October 2025 arXiv papers — page 105

Huyen N. Nguyen, Nils Gehlenborg

Effective visualization retrieval necessitates a clear definition of similarity. Despite the growing body of work in specialized visualization retrieval systems, a systematic approach to understanding visualization similarity remains absent. We introduce the Similarity Framework for Visualization Retrieval (Safire), a conceptual model that frames visualizati

Jing Kong

Many causal estimands, such as average treatment effects under unconfoundedness, can be written as continuous linear functionals of an unknown regression function. We study a weighting estimator that sets weights by a minimax procedure: solving a convex optimization problem that trades off worst-case conditional bias against variance. Despite its growing use

Ryan Liu, Vadim Ponomarenko

In this paper, we study the Pulsar Sequence, an integer sequence derived from Latin-square-based Pulsar puzzles introduced by the Cracking the Cryptic YouTube channel. A Pulsar puzzle consists of two interlocked spirals of circled and uncircled squares, generating the Dual and Pulsar sequences, respectively. We investigate the properties of the Pulsar puzzle

Wjefferson Henrique da Silva Brandão, Anderson Gomes Vieira, Jonathan da Rocha Martins, Andrea Latgé

Proposing new ways to organize carbon in 2D nanomaterials has been a relevant strategy in the search for systems with targeted properties for different applications. One focus is the study of fully sp$^2$ non-graphitic networks, with successfully synthesized examples. Hybrid sp-sp$^2$ systems of the graphyne family are a related approach, and many systems ha

Zhyar Rzgar K. Rostam, Gábor Kertész

The exponential increase in scientific literature and online information necessitates efficient methods for extracting knowledge from textual data. Natural language processing (NLP) plays a crucial role in addressing this challenge, particularly in text classification tasks. While large language models (LLMs) have achieved remarkable success in NLP, their ac

Noah El Rimawi-Fine, Adam Stecklov, Lucas Nelson, Mathieu Blanchette

Modeling dynamical systems and unraveling their underlying causal relationships is central to many domains in the natural sciences. Various physical systems, such as those arising in cell biology, are inherently high-dimensional and stochastic in nature, and admit only partial, noisy state measurements. This poses a significant challenge for addressing the p

Cheik Traoré, Peter Ochs

Stochastic algorithms, especially stochastic gradient descent (SGD), have proven to be the go-to methods in data science and machine learning. In recent years, the stochastic proximal point algorithm (SPPA) emerged, and it was shown to be more robust than SGD with respect to stepsize settings. However, SPPA still suffers from a decreased convergence rate due

Ligen Wang, Konrad Burkmann, Sergey V. Ushakov, Edric X. Wang

Rare earth oxide-phosphates (REOPs) form a largely unexplored family of refractory lanthanides and yttrium compounds with general formula RExOy(PO4)z. They are of interest for applications ranging from thermal barrier coatings to catalysts and magnetic materials. At least four REOPs phases were experimentally identified with RE/P ratios from 7:3 to 6:1, howe

Bruno Scheihing-Hitschfeld

I review recent developments on heavy flavor transport in the QGP medium, along two directions. The first is the transport of individual open heavy quarks. Leveraging the tools of heavy quark effective theory, recent work revealed a novel connection between the evolution equation of the heavy quark phase space distribution, the conditions for kinetic equilib

Zihan Wang, Yi-Ping Chen, Tuba Dolar, Wei Chen

Modern scientific and engineering design increasingly involves distributed optimization, where agents such as laboratories, simulations, or industrial partners pursue related goals under differing conditions. These agents often face heterogeneities in objectives, evaluation budgets, and accessible design variables, which complicates coordination and can lead

Francis Walz, Shashank Kumar, Amirali Sharifi Olounabadi, Yuyan Zhong

Over the past decade, ultrafast electron dynamics in the solid state have been extensively studied using various strong light-matter interaction techniques, such as high-harmonic generation. These studies lead to multiple interpretations of light-matter interaction in the strong-field regime, with exact mechanisms not yet fully understood. It is well known t

Dennis J. Marquis, Blake Wilhelm, Devaprakash Muniraj, Mazen Farhood

This paper presents a reinforcement learning-based path-following controller for a fixed-wing small uncrewed aircraft system (sUAS) that is robust to uncertainties in the aerodynamic model of the sUAS. The controller is trained using the Robust Adversarial Reinforcement Learning framework, where an adversary perturbs the environment (aerodynamic model) to ex

