March 2025 arXiv papers — page 96

Dangyang He

In this note, we study both the Riesz and reverse Riesz transforms on broken line. This model can be described by $(-\infty, -1] \cup [1,\infty)$ equipped with the measure $d\mu = |r|^{d_{1}-1}dr$ for $r \le -1$ and $d\mu = r^{d_{2}-1}dr$ for $r\ge 1$, where $d_{1}, d_{2} >1$. For the Riesz transform, we show that the range of its $L^{p}$ boundedness depends

Shinichi Saito

Skyrmions are topological quasi-particles characterised by local spin textures, which are considered to be robust against structural deformation. N\'eel and Bloch states are famous examples of skyrmions, which exhibit radical and chiral spin profiles, respectively. Here, we show a skyrmion can be continuously transformed to an antiskyrmion or various other f

Kellie Yu Hui Sim, Kenny Tsu Wei Choo

The rapid advancement of Large Language Models (LLMs), reasoning models, and agentic AI approaches coincides with a growing global mental health crisis, where increasing demand has not translated into adequate access to professional support, particularly for underserved populations. This presents a unique opportunity for AI to complement human-led interventi

Kai Zhang, Hengtao He, Shenghui Song, Jun Zhang

Large language models (LLMs) have demonstrated remarkable success across various application domains, but their enormous sizes and computational demands pose significant challenges for deployment on resource-constrained edge devices. To address this issue, we propose a novel distributed on-device LLM inference framework that leverages tensor parallelism to p

Yang Cao, Xiaoyu Li, Yekun Ke, Yingyu Liang

A fundamental challenge in Visual Autoregressive models is the substantial memory overhead required during inference to store previously generated representations. Despite various attempts to mitigate this issue through compression techniques, prior works have not explicitly formalized the problem of KV-cache compression in this context. In this work, we tak

Henrique Morimitsu, Xiaobin Zhu, Roberto M. Cesar, Xiangyang Ji

Optical flow estimation is essential for video processing tasks, such as restoration and action recognition. The quality of videos is constantly increasing, with current standards reaching 8K resolution. However, optical flow methods are usually designed for low resolution and do not generalize to large inputs due to their rigid architectures. They adopt dow

Ruiyi Cui, Liangxia Wan, Fengming Dong

In this article, we introduce the DP color function of a hypergraph, based on the DP coloring introduced by Bernshteyn and Kostochka, which is the minimum value where the minimum is taken over all its k-fold covers. It is an extension of its chromatic polynomial. we obtain an upper bound for the DP color functions of hypergraphs when hypergraphs are connecte

Tung Sum Thomas Kwok, Chi-Hua Wang, Guang Cheng

Tabular data synthesis involves not only multi-table synthesis but also generating multi-modal data (e.g., strings and categories), which enables diverse knowledge synthesis. However, separating numerical and categorical data has limited the effectiveness of tabular data generation. The GReaT (Generate Realistic Tabular Data) framework uses Large Language Mo

Murtaza Zohair, Vidushi Sharma, Eduardo A. Soares, Khanh Nguyen

Designing optimal formulations is a major challenge in developing electrolytes for the next generation of rechargeable batteries due to the vast combinatorial design space and complex interplay between multiple constituents. Machine learning (ML) offers a powerful tool to uncover underlying chemical design rules and accelerate the process of formulation disc

Shengyang Wu, Vladimir Dvorkin

Differential privacy (DP) provides a principled approach to synthesizing data (e.g., loads) from real-world power systems while limiting the exposure of sensitive information. However, adversaries may exploit synthetic data to calibrate cyberattacks on the source grids. To control these risks, we propose new DP algorithms for synthesizing data that provide t

Dinesh Wagle, Yi Li, Mojtaba Taghipour Kaffash, Sergi Lendinez

We experimentally demonstrate strong magnon-magnon coupling by thermal spin excitations in yttrium iron garnet/permalloy (YIG/Py) hybrid structures using microfocused Brillouin light scattering - an optical technique that enables the detection of zero-wavevector and higher-order wavevector spin waves in a broad frequency range. The thermally activated magnon

