October 2025 arXiv papers — page 188

Dewi Endah Kharismawati, Toni Kazic

Accurate maize stand counts are essential for crop management and research, informing yield prediction, planting density optimization, and early detection of germination issues. Manual counting is labor-intensive, slow, and error-prone, especially across large or variable fields. We present MaizeStandCounting (MaSC), a robust algorithm for automated maize se

Mkululi Sikosana, Sean Maudsley-Barton, Oluwaseun Ajao

This study conducts a computational linguistic analysis of pandemic-related online discourse to examine how language distinguishes health misinformation from factual communication. Drawing on three corpora: COVID-19 false narratives (n = 7588), general COVID-19 content (n = 10700), and Monkeypox-related posts (n = 5787), we identify significant differences i

Shilong Zong, Alex Bierly, Almuatazbellah Boker, Hoda Eldardiry

This review aims to conduct a comparative analysis of liquid neural networks (LNNs) and traditional recurrent neural networks (RNNs) and their variants, such as long short-term memory networks (LSTMs) and gated recurrent units (GRUs). The core dimensions of the analysis include model accuracy, memory efficiency, and generalization ability. By systematically

Daniel E. Martin

In 2013, Darryl McCullough and Marcus Wanderley made a series of conjectures that describe the Nielsen equivalence classes and $T_2$-equivalence classes of pairs of generators for $\text{SL}_2(\mathbb{F}_q)$ and the Markoff equivalence classes of triples in $\mathbb{F}_q^3$ that solve $x^2+y^2+z^2=xyz+\kappa$ for some $\kappa\in\mathbb{F}_q$. (The case $\kap

Teng Fei, Srinivas Ravishankar, Hoko Nakada, Abhinav Uppal

Understanding how the primate brain transforms complex visual scenes into coherent perceptual experiences remains a central challenge in neuroscience. Here, we present a comprehensive framework for interpreting monkey visual processing by integrating encoding and decoding approaches applied to two large-scale spiking datasets recorded from macaque using THIN

Zerui Cheng, Stella Wohnig, Ruchika Gupta, Samiul Alam

The meteoric rise of AI, with its rapidly expanding market capitalization, presents both transformative opportunities and critical challenges. Chief among these is the urgent need for a new, unified paradigm for trustworthy evaluation, as current benchmarks increasingly reveal critical vulnerabilities. Issues like data contamination and selective reporting b

Uladzislau Makartsou, Mateusz Gołębiewski, Attila Kákay, Olena Tartakivska

Recent studies on spin-wave propagation in ferromagnetic waveguides has highlighted the role of nonreciprocity resulting from the chiral nature of dipolar interactions in curved elements. However, the impact of spin-wave mode type on nonreciprocity remains unexplored. Using micromagnetic simulations supported by analytical modeling, we systematically analyze

Jannatul Ferdous, G. Matthew Fricke, Judy L. Cannon, Melanie E. Moses

Zoonotic pathogens represent a growing global risk, yet the speed of adaptive immune activation across mammalian species remains poorly understood. Despite orders-of-magnitude differences in size and metabolic rate, we show that the time to initiate adaptive immunity is remarkably consistent across species. To understand this invariance, we analyse empirical

Reinhold Willcox, Fabian R. N. Schneider, Eva Laplace, Philipp Podsiadlowski

The latest GWTC-4 release from the LIGO-Virgo-KAGRA (LVK) collaboration nearly doubles the known population of double compact object mergers and reveals a new trimodal structure in the chirp-mass distribution of merging binary black holes (BBHs) below 30 Msun. Recent detailed stellar evolution models show that features in the pre-collapse cores of massive st

Jesse D Wei, Guo Wei

Suppose that $n$ computer devices are to be connected to a network via inhomogeneous Bernoulli trials. The Shapley value of a device quantifies how much the network's value increases due to the participation of that device. Characteristic functions of such games are naturally taken as the belief function (containment function) and Choquet capacity (hitting p

Vasili Baranau

What is the fastest possible "diffusion"? A trivial answer would be "a process that converts a Dirac delta-function into a uniform distribution infinitely fast". Below, we consider a more reasonable formulation: a process that maximizes differential entropy of a probability density function (pdf) $f(\vec{x}, t)$ at every time $t$, under certain restrictions.

