March 2025 arXiv papers — page 120

Jiaqi Wu, Jingyi Yuan, Yang Weng, Guangwen Wang

Power system voltage regulation is crucial to maintain power quality while integrating intermittent renewable resources in distribution grids. However, the system model on the grid edge is often unknown, making it difficult to model physical equations for optimal control. Therefore, previous work proposes structured data-driven methods like input convex neur

Andris Huang, Edith Hausten, Qian Yu, Kento Taniguchi

Trapped electrons have emerged as an interesting platform for quantum information processing due to their light mass, two-level spin states, and potential for fully electronic manipulation. Previous experiments have demonstrated electron trapping in Penning traps, Paul traps, on solid neon, and superfluid films. In this work, we consider electrons confined i

Masato Shimokawa, Kou Fujimori

Structural vector autoregressive (SVAR) models are widely used to analyze the simultaneous relationships between multiple time-dependent data. Various statistical inference methods have been studied to overcome the identification problems of SVAR models. However, most of these methods impose strong assumptions for innovation processes such as the uncorrelati

Jonas Chris Ferrao, Dickson Dias, Sweta Morajkar, Manisha Gokuldas Fal Dessai

Identifying transcription factor binding sites (TFBS) is crucial for understanding gene regulation, as these sites enable transcription factors (TFs) to bind to DNA and modulate gene expression. Despite advances in high-throughput sequencing, accurately identifying TFBS remains challenging due to the vast genomic data and complex binding patterns. GCBLANE, a

Stephan Ramon Garcia, Jurij Volčič

Hunter proved that the complete homogeneous symmetric polynomials of even degree are positive definite. We prove a noncommutative generalization of this result, in which the scalar variables are replaced with hermitian operators. We provide a sharp lower bound and a sum of hermitian squares representation that are novel even in the scalar case.

Liang Fang, Xiandong Liu, Lei Zhang

We propose a numerical approach for solving conjugate heat transfer problems using the finite volume method. This approach combines a semi-implicit scheme for fluid flow, governed by the incompressible Navier-Stokes equations, with an optimization-based approach for heat transfer across the fluid-solid interface. In the semi-implicit method, the convective t

Zhi Chen, Wei Ma, Lingxiao Jiang

AI-driven software development has rapidly advanced with the emergence of software development agents that leverage large language models (LLMs) to tackle complex, repository-level software engineering tasks. These agents go beyond just generation of final code; they engage in multi-step reasoning, utilize various tools for code modification and debugging, a

Haruka Kogure, Taishi Kurahashi

The purpose of the present paper is to analyze several variants of Solovay's theorem on the existence of doubly partially conservative sentences. First, we investigate $\Theta$ sentences that are doubly $(\Gamma, \Lambda)$-conservative over $T$ for several triples $(\Theta, \Gamma, \Lambda)$. Among other things, we prove that the existence of a $\Delta_{n+1}

Rayani Venkat Sai Rithvik, Shantanu Desai

We examine the long-term trends in impact factor over the past decade (2015-2014) for seven flagship journals widely used in Astrophysics: ApJ, AJ, ApJL, ApJS,A &A, JCAP, and MNRAS. We check the variation of both the traditional impact factor as well as the median-based impact factor, which we had studied in a previous work. We find that ApJS exhibits the la

Andrei Stan

Using the Nehari manifold method, we establish sufficient conditions such that a smooth functional attains a ground state within an annular domain of a closed cone. The localization we obtain immediately allows for multiplicity when applied to disjoint conical sets. To illustrate our results, we consider a two-point boundary value problem and obtain a soluti

Rupak Sarkar, Neha Srikanth, Taylor Hudson, Rachel Rudinger

While it is commonly accepted that maintaining common ground plays a role in conversational success, little prior research exists connecting conversational grounding to success in task-oriented conversations. We study failures of grounding in the Ubuntu IRC dataset, where participants use text-only communication to resolve technical issues. We find that disr

Ruoyu Wang, Yukai Ma, Yi Yao, Sheng Tao

Semantic Scene Completion (SSC) constitutes a pivotal element in autonomous driving perception systems, tasked with inferring the 3D semantic occupancy of a scene from sensory data. To improve accuracy, prior research has implemented various computationally demanding and memory-intensive 3D operations, imposing significant computational requirements on the p

Syed Rifat Raiyan, Md. Hasanul Kabir

Image steganography is an information-hiding technique that involves the surreptitious concealment of covert informational content within digital images. In this paper, we introduce ${\rm SCR{\small EED}S{\small OLO}}$, a novel framework for concealing arbitrary binary data within images. Our approach synergistically leverages Random Shuffling, Fernet Symmet

