March 2026 arXiv papers — page 63

Yoshikazu Terada, Atsutomo Yara

We study nonparametric covariance function estimation for functional data observed with noise at discrete locations on a $d$-dimensional domain. Estimating the covariance function from discretely observed data is a challenging nonparametric problem, particularly in multidimensional settings, since the covariance function is defined on a product domain and th

Simran Khanuja, Hongbin Liu, Shujian Zhang, John Lambert

LLMs are deployed globally, yet produce responses biased towards cultures with abundant training data. Existing cultural localization approaches such as prompting or post-training alignment are black-box, hard to control, and do not reveal whether failures reflect missing knowledge or poor elicitation. In this paper, we address these gaps using mechanistic i

Mehmet Caner, Agostino Capponi, Nathan Sun, Jonathan Y. Tan

We introduce a new agentic artificial intelligence (AI) platform for portfolio management. Our architecture consists of three layers. First, two large language model (LLM) agents are assigned specialized tasks: one agent screens for firms with desirable fundamentals, while a sentiment analysis agent screens for firms with desirable news. Second, these agents

Giacomo Lastrucci, Tanuj Karia, Victor Schulte, Dominik Bongartz

Neural networks are increasingly used as surrogates in optimization problems to replace computationally expensive models. However, embedding ReLU neural networks in mathematical programs introduces significant computational challenges, particularly for deep and wide networks, due to both the formulation of the ReLU disjunction and the resulting large-scale o

Jinhao Xie, Chengkai Yuan

This research paper explores the potential of nonlinear magnetic levitation systems for energy harvesting by developing a modified system that incorporates a more realistic energy harvesting circuit, enabling a better representation of practical operating conditions. Methodologically, approximate solutions for the system dynamics were obtained using the meth

Konstantinos Barmpounakis, Theodoros P. Vagenas, Maria Vakalopoulou, George K. Matsopoulos

Radiotherapy workflows for oncological patients increasingly rely on multi-modal medical imaging, commonly involving both Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). MRI-only treatment planning has emerged as an attractive alternative, as it reduces patient exposure to ionizing radiation and avoids errors introduced by inter-modality regis

Roberto Fuentes-Martínez, Irene Crimaldi

A model-free measure of Granger causality in expectiles is proposed, generalizing the traditional mean-based measure to arbitrary positions of the conditional distribution. Expectiles are the only law-invariant risk measures that are both coherent and elicitable, making them particularly well-suited for studying distributional Granger causality where risk qu

Oleg Kiriukhin

I study orbit-level enstrophy stretching in a cubic Fourier-Galerkin truncation of the three-dimensional incompressible Navier-Stokes equations, reduced by the full octahedral symmetry group $O_h$. The nonlinear transfer compresses to an orbit-level matrix whose symmetric part $V_N$ governs net enstrophy growth. I reduce the stretching problem to an orbit--t

Jan Christian Blaise Cruz, Alham Fikri Aji

Benchmarks and leaderboards are how NLP most often communicates progress, but in the LLM era they are increasingly easy to misread. Scores can reflect benchmark-chasing, hidden evaluation choices, or accidental exposure to test content -- not just broad capability. Closed benchmarks delay some of these issues, but reduce transparency and make it harder for t

Steven R. Spangler

Variations in the luminosity of the Sun on timescales of thousands to millions of years could potentially be responsible for terrestrial climate variations in the Phanerozooic geological period (last 540 million years). In this paper, I consider a method that utilizes observations of an open star cluster with approximately the age of the Sun, specifically M6

K. B. Vedamurthy, Manojkumar Patil, Vaishnavi, Priyanka V

India generates substantial volumes of public agricultural data, yet artificial intelligence (AI) adoption in farming remains limited and largely confined to pilot initiatives. This paper examines this gap by assessing India's agricultural data infrastructure against the requirements of AI systems deployed at scale. Drawing on a systematic review of major na

Chang Lv

We develop a formalism of cohomological descent encoding adelic points and obstructions to local-global principle on algebraic stacks. As an application, by constructing new obstructions using the formalism, we obtain some comparison results of obstructions on some classes of algebraic stacks.

