April 2026 arXiv papers — page 146

Maurizio Quadrio, Federica Gattere, Marco Castelletti, Alessandro Chiarini

Turbulent channel flow controlled by spanwise wall oscillations is studied using direct numerical simulations to improve how spanwise forcing reduces skin-friction drag. Harmonic wall oscillations generate a periodic transverse Stokes layer whose thickness $\delta$ is determined by the forcing period $T$. Although an optimal $T$ that maximizes drag reduction

Ion Matei, Maksym Zhenirovskyy

We present a method to quantify a system's resilience capacity, i.e., the set of degradation magnitudes for which all functional requirements remain satisfied. These requirements come from human stakeholders (e.g., operators, planners) who define the acceptable performance envelope. By representing the resilience capacity in degradation space, we obtain an a

Samrendra Roy, Sajedul Talukder, Syed Bahauddin Alam

Anomaly detection in nuclear industrial control systems (ICS) requires continuous, energy-efficient monitoring across multiple subsystems that are often deployed at different stages of plant commissioning. When a conventional neural network is sequentially trained to monitor new subsystems, it catastrophically forgets previously learned anomaly patterns, a s

Saket Shah

Kuznetsov showed that for a flat quadric fibration $\mathcal{Q}$ over a smooth base $S$, $\mathrm{D}^b(\mathcal{Q})$ admits a semiorthogonal decomposition where one of the components is the derived category of the sheaf of even parts of a Clifford algebra $\mathrm{D}^b(S,\mathcal{C}l_0)$. \par As progress towards a generalization, we show that for a quadric

Yitong Wang

Let $p\geq5$ be a prime number. Let $L$ be a finite unramified extension of $\mathbb{Q}_p$ with ring of integers $\mathcal{O}_L$ and residue field $\mathbb{F}_q$. Given two Serre weights for $\mathrm{GL}_3(\mathbb{F}_q)$, we prove that in most cases the extensions between them for $\mathrm{GL}_3(\mathcal{O}_L)$ modulo the center coincide with their $\mathrm{

Luke Marshall, Prachi Shah, Santanu S. Dey

We propose a new pricing strategy for column generation (CG), referred to as Template pricing. This method is motivated by the desire to coordinate solutions of different pricing subproblems in order to accelerate the convergence of the CG process and simultaneously obtain good quality integer feasible solutions. Instead of finding a column with the optimal

Vojtech Panek, Patrik Beliansky, Zuzana Kukelova, Torsten Sattler

Visual localization is the task of estimating the camera pose of an image relative to a scene representation. In practice, visual localization systems are often cloud-based. Naturally, this raises privacy concerns in terms of revealing private details through the images sent to the server or through the representations stored on the server. Privacy-preservin

Shreyansh Pathak, Jyotishman Das

The need to selectively and efficiently erase learned information from deep neural networks is becoming increasingly important for privacy, regulatory compliance, and adaptive system design. We introduce Graph-Propagated Projection Unlearning (GPPU), a unified and scalable algorithm for class-level unlearning that operates across both vision and audio models

Mirsad Cosovic, Armin Teskeredzic, Antonello Monti, Dejan Vukobratovic

Electric power systems require accurate, scalable, distributed, and near real-time state estimation (SE) to support reliable monitoring and control under increasingly complex operating conditions. Limited monitoring capabilities can lead to inefficient operation and, in extreme cases, large-scale disturbances such as blackouts. To address these challenges, t

Candace Walkington, Theodora Beauchamp, Fareya Ikram, Merve Koçyiğit Gürbüz

Large language models can increasingly adapt educational tasks to learners characteristics. In the present study, we examine a multi-agent teacher-in-the-loop system for personalizing middle school math problems. The teacher enters a base problem and desired topic, the LLM generates the problem, and then four AI agents evaluate the problem using criteria tha

Mohamed Ouzahra

This manuscript addresses the analysis and design of feedback laws for the stabilization of bilinear control systems in infinite-dimensional spaces. It first examines weak, strong, and polynomial stabilization within a Hilbert space framework, emphasizing the role of observability conditions. It then studies exponential stabilization in Banach spaces, highli

Justice Owusu Agyemang, Jerry John Kponyo, Elliot Amponsah, Godfred Manu Addo Boakye

Coding agents and LLM-powered applications routinely send potentially sensitive content to cloud LLM APIs where it may be logged, retained, used for training, or subpoenaed. Existing privacy tooling focuses on network-level encryption and organization-level DLP, neither of which addresses the content of prompts themselves. We present a systematic empirical e