Xing-Liang Dong, Peng-Bo Li, Zongping Gong, Franco Nori

Dissipative light-matter coupling plays a vital role in non-Hermitian physics, but it remains largely unexplored in waveguide QED systems. In this work, we find that by employing pseudo-Hermitian symmetry rather than anti-PT symmetry, the concept of dissipative coupling could be generalized and applied to the field of waveguide QED. This leads to a series of

Seyed Mojtaba Mohasel, Hamidreza Koosha

This study introduces a novel formulation to enhance Support Vector Machines (SVMs) in handling class imbalance and noise. Unlike the conventional Soft Margin SVM, which penalizes the magnitude of constraint violations, the proposed model quantifies the number of violations and aims to minimize their frequency. To achieve this, a binary variable is incorpora

Osamu Hirota

This paper is dedicated to the late Professor H.P. Yuen in commemoration to our 50-year friendship. He invented the concept of quantum stream cipher. It is designed based on a completely different concept from conventional ciphers. The purpose of this cipher is to provide information-theoretic security of long data and secret key with short key length. It is

Mingze Wu, Yan Pan, Junhui Li, Heng Wang

Continuous-variable quantum key distribution holds the potential to generate high secret key rates, making it a prime candidate for high-rate metropolitan quantum network applications. However, despite these promising opportunities, the realization of high-rate continuous-variable quantum key distribution systems with composable security remains an elusive g

Tomohito Otobe, Tatsuya Ogusu, Mizuki Tani, Shunsuke Yamada

This study presents SALMON VR, a visualization program designed to visualize the time evolution of electronic density changes and vector potentials in virtual reality (VR) space. The time-series electronic density data computed by SALMON are stored in CUBE format. SALMON VR processes these data to construct isosurfaces of electronic density variations and tw

Li Zhang

Time series data is one of the most ubiquitous data modalities existing in a diverse critical domains such as healthcare, seismology, manufacturing and energy. Recent years, there are increasing interest of the data mining community to develop time series deep learning models to pursue better performance. The models performance often evaluate by certain eval

Hoigi Seo, Wongi Jeong, Kyungryeol Lee, Se Young Chun

Diffusion models have shown remarkable performance in image synthesis, but they demand extensive computational and memory resources for training, fine-tuning and inference. Although advanced quantization techniques have successfully minimized memory usage for inference, training and fine-tuning these quantized models still require large memory possibly due t

Caoshuo Li, Tanzhe Li, Xiaobin Hu, Donghao Luo

Recently, Vision Graph Neural Network (ViG) has gained considerable attention in computer vision. Despite its groundbreaking innovation, Vision Graph Neural Network encounters key issues including the quadratic computational complexity caused by its K-Nearest Neighbor (KNN) graph construction and the limitation of pairwise relations of normal graphs. To addr

Rafid Umayer Murshed, Md Shoaib Akhter Rafi, Sakib Reza, Mohammad Saquib

Metasurfaces, and in particular reconfigurable intelligent surfaces (RIS), are revolutionizing wireless communications by dynamically controlling electromagnetic waves. Recent wireless communication advancements necessitate broadband and multi-band RIS, capable of supporting dynamic spectrum access and carrier aggregation from sub-6 GHz to mmWave and THz ban

Hsuan-Yi Liao, Zachary McGuirk, Dang Khoa Nguyen, Byungdo Park

We prove that any digraph Brown functor -- i.e. a contravariant functor from the homotopy category of finite directed graphs to the category of abelian groups, satisfying the triviality axiom, the additivity axiom, and the Mayer-Vietoris axiom -- is representable. Furthermore, we show that the first path cohomology functor is a digraph Brown functor.

Dangyang He

We study Riesz and reverse Riesz inequalities on manifolds whose Ricci curvature decays quadratically. First, we refine existing results on the boundedness of the Riesz transform by establishing a Lorentz-type endpoint estimate. Next, we explore the relationship between the Riesz and reverse Riesz transforms, proving that the reverse Riesz, Hardy, and weight

Hengkang Wang, Yang Liu, Huidong Liu, Chien-Chih Wang

Video restoration (VR) aims to recover high-quality videos from degraded ones. Although recent zero-shot VR methods using pre-trained diffusion models (DMs) show good promise, they suffer from approximation errors during reverse diffusion and insufficient temporal consistency. Moreover, dealing with 3D video data, VR is inherently computationally intensive.

