November 2025 arXiv papers — page 183

Satchel Grant, Simon Jerome Han, Alexa R. Tartaglini, Christopher Potts

A common approach to mechanistic interpretability is to causally manipulate model representations via targeted interventions in order to understand what those representations encode. Here we ask whether such interventions create out-of-distribution (divergent) representations, and whether this raises concerns about how faithful their resulting explanations a

Venus Keus, Lucy Lewitt, Jasmine Thomson-Cooke

Within a well-motivated 3-Higgs doublet model, in which the extended dark sector accommodates CP violation, we analyse the electroweak phase transition (EWPT) at one- and two-loop order. We show the importance of higher loop calculations in EWPT analyses and identify the regions of the parameter space of our model where EWPT is of first order while in agreem

Qingbin Li, Jian Pang

This paper presents a switch-type attenuator working from 20 to 100 GHz. The attenuator adopts a capacitive compensation technique to reduce phase error. The small resistors in this work are implemented with metal lines to reduce the intrinsic parasitic capacitance, which helps minimize the amplitude and phase errors over a wide frequency range. Moreover, th

Koki Okada, Kenta Kasai

We propose a method for modifying orthogonal sparse matrix pairs used in CSS codes while preserving their matrix row and column weight distributions, which play a crucial role in determining the performance of belief-propagation decoding. Unlike simple row or column permutations that merely reorder existing elements, the proposed local modification introduce

J. Dobaczewski, B. C. Backes, R. P. de Groote, A. Restrepo-Giraldo

Electromagnetic interactions serve as essential probes for studying and testing our understanding of the atomic nucleus, as they reveal emergent properties across the nuclear chart. We analyse their corresponding observables, which relate to charge and current distributions in nuclei expressed through their multipole components. We focus on theoretical resul

Ferris Moser

We investigate phase space transport in a two-dimensional stretched caldera potential using the Origin-Fate Map (OFM) framework, complemented by Lagrangian Descriptor (LD) analysis. The caldera potential, a model for reaction dynamics with multiple exit channels, is adjusted by a stretching factor lambda that controls the directional bias of the four-saddle

Petr Kuznetsov, Nathan Josia Schrodt

Synchronization is the major obstacle to scalability in distributed computing. Concurrent operations on the shared data engage in synchronization when they encounter a \emph{conflict}, i.e., their effects depend on the order in which they are applied. Ideally, one would like to detect conflicts in a \emph{dynamic} manner, i.e., adjusting to the current syste

Stavros Mitrolaris, Subhankar Banerjee, Sennur Ulukus

We consider a time-slotted job-assignment system with a central server, N users and a machine which changes its state according to a Markov chain (hence called a Markov machine). The users submit their jobs to the central server according to a stochastic job arrival process. For each user, the server has a dedicated job queue. Upon receiving a job from a use

Saswata Mandal, Chao-Xing Liu

Experimental evidences of spontaneous time-reversal (TR) symmetry breaking have been reported for the superconducting ground state in the transition metal dichalcogenide (TMD) superconductor 4H$_b$-TaS$_2$ or chiral molecule intercalated TaS$_2$ hybrid superlattices, and is regarded as evidence of emergent chiral superconductivity. However, the $T_c$ of thes

Kylie Cancilla, Alexander Moore, Amar Saini, Carmen Carrano

Video quality assessment (VQA) is vital for computer vision tasks, but existing approaches face major limitations: full-reference (FR) metrics require clean reference videos, and most no-reference (NR) models depend on training on costly human opinion labels. Moreover, most opinion-unaware NR methods are image-based, ignoring temporal context critical for vi

S Chockalingam, Adrian Buganza Tepole, Aditya Kumar

Classical phase-field theories of brittle fracture capture toughness-controlled crack growth but do not account for the material's strength surface, which governs fracture nucleation in the absence of cracks. The phase-field formulation of Kumar et al. (2020) proposed a blueprint for incorporating the strength surface while preserving toughness-controlled pr

Zihao Song, Shirantha Welikala, Panos J. Antsaklis, Hai Lin

In this paper, we focus on recovery control of nonlinear systems from attacks or failures. The main challenges of this problem lie in (1) learning the unknown dynamics caused by attacks or failures with formal guarantees, and (2) finding the invariant set of states to formally ensure the state deviations allowed from the nominal trajectory. To solve this pro

Ghazaleh FakhriVaighan

We establish a general inequality comparing the $F$-thresholds of a local ring and its associated graded ring. As an application, we deduce that the $F$-rationality of the graded ring descends to the local ring.

