March 2025 arXiv papers — page 124

Pavithra Hariharan, P. G. Sankaran

In recurrent event studies, panel binary data arise when subjects are observed at discrete time points and only the recurrent event status within each observation window is recorded. Such data frequently occur in longitudinal studies due to recall difficulties or participants' privacy concerns during follow-ups, necessitating rigorous statistical analysis. W

Gaurav Duggal, Anand M. Kumar, R. Michael Buehrer, Harpreet S. Dhillon

This paper tackles the challenge of accurate positioning in Non-Line-of-Sight (NLoS) environments, with a focus on indoor public safety scenarios where NLoS bias severely impacts localization performance. We explore Diffraction MultiPath Components (MPC) as a critical mechanism for Outdoor-to-Indoor (O2I) signal propagation and its role in positioning. The p

Teng Fei

In this paper, we investigate the geometries associated with 3-forms of various orbital types on a symplectic 6-manifold. We demonstrate that certain unstable 3-forms, which naturally emerge from specific degenerations of Calabi-Yau structures, exhibit remarkably rich geometric properties. This, in turn, offers a novel perspective on the SYZ conjecture.

Sumana Sanyasipura Nagaraju

Effective feature selection, representation and transformation are principal steps in machine learning to improve prediction accuracy, model generalization and computational efficiency. Reinforcement learning provides a new perspective towards balanced exploration of optimal feature subset using multi-agent and single-agent models. Interactive reinforcement

Yujia Wu, Wei Lan, Long Feng, Chih-Ling Tsai

The stochastic block model (SBM) has been widely used to analyze network data. Various goodness-of-fit tests have been proposed to assess the adequacy of model structures. To the best of our knowledge, however, none of the existing approaches are applicable for sparse networks in which the connection probability of any two communities is of order log(n)/n, a

Dharani Chandra

Large Language Models (LLMs) have revolutionized natural language processing through their state of art reasoning capabilities. This paper explores the convergence of LLM reasoning techniques and feature generation for machine learning tasks. We examine four key reasoning approaches: Chain of Thought, Tree of Thoughts, Retrieval-Augmented Generation, and Tho

Hengfei Lu, Nadir Matringe

In this paper we study the problem of explicitly describing the space of invariant linear forms on induced distinguished representations in terms of invariant linear forms on the inducing representation. More precisely, for certain tempered reductive symmetric pairs (G,H) over a local field of characteristic zero, which we call unimodular in this paper, we s

Noy Soffer Aranov

Periodic lattices are natural generalizations of lattices, which arise naturally in diophantine approximations with rationals of bounded denominators. In this paper, we prove analogues of classical theorems in geometry of numbers for periodic lattices in function fields. Moreover, we use special matrices to compute the covering and packing radii of special p

Jiwei Li, Lingyun Qiu, Zhongjing Wang, Hui Yu

This study extends the flow measurement method initially proposed in [22] to three-dimensional scenarios, addressing the growing need for accurate and efficient non-contact flow measurement techniques in complex hydrodynamic environments. Compared to conventional Acoustic Doppler Current Profilers (ADCPs) and remote sensing-based flow monitoring, the propose

Charaka Vinayak Kumar, Ashok Urlana, Gopichand Kanumolu, Bala Mallikarjunarao Garlapati

Advancements in Large Language Models (LLMs) have increased the performance of different natural language understanding as well as generation tasks. Although LLMs have breached the state-of-the-art performance in various tasks, they often reflect different forms of bias present in the training data. In the light of this perceived limitation, we provide a uni

Xinyu Liu, Shuyu Shen, Boyan Li, Nan Tang

Natural Language to SQL (i.e., NL2SQL) translation is crucial for democratizing database access, but even state-of-the-art models frequently generate semantically incorrect SQL queries, hindering the widespread adoption of these techniques by database vendors. While existing NL2SQL benchmarks primarily focus on correct query translation, we argue that a benc

Qian Wang, Jayden John, Ben Dong, Yuntao Liu

In quantum computing, quantum circuits are fundamental representations of quantum algorithms, which are compiled into executable functions for quantum solutions. Quantum compilers transform algorithmic quantum circuits into one compatible with target quantum computers, bridging quantum software and hardware. However, untrusted quantum compilers pose signific

Monica Dutta, Deepali Gupta, Sumegh Tharewal, Deepam Goyal

The rapid growth of the global population and the continuous decline in cultivable land pose significant threats to food security. This challenge worsens as climate change further reduces the availability of farmland. Soilless agriculture, such as hydroponics, aeroponics, and aquaponics, offers a sustainable solution by enabling efficient crop cultivation in