Tibebe Birhanu, Hang-Hyun Jo

Various time series in natural and social processes have been found to be bursty. Events in the time series rapidly occur within short time periods, forming bursts, which are alternated with long inactive periods. As the timescale defining bursts increases, individual events are sequentially merged to become small bursts and then bigger ones, eventually lead

Caleb Robinson, Anthony Ortiz, Allen Kim, Rahul Dodhia

We present a comprehensive global temporal dataset of commercial solar photovoltaic (PV) farms and onshore wind turbines, derived from high-resolution satellite imagery analyzed quarterly from the fourth quarter of 2017 to the second quarter of 2024. We create this dataset by training deep learning-based segmentation models to identify these renewable energy

Shaoguang Zhang, Chenyu Wu, Yufei Zhang

Traditional Reynolds-averaged Navier-Stokes (RANS) equations often struggle to predict separated flows accurately. Recent studies have employed data-driven methods to enhance predictions by modifying baseline equations, such as field inversion and machine learning (FIML) with symbolic regression. However, data-driven turbulence models exhibit limited adaptab

Kevin Wang, Ishaan Javali, Michał Bortkiewicz, Tomasz Trzciński

Scaling up self-supervised learning has driven breakthroughs in language and vision, yet comparable progress has remained elusive in reinforcement learning (RL). In this paper, we study building blocks for self-supervised RL that unlock substantial improvements in scalability, with network depth serving as a critical factor. Whereas most RL papers in recent

Fedor V. Kovalev, Ilya V. Shadrivov

Spatiotemporal metasurfaces, characterized by dynamic variations in both space and time, enable functionalities unattainable with passive metasurfaces. In this study, we propose a novel concept of parametric metasurfaces capable of performing frequency up-conversion and amplification of free-space electromagnetic waves. This functionality is realized through

Akhil Jaini, Adam T. Deller, Yuanming Wang, Emil Lenc

Fast Radio Bursts (FRBs) are short, intense radio signals from distant astrophysical sources, and their accurate localisation is crucial for probing their origins and utilising them as cosmological tools. This study focuses on improving the astrometric precision of FRBs discovered by the Australian Square Kilometre Array Pathfinder (ASKAP) by correcting syst

Sri Harsha Turlapati, Gautami Golani, Mohammad Zaidi Ariffin, Domenico Campolo

Ease of programming is a key factor in making robots ubiquitous in unstructured environments. In this work, we present a sensorized gripper built with off-the-shelf parts, used to record human demonstrations of a box in box assembly task. With very few trials of short interval timings each, we show that a robot can repeat the task successfully. We adopt a Ca

Hanae Elmekki, Saidul Islam, Ahmed Alagha, Hani Sami

Medical Ultrasound (US) imaging has seen increasing demands over the past years, becoming one of the most preferred imaging modalities in clinical practice due to its affordability, portability, and real-time capabilities. However, it faces several challenges that limit its applicability, such as operator dependency, variability in interpretation, and limite

Takuya Fujimura, Tomoki Toda

Deep neural network-based target signal enhancement (TSE) is usually trained in a supervised manner using clean target signals. However, collecting clean target signals is costly and such signals are not always available. Thus, it is desirable to develop an unsupervised method that does not rely on clean target signals. Among various studies on unsupervised

Peipeng Yu, Jianwei Fei, Hui Gao, Xuan Feng

Current Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in understanding multimodal data, but their potential remains underexplored for deepfake detection due to the misalignment of their knowledge and forensics patterns. To this end, we present a novel framework that unlocks LVLMs' potential capabilities for deepfake detection

Tan Le, Van Le

We propose the joint graph attention neural network (GAT), clustering with adaptive neighbors (CAN) and probabilistic graphical model for dynamic power flow analysis and fault characteristics. In fact, computational efficiency is the main focus to enhance, whilst we ensure the performance accuracy at the accepted level. Note that Machine Learning (ML) based