Ryan T. Tymkow, Benjamin D. Schnapp, Mojtaba Valipour, Ali Ghodshi

Symbolic regression refers to the task of finding a closed-form mathematical expression to fit a set of data points. Genetic programming based techniques are the most common algorithms used to tackle this problem, but recently, neural-network based approaches have gained popularity. Most of the leading neural-network based models used for symbolic regression

Prabhant Singh, Pieter Gijsbers, Elif Ceren Gok Yildirim, Murat Onur Yildirim

In this work, we present LOTUS (Learning to Learn with Optimal Transport for Unsupervised Scenarios), a simple yet effective method to perform model selection for multiple unsupervised machine learning(ML) tasks such as outlier detection and clustering. Our intuition behind this work is that a machine learning pipeline will perform well in a new dataset if i

Jaeho Lee, Eunju Hwang

We propose an ARIMA-TX-GARCH model and use it to forecast European Carbon Emission Allowance futures prices, incorporating Brent crude oil futures prices as an exogenous variable.

Karuna Bhaila, Aneesh Komanduri, Minh-Hao Van, Xintao Wu

Vision-Language Models (VLMs) have demonstrated immense capabilities in multi-modal understanding and inference tasks such as Visual Question Answering (VQA), which requires models to infer outputs based on visual and textual context simultaneously. Such inference abilities of large-scale pretrained models are often attributed to the massive scale of pre-tra

Junyi Zhu, Savas Ozkan, Andrea Maracani, Sinan Mutlu

Deploying natural language processing (NLP) models on mobile platforms requires models that can adapt across diverse applications while remaining efficient in memory and computation. We investigate pre-finetuning strategies to enhance the adaptability of lightweight BERT-like encoders for two fundamental NLP task families: named entity recognition (NER) and

Moacyr Rodrigues, Ciro Russo

We study Morita equivalence in the context of quantales with identity, in the wake of Katsov and Nam's analogous work on semirings. Among a number of other results, we prove a characterization of Morita equivalence and an Eilenberg-Watts-type Theorem for quantales.

Shubham Kumar, Anshuman Sahoo

Accurate wellbore trajectory prediction is a paramount challenge in subsurface engineering, governed by complex interactions between the drilling assembly and heterogeneous geological formations. This research establishes a comprehensive, mathematically rigorous framework for trajectory prediction that moves beyond empirical modeling to a geomechanically-inf

Lauritz van Luijk

In quantum systems with infinitely many degrees of freedom, states can be infinitely entangled across a pair of subsystems, but are there different forms of infinite entanglement? To understand entanglement in such systems, we use a framework in which subsystems are described by von Neumann algebras on the full system's Hilbert space. Although this approach

Yixiao Li, Julia Barth, Thomas Kiefer, Ahmad Fraij

Multi-modal behavior cloning faces significant challenges due to mode averaging and mode collapse, where traditional models fail to capture diverse input-output mappings. This problem is critical in applications like robotics, where modeling multiple valid actions ensures both performance and safety. We propose EBGAN-MDN, a framework that integrates energy-b

Abigail Price, Ada Stelzer, Alexander Yong

We develop a theory of bicrystalline ideals, synthesizing Gr\"obner basis techniques and Kashiwara's crystal theory. This provides a unified algebraic, combinatorial, and computational approach that applies to ideals of interest, old and new. The theory concerns ideals in the coordinate ring of matrices, stable under the action of some Levi group, whose quot

Benjamin Major, Dimitri Shlyakhtenko

We show that, for many choices of finite tuples of generators $X = (x_1, \dots , x_d)$ of a tracial von Neumann algebra $(M, \tau)$ satisfying certain decomposition properties (non-primeness, possessing a Cartan subalgebra, or property $\Gamma$), one can find a diffuse, hyperfinite subalgebra $N \subseteq (W^*(X))^{\omega}$ (often in $W^*(X)$ itself), such t

Abhay Bhandarkar, Gaurav Mishra, Khushi Juchani, Harsh Singhal

This study applies BERTopic, a transformer-based topic modeling technique, to the lmsys-chat-1m dataset, a multilingual conversational corpus built from head-to-head evaluations of large language models (LLMs). Each user prompt is paired with two anonymized LLM responses and a human preference label, used to assess user evaluation of competing model outputs.

Rafin Hassan, Zarin Tasnim Roshni, Rafiqul Bari, Alimul Islam

Hyperspectral imaging (HSI) classification is a critical tool with widespread applications across diverse fields such as agriculture, environmental monitoring, medicine, and materials science. Due to the limited availability of high-quality training samples and the high dimensionality of spectral data, HSI classification models are prone to overfitting and o

Sergei B. Rochal, Aleksey S. Roshal, Olga V. Konevtsova, Rudolf Podgornik

Proteinaceous shells useful for various biomedical applications exhibit a wide range of anomalous structures that are fundamentally different from icosahedral viral capsids described by the Caspar-Klug paradigmatic model. Exploring the Protein Data Bank, we have identified nine different types of anomalous shells structurally close to flat octagonal quasicry

