March 2026 arXiv papers — page 130

Leticia Becher

Marine biogeochemical models are widely used to study nutrient dynamics, water quality, and climate-related processes in coastal and estuarine systems. However, developing models that reliably represent specific environments remains computationally demanding, which makes their application to complex systems such as river plumes and estuarine environments cha

Mircea Mustaţă

Given a hypersurface defined by $f$ in a smooth complex algebraic variety $X$, and a point $P$ on this hypersurface, we consider the invariant $\beta_P(f)$ given by the log canonical threshold at $P$ of ${\mathfrak m}_P\cdot J_f$, where ${\mathfrak m}_P$ is the ideal defining $P$ and $J_f$ is the Jacobian ideal of $f$. We show that this invariant satisfies m

Roland Boniface Sogan, Tabea Rebafka

Recovering the random graph model from an observed collection of networks is known to present significant challenges in the setting, where the networks do not share a common node set and have different sizes. More specifically, the goal is the estimation of the graphon function that parametrizes the nonparametric exchangeable random graph model. Existing met

Tameem Albash, N. Tobias Jacobson

We propose a Monte Carlo based method for simulating the open system dynamics of multiple exchange-only (EO) qubits. In the EO encoding, the total spin projection quantum number along the $z$-axis of the three constituent spins remains unchanged under exchange operations, in contrast to the open system (or multi-qubit miscalibration) setting where coherent a

Quoc Tran-Dinh, Nghia Nguyen-Trung

This paper develops new variance-reduction techniques for the forward-reflected-backward splitting (FRBS) method to solve a class of possibly nonmonotone stochastic composite inclusions. Unlike unbiased estimators such as mini-batching, developing stochastic biased variants faces a fundamental technical challenge and has not been utilized before for inclusio

BESIII collaboration

We search for 15 rare decays of $D$ mesons to hadrons accompanied by an electron-positron pair $D\to h(h^{(')})e^{+}e^{-}$, based on 20.3 fb$^{-1}$ of $e^+ e^-$ collision data collected at the center-of-mass energy of 3.773 GeV with the BESIII detector at BEPCII. No significant signals are observed, and the corresponding upper limits on the branching fractio

Aaditya Khanal, Junxiu Zhou

The practical deployment gap -- transitioning from controlled multi-view 3D skeleton capture to unconstrained monocular 2D pose estimation -- introduces a compound domain shift whose safety implications remain critically underexplored. We present a systematic study of this severe domain shift using a novel Gym2D dataset (style/viewpoint shift) and the UCF101

T. L. Howarth, T. Lehmann, M. Gauding, H. Pitsch

Turbulent lean premixed hydrogen jet flames are simulated using direct numerical simulation employing detailed chemistry in both slot and round configurations at various pressures. All cases are simulated at a constant jet Reynolds number ($Re_j = 10000$) and a fixed ratio of characteristic length scales. While normalised macroscopic quantities (e.g., flame

Jay Sarkar, Vamsi Pavan Rayaprolu, Abhijeet Bhalerao

Solid-state storage architectures based on NAND or emerging memory devices (SSD), are fundamentally architected and optimized for both reliability and performance. Achieving these simultaneous goals requires co-design of memory components with firmware-architected Error Management (EM) algorithms for density- and performance-scaled memory technologies. We de

O. Petruk, T. Kuzyo, S. Orlando, L. Chomiuk

T Coronae Borealis (T CrB) is a nearby recurrent nova expected to erupt in the near future, offering a unique opportunity to study particle acceleration and high-energy emission from novae in real time. We investigate the production of gamma-rays and neutrinos following the T CrB outburst by combining three-dimensional hydrodynamical simulations with a detai

Aakash Lahoti, Kevin Y. Li, Berlin Chen, Caitlin Wang

Scaling inference-time compute has emerged as an important driver of LLM performance, making inference efficiency a central focus of model design alongside model quality. While the current Transformer-based models deliver strong model quality, their quadratic compute and linear memory make inference expensive. This has spurred the development of sub-quadrati

Muhammad Shoaib, Eva Riccomagno, Manuele Leonelli, Gherardo Varando

Staged tree models enhance Bayesian networks by incorporating context-specific dependencies through a stage-based structure. In this study, we present a new framework for estimating staged trees using hierarchical clustering on the probability simplex, utilizing simplex basesd divergences. We conduct a thorough evaluation of several distance and divergence m