Shiheng Nie, Yunguang Yue

Physical knot classification is a challenging fine-grained recognition task in which the intended discriminative cue is rope crossing structure; however, high closed-set accuracy may still arise from low-level appearance shortcuts rather than genuine topological understanding. In this work, we introduce dataset (1,440 images, 10 classes), which trains models

Igor A. Andriyash, Cedric Thaury

This work provides a brief review of the numerical methods for modelling the optical propagation of an ultrashort, intense laser pulse in plasmas and ionizable gases. These methods are implemented in an open-source simulation toolkit Axiprop, which is now actively used for design studies in the context of laser plasma acceleration of electrons (LPA). We pres

Xinyong Cai, Runming Xie, Hu Chen, Yuankai Wu

Spatiotemporal predictive learning aims to forecast future frames from historical observations in an unsupervised manner, and is critical to a wide range of applications. The key challenge is to model long-range dynamics while preserving high-frequency details for sharp multi-step predictions. Existing efficient recurrent-free frameworks typically rely on st

Jiaqi Dong

Accurate short-term forecasting of air temperature and relative humidity is critical for urban management, especially in topographically complex cities such as Chongqing, China. This study compares seven machine learning models: eXtreme Gradient Boosting (XGBoost), Random Forest, Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Decision Tree, L

Dipankar Das, Miguel Levy, Shreya Pandey, Ipsita Saha

The soft-breaking parameter, $m_{12}^2$, frequently appearing in the 2HDM scalar potential is much more remarkable than being just a nonstandard parameter that helps make the BSM scalars super heavy. In fact, as we show through explicit calculations, it should be treated as the direct but concise embodiment of new non-electroweak spontaneous symmetry breakin

Luca Sodano, Sofia Sciangula, Amulya Galmarini, Francesco Bertolotti

The rapid diffusion of large language models and the growth in their capability has enabled the emergence of online environments populated by autonomous AI agents that interact through natural language. These platforms provide a novel empirical setting for studying collective dynamics among artificial agents. In this paper we analyze the interaction network

Francesca Bray, Simone Tolomei, Andrei Cramariuc, Cesar Cadena

Robotic collaborative carrying could greatly benefit human activities like warehouse and construction site management. However, coordinating the simultaneous motion of multiple robots represents a significant challenge. Existing works primarily focus on obstacle-free environments, making them unsuitable for most real-world applications. Works that account fo

Paul B May, Andrew Simpson, Semhar Michael

We develop an identifiable reduced-rank spatial multinomial model for categorical data with many classes. The model represents class-specific spatial effects through a low-dimensional set of shared latent factors, substantially reducing parameter dimension while preserving joint dependence across classes. Because standard conjugate and P\'olya-Gamma methods

Yuchen Wu, Kun Wang, Yining Pan, Na Zhao

Multi-modal fusion has emerged as a promising paradigm for accurate 3D object detection. However, performance degrades substantially when deployed in target domains different from training. In this work, focusing on dual-branch proposal-level detectors, we identify two factors that limit robust cross-domain generalization: 1) in challenging domains such as r

Taro Kimura, Sanchita Sharma

We study the Dirac operator on a finite warped cylinder coupled to a background $U(1)$ gauge field. We identify the intrinsic endpoint operators defining the Atiyah-Patodi-Singer (APS) boundary condition and derive a determinant characterization of the modewise APS spectrum. In the constant-gauge, invertible setting, the endpoint reduced $\eta$ contributions

D. Yu. Saltykova, M. V. Nikitin, V. Ya. Pokrovskii, S. G. Zybtsev

In their Letter Fujiwara et al. (10.48550/arXiv.2511.09888. 2025) report a high-quality experiment demonstrating the synchronization of the CDW sliding in NbSe3 whiskers (nanowires) with surface acoustic waves (SAWs). The SAWs are induced in the conventional LiNbO3 piezoelectric substrates through application of rf voltage to an interdigital transducer (IDT)

Kazuki Nakajima, Yuya Sasaki, Sohei Tokuno, George Fletcher

Gender imbalance persists across science, technology, engineering, and mathematics (STEM) fields, including computer science, where it appears in researcher demographics, productivity, recognition, hiring, and career progression. Given computer science's rapid expansion and global influence, addressing this imbalance is essential for broadening participation

Xue Wang, Zheng Guan, Wenhua Qian, Chengchao Wang

Multi-modal image fusion integrates complementary information from different modalities into a unified representation. Current methods predominantly optimize statistical correlations between modalities, often capturing dataset-induced spurious associations that degrade under distribution shifts. In this paper, we propose an intervention-based framework inspi

Shaid Hasan, Breenice Lee, Sujan Sarker, Tariq Iqbal

Human-robot interaction is increasingly moving toward multi-robot, socially grounded environments. Existing systems struggle to integrate multimodal perception, embodied expression, and coordinated decision-making in a unified framework. This limits natural and scalable interaction in shared physical spaces. We address this gap by introducing a multimodal fr