Lénaïc Chizat, Pierre Marion, Yerkin Yesbay

Dropout is a standard training technique for neural networks that consists of randomly deactivating units at each step of their gradient-based training. It is known to improve performance in many settings, including in the large-scale training of language or vision models. As a first step towards understanding the role of dropout in large neural networks, we

Yangxiao Luo, Shunyu Wan

We introduce a theory for encoding and manipulating algebraic data on categories via $\textit{concentration structures}$, which are equivalence relations on morphisms that satisfy certain axioms. For any category with a concentration structure we can functorially construct a $\textit{concentration monoid}$, which can be used to give a precise definition of h

Harshit Rajgarhia, Suryam Gupta, Asif Shaik, Gulipalli Praveen Kumar

The detection of Personally Identifiable Information (PII) is critical for privacy compliance but remains challenging in low-resource languages due to linguistic diversity and limited annotated data. We present RECAP, a hybrid framework that combines deterministic regular expressions with context-aware large language models (LLMs) for scalable PII detection

Saman Motamed, Minghao Chen, Luc Van Gool, Iro Laina

Despite impressive visual fidelity, modern video generative models frequently produce sequences that violate intuitive physical laws, such as objects floating, teleporting, or morphing in ways that defy causality. While humans can easily detect such implausibilities, there remains no robust method for quantitatively assessing physical realism in video. In th

Qifan Chen, Zhongshu Xu, Jinjin Zhang, Dongbin Xiu

We present a numerical framework for constructing a targeted digital twin (tDT) that directly models the dynamics of quantities of interest (QoIs) in a full digital twin (DT). The proposed approach employs memory-based flow map learning (FML) to develop a data-driven model of the QoIs using short bursts of trajectory data generated through repeated execution

Adam Hung, Fan Yang, Abhinav Kumar, Sergio Aguilera Marinovic

Dexterous manipulation tasks often require switching between different contact modes, such as rolling, sliding, sticking, or non-contact contact modes. When formulating dexterous manipulation tasks as a trajectory optimization problem, a common approach is to decompose these tasks into sub-tasks for each contact mode, which are each solved independently. Opt

Harm Derksen, Benjamin Lovitz

We present explicit quantum channels with strictly sub-additive minimum output R\'enyi entropy for all $p>1$, improving upon prior constructions which handled $p>2$. Our example is provided by explicit constructions of linear subspaces with high geometric measure of entanglement. This construction applies in both the bipartite and multipartite settings. As f

Soroush Mehraban, Vida Adeli, Jacob Rommann, Kyryl Truskovskyi

We address the task of video style transfer with diffusion models, where the goal is to preserve the context of an input video while rendering it in a target style specified by a text prompt. A major challenge is the lack of paired video data for supervision. We propose PickStyle, a video-to-video style transfer framework that augments pretrained video diffu

Md Tahmid Rahman Laskar, Mohammed Saidul Islam, Ridwan Mahbub, Mizanur Rahman

Large Vision-Language Models (LVLMs) with only 7B parameters have shown promise as automated judges in chart comprehension tasks. However, tiny models (<=2B parameters) still perform poorly as judges, limiting their real-world use in resource-constrained settings. To address this, we propose two approaches to ensure cost-efficient evaluation: (i) multi-crite

İdris Demir, Ali İhsan Keskin

This study presents a computational and theoretical framework inspired by thermodynamic principles to analyze the dynamics of economic inflation within adiabatic and non-adiabatic systems. In a framework referred to as developmental symmetry, inflation is formulated as a scalar field evolving through continuity equations, drawing an analogy with the Raychaud

Alessandro Pinzi

The goal of this paper is to define an evolution equation for a curve of random probability measures $(M_t)_{t\in[0,T]}\subset \mathcal{P}(\mathcal{P}(\mathbb{R}^d))$ associated to a non-local drift $b:[0,T]\times\mathbb{R}^d \times \mathcal{P}(\mathbb{R}^d) \to \mathbb{R}^d$ and a non-local diffusion term $a:[0,T]\times \mathbb{R}^d \times \mathcal{P}(\math

Nicholas G. Vlamis

Word metrics on finitely generated groups have canonical quasi-isometry classes, making quasi-isometry invariants genuine group invariants. Rosendal generalized this phenomenon to topological groups through CB-generation, but in the general topological setting the resulting quasi-isometry invariants are not invariants of the underlying abstract group. Specia

Cihan Okay

Quantum advantage in computation refers to the existence of computational tasks that can be performed efficiently on a quantum computer but cannot be efficiently simulated on any classical computer. Identifying the precise boundary of efficient classical simulability is a central challenge and motivates the development of new simulation paradigms. In this pa