Lea Beneish, Andrew Granville

We study the set of $D$ such that a given irreducible hypersurface $C$ of degree $d$ has infinitely many points of degree $D$ over $\mathbb{Q}$. We give a new explicit proof that this set contains all (positive) multiples of the index of $C$ with finitely many exceptions. When $D$ is sufficiently large and divisible by the index of $C$, we show there are $\g

Hongjin Du, Ellery J. Hendrix, Richard D. Robinson, Julia Dshemuchadse

Semiconductor magic-size clusters (MSCs) are atomically precise nanoparticles exhibiting unique size-dependent properties, but their ultrasmall dimensions hinder structural characterization, limiting our understanding of their formation and stability. A few MSC structures have been fully resolved, revealing either bulk-like zincblende-type structures or a ra

C. J. Díaz Baso, J. de la Cruz Rodríguez, H. -P. Doerr, M. van Noort

Solar flares are complex phenomena driven by the release of magnetic energy, but a large energy reservoir is not sufficient to determine their eruptive potential; the magnetic topology and plasma dynamics play a key role. We investigate the thermodynamic and magnetic properties of the solar atmosphere during the rise, peak, and decay phases of a C5.1-class f

Jiin Woo, Shaowei Zhu, Allen Nie, Zhen Jia

The rapid evolution of Large Language Models (LLMs) has driven a growing demand for automated, high-performance system kernels to accelerate machine learning workloads. We introduce TritonRL, a domain-specialized 8B-scale LLM for Triton programming, trained via a novel reinforcement learning (RL) framework. While Triton synthesis faces unique challenges, inc

Adam Mielke, Mads Peter Sørensen, John Wyller

We design a Linear Chain Trick (LCT)-algorithm for dynamical systems with distributed time delay where the time histories contain temporal oscillations. The methodology is illustrated by means of an example in population dynamics.

Zhixuan He, Yue Feng

Large Language Models (LLMs) demonstrate strong performance but often lack interpretable reasoning. This paper introduces the Multi-Agent Collaboration Framework for Diverse Thinking Modes (DiMo), which enhances both performance and interpretability by simulating a structured debate among four specialized LLM agents. Each agent embodies a distinct reasoning

Anton Sinner, Alexander J. Much, Oleksandr Dolynchuk

Surface-induced liquid crystalline phase transitions evoke fundamental interest and can provide deeper insight into the nature of low-dimensional phases. Board-like conjugated polymers are of particular interest because they exhibit novel sanidic liquid crystalline mesophases that have not been widely studied. Furthermore, films of these polymers often exhib

Aaron Ray, Jacob Arkin, Harel Biggie, Chuchu Fan

In order to provide a robot with the ability to understand and react to a user's natural language inputs, the natural language must be connected to the robot's underlying representations of the world. Recently, large language models (LLMs) and 3D scene graphs (3DSGs) have become a popular choice for grounding natural language and representing the world. In t

Selim Amar

We present an improved Fredholm theory of non-elliptic operators for when the corresponding classical dynamical system exhibits normally hyperbolic trapping with smooth backward and forward trapped sets. It takes place on coisotropic Sobolev spaces with weak regularity at the backward trapped set $\Gamma_u$, which are roughly speaking made of distributions $

Young-Jun Lee, Byung-Kwan Lee, Jianshu Zhang, Yechan Hwang

Vision-and-Language Models (VLMs) have shown impressive capabilities on single-turn benchmarks, yet real-world applications often demand more intricate multi-turn dialogues. Existing multi-turn datasets (e.g, MMDU, ConvBench) only partially capture the breadth and depth of conversational scenarios encountered by users. In this work, we introduce MultiVerse,

Zhiguo Ding, Wei Xiong, Michael E. Zieve

For each prime power q, we determine all polynomials over F_{q^2} of the form f(X) := aX^{3q}+bX^{2q+1}+cX^{q+2}+dX^3 which induce complete mappings of F_{q^2}, in the sense that each of the functions x --> f(x) and x --> f(x)+x permutes F_{q^2}. This is the first result in the literature which classifies the complete mappings among some class of polynomials

Yassine Sekhmani, Wentao Liu, Weike Deng, Kuantay Boshkayev

We study electrically charged, slowly rotating black hole solutions in Einstein-Bumblebee gravity coupled to the traceless (conformal) ModMax nonlinear electrodynamics. By adopting a quadratic bumblebee potential that fixes the vacuum expectation value of the Lorentz-violating vector, we derive both the static configuration and its first-order rotating exten

Sergey Pugachev

Multi-agent LLM systems fail to realize parallel speedups due to costly coordination. We present CodeCRDT, an observation-driven coordination pattern where agents coordinate by monitoring a shared state with observable updates and deterministic convergence, rather than explicit message passing. Using Conflict-Free Replicated Data Types (CRDTs), CodeCRDT enab