Daniel N. Wilke

This paper introduces Design for Sensing and Digitalisation (DSD), a new engineering design paradigm that integrates sensor technology for digitisation and digitalisation from the earliest stages of the design process. Unlike traditional methodologies that treat sensing as an afterthought, DSD emphasises sensor integration, signal path optimisation, and real

Kohji Matsumoto, Maki Nakasuji

We study the Hurwitz-type analogue of Schur multiple zeta-functions involving shifting parameters. We extend various formulas, known for ordinary Schur multiple zeta-functions, to the case of Hurwitz type. We also mention unpublished results proved by Yamamoto and by Minoguchi. Further we present new formulas obtained by performing differentiation with respe

Zhuoyi Yang, Ian G. Harris

Log anomaly detection refers to the task that distinguishes the anomalous log messages from normal log messages. Transformer-based large language models (LLMs) are becoming popular for log anomaly detection because of their superb ability to understand complex and long language patterns. In this paper, we propose LogLLaMA, a novel framework that leverages LL

Congjun Ma, Quan Xiao, Liangcheng Liu, Xingxing You

Continuum robots with floating bases demonstrate exceptional operational capabilities in confined spaces, such as those encountered in medical surgeries and equipment maintenance. However, developing low-cost solutions for their motion and planning problems remains a significant challenge in this field. This paper investigates the application of geometric it

Andrii Zahorodnii, Dima Yanovsky

Project Jenkins explores how neural activity in the brain can be decoded into robotic movement and, conversely, how movement patterns can be used to generate synthetic neural data. Using real neural data recorded from motor and premotor cortex areas of a macaque monkey named Jenkins, we develop models for decoding (converting brain signals into robotic arm m

Vanderson Lima

On a closed Riemannian surface of negative curvature, we prove a characterization for configurations of closed geodesics arising from one parameter Allen-Cahn min-max constructions. One of the facts we conclude is that every geodesic occurs with multiplicity one. As an application we obtain a uniform sharp lower bound for the first min-max width of closed hy

Yuezhen Xie, Meiying Zhang, Qi Hao

Adverse climate conditions pose significant challenges for autonomous systems, demanding reliable perception and decision-making across diverse environments. To better simulate these conditions, physically-based NeRF rendering methods have been explored for their ability to generate realistic scene representations. However, these methods suffer from slow ren

Hajime Tanaka, Norihide Tokushige

Let $k\geq 2$ and $n\geq 3(k-1)$. Let $\mathcal{F}$ and $\mathcal{G}$ be families of $k$-element subsets of an $n$-element set. Suppose that $|F\cap G|\geq 2$ for all $F\in\mathcal{F}$ and $G\in\mathcal{G}$. We show that $|\mathcal{F}||\mathcal{G}|\leq\binom{n-2}{k-2}^2$, and determine the extremal configurations. This settles the last unsolved case of a rec

Heng Wang, Yang Li, Ting Ye, Li Ma

Quantum key distribution (QKD), providing a way to generate secret keys with information-theoretic security,is arguably one of the most significant achievements in quantum information. The continuous-variable QKD (CV-QKD) offers the potential advantage of achieving a higher secret key rate (SKR) within a metro area, as well as being compatible with the matur

Kazunari Iwasaki, Kengo Tomida

There is no standard numerical implementation of the Hall effect, which is one of the non-ideal magnetohydrodynamic (MHD) effects. Numerical instability arises when a simple implementation is used, in which the Hall electric field is added to the electric field to update magnetic fields without further modifications to the numerical scheme. In this paper, se

Hilkka Kontro, Armando Mastracci, Stephen S. Cheung, Martin J. MacInnis

Athletic training is characterized by physiological systems responding to repeated exercise-induced stress, resulting in gradual alterations in the functional properties of these systems. The adaptive response leading to improved performance follows a remarkably predictable pattern that may be described by a systems model provided that training load can be a