Utkarsh Nath, Rajeev Goel, Rahul Khurana, Kyle Min

Current text-to-3D methods excel at generating single objects but falter on compositional prompts. We argue this failure is fundamental to their optimization schedules, as simultaneous or iterative heuristics predictably collapse under a combinatorial explosion of conflicting gradients, leading to entangled geometry or catastrophic divergence. In this paper,

Murat Yessenov, Ahmed H. Dorrah, Cheng Guo, Layton A. Hall

Skyrmions, topologically non-trivial localized spin structures, are fertile ground for exploring emergent phenomena in condensed matter physics and next-generation magnetic-memory technologies. Although magnetics and optics readily lend themselves to two-dimensional realizations of spin texture, only recently have breakthroughs brought forth three-dimensiona

Runfa Blark Li, Mahdi Shaghaghi, Keito Suzuki, Xinshuang Liu

Simultaneous Localization and Mapping (SLAM) is one of the most important environment-perception and navigation algorithms for computer vision, robotics, and autonomous cars/drones. Hence, high quality and fast mapping becomes a fundamental problem. With the advent of 3D Gaussian Splatting (3DGS) as an explicit representation with excellent rendering quality

Eric M. Chen, Di Liu, Sizhuo Ma, Michael Vasilkovsky

Despite the increasing popularity of avatar systems such as Snapchat Bitmojis, existing production avatar platforms face several limitations, such as a limited number of predefined assets, tedious customization processes, and inefficient rendering requirements. Addressing these shortcomings, we introduce Snapmoji, an avatar generation system that instantly c

George H. Jacoby, Robin Ciardullo

We examine the relationship between circumnebular extinction and core mass for sets of [O III]-bright planetary nebulae (PNe) in the Large Magellanic Cloud and M31. We confirm that for PNe within one magnitude of the Planetary Nebula Luminosity Function's (PNLF's) bright-end cutoff magnitude (M*), higher core-mass PNe are disproportionally affected by greate

Patrick Martin, Radmila Sazdanovic

In this article, we analyze the structure and relationships between magnitude homology and Eulerian magnitude homology of finite graphs. Building on the work of Kaneta and Yoshinaga, Sazdanovic and Summers, and Asao and Izumihara, we provide two proofs of the existence of torsion in Eulerian magnitude homology, offer insights into the types and orders of tor

Sarasi Jayasekara

Solenoids induced by split sequences are introduced, as the inverse limit object of a sequence of fold maps. The topology of a solenoid is explored, and it is established that solenoids have naturally arising singular foliated structures. Our main goal is to answer the question: ``When is a solenoid minimal, both in a topological sense, and a measure theoret

Wei-Chen Fu, Si-Jia Wen, Di Wang

Isospin symmetry, as the most precise flavor symmetry, can be used to extract information about hadronic dynamics. The effective Hamiltonian operators of bottom quark weak decays are zero under a series of isospin lowering operators $I_-^n$, which permits us to generate isospin sum rules without the Wigner-Eckart invariants. In this work, we derive hundreds

Wenxin Zheng, Wenfeng Shi, Tianlong Fan, Linyuan Lü

Identifying influential node groups in complex networks is crucial for optimizing information dissemination, epidemic control, and viral marketing. However, traditional centrality-based methods often focus on individual nodes, resulting in overlapping influence zones and diminished collective effectiveness. To overcome these limitations, we propose Weighted

Haonan Pan, Shuheng Chen, Elham Pishgar, Kamiar Alaei

Coronary artery disease remains one of the leading causes of mortality globally. Despite advances in revascularization treatments like PCI and CABG, postoperative stroke is inevitable. This study aims to develop and evaluate a sophisticated machine learning prediction model to assess postoperative stroke risk in coronary revascularization patients.This resea

Yuchen Xia, Divyam Sharma, Yichao Yuan, Souvik Kundu

Diffusion-based text-to-image generation models trade latency for quality: small models are fast but generate lower-quality images, while large models produce better images but are slow. We present MoDM, a novel caching-based serving system for diffusion models that dynamically balances latency and quality through a mixture of diffusion models. Unlike prior

Bidisha Mukherjee, Surajit Adhikari, Mrinmay Sahu, Asish Kumar Mishra

In this paper, we present a thorough investigation of vibrational, structural, and electronic properties of perovskite-type rhombohedral Ba$_2$ZnTeO$_6$ (BZTO) under systematic application of pressure. To carry out the analysis, we have performed pressure-dependent Raman spectroscopic measurements, synchrotron XRD, and density functional theory-based calcula