Lachlan McGinness, Peter Baumgartner

Large Language Models (LLMs) were used to assist four Commonwealth Scientific and Industrial Research Organisation (CSIRO) researchers to perform systematic literature reviews (SLR). We evaluate the performance of LLMs for SLR tasks in these case studies. In each, we explore the impact of changing parameters on the accuracy of LLM responses. The LLM was task

Rui Xu, Dawen Yao, Yuzhuang Pian, Ruhui Cao

Constructing high resolution air pollution maps at lower cost is crucial for sustainable city management and public health risk assessment. However, traditional fixed-site monitoring lacks spatial coverage, while mobile low-cost sensors exhibit significant data instability. This study integrates PM2.5 data from 320 taxi-mounted mobile low-cost sensors and 52

Suchanuch Piriyasatit, Chaohao Yuan, Ercan Engin Kuruoglu

Autism Spectrum Disorder (ASD) is a complex neurological condition characterized by varied developmental impairments, especially in communication and social interaction. Accurate and early diagnosis of ASD is crucial for effective intervention, which is enhanced by richer representations of brain activity. The brain functional connectome, which refers to the

Xiangfei Fang, Boying Wang, Chengying Huan, Shaonan Ma

Hypergraph representation learning has garnered increasing attention across various domains due to its capability to model high-order relationships. Traditional methods often rely on hypergraph neural networks (HNNs) employing message passing mechanisms to aggregate vertex and hyperedge features. However, these methods are constrained by their dependence on

Pawel Nurowski

We combine the well-known Beltrami-Klein model of non-Euclidean geometry on a $2-$dimensional disk, where the geodesics are the chords of the disk, with the $2-$dimensional de Sitter space. The geometry of the de Sitter space is defined on the complement of the Beltrami-Klein disk in the plane, with the de Sitter metric being the unique Lorentzian Einstein m

Rui-Bo Wang, Lei You, Shi-Jie Ma, Jian-Bo Deng

The thermodynamics and Joule-Thomson expansion of anti-de Sitter black hole (AdS BH) with Gaussian distribution in non-commutative geometry is systematically studied. The metric of Gaussian-distributed BH is obtained, showing a dS geometry at the core of BH. The research indicates that the BH characterized by a Gaussian distribution exhibit thermodynamic pro

Tingting Zhu, Xiongtao Zhang

In this paper, we study the synchronization problem of nonuniform second-order Kuramoto model with homogeneous dampings and frustration effects on an asymmetric network. More precisely, we focus on the second order model defined on an asymmetric graph with depth no greater than two and present theories on the complete frequency synchronization. Due to the ab

Yuan-Heng Tseng, Shang-Wei Li, Fu-Jiun Jiang

It is known rigorously that the phase transition of the $q$-state ferromagnetic Potts model on the square lattice is second order for $q=4$. Despite this fact, some observables of the $q=4$ model show features of a first-order phase transition. For example, negative peak appears for the quantity of Binder ratio $Q_2$ of this model. Such a non-monotonic behav

Zhaohu Nie

Toda systems are generalizations of the Liouville equation to systems using simple Lie algebras. We study the blowup phenomena of their solutions by giving concrete examples demonstrating blowup masses corresponding to the Weyl groups.

Shirin Mozaffari, Seung-Hwan Do, Richa P. Madhogaria, Aikaterini Flessa Savvidou

The interactions between conduction electrons and magnetism can significantly enhance the Hall signal, a phenomenon known as the anomalous Hall effect (AHE). While the AHE is generally not expected in antiferromagnets, a large AHE is observed in certain antiferromagnets with noncollinear spin textures and nonvanishing Berry curvature. In this work, we presen

In-Chang Baek, Sung-Hyun Kim, Seo-Young Lee, Dong-Hyeon Kim

Recent research has highlighted the significance of natural language in enhancing the controllability of generative models. While various efforts have been made to leverage natural language for content generation, research on deep reinforcement learning (DRL) agents utilizing text-based instructions for procedural content generation remains limited. In this

Krishna Chaitanya Polavaram

This study presents a fascinating linguistic property related to the number of letters in words and their corresponding numerical values. By selecting any arbitrary word, counting its constituent letters, and subsequently spelling out the resulting count and tallying the letters anew, an unanticipated pattern is observed. Remarkably, this iterative sequence,