Raul Figueroa-Sierra, Osvaldo Guzmán, Michael Hrušák, Adam Kwela

We continue the study of Dow spaces of a $\mathfrak{b}$-scale, originally introduced by Alan Dow in "$\pi$-Weight and the Fr\'echet-Urysohn property" (Topology and its Applications, Vol. 174, pp. 56-61). We prove that it is consistent that all such spaces are Fr\'echet, but it is also consistent that none of them is. We use these spaces to exhibit (consisten

Lit Sin Tan, Junzhe Chen, Xiaolong Fu, Lichen Ma

Existing test-time scaling (TTS) methods for unified multimodal models (UMMs) in text-to-image (T2I) generation primarily rely on search or sampling strategies that produce only instance-level improvements, limiting the ability to learn from prior inferences and accumulate knowledge across similar prompts. To overcome these limitations, we propose Meta-TTRL,

Ivan Stetsenko

As AI coding agents become both primary producers and consumers of source code, the software industry faces an accelerating loss of institutional knowledge. Each commit captures a code diff but discards the reasoning behind it - the constraints, rejected alternatives, and forward-looking context that shaped the decision. I term this discarded reasoning the D

Lixi Ye

Let $R_1 := \begin{pmatrix}1&1\\1&0\end{pmatrix}$, and write $R_n := R_1^{\otimes n}$ for the $N \times N$ disjointness matrix, where $N = 2^n$. We construct new depth-2 linear circuits for $R_n$: one of size $O(N^{1.2449})$ and one of maximum input-output degree $O(N^{0.3199})$, improving upon the results of Alman and Li [AL25] (FOCS 2025), who achieved siz

Parastoo Pashmchi, Jérôme Benoit, Motonobu Kanagawa

Missing values are common in photovoltaic (PV) power data, yet the uncertainty they induce is not propagated into predictive distributions. We develop a framework that incorporates missing-data uncertainty into short-term PV forecasting by combining stochastic multiple imputation with Rubin's rule. The approach is model-agnostic and can be integrated with st

Seth Karten, Jake Grigsby, Tersoo Upaa, Junik Bae

We present the PokeAgent Challenge, a large-scale benchmark for decision-making research built on Pokemon's multi-agent battle system and expansive role-playing game (RPG) environment. Partial observability, game-theoretic reasoning, and long-horizon planning remain open problems for frontier AI, yet few benchmarks stress all three simultaneously under reali

Yunling Chen, Dinh Tuan Huynh

We apply the technique of jet differentials to establish a Gauss curvature estimate for an open Riemann surface $M$, equipped with a conformal metric induced from a nonconstant holomorphic map that is highly ramified over a generic hypersurface of sufficiently high degree.

Ohad Lib, Hendrik Timme, Maximilian Ammenwerth, Flavien Gyger

Realizing error-corrected logical qubits is a central goal for the current development of digital quantum computers. Neutral atoms offer the opportunity to coherently shuttle atoms for realizing efficient quantum error correction based on long-range connectivity and parallel atom transport. Nevertheless, time overheads in shuttling atoms and complex control

Andreas Gleis, Gabriel Kotliar

We propose an interacting model that is exactly solvable in any spatial dimension and gives rise to a Fermi liquid (FL) featuring a pseudogapped (PG) single-particle spectral function and a vanishing quasiparticle (QP) weight at half-filling, without invoking Mott physics. The PG originates from a purely fermionic mechanism through emergent QPs arising from

Matthias Volk, Linus Heck, Sebastian Junges, Joost-Pieter Katoen

This tutorial paper presents a hands-on perspective on probabilistic model checking with the Storm model checker. Storm is a decade-old model checker that excels in performance and a rich Python-based ecosystem, which makes it easy to integrate in various workflows. This tutorial focuses on Markov decision processes (MDP), which are popular in a variety of f

Zixin Zhang, Chenfei Liao, Hongfei Zhang, Harold Haodong Chen

Affordance prediction serves as a critical bridge between perception and action in embodied AI. However, existing research is confined to pinhole camera models, which suffer from narrow Fields of View (FoV) and fragmented observations, often missing critical holistic environmental context. In this paper, we present the first exploration into Panoramic Afford

Lexiang Xiong, Qi Li, Jingwen Ye, Xinchao Wang

Vision-Language Models (VLMs) frequently "hallucinate" - generate plausible yet factually incorrect statements - posing a critical barrier to their trustworthy deployment. In this work, we propose a new paradigm for diagnosing hallucinations, recasting them from static output errors into dynamic pathologies of a model's computational cognition. Our framework

Melvyn B. Nathanson

This is a survey of the diversity of problems in additive number theory. Equity requires the consideration of less currently popular problems, and suggests their inclusion in the additive canon. Of particular interest are problems about the sizes of sumsets of finite sets of integers and problems about the arithmetical structure of intersections of sumsets.