Rucha Deshpande, Tahsin Rahman, Miguel Lago, Adarsh Subbaswamy

Synthetic data provides a promising approach to address data scarcity for training machine learning models; however, adoption without proper quality assessments may introduce artifacts, distortions, and unrealistic features that compromise model performance and clinical utility. This work introduces a novel knowledge-based anomaly detection method for detect

Yutong Wen, Ke Chen, Prem Seetharaman, Oriol Nieto

Recent breakthroughs in language-queried audio source separation (LASS) have shown that generative models can achieve higher separation audio quality than traditional masking-based approaches. However, two key limitations restrict their practical use: (1) users often require operations beyond separation, such as sound removal; and (2) relying solely on text

Steffen Dereich, Arnulf Jentzen, Sebastian Kassing

The Adam optimizer is currently presumably the most popular optimization method in deep learning. In this article we develop an ODE based method to study the Adam optimizer in a fast-slow scaling regime. For fixed momentum parameters and vanishing step-sizes, we show that the Adam algorithm is an asymptotic pseudo-trajectory of the flow of a particular vecto

Daniyal Ganiuly, Assel Smaiyl

Phishing emails continue to pose a persistent challenge to online communication, exploiting human trust and evading automated filters through realistic language and adaptive tactics. While large language models (LLMs) such as GPT-4 and LLaMA-3-8B achieve strong accuracy in text classification, their deployment in security systems requires assessing reliabili

Ankan Pandey, Sandip Saha, Dibakar Ghosh

The coupled Stuart-Landau equation serves as a fundamental model for exploring synchronization and emergent behavior in complex dynamical systems. However, understanding its dynamics from a comprehensive nonlinear perspective remains challenging due to the multifaceted influence of coupling topology, interaction strength, and oscillator frequency detuning. D

Leandro J. O. Silva, William R. Wolf

Coherent motions associated with extreme wall shear stress events are investigated for adverse pressure gradient turbulent boundary layers (APG-TBLs). The analyses are performed using wall-resolved large eddy simulations of a NACA0012 airfoil at angles of attack of 9 and 12 deg. and Reynolds number 400000. The suction side exhibits attached TBLs which develo

Ajinkya Gavane, Benjamin Watson

Rendering for light field displays (LFDs) requires rendering of dozens or hundreds of views, which must then be combined into a single image on the display, making real-time LFD rendering extremely difficult. We introduce light field display point rendering (LFDPR), which meets these challenges by improving eye-based point rendering [Gavane and Watson 2023]

Natalia Glazman, Jyoti Mangal, Pedro Borges, Sebastien Ourselin

The application of causal discovery to diseases like Alzheimer's (AD) is limited by the static graph assumptions of most methods; such models cannot account for an evolving pathophysiology, modulated by a latent disease pseudotime. We propose to apply an existing latent variable model to real-world AD data, inferring a pseudotime that orders patients along a

Muqing Yu, Jieun Kim, Ahmed Omran, Zhuan Li

The supercurrent diode effect (SDE), characterized by nonreciprocal critical currents, represents a promising building block for future dissipationless electronics and quantum circuits. Realizing SDE requires breaking both time-reversal and inversion symmetry in the device. Here we use conductive atomic force microscopy (c-AFM) lithography to pattern reconfi

Ana Čolović

We resolve the question of the boundedness of the composition of dyadic paraproducts, first posed by Pott, Reguera, Sawyer, and Wick in~\cite{PotCarSawWic}, by providing necessary and sufficient conditions for their boundedness.

Elvis Agbenyega, Cody Quick

Resilient supply chains are critical, especially for Original Equipment Manufacturers (OEMs) that power today's digital economy. Safety Stock dimensioning-the computation of the appropriate safety stock quantity-is one of several mechanisms to ensure supply chain resiliency, as it protects the supply chain against demand and supply uncertainties. Unfortunate

Tushar Kataria, Shikha Dubey, Mary Bronner, Jolanta Jedrzkiewicz

Deep learning models can generate virtual immunohistochemistry (IHC) stains from hematoxylin and eosin (H&E) images, offering a scalable and low-cost alternative to laboratory IHC. However, reliable evaluation of image quality remains a challenge as current texture- and distribution-based metrics quantify image fidelity rather than the accuracy of IHC staini

Seok-Hyun Ga, Chun-Yen Chang, Sonya Martin

This study examines high school students' acceptance of Arduino technology in a student-led, inquiry-based science class, using the extended Technology Acceptance Model (TAM2) as a guiding framework. Through qualitative analysis of interviews and classroom observations, we explored how students perceived Arduino's usefulness and ease of use. Going beyond tra