Rong-Xin Nie, Xue-Hui Ai, Xin Guan, Jie Yang

Rotational properties of [523]5/2 (h11/2) and [642]5/2 (i13/2) signature partner bands in 169Hf are investigated using the Total Routhian Surface (TRS) method. Experimental data show a distinct signature inversion in the [523]5/2 band at high spin, while the [642]5/2 band exhibits conventional signature splitting. Our analysis identifies a proton subshell ga

Wenyu Chen, Xiangtao Meng, Chuanchao Zang, Li Wang

Large Language Models(LLMs) are widely deployed, yet are vulnerable to jailbreak prompts that elicit policy-violating outputs. Although prior studies have uncovered these risks, they typically treat all tokens as equally important during prompt mutation, overlooking the varying contributions of individual tokens to triggering model refusals. Consequently, th

Enrico Ghiorzi, Armando Tacchella

This paper lays out the formal foundations upon which the SCAN statistical model checker is built.

Miao Yu, Siyuan Fu, Moayad Aloqaily, Zhenhong Zhou

Mechanistic interpretability reveals that safety-critical behaviors (e.g., alignment, jailbreak, backdoor) in Large Language Models (LLMs) are grounded in specialized functional components. However, existing safety attribution methods struggle with generalization and reliability due to their reliance on heuristic, domain-specific metrics and search algorithm

Kailun Tian, Kaili Jiang, Dechang Wang, Hancong Feng

Traditional Direction of Arrival (DOA) estimation methods struggle to simultaneously address three physical constraints in Ultra-Wideband (UWB) electromagnetic sensing: spatial undersampling, asynchronous array phase, and beam squint. Existing solutions treat these issues in isolation, leading to limited performance in complex scenarios. This paper proposes

Christof Schütte, Alexander Sikorski, Jakob Kresse, Marcus Weber

Effective dynamics on a low-dimensional collective-variable (CV) or latent space can be simulated far more cheaply than the underlying high-dimensional stochastic system, but exploiting such coarse predictions requires lifting: turning a coarse CV trajectory into dynamically consistent full-dimensional states and path ensembles, without relying on global sam

Rongxiu Chen, Yuting Su

Online safety fault diagnosis is essential for lithium-ion batteries in electric vehicles(EVs), particularly under complex and rare safety-critical conditions in real-world operation. In this work, we develop an online battery fault diagnosis network based on a deep anomaly detection framework combining kernel one-class classification and minimum-volume esti

Youssef Fattasse, Hocine Bahlouli, Clarence Cortes, David Laroze

We investigate the Goos--H\"anchen (GH) shifts and group delay time of Dirac fermions traversing a rectangular electrostatic potential barrier in silicene. By analyzing their dependence on the incident angle, barrier height, barrier width, and incident energy, we demonstrate that the GH shifts exhibit pronounced oscillations arising from quantum interference

Chengsheng Mu, Shuxun Tian, Shuo Cao, Zong-Hong Zhu

Vacuum energy, a prediction of quantum field theory, manifests itself as a cosmological constant in general relativity. In this Letter, we propose a novel inflationary scenario driven by a bare cosmological constant $\Lambda$, which terminates naturally through a self-tuning mechanism. Within Fab-Four gravity, self-tuning destabilizes the de Sitter state and

Bastian Heinlein, Nuria Zurita Jiménez, Kaikai Zhu, Sümeyye Carkit-Yilmaz

In this paper, we introduce an autoencoder (AE)-based scheme for end-to-end optimization of a multi-user molecule mixture communication system. In the proposed scheme, each transmitter leverages an encoder network that maps the user symbol to a molecule mixture. The mixtures then propagate through the channel to the receiver, which samples the channel using

Bennet Gebken, Michael Ulbrich

We propose a globally convergent trust-region bundle method for minimizing lower-$C^2$ functions using higher-order cutting-plane models. Under certain growth assumptions on the objective around its minimum, the method is able to compute infinitely many trust regions of decreasing size that contain the minimum. We show that these growth assumptions are satis

Minhao Zhu, Kaiming Shen

This paper addresses the weighted sum-rate (WSR) maximization problem in a downlink distributed antenna system subject to per-cluster power constraints. This optimization scenario presents significant challenges due to the high dimensionality of beamforming variables in dense antenna deployments and the structural complexity of multiple independent power con