Kevin B. Xu, John L. Bohn

The puzzle of "sticky collisions," in which molecular collision complexes exhibit unexpectedly long lifetimes, remains an unresolved mystery. A central challenge to solving this mystery is that traditional close-coupling calculations remain limited by the vast computational cost needed to take into account all the degrees of freedom involved in the collision

Abir Sarkar, Martin T. Wells

The recent surge in valuations among AI related firms has renewed concerns that markets may be entering a new phase of speculative exuberance, especially in the technology and semiconductor sectors at the center of the AI investment wave. This paper develops a practical econometric framework for detecting, date-stamping, and drawing inference on the originat

Tiziano De Angelis, Giulia Livieri, Maddalena Ghio

We prove that the optimal boundary surface that splits the action and inaction regions in a mean-field game of capacity expansion studied in (Campi et al.,\ Ann.\ Appl.\ Probab.,\ {\bf 32}(5),\, pp.\,3674-3717, 2022) is the unique continuous solution of a nonlinear integral equation of Volterra type. In order to do that, we first establish continuity of the

Nicolas Huynh, Krzysztof Kacprzyk, Ryan Sheridan, David Bentley

The analysis of DNA sequences has become critical in numerous fields, from evolutionary biology to understanding gene regulation and disease mechanisms. While deep neural networks can achieve remarkable predictive performance, they typically operate as black boxes. Contrasting these black boxes, axis-aligned decision trees offer a promising direction for int

Richard Evan Schwartz

We consider solutions to the $4$-color problem for the vertices of sphere triangulations with degree sequence $6,...,6,4,4,4,4,4,4$. We sort these solutions into combinatorial types and show that each generic type $\tau$ is parametrized by the set of integer lattice points inside a $4$-dimensional rational polyhedral convex cone ${\cal C\/}_{\tau}$. There is

Haocheng Xi, Harman Singh, Yuezhou Hu, Coleman Hooper

Block-wise diffusion language models (DLMs) generate multiple tokens in any order, offering a promising alternative to the autoregressive decoding pipeline. However, they still remain bottlenecked by memory-bound attention in long-context scenarios. Naive sparse attention fails on DLMs due to a KV Inflation problem, where different queries select different p

Eirik Høgmoe Kjelsnes, Eirik Eik Svanes, Vegard Undheim

We deform classical holomorphic Chern--Simons theory on a Calabi--Yau three-fold $X$ by deforming the complex structure by a deformation parameter $h \in\mathscr{H}^{0,1}(T^{1,0}X)$. The corresponding equations of motion admit new "instanton solutions" which which are invariant under re-scalings of $h$, and are perhaps more reminiscent of $G_2$-instantons fo

Mohammed Ali, Abdelrahman Abdallah, Adam Jatowt

Extracting structured, machine-readable compliance criteria from regulatory documents remains an open challenge. Single-pass language models hallucinate structural elements, lose hierarchical relationships, and fail to resolve inter-document dependencies. We introduce \textsc{RegReAct}, a self-correcting multi-agent framework that decomposes regulatory infor

T. Asano, S. Portegies Zwart

Self-gravitating systems of $N$ particles are chaotic. We study how chaotic the Galaxy is, and what the consequences are. We therefore simulated the dynamical evolution of a galaxy-scale distribution of point masses in order to measure the degree of chaos in such a system. These calculations were performed using the softened gravitational $N$-body tree-code

Ailixier Yaermaimaiti, Jiaxin Wang, Yunjie Gu, Huanhai Xin

Non-minimum-phase (NMP) zeros in multi-converter power systems impose bandwidth ceilings on feedback control, yet quantifying them at the system level has been impractical because commercial converters withhold their internal controller models. This paper develops a Jacobian-based framework that decouples the NMP zeros from individual converter dynamics, pro

Noam Mazor, Andrew Morgan, Rafael Pass

A recent and exciting thread of work focuses on developing methods for watermarking the output of large language models (LLMs). We focus on provably undetectable watermarking-that is, schemes that do not alter the output distribution of the LLM, yet enable embedding a watermark in the output that identifies the output as having been generated by the particul

Souvik Agasti

We explore here how to generate a two-mode squeezed output using reservoir engineering in a double-cavity optomechanical system coupled to a common mechanical resonator. Such hybrid platforms are experimentally accessible in electro-optomechanical interfaces and are relevant for high-fidelity state transfer, quantum communication, and metrological applicatio