Byung Hyun Lee, Sungjin Lim, Se Young Chun

Fine-tuning based concept erasing has demonstrated promising results in preventing generation of harmful contents from text-to-image diffusion models by removing target concepts while preserving remaining concepts. To maintain the generation capability of diffusion models after concept erasure, it is necessary to remove only the image region containing the t

Kumar Krishna Agrawal, Long Lian, Longchao Liu, Natalia Harguindeguy

Efficiently modeling massive images is a long-standing challenge in machine learning. To this end, we introduce Multi-Scale Attention (MSA). MSA relies on two key ideas, (i) multi-scale representations (ii) bi-directional cross-scale communication. MSA creates O(log N) scales to represent the image across progressively coarser features and leverages cross-at

Farhad Pourkamali-Anaraki

Traditional neural network regression models provide only point estimates, failing to capture predictive uncertainty. Probabilistic neural networks (PNNs) address this limitation by producing output distributions, enabling the construction of prediction intervals. However, the common assumption of Gaussian output distributions often results in overly wide in

Aditi Godbole

In today's business landscape, organizations need to find the right balance between using their customers' data ethically to power AI solutions and being compliant regarding data privacy and data usage regulations. In this paper, we discuss synthetic data as a possible solution to this dilemma. Synthetic data is simulated data that mimics the real data. We e

Zhaohu Nie

For $N>1$, we constructed a canonical connected fundamental domain for $\Gamma_0(N)$ in [Nie, Parent], utilizing an interesting function $W: {\mathbb Z}/N\to {\mathbb N}$. In this paper, we further study the function $W$, prove some identities, and use it to match the cusps, with widths, produced by our connected fundamental domain with the known cusp classe

Charles Zhao

The spatial transcriptomics (ST) data produced by recent biotechnologies, such as CosMx and Xenium, contain huge amount of information about cancer tissue samples, which has great potential for cancer research via detection of community: a collection of cells with distinct cell-type composition and similar neighboring patterns. But existing clustering method

Xinghao Wu, Jianwei Niu, Xuefeng Liu, Guogang Zhu

Federated Prototype Learning (FedPL) has emerged as an effective strategy for handling data heterogeneity in Federated Learning (FL). In FedPL, clients collaboratively construct a set of global feature centers (prototypes), and let local features align with these prototypes to mitigate the effects of data heterogeneity. The performance of FedPL highly depend

Lulu Ban, Tao Zhu, Xiangqing Lu, Qi Qiu

Cross-dataset Human Activity Recognition (HAR) suffers from limited model generalization, hindering its practical deployment. To address this critical challenge, inspired by the success of DoReMi in Large Language Models (LLMs), we introduce a data mixture optimization strategy for pre-training HAR models, aiming to improve the recognition performance across

Yuxuan Yu, Yuzhuo Fang, Hua Tong, Jiashuo Liu

In this paper, we propose DDPM-Polycube, a generative polycube creation approach based on denoising diffusion probabilistic models (DDPM) for generating high-quality hexahedral (hex) meshes and constructing volumetric splines. Unlike DL-Polycube methods that rely on predefined polycube structure templates, DDPM-Polycube models the deformation from input geom

Wenqing Kuang, Xiongwei Zhao, Yehui Shen, Congcong Wen

LiDAR-based place recognition (LPR) is a key component for autonomous driving, and its resilience to environmental corruption is critical for safety in high-stakes applications. While state-of-the-art (SOTA) LPR methods perform well in clean weather, they still struggle with weather-induced corruption commonly encountered in driving scenarios. To tackle this

Jianzhu Yao, Kevin Wang, Ryan Hsieh, Haisu Zhou

Reasoning and strategic behavior in social interactions is a hallmark of intelligence. This form of reasoning is significantly more sophisticated than isolated planning or reasoning tasks in static settings (e.g., math problem solving). In this paper, we present Strategic Planning, Interaction, and Negotiation (SPIN-Bench), a new multi-domain evaluation desi

Mo Zhou, Jianwei Wang, Xuanmeng Zhang, Dylan Campbell

This paper studies optical flow estimation, a critical task in motion analysis with applications in autonomous navigation, action recognition, and film production. Traditional optical flow methods require consecutive frames, which are often unavailable due to limitations in data acquisition or real-world scene disruptions. Thus, single-frame optical flow est

Bowen Tan, Zheng Xu, Eric Xing, Zhiting Hu

Synthetic data offers a promising path to train models while preserving data privacy. Differentially private (DP) finetuning of large language models (LLMs) as data generator is effective, but is impractical when computation resources are limited. Meanwhile, prompt-based methods such as private evolution depend heavily on the manual prompts, and ineffectivel