Randolph Wiredu-Aidoo

In clustering, strong dominance in the size of a particular cluster is often undesirable, motivating a measure of cluster size uniformity that can be used to filter such partitions. A basic requirement of such a measure is stability: partitions that differ only slightly in their point assignments should receive similar uniformity scores. A difficulty arises

Joshua R. Border, Alain Nogaret, Andrew Lefevre, Vishal Jain

Dissipative coupling is known to induce synchronization. Conversely it may be hypothesized that oscillators driven to synchronize may reduce power dissipation in their coupling. The latter scenario is realized in the human cardiorespiratory system where cardiac and respiratory rhythms are controlled by the central nervous system while interacting viscoelasti

Maximilian Mandelt Buxadé, Stefan Langer, Philipp Bekemeyer

To approximate solutions of complex nonlinear partial differential equations remains a computational challenge, especially for sets of equations relevant in industry, such as Euler or Navier-Stokes equations. Even the most sophisticated computational fluid dynamic algorithms coupled with powerful supercomputers can not find approximate solutions for several

Wei Sun, Ting Wang, Xinran Tian, Wanshun Lan

Existing LLM-based Kubernetes diagnostic systems cannot learn from operational experience, operating on static knowledge bases without improving from past resolutions. We present MetaKube, an experience-aware LLM framework through three synergistic innovations: (1) an Episodic Pattern Memory Network (EPMN) that abstracts diagnostic patterns from historical r

Marco A. S. Trindade

In this work, we derive information-theoretic properties for a modified Tsallis entropy, hereinafter referred to as q-entropy. We introduce the notions of joint q-entropy, conditional q-entropy, relative q-entropy, conditional mutual q-information, and establish several inequalities analogous to those of classical information theory. Within the context of Ma

H. Acharya, M. Aker, D. Batzler, A. Beglarian

Sterile neutrinos in the keV mass range are a well-motivated extension of the Standard Model and viable dark matter candidates. Their existence can be probed in laboratory experiments, as the admixture of a sterile state would induce a characteristic kink-like distortion in the $\beta$-decay electron energy spectrum. The KATRIN experiment is designed to meas

Gyeonghoon Ko, Juho Lee

Permutation invariance is fundamental in molecular point-cloud generation, yet most diffusion models enforce it indirectly via permutation-equivariant networks on an ordered space. We propose to model diffusion directly on the quotient manifold $\tilde{\calX}=\sR^{d\times N}/S_N$, where all atom permutations are identified. We show that the heat kernel on $\

Jianan Mu, Ge Yu, Zhaoxuan Kan, Song Bian

Fully Homomorphic Encryption (FHE) is rapidly emerging as a promising foundation for privacy-preserving cloud services, enabling computation directly on encrypted data. As FHE implementations mature and begin moving toward practical deployment in domains such as secure finance, biomedical analytics, and privacy-preserving AI, a critical question remains insu

Daniele Tarchi

Integrating Artificial Intelligence (AI) into Non-Terrestrial Networks (NTN) is constrained by the joint limits of satellite SWaP and feeder-link capacity, which directly impact O-RAN closed-loop control and model lifecycle management. This paper studies the feasibility of distributing the O-RAN control hierarchy across Ground, LEO, and GEO segments through

Nasser A Alsadhan

The advancing fluency of LLMs raises important questions about their ability to emulate complex human traits, including emotional expression and personality, across diverse linguistic and cultural contexts. This study investigates whether LLMs can convincingly mimic emotional nuance in English and personality markers in Arabic, a critical under-resourced lan

Jiseong Kim

In this paper, we investigate the average behavior of ternary correlations for general $k$-divisor-bounded multiplicative functions, assuming certain second moment integral bounds for the associated $L$-functions. Our approach differs from previous methods based on spectral theory or Heath-Brown-type decompositions, and instead combines the circle method wit

Anina Dufter, Sabrina Weber, Orkun Furat, Johannes Schubert

The performance of all-solid-state battery (ASSB) cathodes strongly depends on their microstructure. Optimizing the cathode morphology can therefore enhance effective macroscopic properties such as ionic and electronic conductivity. The search for optimized microstructures can be facilitated by virtual materials testing: By integrating image analysis and sto

Mohit Yadav, Pankaj Chavan, Tapomoy Guha Sarkar

Cosmographic reconstructions provide a model-agnostic approach towards constraining cosmic evolution. In this work, we develop a semi-cosmographic framework that adopts a Pad\'e-rational fraction parametrization of the Luminosity distance, but also invokes a phenomenology-motivated two-body decaying dark matter (DDM) sector. In this approach, we do not assum