Haoze Zheng, Zihao Wang, Xianfeng Wu, Yajing Bai

Single-image relighting is highly under-constrained: small illumination changes can produce large, nonlinear variations in shading, shadows, and specularities, while geometry and materials remain unobserved. Existing diffusion-based approaches either rely on intrinsic or G-buffer pipelines that require dense and fragile supervision, or operate purely in late

Jishad Kumar, Achilleas Lazarides, Tapio Ala-Nissila

In resetting dynamics, a system is repeatedly coupled to and decoupled from ancillary degrees of freedom that are reinitialized between interactions. This provides a versatile route to engineer nonequilibrium steady states and constitutes a powerful and analytically transparent framework for studying nonequilibrium dynamics in quadratic fermionic models. The

Trishita Dhara, Siddhesh Sheth

Large language models are increasingly deployed in settings where relevant information is embedded within long and noisy contexts. Despite this, robustness to growing context length remains poorly understood across different question answering tasks. In this work, we present a controlled empirical study of context-length robustness in large language models u

Oumarou Oumarou, Pauline J. Ollitrault, Stefano Polla, Christian Gogolin

We develop a framework that enables direct and meaningful comparison of two early fault-tolerant methods for the computation of eigenenergies, namely \gls{qksd} and \gls{spe}, within which both methods use expectation values of Chebyshev polynomials of the Hamiltonian as input. For \gls{qksd} we propose methods for optimally distributing shots and ensuring s

Yiren Chen, Padi Fuster Aguilera, Vincent Martinez, Kun Zhao

We establish global stability for a chemotaxis-growth model with logarithmic sensitivity under dynamic Dirichlet boundary conditions on a 1D domain. We analyze both parabolic-parabolic and parabolic-hyperbolic systems. The key challenge is handling time-dependent boundary data for the unknown functions. We overcome this by introducing dynamic reference profi

Bogdan S. Damski, Rafał Bistroń, Diego Ponterio, Jakub Czartowski

In this work we investigate discrete structures in product Hilbert spaces. For monopartite systems of size $d$ one relies on the Weyl-Heisenberg group $WH(d)$, while in the case of composite Hilbert spaces we identify designs covariant with respect to the product group, $[WH(p)]^{\otimes n}$. In analogy with magic -a quantity attaining its maximum for states

Miki Imura

We present a substitution rule for a rhomb tiling with 10-fold rotational symmetry. The tiling is closely related to the Penrose rhomb tilings and can be obtained from the pentagrid construction. We introduce a finite set of marked prototiles and describe an explicit substitution rule with inflation factor phi^3. Our main result is that the substitution is r

Chris Engh

This paper points out that rational inattention is a nested regularized optimal transport problem. We use entropic optimal transport to establish the main results in Matejka and McKay (2015) and Caplin, Dean, and Leahy (2019) and extend them to arbitrary choice sets.

Yanick Zengaffinen, Andreas Opedal, Donya Rooein, Kv Aditya Srivatsa

Modeling plausible student misconceptions is critical for AI in education. In this work, we examine how large language models (LLMs) reason about misconceptions when generating multiple-choice distractors, a task that requires modeling incorrect yet plausible answers by coordinating solution knowledge, simulating student misconceptions, and evaluating plausi

Davis Rempe, Mathis Petrovich, Ye Yuan, Haotian Zhang

High-quality human motion data is becoming increasingly important for applications in robotics, simulation, and entertainment. Recent generative models offer a potential data source, enabling human motion synthesis through intuitive inputs like text prompts or kinematic constraints on poses. However, the small scale of public mocap datasets has limited the m

H. Sinan Bank, Daniel R. Herber

The proliferation of data across the system lifecycle presents both a significant opportunity and a challenge for Engineering Design and Systems Engineering (EDSE). While this "digital thread" has the potential to drive innovation, the fragmented and inaccessible nature of existing datasets hinders method validation, limits reproducibility, and slows

David Berger, Rene L. Schilling

The unique continuation property (UCP) for an operator $A$ says that, if $Au = 0 = u$ holds on an open set $G$, then one has $u=0$ everywhere. We establish necessary and sufficient conditions for the UCP for the class of L\'evy operators. We prove a connection between the UCP of the L\'evy operator and its resolvent. Our results are applied to obtain a new e

Fabian Gundlach, Béranger Seguin

We prove that two-step nilpotent $p$-extensions of rational global function fields of characteristic $p$ satisfy a quantitative local-global principle when they are counted according to their largest upper ramification break ("last jump"). We had previously shown this only for $p\neq2$. Compared to our previous proof, this proof is also more self-contained,