Taja Yaying, Pinakadhar Baliarsingh, Bipan Hazarika

This paper intends to develop a $q$-difference operator $\nabla^{(\gamma)}_q$ of fractional order $\gamma$, and give several intriguing properties of this new difference operator. Our main focus remains on the construction of sequence spaces $\ell_p(\nabla^{(\gamma)})$ and $\ell_\infty (\nabla^{(\gamma)})$, at the same time comparing these spaces with those

Victor-Emmanuel Brunel

M-estimation, aka empirical risk minimization, is at the heart of statistics and machine learning: Classification, regression, location estimation, etc. Asymptotic theory is well understood when the loss satisfies some smoothness assumptions and its derivatives are dominated locally. However, these conditions are typically technical and can be too restrictiv

Maximilian Matthe

This paper presents evomap, a Python package for dynamic mapping. Mapping methods are widely used across disciplines to visualize relationships among objects as spatial representations, or maps. However, most existing statistical software supports only static mapping, which captures objects' relationships at a single point in time and lacks tools to analyze

Ali Faraz, Akash, Shaharukh Khan, Raja Kolla

Vision-language models (VLMs) have demonstrated impressive generalization across multimodal tasks, yet most evaluation benchmarks remain Western-centric, leaving open questions about their performance in culturally diverse and multilingual settings. To address this gap, we introduce IndicVisionBench, the first large-scale benchmark centered on the Indian sub

Mladen Dimitrov, Dinakar Ramakrishnan

Half a century ago Manin showed that given a number field $k$ and a rational prime $\ell$, there exists a uniform bound for the order of cyclic $\ell$-power isogenies between two non-CM elliptic curves over $k$. We generalize this to certain $2$-dimensional families of abelian $3$-folds with multiplication by an imaginary quadratic field.

R. Barzaga, B. Kerkeni, D. A. García-Hernández, X. Ribas

The coexistence of PAHs and the C$_{60}$ fullerene in different astrophysical environments can give rise to the formation of new complex species denoted as PAH-C$_{60}$ adducts, which may contribute to the infrared (IR) emission observed. These PAH-C$_{60}$ adducts have been previously reported experimentally due to the high reactivity between PAHs and C$_{6

Fabian Zhou, Giuliano Giacalone, Jean-Yves Ollitrault

Recent experiments have shown that the mean transverse momentum $\langle p_T\rangle$ of outgoing particles increases as a function of the particle multiplicity in ultracentral nucleus-nucleus collisions at collider energies. This increase was originally predicted on the basis of simulations where the multiplicity increase occurred at constant volume, so that

Michael Robinson, Michael L. Szulczewski, James T. Thorson

This chapter explores dynamical structural equation models (DSEMs) and their nonlinear generalizations into sheaves of dynamical systems. It demonstrates these two disciplines on part of the food web in the Bering Sea. The translation from DSEMs to sheaves passes through a formal construction borrowed from electronics called a netlist that specifies how data

Yunrui Wang, Cheng Guo

We establish a comprehensive probability theory for coherent transport of random waves through arbitrary linear media. The transmissivity distribution for random coherent waves is a fundamental B-spline with knots at the transmission eigenvalues. We analyze the distribution's shape, bounds, moments, and asymptotic behaviors. In the large n limit, the distrib

Yicheng Xiao, Yu Chen, Haoxuan Ma, Jiale Hong

While the Contrastive Language-Image Pretraining(CLIP) model has achieved remarkable success in a variety of downstream vison language understanding tasks, enhancing its capability for fine-grained image-text alignment remains an active research focus. To this end, most existing works adopt the strategy of explicitly increasing the granularity of visual info

SNO+ Collaboration, :, M. Abreu, A. Allega

Accurate measurement of the cosmogenic muon-induced neutron yield is crucial for constraining a significant background in a wide range of low-energy physics searches. Although previous underground experiments have measured this yield across various cosmogenic muon energies, SNO+ is uniquely positioned due to its exposure to one of the highest average cosmoge

Pawel Gajer, Jacques Ravel

Understanding feature-outcome associations in high-dimensional data remains challenging when relationships vary across subpopulations, yet standard methods assuming global associations miss context-dependent patterns, reducing statistical power and interpretability. We develop a geometric decomposition framework offering two strategies for partitioning infer