Zhang di

Motion aftereffect (MAE) offers valuable insights into the mechanisms underlying motion-in-depth (MID) perception. This study investigates two critical aspects of MAE in depth: (1) the potential directional asymmetry between motion toward versus away from the observer, and (2) the effect of induced eye vergence on MAE magnitude. We conducted two experiments

Nakul Poudel, Zixin Yang, Kelly Merrell, Richard Simon

The registration between the pre-operative model and the intra-operative surface is crucial in image-guided liver surgery, as it facilitates the effective use of pre-operative information during the procedure. However, the intra-operative surface, usually represented as a point cloud, often has limited coverage, especially in laparoscopic surgery, and is pro

Iren Simko, Norah M. Hoffmann

Understanding whether a polaritonic phenomenon is fundamentally quantum or classical is essential for building accurate theoretical models and guiding experimental design. Here, we address this question in the context of polaritonic spectra, and report an intriguing new feature: the twin-polariton, an additional splitting beyond the primary resonant polarito

Meng Song, Xinyi Jing, Jianyong Ding, Ciwei Gao

Virtual energy stations (VESs) work as retailers to provide electricity and natural gas sale services for integrated energy systems (IESs), and guide IESs energy consumption behaviors to tackle the varying market prices via integrated demand response (IDR). However, IES customers are risk averse and show low enthusiasm in responding to the IDR incentive sign

Xi Wang

We introduce a novel framework for learning in neural networks by decomposing each neuron's weight vector into two distinct parts, $W_1$ and $W_2$, thereby modeling contrastive information directly at the neuron level. Traditional gradient descent stores both positive (target) and negative (non-target) feature information in a single weight vector, often obs

Jasmeet Kaur

Effective uncertainty quantification is important for training modern predictive models with limited data, enhancing both accuracy and robustness. While Bayesian methods are effective for this purpose, they can be challenging to scale. When employing approximate Bayesian inference, ensuring the quality of samples from the posterior distribution in a computat

Zhihao Zeng, Ziquan Fang, Yuting Huang, Lu Chen

Traffic prediction aims to forecast future traffic conditions using historical traffic data, serving a crucial role in urban computing and transportation management. While transfer learning and federated learning have been employed to address the scarcity of traffic data by transferring traffic knowledge from data-rich to data-scarce cities without traffic d

Badr Souani, Ezekiel Soremekun, Mike Papadakis, Setsuko Yokoyama

Large Language Models (LLMs) may portray discrimination towards certain individuals, especially those characterized by multiple attributes (aka intersectional bias). Discovering intersectional bias in LLMs is challenging, as it involves complex inputs on multiple attributes (e.g. race and gender). To address this challenge, we propose HInter, a test techniqu

Chenxi Wang, Lijun Song, Jianting Wang, Jing Zhou

Optical waveguides with miniature dimensions to the nanoscale can facilitate the development of highly integrated photonic devices, integrated optical circuits and hybrid quantum system coupling with emitters. Nondegenerate intrinsic flexural mechanical modes of nanowaveguides provide unique insights into the mechanical properties and structural integrity of

Jiawei Li, David Faragó, Christian Petrov, Iftekhar Ahmed

Commit messages are crucial in software development, supporting maintenance tasks and communication among developers. While Large Language Models (LLMs) have advanced Commit Message Generation (CMG) using various software contexts, some contexts developers consider to write high-quality commit messages are often missed by CMG techniques and can't be easily r

Wei-Yang Liu, Ismail Zahed

We analyse the rho meson unpolarized and polarized transverse momentum dependent parton distribution functions (TMDPDFs) in the instanton liquid model (ILM). The corresponding TMDs in ILM are approximated by a constituent quark beam function in the leading Fock state multiplied by a rapidity-dependent factor resulting from the staple-shaped Wilson lines, for

Chong Su, Yingbin Fu, Zheyuan Hu, Jing Yang

We introduce CHOrD, a novel framework for scalable synthesis of 3D indoor scenes, designed to create house-scale, collision-free, and hierarchically structured indoor digital twins. In contrast to existing methods that directly synthesize the scene layout as a scene graph or object list, CHOrD incorporates a 2D image-based intermediate layout representation,

Wilson Román Acevedo, Myriam H. Aguirre, Beatriz Noheda, Diego Rubi

We demonstrate the tunability of oxygen content in pulsed laser deposition (PLD)-grown barium stannate (BaSn$O_3$, BSO) thin films by precisely controlling the background oxygen pressure over a broad range from 0.0004 mbar to 0.13 mbar. The introduction of oxygen vacancies significantly alters the structural properties of BSO films, inducing a monotonic expa