Quansheng Yue, Yanyong Guo, Tarek Sayed, Lai Zheng

Extreme value theory (EVT) has been utilized to estimate crash risk from traffic conflicts with the peak over threshold approach. However, it's challenging to determine a suitable threshold to distinguish extreme conflicts in an objective way. The subjective and arbitrary selection of the threshold in the peak over threshold approach can result in biased est

Chengran Yang, Zhensu Sun, Hong Jin Kang, Jieke Shi

Large Language Models (LLMs) have significantly advanced automated code generation, yet they struggle with complex coding tasks requiring multi-step logical reasoning. High-quality reasoning data is crucial for improving LLMs' reasoning capabilities, but such datasets remain scarce. Existing approaches either rely on computationally expensive reinforcement l

Yinqi Chen, Meiying Zhang, Qi Hao, Guang Zhou

Accurate perception of dynamic traffic scenes is crucial for high-level autonomous driving systems, requiring robust object motion estimation and instance segmentation. However, traditional methods often treat them as separate tasks, leading to suboptimal performance, spatio-temporal inconsistencies, and inefficiency in complex scenarios due to the absence o

Kunyang Li, Jean-Charles Noirot Ferrand, Ryan Sheatsley, Blaine Hoak

Fine-tuning has become the standard practice for adapting pre-trained models to downstream tasks. However, the impact on model robustness is not well understood. In this work, we characterize the robustness-accuracy trade-off in fine-tuning. We evaluate the robustness and accuracy of fine-tuned models over 6 benchmark datasets and 7 different fine-tuning str

Alireza Khalili Golmankhaneh, Elham Hashemzadeh, Carlo Cattani, Donal O'Regan

This paper presents a new Lie theoretic approach to fractal calculus, which in turn yields such new results as a Fractal Noether's Theorem, a setting for fractal differential forms, for vector fields, and Lie derivatives, as well as k-fractal jet space, and algorithms for k-th fractal prolongation. The symmetries of the fractal nonlinear \(n\)-th \(\alpha\)-

Xin-Xu Wang, Bao-Hua Sun, Toshitaka Kajino, Zhen-Yu He

The $\beta^{-}$-decay rate of $^{63}$Ni, an important branching point, affects the subsequent nucleosynthesis in the weak component of the slow-neutron capture process (weak $s$-process). To evaluate the impact of the uncertainties of stellar lifetime of $^{63}$Ni on abundances, we calculate the contribution to $\beta^{-}$-decay rates from its excited states

Liguo Zhang, San-Dong Guo, Gangqiang Zhu

Owing to their inherent characteristics of zero stray field and terahertz dynamics, two-dimensional (2D) zero-net-magnetization magnets demonstrate the potential for miniaturization, ultradensity and ultrafast performance. Recently, fully-compensated ferrimagnet of 2D zero-net-magnetization magnets has already attracted attention, as it can exhibit global sp

Yuhang Liu, Wenjie Zhao, Yunhui Guo

Task Incremental Learning (TIL) is a specialized form of Continual Learning (CL) in which a model incrementally learns from non-stationary data streams. Existing TIL methodologies operate under the closed-world assumption, presuming that incoming data remains in-distribution (ID). However, in an open-world setting, incoming samples may originate from out-of-

Ryne Beeson, Uwe Hanebeck

Efficiently solving the continuous-time signal and discrete-time observation filtering problem for chaotic dynamical systems presents unique challenges in that the advected distribution between observations may encounter a separatrix structure that results in the prior distribution being far from the observation or the distribution may become split into mult

Sojeong Park, Hyeonho Noh, Hyun Jong Yang

With the recent advancements in deep learning, semantic communication which transmits only task-oriented features, has rapidly emerged. However, since feature extraction relies on learning-based models, its performance fundamentally depends on the training dataset or tasks. For practical scenarios, it is essential to design a model that demonstrates robust p

Junfeng Ni, Yu Liu, Ruijie Lu, Zirui Zhou

Decompositional reconstruction of 3D scenes, with complete shapes and detailed texture of all objects within, is intriguing for downstream applications but remains challenging, particularly with sparse views as input. Recent approaches incorporate semantic or geometric regularization to address this issue, but they suffer significant degradation in undercons