Hampus Åström, Elin Anna Topp, Jacek Malec

In this paper we study how transforming regular reinforcement learning environments into goal-conditioned environments can let agents learn to solve tasks autonomously and reward-free. We show that an agent can learn to solve tasks by selecting its own goals in an environment-agnostic way, at training times comparable to externally guided reinforcement learn

Chen Shi, Shaoshuai Shi, Xiaoyang Lyu, Chunyang Liu

Feed-forward 3D reconstruction for autonomous driving has advanced rapidly, yet existing methods struggle with the joint challenges of sparse, non-overlapping camera views and complex scene dynamics. We present UniSplat, a general feed-forward framework that learns robust dynamic scene reconstruction through unified latent spatio-temporal fusion. UniSplat co

Utkarsh U. Chavan, Prashant Trivedi, Nandyala Hemachandra

Multi-agent systems (MAS) are central to applications such as swarm robotics and traffic routing, where agents must coordinate in a decentralized manner to achieve a common objective. Stochastic Shortest Path (SSP) problems provide a natural framework for modeling decentralized control in such settings. While the problem of learning in SSP has been extensive

Shaunak Bhandarkar, James L. McClelland

Many models used in artificial intelligence and cognitive science rely on multi-element patterns stored in "slots" - dedicated storage locations - in a digital computer. As biological brains likely lack slots, we consider how they might achieve similar functional outcomes without them by building on the neurally-inspired modern Hopfield network (MHN; Krotov

Gregory J. Herczeg, Bo Reipurth

FU Ori outbursts are thought to play an important role in stellar assembly and the evolution of protoplanetary disks. However, the progenitor young stellar objects are largely uncharacterized. We obtained a low-resolution optical spectrum of HBC 722 before its FU Ori outburst as part of a survey of young stellar objects in the North America Nebula. The spect

Mariia Churilova, Zdeněk Stuchlík

We study stability of the circular string loops rotating in the equatorial plane of Kerr spacetime against equatorial and polar perturbations. We consider motion of such string loops for different modes in the case of arbitrary equation of state. We also obtain analytical expression for the fundamental frequencies of the string loop oscillations under equato

Oshri Naparstek

We introduce Complexity as Advantage (CAA), a framework that defines the complexity of a system relative to a family of observers. Instead of measuring complexity as an intrinsic property, we evaluate how much predictive regret a system induces for different observers attempting to model it. A system is complex when it is easy for some observers and hard for

Francisco A. Iglesias, Diego G. Lloveras, Florencia L. Cisterna, Hebe Cremades

Coronal mass ejections (CMEs) are a major driver of space weather. To assess CME geoeffectiveness, among other scientific goals, it is necessary to reliably identify and characterize their morphology and kinematics in coronagraph images. Current methods of CME identification are either subjected to human biases or perform a poor identification due to deficie

Soham De, Lodewijk Gelauff, Ashish Goel, Smitha Milli

A central feature of many deliberative processes, such as citizens' assemblies and deliberative polls, is the opportunity for participants to engage directly with experts. While participants are typically invited to propose questions for expert panels, only a limited number can be selected due to time constraints. This raises the challenge of how to choose a

Antonio Pich

The Standard Model does not provide any dynamical explanation of the existence of different families of fermions. To account for this experimental fact, it just replicates three times its single-family gauge structure. The equal-charge fermions of the different families couple to the gauge bosons with exactly the same coupling strength. We overview the empir

Antonio Colanera, Luca Magri

This work develops quantized local reduced-order models (ql-ROMs) of the turbulent Minimal Flow Unit (MFU) for the analysis and interpretation of intermittent dissipative dynamics and extreme events. The ql-ROM combines data-driven clustering of the flow state space with intrusive Galerkin projection on locally defined Proper Orthogonal Decomposition (POD) b

Wouter van Doorn, Anneroos R. F. Everts

Solving a problem by Erd\H{o}s, we prove that every positive integer $n$ can be written as a sum $$n = b_{1} + b_{2} + \ldots + b_{r}$$ of distinct $3$-smooth integers with $1 \le b_{1} < b_{2} < \ldots < b_{r} < 6b_{1}$.