Shreeya Verma Kathuria, Nitin Mayande, Sharookh Daruwalla, Nitin Joglekar

The use of Large Language Models (LLMs) for reliable, enterprise-grade analytics such as text categorization is often hindered by the stochastic nature of attention mechanisms and sensitivity to noise that compromise their analytical precision and reproducibility. To address these technical frictions, this paper introduces the Weighted Syntactic and Semantic

Muhammad Farrukh, Baris Coskun, Tapti Palit, Michalis Polychronakis

Large-scale migration of legacy C code to Rust offers a promising path toward improving memory safety, but LLM-based C-to-Rust translation remains challenging due to limited context windows and hallucinations. Prior approaches are evaluated primarily on small programs or datasets skewed toward small codebases, providing limited insight into scalability on re

Omar El Bachyr, Yewei Song, Saad Ezzini, Jacques Klein

PDF files are primarily intended for human reading rather than automated processing. In addition, the heterogeneous content of PDFs, such as text, tables, and images, poses significant challenges for parsing and information extraction. To address these difficulties, both practitioners and researchers are increasingly developing new methods, including the pro

Xin Liu, Lu Wang

Large language models (LLMs) often hallucinate in long-form generation. Existing approaches mainly improve factuality through post-hoc revision or reinforcement learning (RL) with correctness-based rewards, but they do not teach the model to estimate which parts of its generation are reliable. As a result, models may still state incorrect claims confidently

Vinzenz Thoma, Zebang Shen, Niao He

Understanding the topology of sublevel sets yields crucial insights into the optimization landscape of non-convex functions. If sublevel sets are connected, local search algorithms are less likely to be trapped in isolated valleys, facilitating convergence to global minimizers. However, few results exist to establish connectedness in the nonconvex setting. I

Eli Corn, Daphna Weinshall

Deep learning models may converge to suboptimal solutions despite strong validation accuracy, masking an optimization failure we term Trajectory Deviation. This is because as training proceeds, models can abandon high generalization states for specific data sub-populations, thus discarding previously learned latent features without triggering classical overf

Trajan Murphy

The Karhunen-Lo\`eve Expansion (KLE) of a stochastic process is a well understood eigenfunction expansion used widely in time series analysis, stochastic PDEs, and signal processing. Karhunen-Lo\`eve expansions have also been proven to exist for other types of stochastic elements whose values lie in certain $L^2$ spaces. This article provides a concise proof

Jeff Calder, Zhonggan Huang, Ryan Murray, Adam Pickarski

This work is concerned with the continuum limit of a graph-based data visualization technique called the t-Distributed Stochastic Neighbor Embedding (t-SNE), which is widely used for visualizing data in a variety of applications, but is still poorly understood from a theoretical standpoint. The t-SNE algorithm produces visualizations by minimizing the Kullba

Tian Tong, Feng Lin, Wei Zhang, Runjia Li

Ferromagnetic materials are widely used in Tesla thermomagnetic engines, whereas diamagnetic counterparts have remained unexplored. Here, we demonstrate the first diamagnetic Tesla engine by exploiting the strong diamagnetism of graphene. A graphene disk, fabricated by stacking graphene sheets, serves as the engine wheel. We first show that the conventional

Daniel Begimher, Cristian Leo, Jack Huang, Pat Gaw

We present SIR-Bench, a benchmark of 794 test cases for evaluating autonomous security incident response agents that distinguishes genuine forensic investigation from alert parroting. Derived from 129 anonymized incident patterns with expert-validated ground truth, SIR-Bench measures not only whether agents reach correct triage decisions, but whether they di

Paolo A. Sossi, Dan J. Bower

Meteorites are classified as either non-carbonaceous- (NC) or carbonaceous (CC), representing bodies that likely formed in the inner- or outer solar system, respectively. Despite its location in the inner solar system, the Earth is thought to contain either minor- (~6 %) or substantial amounts (~40 %) of outer solar system material. However, because neither

Michał Szyfelbein

We study a well-known task of constructing a decision tree identifying an unknown hypothesis from a given ground set of hypotheses under both the average- and worst-case cost. The Optimal Decision Tree problem has been extensively studied in the literature and $\mathcal{O}(\log n)$-approximation guarantees are known for both cost criteria (here $n$ is the nu

Manuel Ceballos, Raúl Falcón, Juan Núñez-Valdés, Ángel F. Tenorio

Even if it has been less than a decade and a half since Tian introduced his concept of evolution algebras to represent algebraically non-Mendelian rules in Genetics, their study is becoming increasingly widespread mainly due to their applications to many scientific disciplines. In order to facilitate further research on the topic, this paper deals with the p