Emanuele Macca, Walter Boscheri, Mario Ricchiuto

The Serre-Green-Naghdi (SGN) equations provide a valuable framework for modelling fully nonlinear and weakly dispersive shallow-water flows. However, their elliptic formulation can considerably increase the computational cost compared to the Saint-Venant equations. To overcome this difficulty, hyperbolic models (hSGN) have been proposed that replace the elli

V. Araya, L. Chemin, O. Jimenez-Arranz, M. Romero-Gomez

The bar pattern speed of the LMC has been measured using Gaia data, suggesting the presence of a slow pattern, perhaps not rotating at all. Numerical simulations of interacting LMC-SMC systems were able to reproduce a bar stoppage. Here, we report on the first measurement of the bar pattern speed of the LMC as a function of the evolutionary phase of its stel

Madeline Navarro, Andrei Buciulea, Samuel Rey, Antonio G. Marques

We estimate fair graphs from graph-stationary nodal observations such that connections are not biased with respect to sensitive attributes. Edges in real-world graphs often exhibit preferences for connecting certain pairs of groups. Biased connections can not only exacerbate but even induce unfair treatment for downstream graph-based tasks. We therefore cons

Jaeseong Lee, seung-won hwang, Aurick Qiao, Gabriele Oliaro

Speculative decoding promises faster inference for large language models (LLMs), yet existing methods fail to generalize to real-world settings. Benchmarks typically assume short contexts (e.g., 2K tokens), whereas practical workloads involve long contexts. We find current approaches degrade severely with long contexts; for instance, EAGLE3 even slows down t

Martin G. H. Krause, Martin A. Bourne, Silke Britzen, Adi Foord

We review the state of the evidence for the existence and observational appearance of supermassive black hole binaries. Such objects are expected from standard hierarchical galaxy evolution to form after two galaxies, each containing a supermassive black hole, have merged, in the centre of the merger remnant. A complex interaction is predicted to take place

Mostafa Mohammadpour, Mehdi Zekriyapanah Gashti, Yusif S. Gasimov

High-frequency oscillations (HFOs) are a new biomarker for identifying the epileptogenic zone. Mapping HFO-generating regions can improve the precision of resection sites in patients with refractory epilepsy. However, detecting HFOs remains challenging, and their clinical features are not yet fully defined. Visual identification of HFOs is time-consuming, la

Debadrito Roy, Aryaman Manish Kolhe, V. Lalitha, Navin Kashyap

Certain types of quantum computing platforms, such as those realized using Rydberg atoms or Kerr-cat qubits, are natively more susceptible to Pauli-Z noise than Pauli-X noise, or vice versa. On such hardware, it is useful to ensure that computations use only gates that maintain the Z-bias (or X-bias) in the noise. This is so that quantum error-correcting cod

Luis H. Gallardo, Olivier Rahavandrainy

We study a natural analogue of Collatz's Conjecture for polynomials over $\mathbb{F}_2$.

Carol Hanna, Federica Sarro, Mark Harman, Justyna Petke

Hot fixes are urgent, unplanned changes deployed to production systems to address time-critical issues. Despite their importance, no existing evaluation benchmark focuses specifically on hot fixes. We present HotBugs$.$jar, the first dataset dedicated to real-world hot fixes. From an initial mining of 10 active Apache projects totaling over 190K commits and

Galina García, Joaquín Vidal, Sebastián Zamorano

This article addresses the inverse source problem for a nonlocal heat equation involving the fractional Laplacian. The primary goal is to reconstruct the spatial component of the source term from partial observations of the system's state and its time derivative over a subset of the domain. A reconstruction formula for the Fourier coefficients of the unknown

CMS Collaboration

This paper presents a combination of searches for the nonresonant production of Higgs boson pairs (HH) in proton-proton collisions at a centre-of-mass energy of 13 TeV. The data set was collected by the CMS experiment at the LHC from 2016 to 2018 and corresponds to a total integrated luminosity of 138 fb$^{-1}$. The observed (expected) upper limit on the inc

Heinrich Freistuhler

Within the FTBDNK family of formulations of relativistic Navier-Stokes (H. Freist\"uhler and B. Temple, Proc. R. Soc. A 470, 20140055 (2014), Proc. R. Soc. A 473 (2017), 20160729; F. S. Bemfica, M. Disconzi, and J. Noronha, Phys. Rev. D 98, 104064 (2018), Phys. Rev. D 100, 104020 (2019); P. Kovtun, J. High Energy Phys. 2019, 034 (2019)), this paper collects