Daniel Gomm, Cornelius Wolff, Madelon Hulsebos

Natural language interfaces to tabular data must handle ambiguities inherent to queries. Instead of treating ambiguity as a deficiency, we reframe it as a feature of cooperative interaction where users are intentional about the degree to which they specify queries. We develop a principled framework based on a shared responsibility of query specification betw

Atsuyuki Miyai, Mashiro Toyooka, Takashi Otonari, Zaiying Zhao

Understanding the current capabilities and risks of AI Scientist systems (autoresearch) is essential for ensuring trustworthy and sustainable AI-driven scientific progress while preserving the integrity of the academic ecosystem. To this end, we develop Jr. AI Scientist, a state-of-the-art autonomous AI scientist system that mimics the core research workflow

Hyeonhee Jin

We study Goodwillie-Weiss embedding calculus through its relationship with Goodwillie's functor calculus. Specifically, building on a result of Tillmann and Weiss, we construct a functorial complement for \(T_{n}\)-embeddings that takes values in Heuts's categorical \(n\)-excisive approximation of pointed spaces. We also establish an analogue of Stallings' t

Valentino Smaldore, Corrado Zanella, Ferdinando Zullo

In this work, we introduce $(r,i)$-regular fat linear sets, which are defined as linear sets containing exactly $r$ points of weight $i$ and all other points of weight one. This notion generalizes and unifies existing constructions; scattered linear sets, clubs, and other previously studied families are special cases. We present new classes of regular fat li

Gohar T. Khokhar, Kyle Hanquist, Parham Oveissi, Alex Dorsey

Solid-fuel ramjets offer a compact, energy-dense propulsion option for long-range, high-speed flight but pose significant challenges for thrust regulation due to strong nonlinearities, limited actuation authority, and complex multi-physics coupling between fuel regression, combustion, and compressible flow. This paper presents a computational and control fra

Ricardo Baptista, Franca Hoffmann, Minh Van Hoang Nguyen, Benjamin Zhang

In the theory of optimal transport, the Knothe-Rosenblatt (KR) rearrangement provides an explicit construction to map between two probability measures by building one-dimensional transformations from the marginal conditionals of one measure to the other. The KR map has shown to be useful in different realms of mathematics and statistics, from proving functio

Tiphaine Kouadou, Elie Gozlan, Loïc Garcia, David Polizzi

Homodyne detection is a phase-sensitive measurement technique, essential for the characterization of continuous-variable (CV)-encoded quantum states of light. It is a key component to the implementation of CV quantum-information protocols and benefits from operating, by design, at room temperature. However, performing high-speed quantum information processin

Zhuo Zhang, Xiong Xiong, Sen Zhang, Yuan Zhao

PDEs arise ubiquitously in science and engineering, where solutions depend on parameters (physical properties, boundary conditions, geometry). Traditional numerical methods require re-solving the PDE for each parameter, making parameter space exploration prohibitively expensive. Recent machine learning advances, particularly physics-informed neural networks

Derek Marsh

Basic and instantaneous reproduction numbers, "R" _"0" and "R" _"t" , are important metrics to assess progress of an epidemic and effectiveness of preventative interventions undertaken, and also to estimate coverage needed for vaccination. Reproduction numbers are related to the daily number of positive cases recorded by the national public health authoritie

Vasco V. Branco, Jandó Benedek, Lidia Pivovarova, Luís Correia

1. A hard stop for the implementation of rigorous conservation initiatives is our lack of key species data, especially occurrence data. Furthermore, researchers have to contend with an accelerated speed at which new information must be collected and processed due to anthropogenic activity. Publications ranging from scientific papers to gray literature contai

Yixin Tao, Weiqiang Zheng

The Fisher market equilibrium for private goods and the Lindahl equilibrium for public goods are classic and fundamental solution concepts for market equilibria. While Fisher market equilibria have been well-studied, the theoretical foundations for Lindahl equilibria remain substantially underdeveloped. In this work, we propose a unified duality framework fo

Leonid Positselski

This paper is an MGM version of arXiv.org:1703.04266 and arXiv:1907.03364, and a follow-up to Section 5 of arXiv:1503.05523. In the setting of a commutative ring $S$ with a weakly proregular finitely generated ideal $J\subset S$, we consider the maximal, abstract, and minimal corresponding classes of $J$-torsion $S$-modules and $J$-contramodule $S$-modules w

Jingqi Tong, Yurong Mou, Hangcheng Li, Mingzhe Li

The "Thinking with Text" and "Thinking with Images" paradigms significantly improve the reasoning abilities of large language models (LLMs) and Vision-Language Models (VLMs). However, these paradigms have inherent limitations. (1) Images capture only single moments and fail to represent dynamic processes or continuous changes, and (2) The separation of text

Aleksandr Shestakov, Valery Parfenov, Aleksandr Beznosikov

Variance reduction is a family of powerful mechanisms for stochastic optimization that appears to be helpful in many machine learning tasks. It is based on estimating the exact gradient with some recursive sequences. Previously, many papers demonstrated that methods with unbiased variance-reduction estimators can be described in a single framework. We genera