Sepideh Shafiei, Shapour Hakam

This paper presents a computational methodology for analyzing intonation and deriving tuning systems in microtonal oral traditions, utilizing pitch histograms, Dynamic Time Warping (DTW), and optimization techniques, with a case study on a complete repertoire performed by a master of Iranian Classical Vocal Music (145 pieces). Pitch frequencies are extracted

G. Shibukawa, S. Tsuchimi

We introduce a one parameter deformation of Zwegers' multivariable $\mu$-function by applying iterations of the $q$-Borel summation method, which is also a multivariate analogue of the generalized $\mu$-function introduced by the authors. For this deformed multivariable $\mu$-function, we give some formulas, for example, forward shift formula, translation an

Ahcen Aliouat, Elsa Dupraz

In the emerging field of goal-oriented communications, the focus has shifted from reconstructing data to directly performing specific learning tasks, such as classification, segmentation, or pattern recognition, on the received coded data. In the commonly studied scenario of classification from compressed images, a key objective is to enable learning directl

Priyanka Mandikal, Tushar Nagarajan, Alex Stoken, Zihui Xue

Object state changes in video reveal critical cues about human and agent activity. However, existing methods are limited to temporal localization of when the object is in its initial state (e.g., cheese block) versus when it has completed a state change (e.g., grated cheese), offering no insight into where the change is unfolding. We propose to deepen the pr

J. Scott Carter, Seonmi Choi, Byeorhi Kim

A folding of a branched cover of the 3-sphere that is branched over a knot is a continuous map of the cover into the product of the sphere with a disk that has the property that the projection onto the sphere factor induces the covering. Moreover, away from the branch set, the map is a general position immersion. Cyclic branched covers can be folded so that

Edward Y. Chang, Longling Geng

This paper introduces SagaLLM, a structured multi-agent architecture designed to address four foundational limitations of current LLM-based planning systems: unreliable self-validation, context loss, lack of transactional safeguards, and insufficient inter-agent coordination. While recent frameworks leverage LLMs for task decomposition and multi-agent commun

Ankur Barthwal, Molly Campbell, Ajay Kumar Shrestha

The increasing integration of artificial intelligence (AI) in digital ecosystems has reshaped privacy dynamics, particularly for young digital citizens navigating data-driven environments. This study explores evolving privacy concerns across three key stakeholder groups-young digital citizens, parents/educators, and AI professionals-and assesses differences

Peishi Li, Ming Li, Rang Liu, Qian Liu

Integrated sensing and communication (ISAC) is a pivotal enabler for next-generation wireless networks. A key challenge in ISAC systems lies in designing dual-functional waveforms that can achieve satisfactory radar sensing accuracy by effectively suppressing range-Doppler sidelobes. However, existing solutions are often computationally intensive, limiting t

Thivya Thogesan, Anupiya Nugaliyadde, Kok Wai Wong

Interpretability remains a key difficulty in sentiment analysis with Large Language Models (LLMs), particularly in high-stakes applications where it is crucial to comprehend the rationale behind forecasts. This research addressed this by introducing a technique that applies SHAP (Shapley Additive Explanations) by breaking down LLMs into components such as em

Austin Shouli, Ankur Barthwal, Molly Campbell, Ajay Kumar Shrestha

The rapid expansion of Artificial Intelligence (AI) in digital platforms used by youth has created significant challenges related to privacy, autonomy, and data protection. While AI-driven personalization offers enhanced user experiences, it often operates without clear ethical boundaries, leaving young users vulnerable to data exploitation and algorithmic b

Ye Zhang, Zhishu Shen, Dawen Jiang, Xiangrui Liu

In satellite computing applications, such as remote sensing, tasks often involve similar or identical input data, leading to the same processing results. Computation reuse is an emerging paradigm that leverages the execution results of previous tasks to enhance the utilization of computational resources. While this paradigm has been extensively studied in te

Naresh Kumar Devulapally, Mingzhen Huang, Vishal Asnani, Shruti Agarwal

Invisible watermarking of AI-generated images can help with copyright protection, enabling detection and identification of AI-generated media. In this work, we present a novel approach to watermark images of T2I Latent Diffusion Models (LDMs). By only fine-tuning text token embeddings $W_*$, we enable watermarking in selected objects or parts of the image, o

Melvin Mokhtari

Digital twins (DTs) are redefining healthcare by paving the way for more personalized, proactive, and intelligent medical interventions. As the shift toward personalized care intensifies, there is a growing need for an individual's virtual replica that delivers the right treatment at the optimal time and in the most effective manner. The emerging concept of

Patricia Medina

In this paper, we address the enhancement of classification accuracy for 3D point cloud Lidar data, an optical remote sensing technique that estimates the three-dimensional coordinates of a given terrain. Our approach introduces product coefficients, theoretical quantities derived from measure theory, as additional features in the classification process. We