Ekaterina Nistiuk, Yulia Zaitseva

We describe affine monoids whose group of invertible elements is an active semidirect product of a unipotent group and a torus, in terms of comultiplications on the algebra of regular functions. We introduce the notion of a root monoid, which is constructed from a set of Demazure root pairs on an affine toric variety, and study the properties of such monoids

Alireza Heshmati, Saman Soleimani Roudi, Sajjad Amini, Shahrokh Ghaemmaghami

Existing adversarial attacks often neglect perturbation sparsity, limiting their ability to model structural changes and to explain how deep neural networks (DNNs) process meaningful input patterns. We propose ATOS (Attack Through Overlapping Sparsity), a differentiable optimization framework that generates structured, sparse adversarial perturbations in ele

Abraham Atsiwo

This study presents a three-step machine learning framework to predict bubbles in the S&P 500 stock market by combining financial news sentiment with macroeconomic indicators. Building on traditional econometric approaches, the proposed approach predicts bubble formation by integrating textual and quantitative data sources. In the first step, bubble periods

Wonduk Seo, Juhyeon Lee, Junseo Koh, Wonseok Choi

Prompt optimization has become a practical way to improve the performance of Large Language Models (LLMs) without retraining. However, most existing frameworks treat evaluation as a black box, relying solely on outcome scores without explaining why prompts succeed or fail. Moreover, they involve repetitive trial-and-error refinements that remain implicit, of

Abeer Al Ghamdi, Gin Jose, Almut Beige

As atom-cavity systems are becoming more sophisticated, the limitations of the Jaynes-Cummings model are becoming more apparent. In this paper, we therefore take a more dynamical approach to the modelling of atom-cavity systems and do not reduce the electromagnetic field inside the resonator to a single mode. Our approach shows that the decay rate Gamma_cav

Xiaoshan Huang, Tianlong Zhong, Haolun Wu, Yeyu Wang

Computer-supported simulation enables a practical alternative for medical training purposes. This study investigates the co-occurrence of facial-recognition-derived emotions and socially shared regulation of learning (SSRL) interactions in a medical simulation training context. Using transmodal analysis (TMA), we compare novice and expert learners' affective

Daniel Larsen, Thomas Wright

For every sufficiently large integer $R$, there exists a Carmichael number with exactly $R$ prime factors.

Anna Horváth, Aneta Wojnar, Gergely Gábor Barnaföldi

We derive a modified dispersion relation for massive particles within the frameworks of five-dimensional Kaluza-Klein theory and general relativity, taking into account strong gravitational effects. The resulting effective mass depends on the curvature of the underlying phase space. Notably, in regions with strong gravitational fields, the effective mass may

Howard E. Bond, Nate Bastian, Andrea Bellini, Sebastian Kamann

During an integral-field spectroscopic study of stars in the massive young open cluster NGC 1866 in the Large Magellanic Cloud, we serendipitously discovered a faint planetary nebula (PN). We designate it "Ka LMC 1," and find that its location near the cluster center, along with the agreement of its radial velocity with that of the cluster, imply a high prob

Jiatong Yu, Yinghui He, Anirudh Goyal, Sanjeev Arora

Machine unlearning seeks to selectively remove the "influence" of specific training data on a model's outputs. The ideal goal is Retrain Equivalence--behavior identical to a model trained from scratch on only the retained data. This goal was formulated for models trained on i.i.d. data batches, but modern pipelines often involve multi-stage training, with ea

Seyed Mohammad Hosseiny, Abolfazl Pourhashemi Khabisi, Jamileh Seyed-Yazdi, Milad Norouzi

Quantum thermometry leveraging quantum sensors is investigated with an emphasis on fundamental precision bounds derived from quantum estimation theory. The proposed sensing platform consists of two dissimilar qubits coupled via capacitor, which induce quantum oscillations in the presence of a thermal environment. Thermal equilibrium states are modeled using

Maria Nareklishvili, Nick Polson, Vadim Sokolov

Prediction is a central task of machine learning. Our goal is to solve large scale prediction problems using Generative Quantile Bayesian Prediction (GQBP).By directly learning predictive quantiles rather than densities we achieve a number of theoretical and practical advantages. We contrast our approach with state-of-the-art methods including conformal pred

Bernardo Boatini, Cristina Gavazzoni, Leonardo Gregory Brunnet, Carolina Brito

Wetting phenomena are relevant in several technological applications, particularly those involving hydrophobic or hydrophilic surfaces. Many substrates support multiple wetting states depending on surface conditions or droplet history, a behavior known as metastability. This feature is crucial both for its theoretical complexity and for its relevance in prac