Masahiro Kato

This study clarifies the relationship between Riesz regression [Chernozhukov et al., 2021] and density ratio estimation (DRE) in causal inference problems, such as average treatment effect estimation. We first show that the Riesz representer can be written as a signed density ratio and then demonstrate that the Riesz regression objective coincides with the l

Saee A. Joshi, Geetanjali M. Phatak, Vinayak M. Sholapurkar

The Cauchy dual subnormality problem (CDSP, for short) asks whether the Cauchy dual of a $2-$isometry is subnormal. In this article, we provide a counter-example to CDSP by constructing a cyclic, analytic, $2-$isometry whose defect operator is of rank $3$. In particular, we prove that the Cauchy dual $M_z'$ of the multiplication operator $M_z$ on the Dirichl

Yoh-ichi Mototake, Makoto Sasaki

Physics-informed machine learning (PIML) integrates partial differential equations (PDEs) into machine learning models to solve inverse problems, such as estimating coefficient functions (e.g., the Hamiltonian function) that characterize physical systems. This framework enables data-driven understanding and prediction of complex physical phenomena. While coe

Vincent Gierisch, Wolfgang Mauerer

Commercially available Noisy Intermediate-Scale Quantum (NISQ) devices now make small hybrid quantum-classical experiments practical, but many tools hide configuration or demand ad-hoc scripting. We introduce the Quantum Experiment Framework (QEF): A lightweight framework designed to support the systematic, hypothesis-driven study of quantum algorithms. Unli

Li Yang, Dandan Jiang, Jiang Hu, Zhidong Bai

In this paper, we analyze the asymptotic behavior of a system of interacting reinforced stochastic processes $({\bf Z}_n, {\bf N}_n)_n$ on a directed network of $N$ agents. The system is defined by the coupled dynamics ${\bf Z}_{n+1}=(1-r_{n}){\bf Z}_{n}+r_{n}{\bf X}_{n+1}$ and ${\bf N}_{n+1}=(1-\frac{1}{n+1}){\bf N}_n+\frac{1}{n+1}{\bf X}_{n+1}$, where agen

Mattia Morbello

We study the compactification of the moduli space of a certain class of rank-two irregular connections on the Riemann sphere, presenting one double pole and two simple poles. To construct the compactification explicitly, we identify a class of such irregular connections with the data of a rational irregular curve together with an extra complex parameter. As

Sadia Sultana, Saiyma Sittul Muna, Mosammat Zannatul Samarukh, Ajwad Abrar

Developing accurate biomedical Question Answering (QA) systems in low-resource languages remains a major challenge, limiting equitable access to reliable medical knowledge. This paper introduces BanglaMedQA and BanglaMMedBench, the first large-scale Bangla biomedical Multiple Choice Question (MCQ) datasets designed to evaluate reasoning and retrieval in medi

Junhyeok Bang

Beyond the adiabatic regime, our understanding of quantum dynamics in coupled systems remains limited, and the choice of representation continues to obscure physical interpretation and simulation accuracy. Here we propose a natural and efficient basis for electron nuclear dynamics by drawing on the concepts of pointer and preferred states from decoherence th

Mark Chen, Xi Chen, Hao Cui, William Pires

We show that for any constant $c>0$, any (two-sided error) adaptive algorithm for testing monotonicity of Boolean functions must have query complexity $\Omega(n^{1/2-c})$. This improves the $\tilde\Omega(n^{1/3})$ lower bound of [CWX17] and almost matches the $\tilde{O}(\sqrt{n})$ upper bound of [KMS18].

Divyansha Lachi, Mahmoud Mohammadi, Joe Meyer, Vinam Arora

In domains such as healthcare, finance, and e-commerce, the temporal dynamics of relational data emerge from complex interactions-such as those between patients and providers, or users and products across diverse categories. To be broadly useful, models operating on these data must integrate long-range spatial and temporal dependencies across diverse types o

Tao Lin, Yilei Zhong, Yuxin Du, Jingjing Zhang

Vision-Language-Action (VLA) models have emerged as a powerful framework that unifies perception, language, and control, enabling robots to perform diverse tasks through multimodal understanding. However, current VLA models typically contain massive parameters and rely heavily on large-scale robot data pretraining, leading to high computational costs during

Felipe A. Asenjo, Swadesh M. Mahajan

We present new electromagnetic plasma wavepacket solutions that propagates in one time and one space coordinates. Differently to the usual plane wave solution, which is written in terms of separation of variables, all our solutions are along the light-cone coordinates. This allow us to find several new solutions whose functionality properties rely on the con