Stefan Miteski

Retrieval-Augmented Generation remains the dominant pattern for giving LLMs persistent memory, but a visible cluster of personal wiki-style memory architectures emerged in April 2026 -- design proposals from Karpathy, MemPalace, and LLM Wiki v2 that compile knowledge into an interlinked artifact for long-term use by a single user. They sit alongside producti

Nicholas Moratelli, Christopher Davis, Leonardo F. R. Ribeiro, Bill Byrne

Large Vision-Language Models (LVLMs) increasingly rely on retrieval to answer knowledge-intensive multimodal questions. Existing benchmarks overlook conflicts between visual and textual evidence and the importance of generating deflections (e.g., Sorry, I cannot answer...) when retrieved knowledge is incomplete. These benchmarks also suffer from rapid obsole

Xinyu Zhou, Yu Mei, Faith Thomson, Christian Luedtke

Worm-inspired robots provide an effective locomotion strategy for constrained environments by combining cyclic body deformation with alternating anchoring. For compliant robots, however, the interaction between deformable anchoring structures and the environment makes predictive modeling and deployable gait optimization challenging. This paper presents an ex

M. O. C. Pires

We present a kinetic description of superfluid currents in ring-shaped Bose-Einstein condensates based on the Wigner phase-space formalism. Starting from the Gross-Pitaevskii equation in a toroidal geometry, we derive a Vlasov-type equation for the angular Wigner function, in which the mean-field interaction generates an effective force proportional to the d

Simon Brezovnik, Janez Žerovnik

Roman-type domination parameters form an important class of graph invariants that model protection and resource allocation problems on networks. Among them, $[k]$-Roman domination provides a unified framework that generalizes Roman, double Roman, and higher-order variants. In this paper we investigate the $[k]$-Roman domination number of cylindrical grids $C

T. S. Morton

The velocity field within a steady toroidal vortex is found for arbitrary mean core radius and section ellipticity. The problem is solved by transforming to coordinates that define invariant sets. The method allows the properties of the coordinate system metric tensor to be exploited in the continuity equation in order to obtain the solution. The vorticity i

Salar Adel Sabri, Ramadhan J. Mstafa

The proliferation of sophisticated generative models has significantly advanced the realism of synthetic facial content, known as deepfakes, raising serious concerns about digital trust. Although modern deep learning-based detectors perform well, many rely on spatial-domain features that degrade under compression. This limitation has prompted a shift toward

Cedric Le Gentil, Daniil Lisus, Timothy D. Barfoot

Recently, the robotics community has regained interest in radar-based perception and state estimation. A 2D imaging radar provides dense 360deg information about the environment. Despite the radar antenna's cone of emission and reception, the collected data is generally assumed to be limited to the plane orthogonal to the radar's spinning axis. Accordingly,

Seungik Cho

Predicting the functional impact of single amino acid substitutions (SAVs) is central to understanding genetic disease and engineering therapeutic proteins. While protein language models and structure-based methods have achieved strong performance on this task, they systematically neglect protein dynamics; residue flexibility, correlated motions, and alloste

Aryan Singh Dalal, Maria Baloch, Asiyah Yu Lin, Anna Maria Masci

The Semantic Web standardizes concept meaning for humans and machines, enabling machine-operable content and consistent interpretation that improves advanced analytics. Reusing ontologies speeds development and enforces consistency, yet selecting the optimal choice is challenging because authors lack systematic selection criteria and often rely on intuition

Giorgio Arcadi, Sarif Khan, Agnese Mariotti

We illustrate, via a simplified model, a scenario in which the baryon-asymmetry and, possibly the dark matter component of the Universe are simultaneously generated by the decay of a WIMP-like mother particle, in turn produced non-thermally during an epoch of Early Matter domination. We first consider the standard evolution of the Universe and introduce TeV-

Tolga Talha Yıldız, Uğur Önal, Vinayak R. Krishnamurthy, Ergun Akleman

We present Twisted Edges, a unified framework for designing Linked Knot (LK) structures using labeled non-manifold surface meshes. While the concept of edge twists, originating in topological graph theory, is foundational to these designs, prior approaches have been strictly limited to binary states. We identify this restriction as a critical barrier; binary

Ritwik Vashistha, Jeff M. Phillips, Abhra Sarkar, Arya Farahi

Modern data analyses frequently encounter settings where samples of variables are contaminated by measurement error. Ignoring measurement noise can substantially degrade statistical inference, while existing correction techniques are often computationally costly and inefficient. Recent advances in kernel methods, particularly those based on Maximum Mean Disc