Zongnan Li, Zhao Su, Sumin Wang, Yufan F. Zhou

Nuclear rings, prevalent in barred galaxies, are essential to understanding gas transport toward galactic nuclei. However, the peculiar nuclear ring in our neighboring galaxy M31 remains poorly understood. Here we present a comprehensive study of this multiphase gas structure, originally revealed by its dust emission, based on newly acquired CO mappings and

Zhongyuan Wang, Richong Zhang, Zhijie Nie, Hangyu Mao

Advanced table question answering (TableQA) methods prompt large language models (LLMs) to generate answer text, SQL query, Python code, or custom operation, which impressively improve the complex reasoning problems in the TableQA task. However, these methods lack the versatility to cope with specific question types or table structures. In contrast, the Spre

Wei-Wei Du, Yung-Chien Wang, Wen-Chih Peng

The demand for property valuation has attracted significant attention from sellers, buyers, and customers applying for loans. Reviews of existing approaches have revealed shortcomings in terms of not being able to handle missing value situations, as well as lacking interpretability, which means they cannot be used in real-world applications. To address these

Xiaoyu Xiong, Changyu Hu, Chunru Lin, Pingchuan Ma

We present TopoGaussian, a holistic, particle-based pipeline for inferring the interior structure of an opaque object from easily accessible photos and videos as input. Traditional mesh-based approaches require tedious and error-prone mesh filling and fixing process, while typically output rough boundary surface. Our pipeline combines Gaussian Splatting with

Zitan Chen

The number of zeros and the number of ones in a binary string are referred to as the composition of the string, and the prefix-suffix compositions of a string are a multiset formed by the compositions of the prefixes and suffixes of all possible lengths of the string. In this work, we present binary codes of length n in which every codeword can be efficientl

Weiyang Geng, Yiming Pan, Zhecong Xing, Dongyu Liu

This study proposes a hybrid model based on Transformers, named MSCMHMST, aimed at addressing key challenges in traffic flow prediction. Traditional single-method approaches show limitations in traffic prediction tasks, whereas hybrid methods, by integrating the strengths of different models, can provide more accurate and robust predictions. The MSCMHMST mod

Abhishek Roy, Narsi G, Sujata Mukherjee

Online scams are a growing threat in India, impacting millions and causing substantial financial losses year over year. This white paper presents ShieldUp!, a novel mobile game prototype designed to inoculate users against common online scams by leveraging the principles of psychological inoculation theory. ShieldUp! exposes users to weakened versions of man

Xin Wang, Samiul Alam, Zhongwei Wan, Hui Shen

Despite significant advancements, the practical deployment of Large Language Models (LLMs) is often hampered by their immense sizes, highlighting the need for effective compression techniques. Singular Value Decomposition (SVD) is a promising LLM compression technique. However, existing SVD-based compression methods fall short in reducing truncation losses,

Zhe Wang, Yanjun Qi

Gradient optimization-based adversarial attack methods automate the learning of adversarial triggers to generate jailbreak prompts or leak system prompts. In this work, we take a closer look at the optimization objective of adversarial trigger learning and propose ATLA: Adversarial Trigger Learning with Augmented objectives. ATLA improves the negative log-li

Karin Dissauer, Graham Barnes, KD Leka, Eric L. Wagner

The physical role played by small-scale activity that occurs before the sudden onset of solar energetic events (SEEs, i.e., solar flares and coronal mass ejections) remains in question, in particular as related to SEE initiation and early evolution. It is still unclear whether such precursor activity, often interpreted as plasma heating, particle acceleratio

Nozomi Higashino, Yasuhiro Tada

We study creation of quasi-particles in fractional Chern insulators (FCI) under magnetic fields. We consider two representative models, the Kapit-Mueller model and the checkerboard model, which have distinct band properties in terms of the quantum geometry. The former satisfies the so-called ideal condition and well mimics the lowest Landau level, while the

Xiaozhe Wang, Jiayue Wang, Yihui Jiang, Suyang Sun

Owing to the intrinsically high crystallization temperatures, layered phase-change materials, such as CrGeTe3 and InGeTe3, are attracting attention for embedded memory applications, In addition to the electrical contrast, a major change in magnetic properties is observed in CrGeTe3 upon switching from the crystalline to the amorphous state. In this work, we