Zekai Gu, Shuoxuan Feng, Yansong Wang, Hanzhuo Huang

Reconstructing a renderable 3D model from images is a useful but challenging task. Recent feedforward 3D reconstruction methods have demonstrated remarkable success in efficiently recovering geometry, but still cannot accurately model the complex appearances of these 3D reconstructed models. Recent diffusion-based generative models can synthesize realistic i

Chao Han, Stefanos Ioannou, Luca Manneschi, T. J. Hayward

We investigate neural ordinary and stochastic differential equations (neural ODEs and SDEs) to model stochastic dynamics in fully and partially observed environments within a model-based reinforcement learning (RL) framework. Through a sequence of simulations, we show that neural SDEs more effectively capture the inherent stochasticity of transition dynamics

Pinzhe Zhao, Emanuele Sansone, Marta Kryven, Bonan Zhao

When learning a novel complex task, people often form efficient reusable abstractions that simplify future work, despite uncertainty about the future. We study this process in a visual puzzle task where participants define and reuse helpers -- intermediate constructions that capture repeating structure. In an online experiment, participants solved puzzles of

Jurgen Julio-Batalla

This paper is concerned with the zero mode equation $D_g\varphi=iA\cdot\varphi$ on product of closed spin manifolds $(M_1^{n_1}\times M_2^{n_2},g_1+g_2,\sigma)$ of dimensions $n_1\leq n_2$ respectively. Here $A$ is a real vector field on $M^n=M_1^{n_1}\times M_2^{n_2}$. Under non-increasing condition on $|\varphi|$ we prove that $$\parallel A\parallel_n^2\ge

N. Biver, D. Bockelée-Morvan, R. Moreno, J. Crovisier

3I/ATLAS is the third interstellar comet identified as passing through the Solar System. Its high outgassing activity and favourable perihelion passage on October 29, 2025 UT provided an excellent opportunity to investigate the composition of its coma gases through millimeter spectroscopy. We present observations undertaken with the IRAM 30-m telescope on No

Jean-Christophe Pain

We establish a structural connection between the classical Olech-Opial inequality and the Wirtinger inequality. Using an integral identity involving the mixed energy term $uu'$, we derive a nonlinear interpolation inequality linking these two results. The optimal constant is characterized by a variational problem whose extremals satisfy an Emden-Fowler equat

Cheng Zhang, Zhifei Zhu

We establish a precise hierarchy for the maximal growth of the Stein-Wainger oscillatory integral as the regularity of the phase varies over Denjoy-Carleman classes, such as the Gevrey classes and their generalizations. In particular, we resolve a problem posed by Wang--Zhang, motivated by eigenfunction restriction estimates on curves, and also provide a new

Bennet Gebken, Michael Ulbrich

A cutting-plane model for a nonsmooth function is the maximum of several first-order expansions centered at different points. Using such a model in a bundle method leads to linear convergence (of serious steps) to a minimum. In smooth optimization, superlinear convergence can be achieved by using higher-order models. We show that the same is true for the non

Tomas T. Osterholt, Lumen Eek, Cristiane Morais Smith, Rembert A. Duine

Altermagnets have recently emerged as a new platform for spintronics applications, offering spin-split electronic bands despite vanishing net magnetization. Here, we investigate spin-current generation in Dirac altermagnets and identify Klein tunneling as an efficient mechanism for enhancing spin transport. Using a low-energy Dirac model combined with scatte

Ryo Nasu, Yoshihiro Maruyama

The disjunction effect in human decision making is often taken to show that the classical law of total probability is violated, motivating quantum-like models. We re-examine this claim for the Prisoner's Dilemma disjunction effect. Under the mental-event reading of the opponent-choice events, the conventional classical decision-process model implicitly build

Haoyu Wang, Jingcheng Wang, Shunyu Wu, Xinwei Xiao

Offline reinforcement learning (RL) can fit strong value functions from fixed datasets, yet reliable deployment still hinges on the action selection interface used to query them. When the dataset induces a branched or multimodal action landscape, unimodal policy extraction can blur competing hypotheses and yield "in-between" actions that are weakly supported

Michele Battagliola, Anna-Lena Horlemann, Abhinaba Mazumder, Rocco Mora

Given two linear codes, the Linear Equivalence Problem (LEP) asks to find (if it exists) a linear isometry between them; as a special case, we have the Permutation Equivalence Problem (PEP), in which isometries must be permutations. LEP and PEP have recently gained renewed interest as the security foundations for several post-quantum schemes, including LESS.