Manfried Faber, Rudolf Golubich

We determine the interaction potential of a solitonic dipole in the singlet state, modeled as an SU(2) field, using improved lattice simulations of two stationary solitons at varying separations. The potential is extracted from the energy of two-soliton configurations as a function of distance. At large separations, the interaction reproduces the classical C

Juan Garcia-Bellido

Cosmology is living through fascinating times, where new observations from ground and space telescopes are questioning the established paradigm, the so-called Lambda Cold Dark Matter model. The particle nature of Dark Matter is severely constrained by underground experiments, while recent observations by galaxy surveys indicate that the cosmological constant

Georgianna Lin, Rencong Jiang, Noémie Elhadad, Xuhai "Orson" Xu

Although artificial intelligence (AI) agents are increasingly proposed to support potentially longitudinal health tasks, such as symptom management, behavior change, and patient support, most current implementations fall short of facilitating user intent and fostering accountability. This contrasts with prior work on supporting longitudinal needs, both withi

Hamoud Alhazmi, Jiachen Jiang

Understanding abstract meanings is crucial for advanced language comprehension. Despite extensive research, abstract words remain challenging due to their non-concrete, high-level semantics. SemEval-2021 Task 4 (ReCAM) evaluates models' ability to interpret abstract concepts by presenting passages with questions and five abstract options in a cloze-style for

Cuiran Shi, Shuying Han, Shreya Kusumanchi, Mia Zhou

This study presents a large-scale network dataset, NIH-MPINet, curated from NIH RePORTER and PubMed, characterizing collaboration among multiple Principal Investigators (multi-PIs) on NIH R01-equivalent grants from 2006 to 2023. The network characterizes 30,127 PIs as nodes and their collaborations on 86,743 NIH R01-equivalent grants as edges, spanning 888 r

Mohan Wang, Martin J. Booth, Patrick S. Salter

A novel design for straight directional waveguide couplers and interferometers is demonstrated in glass, fabricated using femtosecond laser direct writing and operating at telecommunication wavelengths (~1550 nm). The devices consisted of parallel waveguides with a spacing of 15 um, where the coupling strength was controlled by scan-engineered refractive ind

Vladimir Vasilenko

Large language models map semantically related prompts to similar internal representations -- a phenomenon interpretable as attractor-like dynamics. We ask whether the identity document of a persistent cognitive agent (its cognitive_core) exhibits analogous attractor-like behavior. We present a controlled experiment on Llama 3.1 8B Instruct, comparing hidden

Jiayi Xin, Xiang Li, Evan Qiang, Weiqing He

In-context learning (ICL) performance depends critically on which demonstrations are placed in the prompt, yet most existing selectors prioritize heuristic notions of relevance or diversity and provide limited insight into the coverage of a demonstration set. We propose Unseen Coverage Selection (UKS), a training-free, subset-level coverage prior motivated b

Ergun Simsek

We present a full-vector finite element method (FEM) mode solver for dielectric waveguides based on a mixed Nedelec-Lagrange discretization of Maxwell's curl equations in the frequency domain. The formulation combines edge elements for transverse field components with nodal elements for the longitudinal component, enabling accurate modeling of hybrid modes w

Steve Hanneke, Idan Mehalel, Shay Moran

Modern large language models generate text autoregressively, producing tokens one at a time. To study the learnability of such systems, Joshi et al. (COLT 2025) introduced a PAC-learning framework for next-token generators, the primitive underlying autoregressive models. In this framework, an unknown next-token generator maps a sequence of tokens to the next

Bingyi Cao, Koert Chen, Kevis-Kokitsi Maninis, Kaifeng Chen

Recent progress in vision-language pretraining has enabled significant improvements to many downstream computer vision applications, such as classification, retrieval, segmentation and depth prediction. However, a fundamental capability that these models still struggle with is aligning dense patch representations with text embeddings of corresponding concept

Bowen Liu, Malwane M. A. Ananda, Sam Weerahandi

Reliability inference based on parametric distributions is an important problem in electrical and mechanical engineering. Most existing methods rely on approximations or bootstrap procedures, which may not perform satisfactorily when data are censored or sample sizes are small. Hence, there is an urgent need to develop exact inference approaches for these si

Rodrigo Hernández

We obtain upper bounds for the norm of the Schwarzian derivative of convex holomorphic mappings defined on the polydisk and the unit ball in $\mathbb{C}^n$. For coordinate-wise convex mappings on the polydisk, we derive a sharp estimate extending the classical one-variable result of Chuaqui--Duren--Osgood to higher dimensions. For the Roper--Suffridge extens