C. Mendes Araújo, Faustino Maciala, Pedro Patrício

We investigate the Drazin invertibility of adjacency matrices associated with a class of oriented graphs known as oriented Dutch windmill graphs. By analyzing walks of prescribed lengths and exploiting the structure of the minimal polynomial, we obtain explicit expressions for the Drazin inverse and determine its index. The approach combines combinatorial en

Supriya Khadka, Sanchari Das

In decentralized web applications, users face an inherent conflict between public verifiability and personal privacy. To participate in regulated on-chain services, users must currently disclose sensitive identity documents to centralized intermediaries, permanently linking real-world identities to public transaction histories. This binary choice between tot

Shaojie Shi, Zhengyu Shi, Lingran Zheng, Xinyu Su

Causal inference in social science relies on end-to-end, intervention-centered research-design reasoning grounded in real-world policy interventions, but current benchmarks fail to evaluate this capability of large language models (LLMs). We present InterveneBench, a benchmark designed to assess such reasoning in realistic social settings. Each instance in I

Yung-Fu Chen, Anish Arora

While deep learning models that leverage local features have demonstrated significant potential for near-optimal routing in dense Euclidean graphs, they struggle to generalize well in sparse networks where topological irregularities require broader structural awareness. To address this limitation, we train a Cascaded Mixture of Experts (Ca-MoE) to solve the

Yifan Wang, Debabrota Basu, Pierre Bourhis, Romain Rouvoy

Database Management Systems (DBMS) are crucial for efficient data management and access control, but their administration remains challenging for Database Administrators (DBAs). Tuning, in particular, is known to be difficult. Modern systems have many tuning parameters, but only a subset significantly impacts performance. Focusing on these influential parame

Guorui Lu, Xiaohui Cai, Todor Stefanov, Qinyu Chen

Twelve-lead electrocardiography (ECG) is essential for cardiovascular diagnosis, but its long-term acquisition in daily life is constrained by complex and costly hardware. Recent efforts have explored reconstructing ECG from low-cost cardiac vibrational signals such as seismocardiography (SCG), however, due to the lack of a dataset, current methods are limit

Lorenzo Esposito, Andrea Papaluca, Stefano Carrazza

We introduce QiboAgent, a reference implementation designed to serve as a practitioner's guideline for developing specialized coding assistants in Quantum Computing middleware. Addressing the limitations in scientific software development of general-purpose proprietary models, we explore how lightweight, open-source Large Language Models (LLMs) provided with

Ryan O'Loughlin, Jyoti Rani

We study spectral constants for convex domains $\Omega$ containing the spectrum of an operator. We extend the Crouzeix--Palencia framework by obtaining bounds depending on a parameter $\gamma$ and relating these bounds to geometric properties of $\Omega$ and the numerical range $W(A)$. We generalise the proof that the numerical range is a $1+\sqrt{2}$-spectr

Emil Y. Sidky, Xiaochuan Pan

The purpose of these notes is to provide background on understanding the primal-dual algorithm of Chambolle and Pock [1] for imaging scientists. The presentation focuses on providing intuition and an algorithmic system that is amenable to pre-conditioning. The document aims to be self-contained, providing background on the essential facts of non-smooth conve

Rahul Deshpande, Majid Kheirkhah, Chris Rich, Richard Harris

Quantum annealing processors typically control qubits in unison, attenuating quantum fluctuations uniformly until the applied system Hamiltonian is diagonal in the computational basis. This simplifies control requirements, allowing annealing QPUs to scale to much larger sizes than gate-based systems, but constraining the class of available operations. Here w

Phi Dung Hoang, Hong Duc Nguyen

The main aim of the paper is to give a formula for computing the separation \L ojasiewicz exponents for two real analytic set germs via the Newton--Puiseux expansions of their defining functions. Moreover, we present an effective exponent for the case of two real algebraic sets in terms of their degrees.

R. Maccary, C. Guidorzi, L. Amati, M. Bulla

Context. The prompt-emission time profiles of GRB 230307A and other long-duration compact object merger (COM) candidates exhibit a unique set of temporal properties, characterised by a deterministic evolution of waiting times and pulse widths. Aims. We searched the Fermi/GBM catalogue for other unidentified long COM candidates exhibiting temporal properties

Alish Kanani, Sangwan Lee, Han Lyu, Jiahao Lin

Large language models operate in distinct compute-bound prefill followed by memory bandwidth-bound decode phases. Hybrid Mamba-Transformer models inherit this asymmetry while adding state space model (SSM) recurrences and element-wise operations that map poorly to matmul-centric accelerators. This mismatch causes performance bottlenecks, showing that a homog