Riddhi Kalsi

This paper resolves the empirical puzzle in the public-private wage literature: why studies using similar data reach contradictory conclusions about wage premiums and penalties. Utilizing rich French administrative panel data (2012-2019), this study has two main contributions: first, it presents a set of new, intuitive yet previously undocumented stylized fa

Sebastian Mocanu, Emil Slusanschi, Marius Leordeanu

This paper presents a vision-only autonomous flight system for small UAVs operating in controlled indoor environments. The system combines semantic segmentation with monocular depth estimation to enable obstacle avoidance, scene exploration, and autonomous safe landing operations without requiring GPS or expensive sensors such as LiDAR. A key innovation is a

N. Rimock, Y. Oz

We formalize a generalized type-II fusion operation for qudit cluster states within linear optics. Two designated qudits, one from each input cluster, interfere with optional ancilla qudits via a passive linear-optical network, followed by number-resolving detection; conditioned on measurement outcome, the remaining qudits form the post-selected fused state.

Kazi Ababil Azam, Hasan Masum, Masfiqur Rahaman, A. B. M. Alim Al Islam

The vehicular density in urbanizing cities of developing countries such as Dhaka, Bangladesh result in a lot of traffic congestion, causing poor on-road experiences. Traffic signaling is a key component in effective traffic management for such situations, but the advancements in intelligent traffic signaling have been exclusive to developed countries with st

Eric I. Rosenthal, Christopher S. Wang, Jamison Sloan, Giovanni Scuri

At cryogenic temperatures and microwave frequencies, the perovskite crystals strontium titanate (STO) and potassium tantalate (KTO) have large, tunable permittivity arising from a quantum paraelectric phase. As such, these materials hold promise as a platform to realize compact, variable capacitance elements for use in quantum devices. From modulating this c

Yingyao Zhou, Natasha Devroye, Onur Günlü

We consider reversely-degraded secure-communication channels, for which the secrecy capacity is zero if there is no channel feedback. Specifically, we focus on a seeded modular code design for the block-fading Gaussian wiretap channel with channel-output feedback, combining universal hash functions for security and learned feedback-based codes for reliabilit

I. M. Moiseenko, D. A. Svintsov, Zh. A. Devizorova

Generation of photocurrent via photon drag effect enables very fast light detection with response time limited by momentum relaxation. At the same time, photon drag in bulk uniform samples is small by the virtue of small photon momentum. We show that the edge of metal gate placed above a two-dimensional electron system (2DES) provides highly non-uniform elec

J. D. Turner, B. W. Stappers, E. Barr, M. Burgay

We present the second and final set of TRAPUM searches for pulsars at 1284 MHz inside supernova remnants and pulsar wind nebulae with the MeerKAT telescope. No new pulsars were detected for any of the 80 targets, which include some unidentified TeV sources that could be pulsar wind nebulae. The mean upper limit on the flux density of undetected pulsars is 52

Ruihan Zhao, Tyler Ingebrand, Sandeep Chinchali, Ufuk Topcu

Vision-Language-Action (VLA) models trained on large robot datasets promise general-purpose, robust control across diverse domains and embodiments. However, existing approaches often fail out-of-the-box when deployed in novel environments, embodiments, or tasks. We introduce Mixture of Skills VLA (MoS-VLA), a framework that represents robot manipulation poli

Wen O. Wang, Thomas P. Devereaux

We perform numerically exact determinant quantum Monte Carlo simulations of the Hubbard model and analyze pairing tendencies by evaluating correlation functions at the imaginary-time midpoint ($\tau=\beta/2$), which suppresses high-frequency weight and emphasizes low-energy physics. Using this diagnostic, we identify clear finite-temperature signatures of un

Jordi Grau-Escolano, David Duran-Rodas, Julian Vicens

Bike-sharing systems (BSS) are key components of urban mobility, promoting active travel and complementing public transport. This paper presents a flexible, data-driven framework for optimizing BSS station placement. Existing methods usually focus on a single planning objective, such as maximizing demand, or on a fixed set of two or three objectives, such as

Xuan Zhang, Ruixiao Li, Zhijian Zhou, Long Li

Reinforcement Learning (RL) has become a compelling way to strengthen the multi step reasoning ability of Large Language Models (LLMs). However, prevalent RL paradigms still lean on sparse outcome-based rewards and limited exploration, which often drives LLMs toward repetitive and suboptimal reasoning patterns. In this paper, we study the central question of