Lara Celik, Bart Horn, Bhavya Mishra, David Muqattash

We discuss the effective field theory of large scale structure in terms of a single scalar degree of freedom, corresponding to the velocity potential of the matter fluid in a $\Lambda$CDM universe. This cosmic ``pion'' field is nonlinearly related to the overdensity and the gravitational potential, and corresponds to the Goldstone boson of spontaneously brok

Mohit Chaudhary, Hans-Christian Weissker, Daniele Toffoli, Mauro Stener

The interest to foster plasmonic applications at energies in the ultra-violet, has escalated research initiatives in clusters of unconventional plasmonic materials like aluminum and indium,for which the surface-plasmon resonance appears above the ionization potential. Naturally, the quantum mechanical description calls for the incorporation of the ionization

Baris Simsek

Agent memory systems accumulate experience but currently lack a principled operational metric for memory quality governance -- deciding which memories to trust, suppress, or deprecate as the agent's task distribution shifts. Write-time importance scores are static; dynamic management systems use LLM judgment or structural heuristics rather than outcome feedb

Xunjie Chen, Liuyin Wang, Xinyan Huang, Jerry Shan

Legged robots face significant challenges in moving and navigating on deformable and highly yielding terrain such as mud. We present a resistive force model for legged foot-mud interactions. The model captures rheological behaviors such as visco-elasticity, thixotropy of the mud suspension and retractive suction. One attractive property of this new model lie

Rahman Ejaz, Varchas Gopalaswamy, Ricardo Luna, Aarne Lees

Bayesian optimization (BO) has for sequential optimization of expensive black-box functions demonstrated practicality and effectiveness in many real-world settings. Meta-Bayesian optimization (meta-BO) focuses on improving the sample efficiency of BO by making use of information from related tasks. Although meta-BO is sample-efficient when task structure tra

Ian Braga, Lucas Wardil, Ricardo Martinez-Garcia

Fixation probabilities are essential for characterizing stochastic evolutionary dynamics, but analytical results remain limited mainly to systems with two competing types. We develop a perturbative framework to compute fixation probabilities in multi-allele Moran processes under weak selection. Exploiting the general structure of the backward Fokker-Planck o

Saswata Goswami, Caique Campos de Oliveira, Abhijith M. B., Varinder Pal

Two-dimensional tsumoite (BiTe), a polymorph of Bi2Te3, has emerged as a promising candidate for nonlinear photonic devices owing to its strong spin-orbit coupling, tunable bandgap, and high carrier mobility characteristics. This work presents a thorough examination of the third-order nonlinear optical response of BiTe dispersions using spatial self-phase mo

Kleyton da Costa, Bernardo Modenesi, Ivan F. M. Menezes, Hélio Lopes

Particle Swarm Optimization (PSO) is susceptible to premature convergence when the swarm collapses around the global best, particularly on multimodal landscapes in higher dimensions. We propose Divergence-guided PSO (DPSO), which augments the velocity update with a modulation term that repels particles whose personal bests have converged near the global best

Harsha Gurram, Li-Jen Chen, Matthew R. Argall, Subash Adhikari

We report the properties of electron distributions and turbulence during a Coronal Mass Ejection (CME) in April 2023 observed by Magnetospheric Multiscale (MMS). The CME exhibits a clear sheath and magnetic cloud (MC), and within the MC, the solar wind becomes sub-Alfv\'enic for two hours. We investigate plasma and turbulence properties of the sub-Alfv\'enic

Yi Ju, Lunlong Li, Jingchun Wang, Scott Moura

Rapid growth in electric-vehicle (EV) charging demand is placing increasing stress on power distribution networks (PDNs), whose hosting capacity is often limited and spatially uneven. Beyond demonstrating that coordination can help, this paper answers an open question that is central for planners: What is the maximal achievable benefit of EV charging demand

Xingyu Qiu, Yuqian Fu, Jiawei Geng, Bin Ren

Cross-domain few-shot object detection (CD-FSOD) remains a challenging problem for existing object detectors and few-shot learning approaches, particularly when generalizing across distinct domains. As part of NTIRE 2026, we hosted the second CD-FSOD Challenge to systematically evaluate and promote progress in detecting objects in unseen target domains under

Matías Bejas, Jun Zhan, Xianxin Wu, Andreas P. Schnyder

The recent discovery of high-$T_c$ superconductivity in pressurized and thin film nickelates is nowadays one of the most relevant and active topics in solid-state physics. The origin of superconductivity together with the relevance of multiorbital physics are highly discussed issues in this field. Knowledge of the size of the gap and its symmetry is of funda