Tengfei Wang, Xin Wang, Yongmao Hou, Zhaoning Zhang

Accurate geometric surface reconstruction, providing essential environmental information for navigation and manipulation tasks, is critical for enabling robotic self-exploration and interaction. Recently, 3D Gaussian Splatting (3DGS) has gained significant attention in the field of surface reconstruction due to its impressive geometric quality and computatio

Edward Hong Wang, Cynthia Xin Wen

We propose a novel dual-loop system that synergistically combines responsive neurostimulation (RNS) implants with artificial intelligence-driven wearable devices for treating post-traumatic stress disorder (PTSD) and enabling naturalistic brain research. In PTSD Therapy Mode, an implanted closed-loop neural device monitors amygdala activity and provides on-d

Vagul Mahadevan, Shangtong Zhang, Rohan Chandra

Safe, agile, and socially compliant multi-robot navigation in cluttered and constrained environments remains a critical challenge. This is especially difficult with self-interested agents with unique, unknown priorities in decentralized settings, where there is no central authority to resolve conflicts induced by spatial symmetry. We address this challenge b

Yunze Liu, Peiran Wu, Cheng Liang, Junxiao Shen

Recent Mamba-based architectures for video understanding demonstrate promising computational efficiency and competitive performance, yet struggle with overfitting issues that hinder their scalability. To overcome this challenge, we introduce VideoMAP, a Hybrid Mamba-Transformer framework featuring a novel pre-training approach. VideoMAP uses a 4:1 Mamba-to-T

Kevin Bradley Dsouza, Enoch Ofosu, Richard Boudreault, Juan Moreno-Cruz

Canada's northern boreal forest edge offers considerable potential for climate change mitigation through large-scale tree planting. Afforestation in these sparsely forested regions could assist the natural northward migration of forests while capitalizing on their carbon removal capacity. However, the sequestration potential is uncertain due to a lack of spa

Hao Huang, Kaijing Xu, Michael Lardelli

Metabolism plays a crucial role in sleep regulation, yet its effects are challenging to track in real time. This study introduces a machine learning-based framework to analyze sleep patterns and identify how metabolic changes influence sleep at specific time points. We first established that sleep periods in Drosophila melanogaster function independently, wi

Kanzhi Cheng, Wenpo Song, Jiaxin Fan, Zheng Ma

Image captioning has been a longstanding challenge in vision-language research. With the rise of LLMs, modern Vision-Language Models (VLMs) generate detailed and comprehensive image descriptions. However, benchmarking the quality of such captions remains unresolved. This paper addresses two key questions: (1) How well do current VLMs actually perform on imag

Gamal Mograby

We introduce a novel approach to portfolio optimization that leverages hierarchical graph structures and the Schur complement method to systematically reduce computational complexity while preserving full covariance information. Inspired by Lopez de Prados hierarchical risk parity and Cottons Schur complement methods, our framework models the covariance matr

Brady Wu, Qinghao Mao, Bryan VanSaders, Heinrich M. Jaeger

In active fluids and active solids the constituents individually generate movement by each extracting energy from their environment or from their own source. Non-reciprocal interactions among these active constituents then enable novel collective behavior that often can be strikingly counterintuitive. However, non-reciprocity in these cases typically require

Maciej P. Polak, Dane Morgan

Automated data extraction from research texts has been steadily improving, with the emergence of large language models (LLMs) accelerating progress even further. Extracting data from plots in research papers, however, has been such a complex task that it has predominantly been confined to manual data extraction. We show that current multimodal large language

Tomoki Watanabe, Keisuke Yamada, Yoshinobu Nakatani

Recently, spin-transfer torque (STT) based magnetization switching has been widely utilized in magnetic resistance-based memories, which have broad applications in microcontroller units and other devices. This study utilizes a macrospin model to simulate magnetization switching in nanoscale magnets with perpendicular anisotropy through spin-splitter torque (

Yuke Song, ShiFang Li, PeiZe Lin, Jin Li

The 7x7 reconstruction of Si(111) surface is widely understood by the dimer-adatom-stacking-fault model (DAS), but the predicted metallicity of DAS contradicts experimental signs of insulation. It is still challenge to predict DAS-like reconstructions by traditional method to solve such a puzzle. Here, we show that low-energy reconstructions of Si(111)-7x7 s

Valeriy A. Buryachenko

We consider the matrix composite materials (CM) of either random (statistically homogeneous or inhomogeneous), periodic, or deterministic (neither random nor periodic) structures. CMs exhibit linear or nonlinear behavior, coupled or uncoupled multi-physical phenomena, locally elastic, weakly nonlocal (strain gradient and stress gradient), or strongly nonloca