Olga S. Rozanova, Evgeniy V. Chizhonkov

In terms of initial data, a sufficient condition for the smoothness of the solution to the Cauchy problem for one-dimensional relativistic cold plasma equations over any given time interval is found. Unlike the non-relativistic case, such sufficient conditions take into account the smallness properties of not only the derivatives of the initial data but also

Eli N. Weinstein, Andrei Slabodkin, Mattia G. Gollub, Elizabeth B. Wood

Biological machine learning is often bottlenecked by a lack of scaled data. One promising route to relieving data bottlenecks is through high throughput screens, which can experimentally test the activity of $10^6-10^{12}$ protein sequences in parallel. In this article, we introduce algorithms to optimize high throughput screens for data creation and model t

Melika Filvantorkaman, Maral Filvan Torkaman

Medical imaging plays a vital role in modern diagnostics; however, interpreting high-resolution radiological data remains time-consuming and susceptible to variability among clinicians. Traditional image processing techniques often lack the precision, robustness, and speed required for real-time clinical use. To overcome these limitations, this paper introdu

Bruno Lourenço, Pedro Adão, João F. Ferreira, Mario Monteiro Marques

This survey investigates how ontologies, semantic log processing, and Large Language Models (LLMs) enhance cybersecurity. Ontologies structure domain knowledge, enabling interoperability, data integration, and advanced threat analysis. Security logs, though critical, are often unstructured and complex. To address this, automated construction of Knowledge Gra

Kailin Chen

This paper studies an exponential bandit model in which a group of agents collectively decide whether to undertake a risky action $R$. This action is implemented if the fraction of agents voting for it exceeds a predetermined threshold $k$. Building on Strulovici (2008), which assumes the agents' payoffs are independent, we explore the case in which the agen

Tianwei Wang, Xinhui Ma, Wei Pang

Motivated by the geometric advantages of quaternions in representing rotations and postures, we propose a quaternion-valued supervised learning Hopfield-structured neural network (QSHNN) with a fully connected structure inspired by the classic Hopfield neural network (HNN). Starting from a continuous-time dynamical model of HNNs, we extend the formulation to

Ertza Warraich, Ali Imran, Annus Zulfiqar, Shay Vargaftik

As distributed machine learning (ML) workloads scale to thousands of GPUs connected by ultra-high-speed inter-connects, tail latency in collective communication has emerged as a primary bottleneck. Prior RDMA designs, like RoCE, IRN, and SRNIC, enforce strict reliability and in-order delivery, relying on retransmissions and packet sequencing to ensure correc

Moon-ki Choi, Daniel Palmer, Harley T. Johnson

We introduce UEIPNet, an equivariant graph neural network designed to predict both interatomic potentials and tight-binding (TB) Hamiltonians for an atomic structure. The UEIPNet is trained using density functional theory calculations followed by Wannier projection to predict energies and forces as node-level targets and Wannier-projected TB matrices as edge

Francisco Jose Cortes Delgado, Eduardo Martinez Gracia, Rafael Valencia Garcia

Recent advances in natural language processing with large neural models have opened new possibilities for syntactic analysis based on machine learning. This work explores a novel approach to phrase-structure analysis by fine-tuning large language models (LLMs) to translate an input sentence into its corresponding syntactic structure. The main objective is to

Nicolas Clarisse, Eduardo O. Pinho, Teerthal Patel, Fabio S. Bemfica

We present a new, first-order, flux-conservative formulation of relativistic viscous hydrodynamics in the BDNK framework, applicable to conformal and nonconformal fluids at zero chemical potential. Focusing on the conformal case in 1+1 dimensions, we numerically solve the equations of motion for two classes of consistent initial data and assess the robustnes

Yuqicheng Zhu, Jingcheng Wu, Yizhen Wang, Hongkuan Zhou

Uncertain knowledge graph embedding (UnKGE) methods learn vector representations that capture both structural and uncertainty information to predict scores of unseen triples. However, existing methods produce only point estimates, without quantifying predictive uncertainty-limiting their reliability in high-stakes applications where understanding confidence

Cristiano Rosa, Sergio Giardino

Within this article one finds the statement of the Klein-Gordon problem within the real Hilbert space formalism ($\mathbbm R$HS) in terms of complex wave functions, and in terms of quaternionic wave functions as well. The complex formulation comprises hermitian and non-hermitian cases, while the quaternionic solutions additionally set in motion self-interact