Fernando Spadea, Oshani Seneviratne

Large language models (LLMs) show promise for personalized financial recommendations but are hampered by context limits, hallucinations, and a lack of behavioral grounding. Our prior work, FLARKO, embedded structured knowledge graphs (KGs) in LLM prompts to align advice with user behavior and market data. This paper introduces RAG-FLARKO, a retrieval-augment

Alvaro Ribot, Anna Seigal, Piotr Zwiernik

Independent Component Analysis (ICA) is a classical method for recovering latent variables with useful identifiability properties. For independent variables, cumulant tensors are diagonal; relaxing independence yields tensors whose zero structure generalizes diagonality. These models have been the subject of recent work in non-independent component analysis.

Gargi Sarkar, Sandeep Kumar Shukla

The rise of ``cyber slavery," a technologically facilitated variant of forced criminality, signifies a concerning convergence of human trafficking and digital exploitation. In Southeast Asia, trafficked individuals are increasingly coerced into engaging in cybercrimes, including online fraud and financial phishing, frequently facilitated by international org

Mehdi Zekriyapanah Gashti, Ghasem Farjamnia

Accurate classification of sleep stages is crucial for the diagnosis and management of sleep disorders. Conventional approaches for sleep scoring rely on manual annotation or features extracted from EEG signals in the time or frequency domain. This study proposes a novel framework for automated sleep stage scoring using time-frequency analysis based on the w

Alessandro Pinzi, Giuseppe Savaré

We study the geometric structure of the space of random measures $\mathcal{P}_p(\mathcal{P}_p(X))$, endowed with the Wasserstein on Wasserstein metric, where $(X, d)$ is a complete separable metric space. In this setting, we prove a metric superposition principle, in the spirit of the result by S. Lisini, that will allow us to recover important geometric fea

Zeyu Zhang, Brian Gould, Maria Barsukova, Mikael C. Rechtsman

Airy wavefunctions are associated with one of the simplest scenarios in wave mechanics: a quantum bouncing ball. In other words, they are the eigenstates of the time-independent Schrodinger equation with a linear potential. In the domain of optics, laser beams that are spatially shaped as Airy functions (`Airy beams') have been shown to exhibit a prominent l

Danielle Mccool, Peter Lugtig, Bella Struminskaya

Smart surveys are surveys that make use of sensors and machine intelligence to reduce respondent burden and increase data quality. Smart surveys have been tests as a way to improve diary surveys in official statistics, where data are collected on topics such as travel, time use and household expenditures. There are often inherent differences both in measurem

Rayyan Merchant, Kevin Tang

As a digraphic language, the Persian language utilizes two written standards: Perso-Arabic in Afghanistan and Iran, and Tajik-Cyrillic in Tajikistan. Despite the significant similarity between the dialects of each country, script differences prevent simple one-to-one mapping, hindering written communication and interaction between Tajikistan and its Persian-

Juan E. Gómez-Morantes, Andrea Herrera, Sonia Camacho

The relationship between microenterprises and information and communication technologies (ICTs) has always been troublesome. Because of the rapid pace of modern digital technologies, digital innovation processes are permeating the industries, markets, and social contexts in which microenterprises exist today. However, microenterprises have severe difficultie

Blake Hansen, Dafne Zorzetto, Valeria Edefonti, Roberta De Vito

Understanding the association between dietary patterns and health outcomes, such as the cancer risk, is crucial to inform public health guidelines and shaping future dietary interventions. However, dietary intake data present several statistical challenges: they are high-dimensional, often sparse with excess zeros, and exhibit heterogeneity driven by individ

Hyeong Kyu Choi, Xiaojin Zhu, Sharon Li

Multi-agent debate (MAD) aims to improve large language model (LLM) reasoning by letting multiple agents exchange answers and then aggregate their opinions. Yet recent studies reveal that agents are not neutral: they are prone to identity-driven sycophancy and self-bias, uncritically adopting a peer's view or stubbornly adhering to their own prior output, un

Md. Nazmul Islam Ananto, Shamit Fatin, Mohammed Eunus Ali, Md Rizwan Parvez

The popular path query - identifying the most frequented routes between locations from historical trajectory data - has important applications in urban planning, navigation optimization, and travel recommendations. While traditional algorithms and machine learning approaches have achieved success in this domain, they typically require model training, paramet

Robin Kothari, Ryan O'Donnell, Kewen Wu

In 2021, Chen, Liu, and Zhandry presented an efficient quantum algorithm for the average-case $\ell_\infty$-Short Integer Solution ($\mathrm{SIS}^\infty$) problem, in a parameter range outside the normal range of cryptographic interest, but still with no known efficient classical algorithm. This was particularly exciting since $\mathrm{SIS}^\infty$ is a simp