Cesar A. Ipanaque Zapata

In this work, we show that the \'{e}tale sectional number (\text{\'{E}tale-sec$(-)$}), i.e., the sectional number in the category of topological spaces with the \'{e}tale quasi Grothendieck topology (as defined in arXiv:2410.22515), is either 1 or infinity. Specifically, given a continuous map $f:X\to Y$, we demonstrate that \[\text{\'{E}tale-sec$(f)$}=\begi

Ha M. Nguyen, Carson D. Ziemke, Narendirakumar Narayanan, Sebastian Amaya-Roncanci

LiBO$_{2}$ is an electronic insulator and a promising surface coating for stabilizing high-voltage cathodes in lithium-ion batteries. Despite its potential, the functional mechanisms of this coating remain unclear, particularly the transport of lithium ions and electrons through LiBO$_{2}$ in the presence of lattice vacancies. This understanding is critical

Dion Bongaerts, Thomas Lambert, Daniel Liebau, Peter Roosenboom

We investigate the drivers of vote delegation in Decentralized Autonomous Organizations (DAOs), using the Uniswap governance DAO as a laboratory. We show that parties with fewer self-owned votes and those affiliated with the controlling venture capital firm, Andreesen Horowitz (a16z), receive more vote delegations. These patterns suggest that while the Unisw

Andrea Urru, Alessio Filippetti, Jorge Íñiguez-Gonzàlez, Vincenzo Fiorentini

Magnetoelectric responses are related in general to magnetic multipoles, and in particular the off-diagonal linear response is proportional to the toroidization order parameter. In multiferroics with three order parameters (polarization, magnetization, and toroidization), such response turns out to be also proportional to the trilinear coupling between the o

Liang-Zhen Wen, Yao Ma, Lu Meng, Shi-Lin Zhu

We investigate the existence of bound states and resonances in the ${\phi NN, J/\psi NN, \eta_c NN}$ systems using HAL QCD interactions for ${\phi N, J/\psi N}$, and ${\eta_c N}$. We employ the Gaussian expansion method to solve the complex-scaled Schr\"odinger equation and find no resonances or bound states in the ${J/\psi NN}$ and ${\eta_c NN}$ systems. We

Wonwoong Cho, Yan-Ying Chen, Matthew Klenk, David I. Inouye

Text-to-Image (T2I) Diffusion Models have achieved remarkable performance in generating high quality images. However, enabling precise control of continuous attributes, especially multiple attributes simultaneously, in a new domain (e.g., numeric values like eye openness or car width) with text-only guidance remains a significant challenge. To address this,

Andrew Claussen, Nicholas Ovenhouse

We study the enumeration of mixed dimer covers on skew Young diagrams of ribbon shape (also called border strips or snake graphs). For the two extreme cases of straight and zigzag shapes, we show that the number of mixed dimer covers are given by the Euler and Catalan numbers. We also give q-analogs by showing that the rank generating functions of the partia

Jun Yu, Yang Zheng, Lei Wang, Yongqi Wang

Facial expression recognition is a challenging classification task that holds broad application prospects in the field of human-computer interaction. This paper aims to introduce the method we will adopt in the 8th Affective and Behavioral Analysis in the Wild (ABAW) Competition, which will be held during the Conference on Computer Vision and Pattern Recogni

Yuli Tan, Junling Zhou

An $r$-block-coloring, simply $r$-coloring, of a Steiner triple system $\mathrm{STS}(v)$ is a partition of the block set into $r$ color classes, each color class being a partial parallel class. The chromatic index of $\mathrm{STS}(v)$, denoted by $\chi^{\prime}(v)$, is the smallest $r$ for which an $r$-coloring of an $\mathrm{STS}(v)$ exists. A minimum color

Yun Tang, Udhaya Chandhar Srinivasan, Benjamin James Scott, Obumneme Umealor

With the advent of 6G, Open Radio Access Network (O-RAN) architectures are evolving to support intelligent, adaptive, and automated network orchestration. This paper proposes a novel Edge AI and Network Service Orchestration framework that leverages Large Language Model (LLM) agents deployed as O-RAN rApps. The proposed LLM-agent-powered system enables inter

Dhruv Kudale, Badri Vishal Kasuba, Venkatapathy Subramanian, Parag Chaudhuri

Table Structure Recognition (TSR) is vital for various downstream tasks like information retrieval, table reconstruction, and document understanding. While most state-of-the-art (SOTA) research predominantly focuses on TSR in English documents, the need for similar capabilities in other languages is evident, considering the global diversity of data. Moreover