Zhuoyue Huang, Freddie Bickford Smith, Tom Rainforth

The central goal of active learning is to gather data that maximises downstream predictive performance, but popular approaches have limited flexibility in customising this data acquisition to different downstream problems and losses. We propose a rigorous loss-driven approach to Bayesian active learning that allows data acquisition to directly target the los

Bhavana Sajja

We introduce behavioral fidelity -- a third evaluation dimension for synthetic tabular data that measures whether generated data preserves the temporal, sequential, and structural behavioral patterns that distinguish real-world entity activity. Existing frameworks evaluate statistical fidelity (marginal distributions and correlations) and downstream utility

Zhongjun Zhang, Sean R. Sinclair

We study offline-online reinforcement learning in linear mixture Markov decision processes (MDPs) under environment shift. In the offline phase, data are collected by an unknown behavior policy and may come from a mismatched environment, while in the online phase the learner interacts with the target environment. We propose an algorithm that adaptively lever

Mandar M. Sohoni, Jérémie Laydevant, Mathieu Ouellet, Shi-Yuan Ma

Illumination using correlated photon sources has been established as an approach to allowing high-fidelity images to be reconstructed from noisy camera frames by taking advantage of the knowledge that signal photons are spatially correlated whereas detector clicks due to noise are uncorrelated. However, in computer-vision tasks, the goal is often not ultimat

Daniel Yang, Jungseok Hong, John J. Leonard, Yogesh Girdhar

3D Gaussian Splatting is a powerful visual representation, providing high-quality and efficient 3D scene reconstruction, but it is crucially dependent on accurate camera poses typically obtained from computationally intensive processes like structure-from-motion that are unsuitable for field robot applications. However, in these domains, multimodal sensor da

Arun L. Bishop, Micah I. Reich, Zachary Manchester

Many problems in robotics require reasoning over a mix of continuous dynamics and discrete events, such as making and breaking contact in manipulation and locomotion. These problems are locally well modeled by linear complementarity quadratic programs (LCQPs), an extension to QPs that introduce complementarity constraints. While very expressive, LCQPs are no

Wei Wu

Functional data registration is a critical challenge in modern statistics, essential for separating phase variability from amplitude variability. While derivative-based frameworks offer mathematically elegant solutions, their dependence on signal velocities renders them susceptible to additive noise. This study proposes and evaluates a family of robust, Sobo

Anil Jangam, Ganesh Karthick Rajendran, Roy Kantharajah

Geographically High-Available (Geo-HA) cluster systems are essential for service continuity in distributed cloud-native environments. However, traditional arbitration mechanisms, which are often predicated on deterministic node-level heartbeats, are resource-intensive and inherently reactive. This necessitates a dedicated arbiter per deployment and leads to

N. Azatyan, L. Kaper, A. Samsonyan, M. Stoop

OB runaway stars are massive stars moving through interstellar space at high velocities (up to 200 km/s), produced by dynamical ejections in young massive clusters or supernova explosions in massive binaries. They can travel several hundred parsec before exploding as supernovae, affecting the dynamical and chemical evolution of the Galaxy. The Vel OB1 associ

Genly Leon, Aleksander Kozak, Claudio Michea

We present a hybrid study that combines a concise review of scalar-field cosmology with new analytic developments that integrate averaging reductions for oscillatory regimes with dynamical-systems techniques. For oscillatory fields, we derive an averaging reduction that yields an effective slow system whose time averages control dissipation; introducing unif

Xiaoxue Han, Libo Zhang, Zining Zhu, Yue Ning

We introduce Graph Concept Bottleneck (GCB) as a new paradigm for self-explainable text-attributed graph learning. GCB maps graphs into a subspace, concept bottleneck, where each concept is a meaningful phrase, and predictions are made based on the activation of these concepts. Unlike existing interpretable graph learning methods that primarily rely on subgr

Charles Bordenave, Xinlong Dong, Dragomir Šarić

Let X be an infinite Riemann surface with an upper-bounded geodesic pants decomposition. The vertices of the corresponding dual graph G are pairs of pants and edges are cuffs with conductances equal to their lengths. We prove that the geodesic flow on X is ergodic if and only if the random walk on G is recurrent. This yields explicit criteria for deciding, i

Jessica O. de Almeida, Wilton J. M. Kort-Kamp, Mathias S. Scheurer

Nonlinear light-matter interaction and, in particular, high-harmonic generation (HHG) are fundamentally interesting and frequently discussed as versatile probes of quantum materials with potential for optical information processing applications. Meanwhile, there has also been significant progress in graphene-based multilayer systems to engineer interesting b