Tianxing Wu, Shutong Zhu, Jingting Wang, Ning Xu

Uncertain knowledge graphs (UKGs) associate each triple with a confidence score to provide more precise knowledge representations. Recently, since real-world UKGs suffer from the incompleteness, uncertain knowledge graph (UKG) completion attracts more attention, aiming to complete missing triples and confidences. Current studies attempt to learn UKG embeddin

Oketa Basha, Tracee Lynn Jamison-Hooks, Philip Mauskopf, Lynn Miles

The Habitable Worlds Observatory (HWO), a nextgeneration ultraviolet/optical/infrared space telescope, will require detector technologies capable of supporting substantially larger pixel-count arrays than those flown on previous missions. Microwave Kinetic Inductance Detectors (MKIDs) provide a scalable solution through microwave multiplexing and have alread

Jean-Paul Décamps, Fabien Gensbittel, Thomas Mariotti, Stéphane Villeneuve

Tipping points characterize situations where a regulated system may experience a sudden and irreversible change and are generally associated with a random state of the system below which the change materializes. In this paper, we study a singular stochastic control problem in which the performance criterion depends on the hitting time of a random state that

Jiaying Zhu, Yurui Zhu, Xin Lu, Wenrui Yan

Multimodal Large Language Models (MLLMs) encounter significant computational and memory bottlenecks from the massive number of visual tokens generated by high-resolution images or multi-image inputs. Previous token compression techniques are often constrained by heuristic rules that risk discarding critical information. They may suffer from biases, such as a

Qiyao Peng, Chen Wang, Yinghui Wang, Hongtao Liu

Reviewer recommendation is a critical task for enhancing the efficiency of academic publishing workflows. However, research in this area has been persistently hindered by the lack of high-quality benchmark datasets, which are often limited in scale, disciplinary scope, and comparative analyses of different methodologies. To address this gap, we introduce FRO

Yiyang Huang, Liang Shi, Yitian Zhang, Yi Xu

Large Vision-Language Models (LVLMs) excel in diverse cross-modal tasks. However, object hallucination, where models produce plausible but inaccurate object descriptions, remains a significant challenge. In contrast to previous work focusing on LLM components, this paper is the first to trace LVLM hallucinations to visual encoders and identifies three key is

Santanu S. Dey, Burak Kocuk

In this paper, we study the set $\mathcal{S}^\kappa = \{ (x,y)\in\mathcal{G}\times\mathbb{R}^n : y_j = x_j^\kappa , j=1,\dots,n\}$, where $\kappa > 1$ and the ground set $\mathcal{G}$ is a nonempty polytope contained in $[0,1]^n$. This nonconvex set is closely related to separable standard quadratic programming and appears as a substructure in potential-base

Moida Praneeth Jain, Venkatesh Choppella

Misconceptions about program execution hinder many novice programmers. We introduce SimpliPy, a notional machine designed around a carefully chosen Python subset to clarify core control flow and scoping concepts. Its foundation is a precise operational semantics that explicitly tracks source code line numbers for each execution step, making the link between

Khandaker Akramul Haque, Katherine R. Davis

This paper presents DESTinE Block, a blockchain-based data storage framework designed for power systems and optimized for resource-constrained environments, including grid-edge devices such as single-board computers. The proposed architecture leverages the InterPlanetary File System (IPFS) for storing large files while maintaining secure and traceable metada

Lisa Sauermann, Zixuan Xu

How many hyperplanes in $\mathbb{R}^n$ are needed in order to slice every edge of the $n$-dimensional hypercube with vertex set $\{\pm 1\}^n$? Here, we say that a hyperplane $H\subseteq \mathbb{R}^n$ slices an edge of the hypercube if it contains exactly one interior point of the edge. The problem of determining the minimum possible size of a collection of h

Cassidy Ashworth, Pietro Liò, Francesco Caso

Deep learning models have proven enormously successful at using multiple layers of representation to learn relevant features of structured data. Encoding physical symmetries into these models can improve performance on difficult tasks, and recent work has motivated the principle of parameter symmetry breaking and restoration as a unifying mechanism underlyin

Zanyar Ebrahimi, Kayoomars Karami

This paper examines the growth of dark matter and dark energy perturbations within a non-canonical scalar field model characterized by an exponential potential. Through dynamical system analysis, we identify critical points and track the background evolution of a spatially flat FLRW universe dominated by dark energy and pressureless dark matter. We systemati

Jiaxi Zhuang, Yu Zhang, Aimin Zhou, Ying Qian

Retrosynthesis prediction is fundamental to drug discovery and chemical synthesis, requiring the identification of reactants that can produce a target molecule. Current template-free methods struggle to capture the structural invariance inherent in chemical reactions, where substantial molecular scaffolds remain unchanged, leading to unnecessarily large sear