Qinghua Liu, Sam Heshmati, Zheda Mai, Zubin Abraham

Effective analysis of time series data presents significant challenges due to the complex temporal dependencies and cross-channel interactions in multivariate data. Inspired by the way human analysts visually inspect time series to uncover hidden patterns, we ask: can incorporating visual representations enhance automated time-series analysis? Recent advance

Jon Nelson, Joel Rajakumar, Michael J. Gullans

In this work, we study the task of encoding logical information via a noisy quantum circuit. It is known that at superlogarithmic depth, the output of any noisy circuit without reset gates or intermediate measurements becomes indistinguishable from the maximally mixed state, implying that all input information is destroyed. This raises the question of whethe

K. K. Boone, P. S. Ferguson, M. Tabbutt, K. Bechtol

Observations of density variations in stellar streams are a promising probe of low-mass dark matter substructure in the Milky Way. However, survey systematics such as variations in seeing and sky brightness can also induce artificial fluctuations in the observed densities of known stellar streams. These variations arise because survey conditions affect both

Tianyu Bell Pan, Damon L. Woodard

This paper explores a multimodal co-training framework designed to enhance model generalization in situations where labeled data is limited and distribution shifts occur. We thoroughly examine the theoretical foundations of this framework, deriving conditions under which the use of unlabeled data and the promotion of agreement between classifiers for differe

Evgeni Dimitrov, Zhengye Zhou

We study line ensembles arising naturally in symmetrized/half-space geometric last passage percolation (LPP) on the $N \times N$ square. The weights of the model are geometrically distributed with parameter $q^2$ off the diagonal and $cq$ on the diagonal, where $q \in (0,1)$ and $c \in [0, q^{-1})$. In the supercritical regime $c > 1$, we show that the ensem

Ebrahim Rahimi, Clara Maathuis

Computational Thinking (CT) is a key skill set for students in higher education to thrive and adapt to an increasingly technology-driven future and workplace. While research on CT education has gained remarkable momentum in K12 over the past decade, it has remained under-explored in higher education, leaving higher education teachers with an insufficient ove

Daniel M. Cherenson, Dimitra Panagou

Many robotic systems are underactuated, meaning not all degrees of freedom can be directly controlled due to lack of actuators, input constraints, or state-dependent actuation. This property, compounded by modeling uncertainties and disturbances, complicates the control design process for trajectory tracking. In this work, we propose an adaptive control arch

Igor de M. Froldi, Hermann Freire

We systematically study the efficiency of the intrinsic superconducting diode effect of several pair-density-wave states that can emerge in two-dimensional $d$-wave metallic altermagnets. To this end, we investigate several scenarios using an effective minimal microscopic model and Ginzburg-Landau analysis in order to derive the corresponding pairing phase d

Shen Dong, Mingxuan Zhang, Pengfei He, Li Ma

Large Language Model (LLM)-based Multi-Agent Systems (MAS) have emerged as a powerful paradigm for tackling complex, multi-step tasks across diverse domains. However, despite their impressive capabilities, MAS remain susceptible to adversarial manipulation. Existing studies typically examine isolated attack surfaces or specific scenarios, leaving a lack of h

Ryan Felton, Caleb Scharf, Stuart Bartlett, Nathalie A. Cabrol

Advances in machine learning over the past decade have resulted in a proliferation of algorithmic applications for encoding, characterizing, and acting on complex data that may contain many high dimensional features. Recently, the emergence of deep-learning models trained across very large datasets has created a new paradigm for machine learning in the form

Paolo Aniello, Lorenzo Guglielmi, Stefano Mancini, Vincenzo Parisi

In the framework of quantum mechanics over a quadratic extension of the ultrametric field of p-adic numbers, we introduce a notion of tensor product of p-adic Hilbert spaces. To this end, following a standard approach, we first consider the algebraic tensor product of p-adic Hilbert spaces. We next define a suitable norm on this linear space. It turns out th

Marcelo A. Colominas, Stefan Steinerberger, Hau-Tieng Wu

We show that the problem of identifying different signal components from a time-frequency representation can be equivalently phrased as a graph clustering problem: given a graph $G=(V,E)$ one aims to identify `clusters', subgraphs that are strongly connected and have relatively few connections between them. The graph clustering problem is well studied, we sh

Arthur Bawin, Stéphane Étienne, Cédric Béguin

We present an anisotropic mesh adaptation procedure based on Riemannian metrics for the simulation of two-phase incompressible flows with non-matching densities. The system dynamics are governed by the Cahn-Hilliard Navier-Stokes (CHNS) equations, discretized with mixed finite elements and implicit time-stepping. Spatial accuracy is controlled throughout the