Shijia Zhou, Saif M. Mohammad, Barbara Plank, Diego Frassinelli

Internet memes represent a popular form of multimodal online communication and often use figurative elements to convey layered meaning through the combination of text and images. However, it remains largely unclear how multimodal large language models (MLLMs) combine and interpret visual and textual information to identify figurative meaning in memes. To add

Fedor B. Lyudogovskiy

We study the partition graph $G_n$, whose vertices are the integer partitions of $n$ and whose edges correspond to elementary transfers of one unit between parts. We introduce the simplex stratification of $G_n$: for each vertex $\lambda$, let $\dim_{\mathrm{loc}}(\lambda)$ denote the largest dimension of a simplex of the clique complex $K_n = \mathrm{Cl}(G_

Qinglun Zhang, Shen Cheng, Tian Dan, Haoqiang Fan

While existing equivariant methods enhance data efficiency, they suffer from high computational intensity, reliance on single-modality inputs, and instability when combined with fast-sampling methods. In this work, we propose E3Flow, a novel framework that addresses the critical limitations of equivariant diffusion policies. E3Flow overcomes these challenges

Yoshimichi Nakatsuka, Nicolas Dutly, Kari Kostiainen, Srdjan Capkun

Private Membership Testing (PMT) protocols enable clients to verify whether a certain data item is included in a database without revealing the item to the database operator or other external parties. This paper examines Source-assisted PMT (SPMT), in which clients leverage compact data source-provided information issued when the data item is first submitted

Ylann Rouzaire, Daniel JG Pearce, Ignacio Pagonabarraga, Demian Levis

We investigate emergent dynamics due to non-reciprocity in the $\mathcal{O}(2)$ model. The lattice XY model, where non-reciprocity stems from vision cone like couplings, can be described by a continuum description in which non-reciprocity translates into a new term depending on the rotational of the orientation field. We argue that non-reciprocity is akin to

Nghia Vu, Tuong Do, Dzung Tran, Binh X. Nguyen

Generative models have shown substantial impact across multiple domains, their potential for scene synthesis remains underexplored in robotics. This gap is more evident in drone simulators, where simulation environments still rely heavily on manual efforts, which are time-consuming to create and difficult to scale. In this work, we introduce AeroScene, a hie

Arturo Tozzi

Mechanical loading in articular cartilage drives interstitial fluid flow through the porous collagen proteoglycan matrix, generating electrokinetic signals. We investigate whether the structural organization of cartilage histology can be translated into a computational representation capable of predicting its electrokinetic behavior. Histological pictures we

Federico Rosato, Lorenzo Nespoli, Vasco Medici

Smart meters provide relevant information for impedance identification, but they lack global phase alignment and internal network nodes are often unobserved. A few methods for this setting were developed, but they have requirements on data correlation and/or network topology. In this paper, we offer a unifying view of data- and structure-driven identifiabili

Jelizaveta Vakarjuk, Alisa Pankova

European Digital Identity (EUDI) Wallet aims to provide end users with a way to get attested credentials from issuers, and present them to different relying parties. An important property mentioned in the regulatory frameworks is the possibility to revoke a previously issued credential. While it is possible to issue a short-lived credential, in some cases it

Aomar Osmani

We study learning under regime variation, where the learner, its memory state, and the evaluative conditions may evolve over time. This paper is a foundational and structural contribution: its goal is to define the core learning-theoretic objects required for such settings and to establish their first theorem-supporting consequences. The paper develops a reg

Nasser A Alsadhan

Amidst the rising capabilities of generative AI to mimic specific human styles, this study investigates the ability of state-of-the-art large language models (LLMs), including GPT-4o, Gemini 1.5 Pro, and Claude Sonnet 3.5, to emulate the authorial signatures of prominent literary and political figures: Walt Whitman, William Wordsworth, Donald Trump, and Bara

Jurgen Julio-Batalla

This paper is concerned with the zero mode equation $D_g\varphi=iA\cdot\varphi$ on closed spin manifold $(M^n,g,\sigma)$ of positive scalar curvature. Here $A$ is a real one form on $M$. We proved that if $(\varphi, A)$ is a non trivial solution of the zero mode equation then $$\parallel dA\parallel_{n/2}>Y(M^n,[g])/(4v_n^{1/2}),$$ where $Y(M^n,[g])$ is the