Noe Barcenas, Mario Velasquez

We examine positive and negative results for the Gromov-Lawson-Rosenberg Conjecture within the class of crystallographic groups. We give necessary conditions within the class of split extensions of free abelian by cyclic groups to satisfy the unstable Gromov-Lawson-Rosenberg Conjecture. We also give necessary conditions within the same class of groups, produ

Tingkai Liu, Ari S. Benjamin, Anthony M. Zador

In the current work, we connect token-level uncertainty in causal language modeling to two types of training objectives: 1) masked maximum likelihood (MLE), 2) self-distillation. We show that masked MLE is effective in reducing epistemic uncertainty, and serve as an effective token-level automatic curriculum learning technique. However, masked MLE is prone t

Jingxuan Zhang, Robert J. Hart, Ziqian Bi, Shiaofen Fang

The use of the iris as a biometric identifier has increased dramatically over the last 30 years, prompting privacy and security concerns about the use of iris images in research. It can be difficult to acquire iris image databases due to ethical concerns, and this can be a barrier for those performing biometrics research. In this paper, we describe and show

Lingyang Liu

This paper is devoted to a study of the controllability of a free-boundary problem for a class of one-dimensional degenerate parabolic equations with distributed controls, locally supported in space. We prove that for any $T>0$, if the initial state is sufficiently small, there exists a control that drives the state exactly to rest at time $t = T$. The proof

Martin J. Gander, Shu-Lin Wu, Tao Zhou

Time parallelization, also known as PinT (Parallel-in-Time) is a new research direction for the development of algorithms used for solving very large scale evolution problems on highly parallel computing architectures. Despite the fact that interesting theoretical work on PinT appeared as early 1964, it was not until 2004, when processor clock speeds reached

Alessandro Coppo, Alexandre Le Boité, Simone Felicetti, Valentina Brosco

Topology and nonlinearity are deeply connected. However, whether topological effects can arise solely from the structure of nonlinear interaction terms, and the nature of the resulting topological phases, remain to large extent open questions. Here we consider a chain of parametrically-driven quantum resonators coupled only via weak nearest-neighbour cross-K

Jiayu David Cao, Konstantin S. Denisov, Igor Zutic

Excitonic states of tightly-bound electron-hole pairs dominate the optical response in a growing class of two-dimensional (2D) materials and their van der Waals (vdW) heterostructures. In transition metal dichalcogenides (TMDs) a useful guidance for the excitonic spectrum is the analogy with the states in the 2D hydrogen atom. From our symmetry analysis and

Meijuan Xie, Liling Luo

The advent of generative artificial intelligence (GAI) has brought about a notable surge in the field of education. The use of GAI to support learning is becoming increasingly prevalent among students. However, the manner and extent of its utilisation vary considerably from one individual to another. And researches about student's utilisation and percept

Meijuan Xie, Liling Luo

As artificial intelligence (AI) technology becomes increasingly prevalent in the filed of education, there is a growing need for mathematics teacher education students (MTES) to demonstrate proficiency in the integration of AI with the technological pedagogical content knowledge (AI-TPACK). To study the issue, we firstly devised an systematic AI-TPACK scale

Farid Labib, Takanori Sugimoto, Hiroyuki Takakura, Asuka Ishikawa

The structural and physical properties of two single-grain Au-Al-Gd 1/1 approximant crystals (ACs) with analyzed compositions Au73.67Al12.24Gd14.09 and Au74.67Al11.35Gd13.98 were thoroughly investigated. The two variants are iso-structural (cubic, space group: Im-3 undergoing sharp antiferromagnetic (AFM) transitions at TN = 9.6 K and TN = 8.3 K, respectivel

Marco Palma, Ruth H Keogh, Siobhán B Carr, Rhonda Szczesniak

Within-individual variability of health indicators measured over time is becoming commonly used to inform about disease progression. Simple summary statistics (e.g. the standard deviation for each individual) are often used but they are not suited to account for time changes. In addition, when these summary statistics are used as covariates in a regression m

Ixandra Achitouv, Vincent Lahoche, Dine Ousmane Samary, Parham Radpay

In this paper, we use a nonperturbative renormalization group approach to construct the dynamical phase space of a quantum spin glass in the large $N$ limit. The disordered Hamiltonian is of ``$2 + p$" type, and we perform a coarse-graining procedure over the Wigner spectrum for the matrix-like disorder. The phase space reconstruction relies on phase tra

Kleber Carrapatoso, Isabelle Gallagher, Isabelle Tristani

In this paper, we investigate the link between kinetic equations (including Boltzmann with or without cutoff assumption and Landau equations) and the incompressible Navier-Stokes equation. We work with strong solutions and we treat all the cases in a unified framework. The main purpose of this work is to be as accurate as possible in terms of functional spac