M. B. Khan, M. A. Shay, S. Oughton, W. H. Matthaeus

We study the statistics of dynamical quantities associated with magnetic reconnection events embedded in a sea of strong background magnetohydrodynamic (MHD) turbulence using direct numerical simulations. We focus on the relationship of the reconnection properties to the statistics of global turbulent fields. For the first time, we show that the distribution

Sihyung Park, Wenbin Lu, Shu Yang

Truncation by death, a prevalent challenge in critical care, renders traditional dynamic treatment regime (DTR) evaluation inapplicable due to ill-defined potential outcomes. We introduce a principal stratification-based method, focusing on the always-survivor value function. We derive a semiparametrically efficient, multiply robust estimator for multi-stage

Kiana Kiashemshaki, Sina Samieirad, Sarvenaz Erfani, Aryan Jalaeianbanayan

Objective: Tinnitus affects 10-15% of the population yet lacks objective diagnostic biomarkers. This study applied machine learning to EEG and fMRI data to identify neural signatures distinguishing tinnitus patients from healthy controls. Methods: Two datasets were analyzed: 64-channel EEG recordings from 80 participants (40 tinnitus, 40 controls) and restin

Soyeong Jeong, Taehee Jung, Sung Ju Hwang, Joo-Kyung Kim

Recent Long-Context Language Models (LCLMs) can process hundreds of thousands of tokens in a single prompt, enabling new opportunities for knowledge-intensive multi-hop reasoning by integrating large sets of retrieved documents or, in some cases, directly all necessary information. However, simply feeding more documents into the context window fails to captu

Catalin-Mihai Halati

We investigate the mechanisms necessary for the stabilization of complex quantum correlations by exploring dissipative couplings to nonreciprocal reservoirs. We analyze the role of locality in the coupling between the environment and the quantum system of interest, as we consider either local couplings throughout the system, or a single global coupling. We c

Jialin Lu, Kye Emond, Kaiyu Yang, Swarat Chaudhuri

We present Lean Finder, a semantic search engine for Lean and mathlib that understands and aligns with the intents of mathematicians. Progress in formal theorem proving is often hindered by the difficulty of locating relevant theorems and the steep learning curve of the Lean 4 language, making advancement slow and labor-intensive. Existing Lean search engine

Yi-Jen Shih, Desh Raj, Chunyang Wu, Wei Zhou

Recent advances in speech large language models (speech LLMs) have enabled seamless spoken interactions, but these systems still struggle with complex reasoning tasks. Previously, chain-of-thought (CoT) prompting or fine-tuning has been to shown to significantly improve the reasoning abilities of text-based LLMs. In this work, we investigate the effect of Co

M. Richard Sayanagi

A power series ring over non-Noetherian rings can fail to be flat over the base ring, and its dimension can be infinite, even when the dimension of the base ring is finite. We study the case when the base ring has Krull dimension 0, and consider a version of the power series ring which preserves flatness and whose dimension remains finite. We consider proper

Nai-Hui Chia, Yu-Ching Shen

We investigate the computational complexity of the Local Hamiltonian (LH) problem and the approximation of the Quantum Partition Function (QPF), two central problems in quantum many-body physics and quantum complexity theory. Both problems are known to be QMA-hard, and under the widely believed assumption that $\mathsf{BQP} \neq \mathsf{QMA}$, no efficient q

Thomas Murff, Xerxes D. Arsiwalla

We address the problem of finding upper bounds on the chromatic index $q(V,E)$ of linear (and loopless) hypergraphs. The first bound we find is defined through a color-preserving group on a proper and minimally edge-colored linear hypergraph, whose orbits serve as a finer partition to the hypergraph's coloring, thereby yielding an upper bound on $q(V,E)$. Th

Xing-Xing Dong, Cai Guo, Wen Lu, Shu-Min Zhao

This study investigates the Higgs rare decays $h^0\rightarrow Z\gamma,V Z$ within the next to minimum B-L supersymmetric model(NB-LSSM), where $V$ represents a vector meson $(\phi,J/\psi,\Upsilon(1S),\rho^0,\omega)$. Compared to the minimal supersymmetric standard model(MSSM), the NB-LSSM introduces three singlet Higgs superfields, which mix with the Higgs d

Guoliang Gong, Man Yu

Computed Tomography (CT) is a vital diagnostic tool in clinical practice, yet the health risks associated with ionizing radiation cannot be overlooked. Low-dose CT (LDCT) helps mitigate radiation exposure but simultaneously leads to reduced image quality. Consequently, researchers have sought to reconstruct clear images from LDCT scans using artificial intel