Jingzhou Shen, Luis Lago Enamorado, Shiwen Mao, Xuyu Wang

In this paper, we propose the geometric algebra-informed neural radiance fields (GAI-NeRF), a novel framework for wireless channel prediction that leverages geometric algebra attention mechanisms to capture ray-object interactions in complex propagation environments. Our approach incorporates global token representations, drawing inspiration from transformer

Gonzalo C. de Elía, Macarena Zanardi, Rebecca G. Martin

With n-body simulations and analytic approximations we study the dynamics and stability of low eccentricity misaligned test particles around binary systems with varying mass fraction and eccentricity. General relativity (GR) plays a primary role in determining the motion of an outer particle since it drives apsidal precession of the binary orbit. The effects

Xunjie Chen, Xinyan Huang, Peter Shan, Jingang Yi

Bipeds have demonstrated high agility and mobility in unstructured environments such as sand. The yielding of such granular media brings significant sinkage and slip of the bipedal feet, leading to uncertainty and instability of walking locomotion. We present a new dynamics-modeling approach to capture and predict bipedal-walking locomotion on granular media

Welington Cordeiro, Maria José Pacifico, Xuan Zhang

In this paper, we introduce and investigate the notions of Mean Dimension and Metric Mean Dimension for generalized iterated function systems (IFS). We establish basic properties of these invariants and prove that Mean Dimension is always bounded above by the lower Metric Mean Dimension and the upper Metric Mean Dimension in this setting. We further show tha

Saeid Pakravan, Mohsen Ahmadzadeh, Ming Zeng, Xingwang Li

This paper considers a multi-user system, where the users first harvest energy from the base station and then use the harvested energy to transmit information via non-orthogonal multiple access (NOMA). A pinching antenna array is adopted to assist the energy transfer and information transmission, owing to its ability to adapt to dynamic propagation condition

Xinyu Jessica Wang, Haoyue Bai, Yiyou Sun, Haorui Wang

Large language model (LLM) agents perform strongly on short- and mid-horizon tasks, but often break down on long-horizon tasks that require extended, interdependent action sequences. Despite rapid progress in agentic systems, these long-horizon failures remain poorly characterized, hindering principled diagnosis and comparison across domains. To address this

Preetam Dwivedi, Akshay Hacholli, Adam Bettigole

At the scale of Uber's monorepos, traditional Git workflows become a fundamental bottleneck. Cloning multi-gigabyte repositories, maintaining local checkouts, periodically syncing from upstream, and executing repetitive fetch or push operations consume substantial compute and I/O across hundreds of automation systems. Although CI (Continuous Integration) sys

Giovanni Pisante, Francesca Prinari

Given a bounded convex open set $\Omega\subseteq \mathbb R^N$, we prove that the Poincar\'e-Sobolev constants $\lambda_{p,q}(\Omega)$ can be bounded from below by the $p$-power of the ratio between the perimeter of $\Omega$ and a suitable power of its volume, with an optimal constant which is explicitly given. This generalises an old result for torsional rig

Zhiwei Fan, Yiming Pan, Daniel Coca

Coherent nonlinear wave dynamics are often strongly shaped by a compact set of physically meaningful descriptors of the initial state. Traditional neural operators typically treat the input-output mapping as a largely black-box high-dimensional regression problem, without explicitly exploiting this structured physical context. Common feature-integration stra

Sunia Tanweer, Narayan Puthanmadam Subramaniyam, Firas A. Khasawneh

Epileptic seizure detection from EEG signals remains challenging due to the high dimensionality and nonlinear, potentially stochastic, dynamics of neural activity. In this work, we investigate whether features derived from topological data analysis (TDA) can improve the classification of brain states in preictal, ictal and interictal iEEG recordings from epi

Somraj Gautam, Anathapindika Dravichi, Gaurav Harit

We introduce INDOTABVQA, a benchmark for evaluating cross-lingual Table Visual Question Answering (VQA) on real-world document images in Bahasa Indonesia. The dataset comprises 1,593 document images across three visual styles (bordered, borderless, and colorful) with one or more than one tables, and 1,593 question-answer sets in four languages: Bahasa Indone

Tarik Can Ozden, Sachidanand VS, Furkan Horoz, Ozgur Kara

We introduce ArcDeck, a multi-agent framework that formulates paper-to-slide generation as a structured narrative reconstruction task. Unlike existing methods that directly summarize raw text into slides, ArcDeck explicitly models the source paper's logical flow. It first parses the input to construct a discourse tree and establish a global commitment docume