Yuhao Liu, Pingping Jiang, Gongqiu Zhang

We propose a new financial model, the stochastic volatility model with sticky drawdown and drawup processes (SVSDU model), which enables us to capture the features of winning and losing streaks that are common across financial markets but can not be captured simultaneously by the existing financial models. Moreover, the SVSDU model retains the advantages of

Firoj Alam, Julia Maria Struß, Tanmoy Chakraborty, Stefan Dietze

The CheckThat! lab aims to advance the development of innovative technologies designed to identify and counteract online disinformation and manipulation efforts across various languages and platforms. The first five editions focused on key tasks in the information verification pipeline, including check-worthiness, evidence retrieval and pairing, and verifica

Chejian Xu, Jiawei Zhang, Zhaorun Chen, Chulin Xie

Multimodal foundation models (MMFMs) play a crucial role in various applications, including autonomous driving, healthcare, and virtual assistants. However, several studies have revealed vulnerabilities in these models, such as generating unsafe content by text-to-image models. Existing benchmarks on multimodal models either predominantly assess the helpfuln

Yang Liu, Andrew Caratenuto, Xuguang Zhang, Ying Mu

Reducing human reliance on high-electricity-consuming cooling technologies like air conditioning is crucial for reshaping the global energy paradigm. Through utilizing natural starch gelatinization, freezedrying and densification processes, we fabricated an ultrawhite cooling starch film with an ultrahigh solar reflectance of 0.96 and strong infrared emittan

Shiyi Jiang, Farshad Firouzi, Krishnendu Chakrabarty

The increasing need for sharing healthcare data and collaborating on clinical research has raised privacy concerns. Health information leakage due to malicious attacks can lead to serious problems such as misdiagnoses and patient identification issues. Privacy-preserving machine learning (PPML) and privacy-enhancing technologies, particularly federated learn

Sławomir Solecki

We explore connections between stellar moves on simplicial complexes (these are fundamental operations of combinatorial topology) and projective Fra{\"i}ss{\'e} limits (this is a model theoretic construction with topological applications). We identify a class of simplicial maps that arise from the stellar moves of welding and subdividing. We call these maps

Hanzhi Zhang, Sumera Anjum, Heng Fan, Weijian Zheng

Hallucinations in generative AI, particularly in Large Language Models (LLMs), pose a significant challenge to the reliability of multilingual applications. Existing benchmarks for hallucination detection focus primarily on English and a few widely spoken languages, lacking the breadth to assess inconsistencies in model performance across diverse linguistic

Zikun Zhou, Yushuai Sun, Wenjie Pei, Xin Li

The traditional paradigm to update retrieval models requires re-computing the embeddings of the gallery data, a time-consuming and computationally intensive process known as backfilling. To circumvent backfilling, Backward-Compatible Learning (BCL) has been widely explored, which aims to train a new model compatible with the old one. Many previous works focu

Mitsuo Oka, Tai D. Phan, Marit Øieroset, Daniel J. Gershman

Particles are heated efficiently through energy conversion processes such as shocks and magnetic reconnection in collisionless plasma environments. While empirical scaling laws for the temperature increase have been obtained, the precise mechanism of energy partition between ions and electrons remains unclear. Here we show, based on coupled theoretical and o

Shunpei Fujii, Kanta Tachibana

This study proposes a novel metric to quantitatively evaluate body synergistic coordination, explicitly addressing dynamic interactions between pairs of body segments in baseball pitching motions. Conventional methods typically compare motion trajectories using individual joint coordinates or velocities independently, employing techniques like Dynamic Time W

Pan Xu

Factor-revealing linear programs (LPs) and policy-revealing LPs arise in various contexts of algorithm design and analysis. They are commonly used techniques for analyzing the performance of approximation and online algorithms, especially when direct performance evaluation is challenging. The main idea is to characterize the worst-case performance as a famil

Shingo Kazama, Naoki Aoyama, Yoshitaka Itow, Masatoshi Kobayashi

Light dark matter searches using ionization signals in dual-phase liquid xenon (LXe) time projection chambers (TPCs) are limited by low-energy ionization backgrounds, including those from the photoelectric effect on the electrodes. To address this, we measured the quantum efficiency (QE) of various electrode materials for vacuum ultraviolet (VUV) photons in