Xinran Fang, Wei Feng, Yanmin Wang, Yunfei Chen

In hazardous environments, sensors and actuators can be deployed to see and operate on behalf of humans, enabling safe and efficient task execution. Functioning as a neural center, the edge information hub (EIH), which integrates communication and computing capabilities, coordinates these sensors and actuators into sensing-communication-computing-control (SC

Anders Aamand, Mikkel Abrahamsen, Reilly Browne, Mayank Goswami

We study the problems of covering or partitioning a polygon $P$ (possibly with holes) using a minimum number of small pieces, where a small piece is a connected sub-polygon contained in an axis-aligned unit square. For covering, we seek to write $P$ as a union of small pieces, and in partitioning, we furthermore require the pieces to be pairwise interior-dis

Yasamin Borhani, Taylor Mordan, Yihan Wang, Reyhaneh Hosseininejad

Object skeletons offer a concise representation of structural information, capturing essential aspects of posture and orientation that are crucial for autonomous driving applications. However, a unified architecture that simultaneously handles multiple instances and categories using only the input image remains elusive. In this paper, we introduce PoseDriver

Xin-Tong Ma, Ding-Hui Xu, Zhen-hua Zhao

The type-I seesaw model with two-zero textures of the Majorana mass matrix for the right-handed neutrinos $M^{}_{\rm R}$ provides a highly predictive framework for neutrino mass generation and baryon asymmetry of the Universe via leptogenesis. In this work, we systematically investigate the compatibility of viable two-zero textures of $M^{}_{\rm R}$ with lep

Xinlei Liu, Andrey Belogaev, Jonathan Oostvogels, Bingwu Fang

The latency gap between wired and wireless networks poses a challenge in the adoption of wireless technologies in latency-sensitive scenarios. The gap is especially notable in multi-hop communication typical for industrial sensor networks and robotic swarms. The main reason behind it is that commonly used wireless protocols rely on store-and-forward routing

Jialiang Fu, Wen-Xuan Lang

In this paper, we establish a new law of large numbers with the rate of convergence for special partial sums in a probability space. The proof relies on nonlinear expectation theory, as the uncertainty of random variables in the special partial sums induces the sublinearity of the expectation. As an application, we apply the new theorem to analyze the feedba

Alba Ordonez, Theodor Johannes Line Forgaard, David Wade, Aina Juell Bugge

We present the NCS-models, a family of seismic foundation models pretrained on a large share of full-stack seismic cubes from the Norwegian Continental Shelf (NCS) available through the public DISKOS database. The model weights are open-sourced for the wider geoscience community. Foundation models trained with large-scale self-supervision are emerging as a p

Jiawei Guo, Daniel Schwalbe-Koda

Determining atomistic structures from characterization data is one of the most common yet intricate problems in materials science. Particularly in amorphous materials, proposing structures that balance realism and agreement with experiments requires expert guidance, good interatomic potentials, or both. Here, we introduce GLASS, a generative framework that i

Noah Bergam, Samuel Deng, Daniel Hsu

We prove the tightest-known upper bounds on the sample complexity of multi-group learning. Our algorithm extends the one-inclusion graph prediction strategy using a generalization of bipartite $b$-matching. In the group-realizable setting, we provide a lower bound confirming that our algorithm's $\log n / n$ convergence rate is optimal in general. If one rel

Davide Bricalli

In this paper we will study the Hessian hypersurface associated with a smooth cubic. We prove that the existence of a Hessian locus, associated with a smooth cubic form f, of dimension bigger then the expected one, forces the cubic f to be of Thom-Sebastiani type. Moreover, we will analyze the existence of some projective linear spaces in such Hessian loci a

Oliver Hennhöfer, Christine Preisach

Standard conformal anomaly detection provides marginal finite-sample guarantees under the assumption of exchangeability . However, real-world data often exhibit distribution shifts, necessitating a weighted conformal approach to adapt to local non-stationarity. We show that this adaptation induces a critical trade-off between the minimum attainable p-value a

Mario Ramos-Hamud

A generic aspect of low-energy effective field theories (EFTs) coming from string compactifications is the appearance of moduli fields. Among these moduli, the axion and dilaton are present as (pseudo-) Goldstone bosons from the spontaneous breaking of an exact (or approximate) global symmetry. These moduli have a different microscopic coupling to matter but

Quang Manh Hoang, Guilherme Vieira Hollweg, Bang Nguyen, Akhtar Hussain

Modern machine learning approaches typically identify the impedance of a single inverter-based resource (IBR) and assume similar impedance characteristics across devices. In modern power systems, however, IBRs will employ diverse control topologies and algorithms, leading to highly heterogeneous impedance behaviors. Training one model per IBR is inefficient