Clotilde Brayé, Aurélien Bricout, Arnaud Gotlieb, Nadjib Lazaar

Medical Intelligent Systems (MIS) are increasingly integrated into healthcare workflows, offering significant benefits but also raising critical safety and ethical concerns. According to the European Union AI Act, most MIS will be classified as high-risk systems, requiring a formal risk management process to ensure compliance with the ethical requirements of

Guillem Domènech, Apostolos Tsabodimos, Nathaniel Sherrill, Alexander Ganz

We study the impact of general disformal metric transformations on fermions, which shift the gravitational metric by an additional rank-2 tensor. This tensor can in principle be constructed from scalar-field gradients, vector fields, or field-strength contractions. We show this transformation results in the conventional Dirac action being modified by additio

Akhil Kumar, Jianliang Leon Zhao, Om Dobariya

In this paper, we report our experience with several LLMs for their ability to understand a process model in an interactive, conversational style, find syntactical and logical errors in it, and reason with it in depth through a natural language (NL) interface. Our findings show that a vanilla, untrained LLM like ChatGPT (model o3) in a zero-shot setting is e

Rasika Muralidharan, Haewoon Kwak, Jisun An

Multi-Agent Systems (MAS) with Large Language Model (LLM)-powered agents are gaining attention, yet fewer studies explore their team dynamics. Inspired by human team science, we propose a multi-agent framework to examine core aspects of team science: structure, diversity, and interaction dynamics. We evaluate team performance across four tasks: CommonsenseQA

Waleed Bin Qaim, Aleksandr Ometov, Claudia Campolo, Antonella Molinaro

Over the years, significant contributions have been made by the research and industrial sectors to improve wearable devices towards the Internet of Wearable Things (IoWT) paradigm. However, wearables are still facing several challenges. Many stem from the limited battery power and insufficient computation resources available on wearable devices. On the other

Shuqing Luo, Yilin Guan, Pingzhi Li, Hanrui Wang

Test-time scaling (TTS) boosts LLM reasoning via long chain-of-thought (CoT), but the linear KV-cache growth amplifies the memory-bound bottleneck of LLM decoding. Query-aware page-level sparse decoding can achieve state-of-the-art performance under constrained FLOPs budgets, but is limited by both sequential-dependent page filtering and coarse-grained token

Benjamin Parpillon, Anthony Badea, Danush Shekar, Cristian Gingu

We present a 28 nm CMOS pixel readout integrated circuit implementing in-pixel analog signal processing and on-chip machine learning data filtering for particle tracking detectors. Our ASIC comprises two $32 \times 8$ pixel matrices with a pixel pitch of $25 \times 25~μ\mathrm{m}^2$, in which each pixel integrates a charge-sensitive amplifier with synchronou

Haoyu Han, Kai Guo, Harry Shomer, Yu Wang

Reasoning over structured graphs remains a fundamental challenge for Large Language Models (LLMs), particularly when scaling to large graphs. Existing approaches typically follow the retrieval-augmented generation (RAG) paradigm: first retrieving subgraphs relevant to the query and then generating answers conditioned on the retrieved subgraphs. However, such

Julian Stetzler, Sophya Garashchuk, Vitaly A. Rassolov

Conventional theoretical and computational approaches to fully coupled quantum molecular dynamics, i.e. when both the electrons and nuclei are treated as quantum-mechanical particles, are impractical for all but the smallest chemical systems. In this paper we describe the formalism and implementation of the Factorized Electron Nuclear Dynamics (FENDy) with e

David Ben-Zvi, David Nadler

We introduce and motivate -- based on ongoing joint work with Germ\'an Stefanich -- the notion of potent categorical representations of a complex reductive group $G$, specifically a conjectural Langlands correspondence identifying potent categorical representations of $G$ and its Langlands dual $\check G$. We emphasize the symplectic nature of potent categor

Oscar Michel, Matthias Werner, Arnau Riera

Quantum state transfer is a fundamental requirement for scalable quantum computation, where fast and reliable communication between distant subsystems is essential. In this work, we present a protocol for quantum state transfer in linear Ising chains. Starting from a perfect state transfer scheme via a Heisenberg Hamiltonian with inhomogeneous couplings, we

Richard Huber, Rolf Clackdoyle, Laurent Desbat

Tomographic techniques are vital in modern medicine, allowing doctors to observe patients' interior features. Individual steps in the measurement process are modeled by `single projection operators' $p$. These are line integral operators over a collection of curves that covers the regions of interest. Then, the entire measurement process can be understood as