Megan Masters

We introduce the annex of an element $x$ in a Coxeter group as the set of elements $y$ such that $x \nleq y$ with respect to Bruhat order. This notion provides a complementary perspective to the study of Bruhat intervals and their interpretation via folded galleries. We establish general properties of annexes and show that in affine Coxeter groups the annex

Stefano Lovato, Michele Tonan, Matteo Bottin, Matteo Massaro

Underactuated robots are characterized by a larger number of degrees of freedom than actuators and if they are designed with a specific mass distribution, they can be controlled by means of differential flatness theory. This structural property enables the development of lightweight and cost-effective robotic systems with enhanced dexterity. However, a key c

Zhenheng Tang, Xiang Liu, Qian Wang, Eunsol Choi

As Large Language Models (LLMs) become more powerful and autonomous, they increasingly face conflicts and dilemmas in many scenarios. We first summarize and taxonomize these diverse conflicts. Then, we model the LLM's preferences to make different choices as a priority graph, where instructions and values are nodes, and the edges represent context-specific p

Aleksander Krasowski, René P. Klausen, Aycan Celik, Sebastian Lapuschkin

Physics-informed neural networks (PINNs) constitute a flexible deep learning approach for solving partial differential equations (PDEs), which model phenomena ranging from heat conduction to quantum mechanical systems. Despite their flexibility, PINNs offer limited insight into how their predictions deviate from the true solution, hindering trust in their pr

Arash Azizi

Janus states, defined as coherent superpositions of two single-mode squeezed vacua, provide a simple but genuinely non-Gaussian setting for studying how interference reshapes quantum Fisher information (QFI) beyond the Gaussian squeezed-vacuum picture. Using an exact analytic treatment, we determine the QFI of Janus states and identify the benchmarks under w

Marta Baselga, Jan-Hendrik Arling, Naomi Davis, Jochen Dingfelder

Strip detectors are populating outer trackers of high-energy particle experiments. They are convenient for covering large areas of sensitive material since they use less power and have fewer readout channels compared to pixels sensors. Nevertheless, they are typically manufactured with a mask set that covers the full wafer, otherwise when using smaller retic

David Števaňák, Marek Šuppa

Keyphrase extraction for morphologically rich, low-resource languages remains understudied, largely due to the scarcity of suitable evaluation datasets. We address this gap for Slovak by constructing a dataset of 227,432 scientific abstracts with author-assigned keyphrases -- scraped and systematically cleaned from the Slovak Central Register of Theses -- re

Georgios Maragkopoulos, Aikaterini Mandilara, Ralntion Komini, Dimitris Syvridis

Multispectral satellite imagery poses significant challenges for deep learning models due to the high dimensionality of spectral data and the presence of structured correlations across channels. Recent work in quantum machine learning suggests that unitary evolutions and Hilbert-space embeddings can introduce useful inductive biases for learning. In this wor

Jian Ji

We report the identification and proof of a universal constant, ln(3) = 1.09861, which governs the onset of bidirectional collective behavior in one-dimensional Poisson proximity networks. The constant - named the cooperative percolation constant and denoted by Lambda_c - is the unique positive solution to 2/(exp(x)-1) = 1 and equals the Shannon entropy of t

Ritajit Kundu, Mandar M. Deshmukh, Herbert A. Fertig, Arijit Kundu

Van der Waals materials may be combined to form moir\'e patterns that are effectively crystal lattices. These systems are unique in that their in-plane unit cell sizes may be orders of magnitude larger than interlayer separations, leading to unique behaviors emerging from interlayer interactions. In this work, we investigate interlayer valley drag in lattice

Leonardo Galliano, Ludovic Berthier

We study a two-dimensional, off-lattice particle model introduced to describe absorbing phase transitions in driven non-Brownian suspensions. We numerically explore the $(\phi,\epsilon)$ phase diagram, where $\phi$ is the packing fraction and $\epsilon$ controls the amplitude of particle jumps. We use a binary mixture to suppress crystallization, which allow

Xiyu Liu, Qingyi Si, Zhengxiao Liu, Chenxu Yang

While locate-then-edit knowledge editing efficiently updates knowledge encoded within Large Language Models (LLMs), a critical generalization failure mode emerges in the practical same-subject knowledge editing scenario: models fail to recall the updated knowledge when following user instructions, despite successfully recalling it in the original edited form

Paul-Alexis Mor, Anne R. Kroo, Carson G. Valdez, Marko Šimić

Recent advances in optical imaging and communication increasingly involve high-dimensional, partially coherent light, creating a growing need for scalable tools to measure and manipulate coherence. Here, we demonstrate the automatic separation of spatially partially coherent light into "coherence modes" -- its orthogonal and mutually incoherent components. T