Anupam Pani, Yanchao Yang

Despite advances in Vision-Language-Action (VLA) models, robotic manipulation struggles with fine-grained tasks because current models lack mechanisms for active visual attention allocation. Human gaze naturally encodes intent, planning, and execution patterns -- offering a powerful supervisory signal for guiding robot perception. We introduce a gaze-regular

L. Martinez, O. G. Benvenuto, M. A. De Vito

The initial masses of red supergiant (RSG) type II supernova (SN II) progenitors are commonly inferred from pre-explosion imaging by converting the progenitor luminosity into an initial mass estimate using non-rotating stellar evolution models. However, stellar rotation affects the evolution and may influence these estimates. We investigate how the observed

Keming He, Yuehan Zhang, Hongshun Yao, Jin-Guo Liu

Coherent Ising machines (CIMs) have emerged as specialized quantum hardware for large-scale combinatorial optimization. However, for large instances that remain challenging for classical methods, some platforms support only finite-precision inputs, and the required scaling and quantization can degrade solution quality. Dynamic portfolio optimization (DPO) ca

Yukinori Yamamoto, Kazuya Nishimura, Tsukasa Fukusato, Hirokazu Nosato

Deep learning-based 3D medical image segmentation methods relies on large-scale labeled datasets, yet acquiring such data is difficult due to privacy constraints and the high cost of expert annotation. Formula-Driven Supervised Learning (FDSL) offers an appealing alternative by generating training data and labels directly from mathematical formulas. However,

Nihat Gokhan Gogus, Rewayat Khan

Matrix valued (asymmetric) truncated Toeplitz operators are generally not complex symmetric. In this paper, we define a new conjugation with unique properties and study its relation to matrix valued asymmetric truncated Toeplitz operators. We also explore the connections between matrix valued asymmetric truncated Toeplitz operators and Hankel operators with

Yuqing Zhou, Ze Tao, Fujun Liu

Efficient thermal management and precise field prediction are critical for the design of advanced energy systems, including electrohydrodynamic transport, microfluidic energy harvesters, and electrically driven thermal regulators. However, the steady-state simulation of these electrothermal coupled multiphysics systems remains challenging for physics-informe

Edoardo Cetin, Stefano Peluchetti, Emilio Castillo, Akira Naruse

Scaling autoregressive large language models (LLMs) has driven unprecedented progress but comes with vast computational costs. In this work, we tackle these costs by leveraging unstructured sparsity within an LLM's feedforward layers, the components accounting for most of the model parameters and execution FLOPs. To achieve this, we introduce a new sparse pa

Chuhao Qin, Lukas Esterle, Evangelos Pournaras

Coordination of view coverage via privacy-aware smart cameras is key to a more socially responsible urban intelligence. Rather than maximizing view coverage at any cost or over relying on expensive cryptographic techniques, we address how cameras can coordinate to legitimately monitor public spaces while excluding privacy-sensitive regions by design. This ar

Long Zhang, Dai-jun Lin, Wei-neng Chen

Large language models (LLMs) generalize smoothly across continuous semantic spaces, yet strict logical reasoning demands the formation of discrete decision boundaries. Prevailing theories relying on linear isometric projections fail to resolve this fundamental tension. In this work, we argue that task context operates as a non-isometric dynamical operator th

Md Nahid Hasan, Vishwam Tiwari, Aditya Challa, Vaskar Raychoudhury

Delivery systems have become a core part of urban life, supporting the demand for food, medicine, and other goods. Yet traditional logistics networks remain fragile, often struggling to adapt to road closures, accidents, and shifting demand. Online Food Delivery (OFD) platforms now represent a cornerstone of urban logistics, with the global market projected

Hyunwoo Kim, Munyoung Lee, Seung Hyub Jeon, Kyu Sung Lee

Understanding wafer-level spatial variations from in-situ process signals is essential for advanced plasma etching process monitoring. While most data-driven approaches focus on scalar indicators such as average etch rate, actual process quality is determined by complex two-dimensional spatial distributions across the wafer. This paper presents a spatial reg

Yan Fang, Jianfei Ge, Jiangjian Xiao

Recent advances in 3D Gaussian Splatting (3DGS) have enabled real-time, photorealistic scene reconstruction. However, conventional 3DGS frameworks typically rely on sparse point clouds derived from Structure-from-Motion (SfM), which inherently suffer from scale ambiguity, limited geometric consistency, and strong view dependency due to the lack of geometric