Clare R. Rees-Zimmerman, José Martín-Roca, David Evans, Mark A. Miller

We compare three model-free numerical methods for inverting structural data to obtain interaction potentials, namely iterative Boltzmann inversion (IBI), test-particle insertion (TPI), and a machine-learning (ML) approach called ActiveNet. Three archetypal models of two-dimensional colloidal systems are used as test cases: Weeks--Chandler--Anderson short-ran

Andrew Kiruluta, Andreas Lemos

In recent years, diffusion based methods have emerged as a powerful paradigm for generative modeling. Although discrete diffusion for natural language processing has been explored to a lesser extent, it shows promise for tasks requiring iterative denoising of token based data. In standard approaches to text generation, transformers dominate, but their relian

Madelaine Martinez Ferguson, Aliza Sharmin, Mustafa Can Camur, Xueping Li

This paper reviews intermodal transportation systems and their role in decarbonizing freight networks from an operations research perspective, analyzing over a decade of studies (2010-2024). We present a chronological analysis of the literature, illustrating how the field evolved over time while highlighting the emergence of new research avenues. We observe

Kira Adaricheva, Jonathan T. Brockman, Gillian Z. Elston, Lawrence Hobbie

Improving undergraduate success in STEM requires identifying actionable factors that impact student outcomes, allowing institutions to prioritize key leverage points for change. We examined academic, demographic, and institutional factors that might be associated with graduation rates at two four-year colleges in the northeastern United States using a novel

Marko Ristić, Richard O'Shaughnessy, Kate Wagner, Christopher J. Fontes

We reassess the capacity for multimessenger inference of AT2017gfo/GW170817 using both kilonova and gravitational wave emission within the context of a recent simulation-based surrogate model for kilonova emission. Independent of the inclusion of gravitational wave observations, comparisons between observations that incorporate our kilonova model favor a nar

Hiroaki Karuo, Han-Bom Moon, Helen Wong

We consider two algebras of curves associated to an oriented surface of finite type - the cluster algebra from combinatorial algebra, and the skein algebra from quantum topology. We focus on generalizations of cluster algebras and generalizations of skein algebras that include arcs whose endpoints are marked points on the boundary or in the interior of the s

Zerong Huang, Kai Yuen Lee, Chun Kit Wong, Liyuan Qiu

We investigate the hollowing transition of a shell-shaped Bose-Einstein condensate using collective excitations. The shell is created using an immiscible dual-species BEC mixture, with its hollowness controlled by tuning the repulsive interspecies interaction via a Feshbach resonance. Our results reveal two distinct monopole modes in which the two condensate

Jianwu Fang, Lei-Lei Li, Zhedong Zheng, Hongkai Yu

Traffic Accident Anticipation (TAA) in traffic scenes is a challenging problem for achieving zero fatalities in the future. Current approaches typically treat TAA as a supervised learning task needing the laborious annotation of accident occurrence duration. However, the inherent long-tailed, uncertain, and fast-evolving nature of traffic scenes has the prob

Shishir Rao, Nouman Ahmed, Gholamreza Salimi-Khorshidi, Christopher Yau

We developed and validated TRisk, a Transformer-based AI model predicting 36-month mortality in heart failure patients by analysing temporal patient journeys from UK electronic health records (EHR). Our study included 403,534 heart failure patients (ages 40-90) from 1,418 English general practices, with 1,063 practices for model derivation and 355 for extern

Yiming Fang, Li Chen, Ang Chen, Weidong Wang

The high computational complexity of the multiple signal classification (MUSIC) algorithm is mainly caused by the subspace decomposition and spectrum search, especially for frequent real-time applications or massive sensors. In this paper, we propose a low-complexity MUSIC algorithm from a finite-precision arithmetic perspective. First, we analyze the comput

Ryan P. Kelly, David J. Warne, David T. Frazier, David J. Nott

Simulation-based Bayesian inference (SBI) methods are widely used for parameter estimation in complex models where evaluating the likelihood is challenging but generating simulations is relatively straightforward. However, these methods commonly assume that the simulation model accurately reflects the true data-generating process, an assumption that is frequ

Yuzheng Hu, Fan Wu, Ruicheng Xian, Yuhang Liu

We propose the notion of empirical privacy variance and study it in the context of differentially private fine-tuning of language models. Specifically, we show that models calibrated to the same $(\varepsilon, \delta)$-DP guarantee using DP-SGD with different hyperparameter configurations can exhibit significant variations in empirical privacy, which we quan