Byoungwoo Park, Juho Lee

Understanding the continuous evolution of populations from discrete temporal snapshots is a critical research challenge, particularly in fields like developmental biology and systems medicine where longitudinal tracking of individual entities is often impossible. Such trajectory inference is vital for unraveling the mechanisms of dynamic processes. While Sch

Hongyi Xiao, Jing Wang, Achim Sack, Ralf Stannarius

We experimentally study a scallop-like swimmer with reciprocally flapping wings in a nearly frictionless, cohesive granular medium consisting of hydrogel spheres. Significant locomotion is found when the swimmer's flapping frequency matches the inverse relaxation time of the material. Remarkably, the swimmer moves in the opposite direction compared to its mo

Pouya M. Kouch, Talvikki Hovatta, Elina Lindfors, Ioannis Liodakis

The IceCube Neutrino Observatory has detected several hundred high-energy neutrinos from cosmic sources. Despite numerous studies searching for their origin, it is still not known which sources emit them. A few likely individual associations exist with active galactic nuclei (AGNs), mostly comprising blazars (AGNs with jets pointed toward Earth). Nonetheless

Pouya M. Kouch, Elina Lindfors, Talvikki Hovatta, Ioannis Liodakis

Active galactic nuclei (AGN) are some of the brightest and most variable objects in the Universe. Those with relativistic jets observed at small viewing angles are blazars. Due to Doppler boosting, blazars exhibit extreme stochastic variability. While the origin of this variability is thought to be changes in the accretion flow and jet dynamics, much about b

Nicholas Gismondi, Alexandru F. Radu

In this paper we construct non-trivial solutions to the stationary dissipative surface quasi-geostrophic equation on the two dimensional torus which lie strictly below the critical regularity threshold of $\dot{H}^{-1/2}(\mathbb{T}^2)$. Specifically, for any $\alpha < 1/2$ and any dissipation exponent $0 < \gamma \leq 2$ we construct non-trivial solutions su

Junchi Yu, Yujie Liu, Jindong Gu, Philip Torr

Retrieval-Augmented Generation (RAG) based on knowledge graphs (KGs) enhances large language models (LLMs) by providing structured and interpretable external knowledge. However, existing KG-based RAG methods struggle to retrieve accurate and diverse information from text-rich KGs for complex real-world queries. Process Reward Models (PRMs) offer a way to ali

Xinfeng Li, Shengyuan Pang, Jialin Wu, Jiangyi Deng

Text-to-image (T2I) models, though exhibiting remarkable creativity in image generation, can be exploited to produce unsafe images. Existing safety measures, e.g., content moderation or model alignment, fail in the presence of white-box adversaries who know and can adjust model parameters, e.g., by fine-tuning. This paper presents a novel defensive framework

J. F Toland

For any compact, connected metric space $(M,d)$ the set of points where $M$ is not weakly locally connected is shown to define a partition $\sP$ of $M$ for which the corresponding quotient metric space $(\sQ, \nabla_\sQ)$ is a Peano continuum with $\sQ = \sP$.

Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang, Pawel Borsukiewicz

Code readability is crucial for software comprehension and maintenance, yet difficult to assess at scale. Traditional static metrics often fail to capture the subjective, context-sensitive nature of human judgments. Large Language Models (LLMs) offer a scalable alternative, but their behavior as readability evaluators remains underexplored. We introduce CoRe

P. R. Casale, J. E. Amaro

We investigate the role of short-range correlations (SRC) in the transverse nuclear response within the quasielastic peak region, focusing on the 1p1h channel. The calculation is performed in nuclear matter by solving the Bethe-Goldstone equation with the realistic Granada 2013 nucleon-nucleon potential, including both one-body and two-body meson-exchange cu

Gines R. Perez Teruel

We propose a geometric framework where dispersion relations are viewed as parametric surfaces in energy-momentum space. Within this picture, the presence and type of critical points of the surface emerge as clear geometric signatures of kinematical restrictions. The Newtonian relation corresponds to a developable surface with no critical points, reflecting t

Zijian Zhang, Mingyao Cui

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for enhancing wireless communications through dense antenna arrays. Accurate channel estimation is critical to unlocking their full performance potential. To enhance RIS channel estimators, this paper proposes a novel observation matrix design scheme. Bayesian optimization fram

Yijing Zhou, Shabnam J. Semnani

Multi-scale simulations of nonlinear heterogeneous materials and composites are challenging due to the prohibitive computational costs of high-fidelity simulations. Recently, machine learning (ML) based approaches have emerged as promising alternatives to traditional multiscale methods. However, existing ML surrogate constitutive models struggle in capturing