Simon Devauchelle, David Doukhan, Rémi Uro, Lucas Ondel Yang

We present spINAch, a large diachronic corpus of French speech from radio and television archives, balanced by speakers' gender, age (20-95 years old), and spanning 60 years from 1955 to 2015. The dataset includes over 320 hours of recordings from more than two thousand speakers. The methodology for building the corpus is described, focusing on the quality o

Daiwei Zhu, Miguel Angel Lopez-Ruiz, François-Henry Rouet, Claudio Girotto

Solving large-scale sparse linear systems is a challenging computational task due to the introduction of non-zero elements, or "fill-in". The Graph Partitioning Problem (GPP) arises naturally when minimizing fill-in and accelerating solvers. In this paper, we measure the end-to-end performance of a hybrid quantum-classical framework designed to accel

Martina Ciprini, Maria Lucia Marcelli, Gianmassimo Tasinato

Stochastic gravitational-wave backgrounds (SGWBs) of primordial origin offer a powerful probe of early-Universe physics and possible dark-sector dynamics. While most searches focus on the GW power spectrum, additional information is encoded in higher-order correlators that characterize the statistical properties of the signal. In this work we study non-Gauss

Duy Vu Minh Nguyen, Chinh Thanh Truong, Phuc Hoang Tran, Hung Tuan Le

Vietnamese medical research has become an increasingly vital domain, particularly with the rise of intelligent technologies aimed at reducing time and resource burdens in clinical diagnosis. Recent advances in vision-language models (VLMs), such as Gemini and GPT-4V, have sparked a growing interest in applying AI to healthcare. However, most existing VLMs la

Federico Nocentini, Thomas Besnier, Claudio Ferrari, Stefano Berretti

Speech-driven 3D facial animation has advanced rapidly, yet most approaches remain tied to registered template meshes, preventing effective deployment on raw 3D scans with arbitrary topology. At the same time, modeling controllable emotional dynamics beyond lip articulation remains challenging, and is often tied to template-based parameterizations. We addres

Claudia Fassino, Pierpaolo Uberti

Given a reference risk measure, the risk budgeting is the portfolio where each asset contributes a predetermined amount to the total risk. We propose a novel approach, alternative to the ones proposed in the literature, for the calculation of the risk budgeting portfolio. This different perspective on the problem has several interesting consequences. For the

Fridrich Egyenes, Daniel Stoffels, Stefan Marinkovic, Bernd Aichner

High current densities can induce the directional diffusion of atoms in metallic films. In YBa$_2$Cu$_3$O$_{7-\delta}$ (YBCO), this electromigration process selectively acts on oxygen atoms lying in the Cu-O chains, permitting to vary the oxygen concentration in a targeted spot of high current density. This approach has proven successful in mapping the phase

Nitin Priyadarshini Shankar, Soham Lahiri, Sheetal Kalyani, Saurav Prakash

Federated Learning (FL) preserves privacy by distributing training across devices. However, using DNNs is computationally intensive at the low-powered edge during inference. Edge deployment demands models that simultaneously optimize memory footprint and computational efficiency, a dilemma where conventional DNNs fail by exceeding resource limits. Traditiona

E. da Hora, Fabiano C. Simas

We study the oscillon/$Q$-ball relation in an extended model with non-canonical kinematics. The model contains a single real scalar field whose kinetic term is enlarged to include a generalizing function. We approximate the real sector up to the third order in a book-keeping parameter. In this context, we implement the Renormalization Group Perturbation Expa

Zhenwei Lin, Zikai Xiong, Dongdong Ge, Yinyu Ye

This technical note documents the implementation and use of the Primal-Dual Conic Programming Solver (PDCS), a first-order solver for large-scale conic optimization problems introduced by Lin et al. (arXiv:2505.00311). It describes the algorithmic and implementation details underlying PDCS, including the restarted primal-dual hybrid gradient method framework

Mengru Zhang, Satyanarayana Bonakala, Taku Watanabe, Karim Hamzaoui

Short-chain per- and polyfluoroalkyl substances (PFASs) are increasingly replacing regulated long-chain PFASs, yet they remain challenging to remove from water due to their high persistence, mobility, and weak affinity toward conventional adsorbents. In this work, we developed a hybrid high-throughput computational screening (HTCS) strategy to identify high-

Leeseok Kim, Milad Marvian

We present a general framework for promoting first-order pulse sequences in quantum simulation to higher-order sequences that maintain robustness in the presence of finite pulse-width effects. Our approach maps a given first-order pulse sequence to a first-order Trotter formula, applies higher-order Trotter-formula constructions, and then compiles the result