Somak Basistha, Soumyajit Mandal, John Jesudasan, Vivas Bagwe

We use a two-coil mutual inductance technique to investigate the non-linear response of the vortex lattice of two type-II superconducting thin films, namely a very weakly pinned amorphous Molybdenum Germanium (a-MoGe) and a moderate-to-strongly pinned Niobium Nitride (NbN). We observe a strong dependence of the magnetic shielding response of the superconduct

Johan de Aguas, Sebastian Krumscheid, Johan Pensar, Guido Biele

We define the Probability of Tiered Benefit in scenarios with a binary exposure and an outcome that is either categorical with $K \geq 2$ ordered tiers or continuous partitioned by $K-1$ fixed thresholds into disjoint intervals. Similarly to other pure counterfactual queries, this parameter is not $g$-identifiable without additional assumptions. We demonstra

Tian Jin, Ahmed Imtiaz Humayun, Utku Evci, Suvinay Subramanian

Pruning eliminates unnecessary parameters in neural networks; it offers a promising solution to the growing computational demands of large language models (LLMs). While many focus on post-training pruning, sparse pre-training--which combines pruning and pre-training into a single phase--provides a simpler alternative. In this work, we present the first syste

Siyi Wu, Leyi Zhao, Haotian Ma, Xinyuan Song

Accurate segmentation of tubular and curvilinear structures, such as blood vessels, neurons, and road networks, is crucial in various applications. A key challenge is ensuring topological correctness while maintaining computational efficiency. Existing approaches often employ topological loss functions based on persistent homology, such as Betti error, to en

Bowen Baker, Joost Huizinga, Leo Gao, Zehao Dou

Mitigating reward hacking--where AI systems misbehave due to flaws or misspecifications in their learning objectives--remains a key challenge in constructing capable and aligned models. We show that we can monitor a frontier reasoning model, such as OpenAI o3-mini, for reward hacking in agentic coding environments by using another LLM that observes the model

Axel J. M. Deenen, Dirk Grundler

Superconductors are characterized by macroscopic phase coherence and have enabled cryogenic electronics and quantum technologies. Recent advances in 3D nanofabrication now offer possibilities for tuning functional properties relevant for on-chip 3D integration of superconductors. However, non-equilibrium phenomena in 3D nanostructures exposed to transport cu

Kun Su, Krishna Sayana, Hubert Pham, James Pine

This paper introduces a novel dataset REGEN (Reviews Enhanced with GEnerative Narratives), designed to benchmark the conversational capabilities of recommender Large Language Models (LLMs), addressing the limitations of existing datasets that primarily focus on sequential item prediction. REGEN extends the Amazon Product Reviews dataset by inpainting two key

Dennis J. Diaz-Diaz, Isaías F. de-la-Fuente-Jimenez, Teresa I. Hoekstra-Mendoza, Miguel E. Licona-Velázquez

In this paper, we introduce the concept of bikernel by monochromatic paths of a bicolored digraph. This concept is strongly motivated by the existing notions of kernels, kernels by monochromatic paths, and double stable augmented categories. We establish sufficient and necessary conditions for several families of bicolored digraphs to have a bikernel by mono

Robert H. Thomson, Quan Nguyen, Essien Ayanam, Matthew Canham

Humanity is currently facing an existential crisis about the nature of truth and reality driven by the availability of information online which overloads and overwhelms our cognitive capabilities, which we call Cyber-Psychosis. The results of this Cyber-Psychosis include the decline of critical thinking coupled with deceptive influences on the Internet which

Sophie Williams, Jonas Schuett, Markus Anderljung

Foundation models - models trained on broad data that can be adapted to a wide range of downstream tasks - can pose significant risks, ranging from intimate image abuse, cyberattacks, to bioterrorism. To reduce these risks, policymakers are starting to impose obligations on the developers of these models. However, downstream developers - actors who fine-tune

Atoosa Malemir Chegini, Keivan Rezaei, Hamid Eghbalzadeh, Soheil Feizi

Fact-checking tabular data is essential for ensuring the accuracy of structured information. However, existing methods often rely on black-box models with opaque reasoning. We introduce RePanda, a structured fact verification approach that translates claims into executable pandas queries, enabling interpretable and verifiable reasoning. To train RePanda, we

Khadija Hafeez, Mubashir Husain Rehmani, Sumita Mishra, Donna OShea

Recent smart grid advancements enable near-realtime reporting of electricity consumption, raising concerns about consumer privacy. Differential privacy (DP) has emerged as a viable privacy solution, where a calculated amount of noise is added to the data by a trusted third party, or individual users perturb their information locally, and only send the random