Yang Su, Shiyu Zhang, Yan Sun, Ji Yang

We uncovered a more tilted molecular gas structure with highly negative velocities located near the dust lane. Our observations also show that the approaching gas flows from the overshoot process are captured by the bar gravitational and then flows towards the Galactic central molecular zone (CMZ) through the bar channel. The recycling gas from the overshoot

Henri Aïdasso

This document presents the artifact associated with the ICSE SEIP 25 paper titled On the Diagnosis of Flaky Job Failures: Understanding and Prioritizing Failure Categories. The original paper identifies and analyzes 46 distinct categories of flaky job failures that developers encounter, using Recency (R), Frequency (F), and Monetary (M) measures. In addition

Yue You, Xiao-Jing Du, Lin Ma, Hua Qiu

As for plasmonic antenna structures that generate localized near-field enhancement, the most effective current implementations are based on electric dipole resonance modes, but this approach also imposes limitations on their further optimization. Here we introduce an ASRR structure whose ASR mode enables differential charge distribution across both sides of

Yueke Hu, Paul Nelson

In this paper we prove a new subconvexity result for the standard L-function of a unitary cuspidal automorphic representation $\pi$ of $\text{GL}_n$, where the finite set of places $S$ with large conductors is allowed to vary, provided that the local parameters at every place in $S$ satisfy certain uniform growth condition.

Ruanqianqian Huang, Savitha Ravi, Michael He, Boyu Tian

Computational notebooks are intended to prioritize the needs of scientists, but little is known about how scientists interact with notebooks, what requirements drive scientists' software development processes, or what tactics scientists use to meet their requirements. We conducted an observational study of 20 scientists using Jupyter notebooks for their day-

Xiaoyun Wang, Shuangfeng Han, Zhiming Liu, Qixing Wang

This paper systematically analyzes the typical application scenarios and key technical challenges of AI in 6G air interface transmission, covering important areas such as performance enhancement of single functional modules, joint optimization of multiple functional modules, and low-complexity solutions to complex mathematical problems. Innovatively, a three

Jiahao Wu, Rui Peng, Zhiyan Wang, Lu Xiao

Novel view synthesis has long been a practical but challenging task, although the introduction of numerous methods to solve this problem, even combining advanced representations like 3D Gaussian Splatting, they still struggle to recover high-quality results and often consume too much storage memory and training time. In this paper we propose Swift4D, a divid

Huriye Gürsel, Mert Mangut, Erdem Sucu

This work mainly focuses on the nonlinear Einstein-Euler-Heisenberg theory and its applications from various aspects. Firstly, thermodynamic variables are analytically determined via Smarr formula for a four dimensional spherically symmetric Einstein-Euler-Heisenberg black hole by taking the Hawking-Bekenstein entropy as the basis. The results are supported

Ronald Orozco López

In this paper, we use the Rogers-Ramanujan type $q$-exponential operator $\mathcal{R}(qD_{q})$ to derive generating functions, and Mehler and Rogers formulas, for the non-normalized homogeneous Stieljes-Wigert polynomials $\mathrm{S}_{n}(x,y;q)$.

Thiha Aung, Mike Ludkovski

We develop a mathematical model for intraday dispatch of co-located wind-battery energy assets. Focusing on the primary objective of firming grid-side actual production vis-a-vis the preset day-ahead hourly generation targets, we conduct a comprehensive study of the resulting stochastic control problem across different firming formulations and wind generatio

Takanori Sugiyama

Precise characterization of noisy quantum operations plays an important role for realizing further accurate operations. Quantum tomography is a popular class of characterization methods, and several advanced methods in the class use error amplification circuit (EAC), a repetition of a sequence of quantum gates, for increasing their estimation precision. Here

Xiaoying Zhang, Da Peng, Yipeng Zhang, Zonghao Guo

Recent progress in (multimodal) large language models ((M)LLMs) has shifted focus from pre-training to inference-time computation and post-training optimization, largely due to concerns over the availability of high-quality human data. However, these strategies alone are insufficient to drive substantial model improvements. We argue that effective model adva

Ryan McCulloch, Marius Tărnăuceanu

A group $G$ is said to have dense ${\cal CD}$-subgroups if each non-empty open interval of the subgroup lattice $L(G)$ contains a subgroup in the Chermak--Delgado lattice ${\cal CD}(G)$. In this note, we study finite groups satisfying this property.