Bo Sundborg

Black shells, a kind of black hole mimickers, are identified thermodynamically as bulk duals of baryon operators in vector models, indicating that such objects are essential for the consistency of higher spin gravity theories. Thermal baryons, with a spectrum of a 2+1-dimensional relativistic Fermi gas, are found to be precursors of the deconfinement phase t

J. R. Sauter, A. von Stauffenberg, G. Bourdarot, W. Brandner

The implementation of the GRAVITY+ Adaptive Optics (GPAO) system at VLTI enables unprecedented sensitivity and stability in optical interferometry. This allows high-precision characterization of directly imaged exoplanets at medium spectral resolution, providing a new pathway for studying planetary atmospheres. We aim to quantify and characterize the short-

Oscar Camacho Ibarra, Jan-Gabriel Hartel, Atzin David Ruiz Perez, Sonja Barkhofen

We present a systematic workflow for the design of one-dimensional photonic crystal nanobeam cavities with non-zero cavity lengths. By simultaneously optimizing the lattice periodicity, air-hole geometry, and cavity length, our approach enables precise control of optical confinement while mitigating radiative losses and linewidth broadening effects. The meth

Zeyu Ding, Yong Zhou, Jiaqi Zhao, Wen-Liang Du

Recent real-time detection transformers have gained popularity due to their simplicity and efficiency. However, these detectors do not explicitly model object rotation, especially in remote sensing imagery where objects appear at arbitrary angles, leading to challenges in angle representation, matching cost, and training stability. In this paper, we propose

Pradip Kumar

Let $X$ be a smooth projective complex curve, $P\subset X$ a reduced effective divisor, and $X^{0}=X\setminus P$. We study logarithmic $V$-twisted Higgs bundles arising from a logarithmic Hecke compactification of a rank-two bundle on $X^{0}$. We show that a pair of induced logarithmic line-twisted fields lifts uniquely exactly under explicit local Hecke con

Wenjie Zhou, Yuan Gao, Xin Zhou, Hao Fu

Retrieving real-time information is a fundamental capability for search-integrated agents in real-world applications. However, existing benchmarks are predominantly static and therefore fail to capture the temporal dynamics of information and the continuously evolving nature of real-world knowledge. To address this limitation, we propose RT-QA, a dynamic eva

Radu-Alexandru Dragomir, Xiaowen Jiang, Bonan Sun, Nicolas Boumal

Without randomization, escaping the saddle points of $f \colon \mathbb{R}^d \to \mathbb{R}$ requires at least $\Omega(d)$ pieces of information about $f$ (values, gradients, Hessian-vector products). With randomization, this can be reduced to a polylogarithmic dependence in $d$. The prototypical algorithm to that effect is perturbed gradient descent (PGD): t

Jacopo Fanini, Gerardo Ganis, Marcello Maggi

The LEP data represents the most precise and highest centre-of-mass energy sample of $e^+e^-$ collision data collected to date. Numerous scientific articles have been published since the conclusion of the experiments, underscoring the ongoing relevance of this dataset and the need to secure its long-term availability according to FAIR data preservation princ

Pratyush Acharya, Habish Dhakal

Standard optimization theories struggle to explain grokking, where generalization occurs long after training convergence. While geometric studies attribute this to slow drift, they often overlook the interaction between the optimizer's noise structure and landscape curvature. This work analyzes AdamW dynamics on modular arithmetic tasks, revealing a ``Spectr

Andrew Ritchhart, Sarah I. Allec, Pravalika Butreddy, Krista Kulesa

We present a multi-agentic workflow for critical materials recovery that deploys a series of AI agents and automated instruments to recover critical materials from produced water and magnet leachates. This approach achieves selective precipitation from real-world feedstocks using simple chemicals, accelerating the development of efficient, adaptable, and sca

Alexander Sivitilli, Lucia Marchetti, Angus Comrie, P. Cilliers Pretorius

As modern astronomy confronts unprecedented data volumes, automated pipelines and machine-learning techniques have become essential for processing and analysis. As these workflows grow more complex, astronomers also require input and inspection tools that can keep pace. To address challenges in navigating multidimensional datasets for quality control and sci

Felix Giering, Rohan Srikumar, Peter Schmelcher

We investigate the non-adiabatic quantum dynamics of ultralong-range Rydberg molecules using a vibronically coupled two-channel treatment. The two-channels are composed of coupled trilobite and butterfly electronic states, formed as a result of $S$-wave and $P$-wave scattering of high angular momentum Rydberg electrons with perturbing ground state atoms. Wit

