reported number of cases of dengue, Zika, chikungunya, malaria and leishmaniasis for distinct geographical areas, in different periods. Data were treated in special but well-known procedure, transforming the raw

Carmen Scheidemann, Andrei Cramariuc, Changan Chen, Jia-Ruei Chiu

Background: Assistance robots have the potential to increase the independence of people who need daily care due to limited mobility or being wheelchair-bound. Current solutions of attaching robotic arms to motorized wheelchairs offer limited additional mobility at the cost of increased size and reduced wheelchair maneuverability. Methods: We present an on-de

Susanta Mondal, Manoj K. Yadav

We introduce a concept of the commuting probability of a skew left brace analogous to group theory. We establish upper and lower bounds for the commuting probability and prove that, for finite non-trivial skew left braces, it is always at most $\frac{3}{4}$. Interestingly, there is no skew left brace with commuting probability in the open interval $(5/8, 1)$

Leonardo Pedroso, Pedro Batista, W. P. M. H. Heemels

Emerging large-scale engineering systems rely on distributed fusion for situational awareness, where agents combine noisy local sensor measurements with exchanged information to obtain fused estimates. However, at the sheer scale of these systems, tracking cross-correlations becomes infeasible, preventing the use of optimal filters. Covariance intersection (

Yi Wang, Meixia Xiao, Hang Xiong

We investigate nonlinear Landau damping for the two-species screened Vlasov-Poisson system with large initial distributions on the phase space $\mathbb{R}^d \times \mathbb{R}^d$ (where $d \geq 3$). Under a structural quasi-neutrality condition, we establish the existence and uniqueness of global strong solutions to the two-species system with arbitrarily lar

Xiaoyan Wang, Li Guo, Huhu Zhang

Motivated by the recent development of noncommutative Novikov algebras and multi-Novikov algebras from the study of regularity structures of stochastic PDEs, this paper gives a general approach to study various multi-Novikov algebras and multi-differential algebras, with close connection with Poisson algebras. The construction of S. Gelfand of Novikov algebr

Eric J. Sung, Charles A. Stafford

In a recent paper [arXiv:2205.06279], Danielson et al. demonstrated that the mere presence of a black hole causes universal decoherence of quantum superpositions (dubbed the DSW decoherence). We analyze decoherence in a superconducting analogue [arXiv:1709.06154] of the event horizon of a black hole, where Andreev reflection plays the role of Hawking radiati

Jiemin Lin, Yongqiang Du, Mingxuan Zhang, Ruiheng Jing

Quantum digital signatures (QDS) offer information-theoretic security for message integrity, authenticity, and non-repudiation, and constitute a fundamental cryptographic primitive for future quantum networks. Despite significant progress, the practical deployment of QDS has been severely constrained by limited signature rates and poor tolerance to channel l

Sam P. Fisher, Marco Linton, Pablo Sánchez-Peralta

We prove that a finitely generated virtually RFRS group of cohomological dimension at most $2$ is coherent if and only if its second $L^{2}$-Betti number vanishes if and only if it is virtually free-by-cyclic. The non-vanishing of the second $L^{2}$-Betti number provides the first known global obstruction to coherence in any reasonably wide class of groups,

Filiberto Ares, Michele Mazzoni, Sara Murciano, Dávid Szász-Schagrin

Fermionic Gaussian states are a fundamental tool in many-body physics, faithfully representing non-interacting quantum systems and allowing for efficient numerical simulations. Given a many-body wave function, it is therefore interesting to ask how much it differs from that of a Gaussian state, as quantified by the notion of non-Gaussianity. In this work, we

Yibo Li, Qiongxiu Li

Gradient inversion attacks reveal that private training text can be reconstructed from shared gradients, posing a privacy risk to large language models (LLMs). While prior methods perform well in small-batch settings, scaling to larger batch sizes and longer sequences remains challenging due to severe signal mixing, high computational cost, and degraded fide

Jinsheng Wei, Xiguang Zhang, Zheng Shi, Guanming Lu

Recongnizing true emotions from masked expressions is extremely challenging due to deliberate concealment. Existing paradigms recognize true emotions from masked-expression clips that contain onsetframes just starting to disguise. However, this paradigm may not reflect the actual disguised state, as the onsetframe leaks the true emotional information without

Victoria Graf, Valentina Pyatkin, Nouha Dziri, Nathan Lambert

Multi-turn conversations are a common and critical mode of language model interaction. However, current open training and evaluation data focus on single-turn settings, failing to capture the additional dimension of these longer interactions. To understand this multi-/single-turn gap, we first introduce a new benchmark, TurnWiseEval, for multi-turn capabilit