Balázs Endre Szigeti, István Szapudi, Imre Ferenc Barna, Gergely Gábor Barnaföldi

The discrepancy between low and high redshift Hubble constant $H_0$ measurements is the highest significance tension within the concordance $\Lambda$CDM paradigm. If not due to unknown systematics, the Hubble puzzle suggests a lack of understanding of the universe's expansion history despite the otherwise spectacular success of the theory. We show that a G\"

Jeonghwan Park, Kang Li, Huiyu Zhou

We present a new wrapper feature selection algorithm for human detection. This algorithm is a hybrid feature selection approach combining the benefits of filter and wrapper methods. It allows the selection of an optimal feature vector that well represents the shapes of the subjects in the images. In detail, the proposed feature selection algorithm adopts the

Peihong Yu, Amisha Bhaskar, Anukriti Singh, Zahiruddin Mahammad

Training robotic manipulation policies traditionally requires numerous demonstrations and/or environmental rollouts. While recent Imitation Learning (IL) and Reinforcement Learning (RL) methods have reduced the number of required demonstrations, they still rely on expert knowledge to collect high-quality data, limiting scalability and accessibility. We propo

Mikel Rodriguez, Raluca Ada Popa, Four Flynn, Lihao Liang

As frontier AI models become more capable, evaluating their potential to enable cyberattacks is crucial for ensuring the safe development of Artificial General Intelligence (AGI). Current cyber evaluation efforts are often ad-hoc, lacking systematic analysis of attack phases and guidance on targeted defenses. This work introduces a novel evaluation framework

Elias Khalife, Pierre-Loic Garoche, Mazen Farhood

This paper focuses on formally verifying invariant properties of control programs both at the model and code levels. The physical process is described by an uncertain discrete-time state-space system, where the dependence of the state-space matrix-valued functions defining the system on the uncertainties can be rational. The proposed approaches make use of p

Khonzoda Umarova, Talia Wise, Zhuoer Lyu, Mina Lee

Writing about a subject enriches writers' understanding of that subject. This cognitive benefit of writing -- known as constructive learning -- is essential to how students learn in various disciplines. However, does this benefit persist when students write with generative AI writing assistants? Prior research suggests the answer varies based on the type of

Jennie J. Y. Chen, Sidney S. Fels

The Steering Law has long been a fundamental model in predicting movement time for tasks involving navigating through constrained paths, such as in selecting sub-menu options, particularly for straight and circular arc trajectories. However, this does not reflect the complexities of real-world tasks where curvatures can vary arbitrarily, limiting its applica

Aman Gupta, Daniel Prasanth, Venkat Chandra Gunja

The future of quantum computing architecture is most likely the one in which a large number of clients are either fully classical or have a very limited quantum capability while a very small number of servers having the capability to perform quantum computations and most quantum computational tasks are delegated to these quantum servers. In this architecture

A. V. Kitaev, A. Vartanian

We give a classification for the small-$\tau$ asymptotic behaviours of solutions to the degenerate third Painlev\'e equation, $u^{''}(\tau) = \frac{(u^{\prime}(\tau))^{2}}{u(\tau)} - \frac{u^{\prime}(\tau)}{\tau} + \frac{1}{\tau}\left(-8 \varepsilon (u(\tau))^{2} + 2ab \right) + \frac{b^{2}}{u(\tau)}, \quad\varepsilon=\pm1,\quad\varepsilon b>0, \quad a\in\ma

Naome A. Etori, Kevin Lu, Randu Karisa, Arturs Kanepajs

As large language models (LLMs) rapidly advance, evaluating their performance is critical. LLMs are trained on multilingual data, but their reasoning abilities are mainly evaluated using English datasets. Hence, robust evaluation frameworks are needed using high-quality non-English datasets, especially low-resource languages (LRLs). This study evaluates eigh

Caterina Morelli, Paolo Maranzano, Philipp Otto

In this study, we propose a novel application of spatiotemporal clustering in the environmental sciences, with a particular focus on regionalised time series of greenhouse gases (GHGs) emissions from a range of economic sectors. Utilising a hierarchical spatiotemporal clustering methodology, we analyse yearly time series of emissions by gases and sectors fro

Ang Li

FastMap was first introduced in the Data Mining community for generating Euclidean embeddings of complex objects. In this dissertation, we first present FastMap to generate Euclidean embeddings of graphs in near-linear time: The pairwise Euclidean distances approximate a desired graph-based distance function on the vertices. We then apply the graph version o