Ariel Goodwin, Adrian S. Lewis

Algorithms for minimal enclosing ball problems are often geometric in nature. To highlight the metric ingredients underlying their efficiency, we focus here on a particularly simple geodesic-based method. A recent subgradient-based study proved a complexity result for this method in the broad setting of geodesic spaces of nonpositive curvature. We present a

Tim Dortmann, Markus Vieth, Bertil Schmidt

Approximate Membership Query (AMQ) structures are essential for high-throughput systems in databases, networking, and bioinformatics. While Bloom filters offer speed, they lack support for deletions. Existing GPU-based dynamic alternatives, such as the Two-Choice Filter (TCF) and GPU Quotient Filter (GQF), enable deletions but incur severe performance penalt

A. R. N. Hanna, M. M. Ferreira-Carvalho, S. H. Chen, C. F. Chang

This work presents a comprehensive investigation of the structural, magnetic, and electronic properties of the double perovskite Ba$_2$CoMoO$_6$ (BCMO). Single crystals were grown via floating-zone and Czochralski techniques and characterized using a set of complementary methods. X-ray diffraction analysis confirmed that BCMO crystallizes in a face-centered

Xianbao Hou, Yonghao He, Zeyd Boukhers, John See

Diffusion models have significantly mitigated the impact of annotated data scarcity in remote sensing (RS). Although recent approaches have successfully harnessed these models to enable diverse and controllable Layout-to-Image (L2I) synthesis, they still suffer from limited fine-grained control and fail to strictly adhere to bounding box constraints. To addr

Penny Chong, Harshavardhan Abichandani, Jiyuan Shen, Atin Ghosh

Agent applications are increasingly adopted to automate workflows across diverse tasks. However, due to the heterogeneous domains they operate in, it is challenging to create a scalable evaluation framework. Prior works each employ their own methods to determine task success, such as database lookups, regex match, etc., adding complexity to the development o

Ryan O. Behunin, Andrew Shepherd, Ruoyu Yuan, Taylor Ray

We present a quantum field theoretic formulation of acoustoelectric interactions in waveguide-like systems of arbitrary cross-section. Building on an open quantum systems approach, we derive a unified description of plasmon-phonon coupling that incorporates dissipation, noise, and the influence of drift currents. Our analysis captures both bulk and surface p

Isabella Molina, Peter Craig, Rebecca Diesing, Laura Chomiuk

We present very long baseline interferometric (VLBI) radio imaging and Fermi/LAT GeV $\gamma$-ray observations of the 2019 eruption of the symbiotic recurrent nova V3890 Sgr.The VLBI imaging spans 8 -- 51 days after eruption, synchronous with the detected $\gamma$-rays. VLBI imaging shows the eruption starts out asymmetric on day 8 with an eastern component

A. Capolupo, I. De Martino, S. Monda, R. Della Monica

We investigate the formation and relaxation of dark matter halos in the context of wave dark matter particles evolving on a background of neutrino condensate. To this aim, we solved numerically the Schrodinger-Poisson system to model the dynamical evolution of ultralight bosonic dark matter particles in the presence of neutrino condensate. The latter appears

Ruonan Yu, Zhenxiong Tan, Zigeng Chen, Songhua Liu

Diffusion Transformers (DiTs) have demonstrated remarkable scalability and quality in image and video generation, prompting growing interest in extending them to controllable generation and editing tasks. However, compared to the image counterparts, progress in video control and editing remains limited, mainly due to the scarcity of paired video data and the

Ayoub Laayoun, Badr Missaoui

In this paper, we investigate a class of Mean Field Games (MFGs) in which the state dynamics are governed by multidimensional reflected stochastic differential equations (SDEs). We establish the existence of an equilibrium and show that it can be approximated by the equilibrium of MFGs with non-reflected SDE. This approximation is constructed via a penalizat

C. L. C. Lacoste, D. Oportus, J. Béard, S. N. Chen

Stimulated Brillouin and Raman scattering (SBS and SRS) are instabilities that affect the propagation of high-power lasers in plasmas. The latter is further affected by Cross-Talk (CT) effects when multiple laser beams are simultaneously propagated in the plasma, as found in the schemes proposed for inertial confinement fusion (ICF). Here we develop a new th

Yuanfan Zheng, Kunyu Peng, Xu Zheng, Kailun Yang

Cross-domain panoramic semantic segmentation has attracted growing interest as it enables comprehensive 360{\deg} scene understanding for real-world applications. However, it remains particularly challenging due to severe geometric Field of View (FoV) distortions and inconsistent open-set semantics across domains. In this work, we formulate an open-set domai