Kohki Horie, Keiichiro Toda, Takuma Nakamura, Takuro Ideguchi

Quantitative phase microscopy (QPM) and interferometric scattering (iSCAT) microscopy are powerful label-free imaging techniques and are widely used for biomedical applications. Each method, however, possesses distinct limitations: QPM, which measures forward scattering (FS), excels at imaging microscale structures but struggles with rapidly moving nanoscale

Minsu Yi, Panayotis Benetatos

We introduce a simple theoretical model, the Freely Jointed Chain with quenched hinges (qFJC), which captures the quenched disorder in the local bending stiffness of the polymer. In this article, we analyze the tensile elasticity of the qFJC in the Gibbs (fixed-force) ensemble. For finite-size systems, we obtain a recurrence relation of the exact free energy

Xiaodong Zhao, Duo Song, Sebastian Mergelsberg, Micah Prange

Rare earth elements (REEs) are critical for advanced technologies, yet in hydrothermal aqueous solutions the molecular level details of their interaction with ligands that control their geochemical transport and deposition remain poorly understood. This study elucidates the coordination behavior of Yb(III) in sulfate-rich hydrothermal fluids using in situ ex

Yu Wang, Hua Pei

Measurement of the Chiral Magnetic Effect (CME) has been a popular topic of high-energy nuclear physics in the last decade. The flow correlation $\gamma$ between charged hadron pairs of the same and opposite charges and their difference $\Delta \gamma$ were measured to separate the CME-driven signal from the collective flow background especially second-order

Neal Batra

This study explores the application of Hawkes processes to model high-frequency data in the context of limit order books. Two distinct Hawkes-based models are proposed and analyzed: one utilizing exponential kernels and the other employing power-law kernels. These models are implemented within a bivariate framework. The performance of each model is evaluated

Zhongyu Ouyang, Chunhui Zhang, Yaning Jia, Soroush Vosoughi

In modern social media, recommender systems (RecSys) rely on the click-through rate (CTR) as the standard metric to evaluate user engagement. CTR prediction is traditionally framed as a binary classification task to predict whether a user will interact with a given item. However, this approach overlooks the complexity of real-world social modeling, where the

Yu-Fu Shen

The stellar initial mass function is of great significance for the study of star formation and galactic structure. Observations indicate that the IMF follows a power-law form. This work derived that when the expected number of stars formed from a spherical molecular cloud is much greater than 1, there is a relationship between the slope $\alpha$ of the IMF a

Filippo Calderoni, Dima Sinapova

In this paper we continue the study of equivalence of generics filters started by Smythe in [Smy22]. We fully characterize those forcing posets for which the corresponding equivalence of generics is smooth using the purely topological property of condensation. Next we leverage our characterization to show that there are non-homogeneous forcing for which equi

Wasura D. Wattearachchi, Erandi Lakshika, Kathryn Kasmarik, Michael Barlow

This paper introduces a framework for human swarm interaction studies that measures situation awareness in dynamic environments. A tablet-based interface was developed for a user study by implementing the concepts introduced in the framework, where operators guided a robotic swarm in a single-target search task, marking hazardous cells unknown to the swarm.

Jeff Jewett, Sandhya Saisubramanian

Decision-making in complex, continuous multi-task environments is often hindered by the difficulty of obtaining accurate models for planning and the inefficiency of learning purely from trial and error. While precise environment dynamics may be hard to specify, human experts can often provide high-fidelity abstractions that capture the essential high-level s

Wenwen Huo, Chao Zhang

In this paper, we investigate the global well-posedness and optimal time-decay of classical solutions for the 3-D full compressible Navier-Stokes system, which is given by the motion of the compressible viscous and heat-conductive gases. First of all, we study the global well-posedness of the Cauchy problem to the system when the initial data is small enough