Stuart McAlpine

We present a publicly available catalogue of massive structures in the nearby Universe, constructed from the Manticore-Local posterior ensemble, a Bayesian field-level reconstruction that infers the underlying dark matter distribution from 2M++ galaxies. We identify massive structures by clustering the central haloes inferred at z = 0 across the 80 posterior

Muhammad Ammar, Hadiya Murad Hadi, Usman Majeed Butt

Large Language Models (LLMs) are now capable of generating text that closely resembles human writing, making them powerful tools for content creation, but this growing ability has also made it harder to tell whether a piece of text was written by a human or by a machine. This challenge becomes even more serious for languages like Urdu, where there are very f

Ayush Chopra, Aman Sharma, Feroz Ahmad, Luca Muscariello

Modern AI agents can exchange messages using protocols such as A2A and ACP, yet these mechanisms emphasize communication over coordination. As agent populations grow, this limitation produces brittle collective behavior, where individually smart agents converge on poor group outcomes. We introduce the Ripple Effect Protocol (REP), a coordination protocol in

Luca Chiantini, Filippo Fagioli

We establish a connection between properties of partially symmetric tensors (i.e. tensors associated to linear systems of quadric hypersurfaces) and the geometry of some related loci, generalization of the Weddle loci introduced in \cite{CFF+22} for their role in the study of configurations of points and interpolation problems. In particular, we consider lin

Richard J. Young, Brandon Gillins, Alice M. Matthews

Despite widespread deployment of Large Language Models, systematic evaluation of instruction-following capabilities remains challenging. While comprehensive benchmarks exist, focused assessments that quickly diagnose specific instruction adherence patterns are valuable. As newer models may be trained on existing benchmarks, novel evaluation approaches are ne

Hadi Abbaszadehpeivasti, Etienne de Klerk, Adrien Taylor

The difference-of-convex algorithm (DCA) is a well-established nonlinear programming technique that solves successive convex optimization problems. These sub-problems are obtained from the difference-of-convex~(DC) decompositions of the objective and constraint functions. We investigate the worst-case performance of the unconstrained DCA, with and without bo

Guo Li, Weihong Chen, Yongfu Fan

Diffusion models have demonstrated powerful performance in generating high-quality images. A typical example is text-to-image generator like Stable Diffusion. However, their widespread use also poses potential privacy risks. A key concern is membership inference attacks, which attempt to determine whether a particular data sample was used in the model traini

Zhipeng Huang, Xinxin Cheng, Hazem Daoud, Wen-Xiong Song

Two classes of Phase Change Materials (PCMs) have emerged as the best candidates for applications requiring the fast reading and writing of data: GeTe-Sb$_{2}$Te$_{3}$ pseudobinary alloys (group 1) and doped Sb-Te compounds near the eutectic composition Sb$_{70}$Te$_{30}$ (group 2). Both material classes undergo reversible switching between a low-resistance

Alkis Koudounas, Moreno La Quatra, Manuel Giollo, Sabato Marco Siniscalchi

Hallucinations in automatic speech recognition (ASR) systems refer to fluent and coherent transcriptions produced by neural ASR models that are completely unrelated to the underlying acoustic input (i.e., the speech signal). While similar to conventional decoding errors in potentially compromising the usability of transcriptions for downstream applications,

Seungho Cho, Changgeon Ko, Eui Jun Hwang, Junmyeong Lee

Large language models (LLMs) are increasingly used across diverse cultural contexts, making accurate cultural understanding essential. Prior evaluations have mostly focused on output-level performance, obscuring the factors that drive differences in responses, while studies using circuit analysis have covered few languages and rarely focused on culture. In t

Anastasiia Topchieva, Tamara Molyarova, Anton Vasyunin

Luminosity outbursts of FU Ori-type objects (FUors) allow us to observe in the gas the molecules that are typically present in the ice in protoplanetary discs. In particular, the fraction of deuterated water, which is usually is mostly frozen in the midplane of a protoplanetary disc, has been measured for the first time in the gas of the disc around a FUor V

Adeel A. Khan, Tasuki Kinjo, Hyeonjun Park, Pavel Safronov

We define a new perverse t-exact pullback operation on derived categories of constructible sheaves which generalizes most perverse t-exact functors in sheaf theory, such as microlocalization, the Fourier-Sato transform and vanishing cycles. This operation is defined for morphisms of algebraic stacks equipped with a relative exact (-1)-shifted symplectic stru