Ben Hawks, Gregor von Laszewski, Matthew D. Sinclair, Marco Colombo

Scientific machine learning research spans diverse domains and data modalities, yet existing benchmark efforts remain siloed and lack standardization. This makes novel and transformative applications of machine learning to critical scientific use-cases more fragmented and less clear in pathways to impact. This paper introduces an ontology for scientific benc

Alejandro Gomez Cadavid, Pranav Chandarana, Sebastián V. Romero, Jan Trautmann

We introduce quantum-enhanced memetic tabu search (QE-MTS), a non-variational hybrid algorithm that achieves state-of-the-art scaling for the low-autocorrelation binary sequence (LABS) problem. By seeding the classical MTS with high-quality initial states from digitized counterdiabatic quantum optimization (DCQO), our method suppresses the empirical time-to-

Edoardo Marcelli, Sean O'Hagan, Veronika Rockova

Generative Bayesian Filtering (GBF) provides a powerful and flexible framework for performing posterior inference in complex nonlinear and non-Gaussian state-space models. Our approach extends Generative Bayesian Computation (GBC) to dynamic settings, enabling recursive posterior inference using simulation-based methods powered by deep neural networks. GBF d

Mohsen Hafez-Torbati, Götz S. Uhrig

We propose N\'eel antiferromagnetic (AF) Mott insulators with a buckled honeycomb structure as potential candidates to host a high-temperature AF Chern insulator (AFCI). Using a generalized Kondo lattice model we show that the staggered potential induced by a perpendicular electric field due to the buckling can drive the AF Mott insulator to an AFCI phase. W

Dhruv Deepak Agarwal, Aswani Kumar Cherukuri

The growth of cloud computing has revolutionized data processing and storage capacities to another levels of scalability and flexibility. But in the process, it has created a huge challenge of security, especially in terms of safeguarding sensitive data. Classical security practices, including encryption at rest and during transit, fail to protect data in us

Christoph Buchheim, Lowig T. Duer

We investigate the feasibility problem for generalized inverse linear programs. Given an LP with affinely parametrized objective function and right-hand side as well as a target set Y, the goal is to decide whether the parameters can be chosen such that there exists an optimal solution that belongs to Y (optimistic scenario) or such that all optimal solution

Qing Wang, Yong Zhang

Although microservices have physically isolated modules, they have failed to prevent the propagation and diffusion of dependencies. To trace the root cause of the inter-module coupling, this paper, starting from the impact assessment approach for module changes, proposes a conceptual method for calculating module independence and utilizes this method to deri

A. A. Saharian, L. Sh. Grigoryan, V. Kh. Kotanjyan

We investigate the local characteristics of the Fulling-Rindler vacuum for a massive Dirac field induced by a planar boundary moving with constant proper acceleration in $(D+1)$-dimensional flat spacetime. On the boundary, the field operator obeys the bag boundary condition. The boundary divides the right Rindler wedge into two separate regions, called RL an

Goureesankar Sathyanathan, Josquin Errard, Soumen Basak

We introduce a noise-aware extension to the parametric maximum-likelihood framework for component separation by modeling correlated $1/f^\alpha$ noise as a harmonic-space power law. This approach addresses a key limitation of existing implementations, for which a mismodelling of the statistical properties of the noise can lead to biases in the characterizati

Erickson Tjoa, J. Ignacio Cirac

In this work we introduce an ansatz for continuous matrix product operators for quantum field theory. We show that (i) they admit a closed-form expression in terms of finite number of matrix-valued functions without reference to any lattice parameter; (ii) they are obtained as a suitable continuum limit of matrix product operators; (iii) they preserve the en

Ruth G. Curry, Michael W. Lomas, Megan R. Sullivan, Damian Grundle

Despite decades of ship-based observations at the Bermuda Atlantic Timeseries Study (BATS) site, ambiguities linger in our understanding of the region's annual carbon cycle. Difficulties reconciling geochemical estimates of annual net community production (ANCP) with direct measurements of nutrient delivery and carbon exports (EP) have implied either an insu

Brandon Severin, Tim Botzem, Federico Fedele, Xi Yu

Donor spin qubits in silicon offer one- and two-qubit gates with fidelities beyond 99%, coherence times exceeding 30 seconds, and compatibility with industrial manufacturing methods. This motivates the development of large-scale quantum processors using this platform, and the ability to automatically tune and operate such complex devices. In this work, we pr