Sreekar Chigurupati, Eleftherios Garyfallidis

We present SuCor, a method for correcting susceptibility induced geometric distortions in echo planar imaging (EPI) using optimal transport (OT) along the phase encoding direction. Given a pair of reversed phase encoding EPI volumes, we model each column of the distortion field as a Wasserstein-2 barycentric displacement between the opposing-polarity intensi

Amirhossein Mollaali, Bongseok Kim, Christian Moya, Guang Lin

Generalizing across disparate physical laws remains a fundamental challenge for artificial intelligence in science. Existing deep-learning solvers are largely confined to single-equation settings, limiting transfer across physical regimes and inference tasks. Here we introduce pADAM, a unified generative framework that learns a shared probabilistic prior acr

Hao Zhu, Markus Hainy

In many scientific and engineering domains, physical experiments are often costly, non-replicable, or time-consuming. The Kennedy and O'Hagan (KOH) model framework has become a widely used approach for combining simulator runs with limited experimental observations. Under a Bayesian implementation, the simulator output, model discrepancy, and observation noi

Wei Min Loh, Sajib Kumer Sinha, Ankur Agarwal, Pascal Poupart

Contextual bandits are incredibly useful in many practical problems. We go one step further by devising a more realistic problem that combines: (1) contextual bandits with dense arm features, (2) non-linear reward functions, and (3) a generalization of correlated bandits where reward distributions change over time but the degree of correlation maintains. Thi

Enrico Di Lucente, Nicola Marzari, Michele Simoncelli

In crystals, macroscopic technological properties such as thermal conductivity originate from the microscopic drift and scattering of phonons, commonly described by the Boltzmann Transport Equation (BTE). Despite its widespread use, the most general space-time nonlocal form of the BTE still lacks a rigorous derivation of its collisional part based on Fermi's

Jay Chooi, Paul Gölz, Ariel D. Procaccia, Benjamin Schiffer

We study the problem of selecting a statement that finds common ground across diverse population preferences. Generative AI is uniquely suited for this task because it can access a practically infinite set of statements, but AI systems like the Habermas machine leave the choice of generated statement to a voting rule. What it means for this rule to find comm

Krishna Kumar

Machine learning models for geotechnical hazard prediction can achieve high accuracy while learning physically inconsistent relationships from sparse or biased training data. Current remedies (post-hoc explainability, such as SHAP and LIME, and training-time constraints) either diagnose individual predictions approximately or restrict model capacity without

Valentin Lafargue, Ariel Guerra-Adames, Emmanuelle Claeys, Elouan Vuichard

Large language models (LLMs) are increasingly deployed in applications with societal impact, raising concerns about the cultural biases they encode. We probe these representations by evaluating whether LLMs can perform author profiling from song lyrics in a zero-shot setting, inferring singers&#39; gender and ethnicity without task-specific fine-tuning. Acro

Huiwen Zhang, Feng Ye

Accurate network traffic classification is vital for managing modern applications with strict Quality of Service (QoS) demands, such as edge computing, real-time XR, and autonomous systems. While recent advances in application-level classification show high accuracy, they often miss fine-grained in-app QoS variations critical for service differentiation. Thi

Chenggong Hu, Yi Wang, Mengqi Xue, Haofei Zhang

Textile pattern generation (TPG) aims to synthesize fine-grained textile pattern images based on given clothing images. Although previous studies have not explicitly investigated TPG, existing image-to-image models appear to be natural candidates for this task. However, when applied directly, these methods often produce unfaithful results, failing to preserv

Akira Saito, Hiromu Fujita

This paper discusses a novel data-driven nonlinearity identification method for mechanical systems with nonlinear restoring forces such as polynomial, piecewise-linear, and general displacement-dependent nonlinearities. The proposed method is built upon the universal approximation theorem that states that a nonlinear function can be approximated by a linear

Premanand Seralathan

Modern operating systems increasingly randomize Media Access Control (MAC) addresses to protect user privacy, fundamentally disrupting Network Access Control (NAC) systems that have relied on MAC addresses as persistent device identifiers for over two decades. This disruption affects critical enterprise environments including federal government agencies oper

Ruijiang Gao, Steven Chong Xiao

We study whether state-of-the-art AI coding agents, given the same data and research question, produce the same empirical results. Deploying 150 autonomous Claude Code agents to independently test six hypotheses about market quality trends in NYSE TAQ data for SPY (2015--2024), we find that AI agents exhibit sizable \textit{nonstandard errors} (NSEs), that i

Zhen Xu, Jinsu Yoo, Cristian Bautista, Zanming Huang

Building robust 3D perception for self-driving still relies heavily on large-scale data collection and manual annotation, yet this paradigm becomes impractical as deployment expands across diverse cities and regions. Meanwhile, modern cities are increasingly instrumented with roadside units (RSUs), static sensors deployed along roads and at intersections to