Amirabbas Afzali, Amirhossein Afsharrad, Seyed Shahabeddin Mousavi, Sanjay Lall

Large Language Models (LLMs) have made significant strides in generating human-like responses, largely due to preference alignment techniques. However, these methods often assume unbiased human feedback, which is rarely the case in real-world scenarios. This paper introduces Content-Aware Noise-Resilient Preference Optimization (CNRPO), a novel framework tha

Ryan McCulloch, Marius Tărnăuceanu

By imposing conditions upon the index of a self-centralizing subgroup of a group, and upon the index of the center of the group, we are able to classify the Chermak-Delgado lattice of the group. This is our main result. We use this result to classify the Chermak-Delgado lattices of dicyclic groups and of metabelian $p$-groups of maximal class.

Naihuan Jing, Ning Liu, Yu Wu

Let $\chi^{\lambda}_{\mu}$ be the value of the irreducible character $\chi^{\lambda}$ of the Hecke algebra of the symmetric group on the conjugacy class of type $\mu$. The usual Murnaghan-Nakayama rule provides an iterative algorithm based on reduction of the lower partition $\mu$. In this paper, we establish a dual Murnaghan-Nakayama rule for Hecke algebras

Jinghan Wang, Michael W. Fisher

Engineered systems naturally experience large disturbances that can disrupt desired operation because the system may fail to recover to a stable equilibrium point. It is valuable to determine the mechanism of instability when the system is subject to a particular finite-time disturbance, because this information can be used to improve vulnerability detection

Ronas Shakya, Sam Urmian, Mohammad Khalil

The advancement of large language models (LLMs) has created a competitive landscape for AI-assisted programming tools. This study evaluates two leading models: ChatGPT 03-mini and DeepSeek-R1 on their ability to solve competitive programming tasks from Codeforces. Using 29 programming tasks of three levels of easy, medium, and hard difficulty, we assessed th

Stefan Großkinsky, Gunter Schütz, Ali Zahra

We introduce a novel exclusion process with a simple local kinetic constraint that leads to a remarkable transition between a homogeneous phase with short-range correlations and a clustered phase with long-range correlations and spontaneous breaking of translation invariance. The metastable dynamics of particle clusters lead to a coarsening cascade and glass

S. Sajad Dabiri, Reza Asgari

Using the velocity gauge formalism, we develop a theoretical framework for computing the nonlinear optical responses of time-periodic quantum systems. This approach complements the length gauge formulation and offers distinct advantages in both numerical and analytical treatments, particularly for atomic and solid-state systems with well-defined momentum-spa

Bishnu Paudel, James A. Sellers, Haiyang Wang

Let $T_{\ell,k}(n)$ denote the number of $\ell$-regular $k$-tuple partitions of $n$. In a recent work, Nath, Saikia, and Sarma derived several families of congruences for $T_{\ell,k}(n)$, with particular emphasis on the cases $T_{2,3}(n)$ and $T_{4,3}(n)$. In the concluding remarks of their paper, they conjectured that $T_{2,3}(n)$ satisfies an infinite set

Thomas R. Beauchamp, Hana Jirovská, Scarlett Gauthier, Stephanie Wehner

We propose an architecture for scheduling network operations enabling the end-to-end generation of entanglement according to user demand. The main challenge solved by this architecture is to allow for the integration of a network schedule with the execution of quantum programs running on processing end nodes in order to realise quantum network applications.

Bardia Nadimi, Ghali Omar Boutaib, Hao Zheng

Designing Verilog modules requires meticulous attention to correctness, efficiency, and adherence to design specifications. However, manually writing Verilog code remains a complex and time-consuming task that demands both expert knowledge and iterative refinement. Leveraging recent advancements in large language models (LLMs) and their structured text gener

Gagan Khandate, Boxuan Wang, Sarah Park, Weizhe Ni

Pre-training on large datasets of robot demonstrations is a powerful technique for learning diverse manipulation skills but is often limited by the high cost and complexity of collecting robot-centric data, especially for tasks requiring tactile feedback. This work addresses these challenges by introducing a novel method for pre-training with multi-modal hum

Hsiang-Ting Chen, Yuan Zhang, Gustavo Carneiro, Rajvinder Singh

While AI-assisted colonoscopy promises improved colorectal cancer screening, its success relies on effective integration into clinical practice, not just algorithmic accuracy. This paper, based on an Australian field study (observations and gastroenterologist interviews), highlights a critical disconnect: current development prioritizes machine learning mode