Nicolas Moens, Dwaipayan Debnath, Olivier Verhamme, Frank Backs

The atmospheres of massive O-type stars (O stars) are dynamic, turbulent environments resulting from radiatively driven instabilities over the iron bump, located slightly beneath the stellar surface. Here, complex radiation hydrodynamic processes affect the structure of the atmosphere as well as the formation of spectral lines. In quantitative spectroscopic

Baptiste Bonin, Maxime Heuillet, Audrey Durand

Modeling user preferences across domains remains a key challenge in slate recommendation (i.e. recommending an ordered sequence of items) research. We investigate how Large Language Models (LLM) can effectively act as world models of user preferences through pairwise reasoning over slates. We conduct an empirical study involving several LLMs on three tasks s

Cyril Vallez, Alexander Sternfeld, Andrei Kucharavy, Ljiljana Dolamic

As the role of Large Language Models (LLM)-based coding assistants in software development becomes more critical, so does the role of the bugs they generate in the overall cybersecurity landscape. While a number of LLM code security benchmarks have been proposed alongside approaches to improve the security of generated code, it remains unclear to what extent

Yoshitaka Hatta, Jakob Schoenleber

Within the collinear factorization framework based on Generalized Parton Distributions (GPDs), we calculate the spin density matrix of exclusively produced quark and antiquark pairs $u\bar{u}$, $d\bar{d}$, $s\bar{s}$, $c\bar{c}$, $b\bar{b}$ in electron-proton scattering. The presence of both real and imaginary parts in the scattering amplitudes leads to a ri

Jean-Marcel Rax

Experiments devoted to charge parity (CP) violation are normally interpreted by adjusting the elements of the Cabibbo-Kobayashi-Maskawa matrix to the measured violation parameters. However, the physical origin of these violations remains an open issue. To resolve this issue, the impact of Earth's gravity on meson oscillations is analysed. The effect of gravi

Ziling Cheng, Jieliang Hong, Dan Yao

Let $X=(X_t, t\geq 0)$ be a superprocess in a random environment governed by a Gaussian noise $W=\{W(t, x),t\geq 0,x\in\mathbb{R}^d\}$ white in time and colored in space with correlation kernel $g$. We consider the occupation time process of the model starting from a finite measure. It is shown that the occupation time process of $X$ is absolutely continuous

Jonas E. Katona, Emily K. de Jong, Nipun Gunawardena

Reduced-order models (ROMs) can efficiently simulate high-dimensional physical systems but lack robust uncertainty quantification methods. Existing approaches are frequently architecture- or training-specific, which limits flexibility and generalization. We introduce a post hoc, model-agnostic framework for predictive uncertainty quantification in latent spa

Vladimir Despotovic, Peter Pocta, Andrej Zgank

Remote monitoring of cardiovascular diseases plays an essential role in early detection of abnormal cardiac function, enabling timely intervention, improved preventive care, and personalized patient treatment. Abnormalities in the heart sounds can be detected automatically via computer-assisted decision support systems, and used as the first-line screening t

Arkadeep Saha, Pieter van Goor, Antonio Franchi, Ravi Banavar

Landmark Inertial Simultaneous Localisation and Mapping (LI-SLAM) is the problem of estimating the locations of landmarks in the environment and the robot's pose relative to those landmarks using landmark position measurements and measurements from Inertial Measurement Unit (IMU). This paper proposes a nonlinear observer for LI-SLAM posed in continuous time

Wilfried Raffi, Jochem G. Meijer, Detlef Lohse

Freeze-thaw cycles can be regularly observed in nature in water and are essential in industry and science. Objects present in the medium will interact with either an advancing solidification front during freezing or a retracting solidification front, i.e., an advancing melting front, during thawing. It is well known that objects show complex behaviours when

Kalyan Banerjee, Kalyan Chakraborty, Arkabrata Ghosh

We present two distinct families of imaginary biquadratic fields, each of which contains infinitely many members, with each member having large class groups. Construction of the first family involves elliptic curves and their quadratic twists, whereas to find the other family, we use a combination of elliptic and hyperelliptic curves. Two main results are us

Kaveh Eskandari Miandoab, Katharine Kowalyshyn, Kabir Pamnani, Anesu Gavhera

We present IntelliProof, an interactive system for analyzing argumentative essays through LLMs. IntelliProof structures an essay as an argumentation graph, where claims are represented as nodes, supporting evidence is attached as node properties, and edges encode supporting or attacking relations. Unlike existing automated essay scoring systems, IntelliProof