Xuenan Li, Mihnea Leonte, Christian D. Santangelo, Miranda Holmes-Cerfon

Singularity analysis is essential in robot kinematics, as singular configurations cause loss of control and kinematic indeterminacy. This paper models singularities in bar frameworks as saddle points on constrained manifolds. Given an under-constrained, non-singular bar framework, by allowing one edge to vary its length while fixing lengths of others, we def

Nicolás Laverde, Melissa Robles, Johan Rodríguez

We present Shap-MeD, a text-to-3D object generative model specialized in the biomedical domain. The objective of this study is to develop an assistant that facilitates the 3D modeling of medical objects, thereby reducing development time. 3D modeling in medicine has various applications, including surgical procedure simulation and planning, the design of per

Pawel Rubach

This paper presents the Kafka Slurm Agent (KSA), an open source (Apache 2.0 license) distributed computing and stream processing engine designed to help researchers distribute Python-based computational tasks across multiple Slurm-managed HPC clusters and workstations. Written entirely in Python, this extensible framework utilizes an Apache Kafka broker for

Hunter Ellis, Wei Jia, Imteaz Rahaman, Apostoli Hillas

Ga2O3 Schottky barrier diodes featuring a field plate and a composite SiO2/SiNx dielectric layer beneath the field plate were fabricated, achieving a breakdown voltage of 2.4 kV at room temperature. Electrical performance and degradation were analyzed via I-V and C-V measurements from 25 {\deg}C to 500 {\deg}C, revealing temperature-dependent transport, inte

Wuming Yang, Haibo Yuan, Yaqian Wu, Shaolan Bi

The chemical composition of the Sun is still a highly controversial issue. No solar model has yet been able to simultaneously reproduce the solar lithium and beryllium abundances, along with helioseismic results, including the rotation profile. Lithium and beryllium are fragile elements that are highly sensitive to the physical conditions, as well as to tran

Michelle Blom, Alexander Ek, Peter J. Stuckey, Vanessa Teague

Constructing efficient risk-limiting audits (RLAs) for multiwinner single transferable vote (STV) elections is a challenging problem. An STV RLA is designed to statistically verify that the reported winners of an election did indeed win according to the voters' expressed preferences and not due to mistabulation or interference, while limiting the risk of acc

Mariam Nour, Mohamed H. Zaki, Mohamed Abdel-Aty

Work zones play a key role in road and highway maintenance but can lead to significant risks to both drivers and workers. Smart Work Zones (SWZs) have emerged as a potential solution, offering decision-makers real-time insights into the status of the work zone. By utilizing work zone barrels equipped with sensors and communication nodes, SWZs facilitate coll

Fatemeh Dehrouyeh, Ibrahim Shaer, Soodeh Nikan, Firouz Badrkhani Ajaei

With the growing need for real-time processing on IoT devices, optimizing machine learning (ML) models' size, latency, and computational efficiency is essential. This paper investigates a pruning method for anomaly detection in resource-constrained environments, specifically targeting Electric Vehicle Charging Infrastructure (EVCI). Using the CICEVSE2024 dat

Matthew P. Bland, Faranak Bahrami, Jeronimo G. C. Martinez, Paal H. Prestegaard

Materials improvements are a powerful approach to reducing loss and decoherence in superconducting qubits because such improvements can be readily translated to large scale processors. Recent work improved transmon coherence by utilizing tantalum (Ta) as a base layer and sapphire as a substrate. The losses in these devices are dominated by two-level systems

Varich Boonsanong, Vidhisha Balachandran, Xiaochuang Han, Shangbin Feng

With the widespread consumption of AI-generated content, there has been an increased focus on developing automated tools to verify the factual accuracy of such content. However, prior research and tools developed for fact verification treat it as a binary classification or a linear regression problem. Although this is a useful mechanism as part of automatic

Mark Braverman, Jingyi Liu, Jieming Mao, Jon Schneider

The adversarial Bandit with Knapsack problem is a multi-armed bandits problem with budget constraints and adversarial rewards and costs. In each round, a learner selects an action to take and observes the reward and cost of the selected action. The goal is to maximize the sum of rewards while satisfying the budget constraint. The classical benchmark to compa

Jake Fawkes, Michael O'Riordan, Athanasios Vlontzos, Oriol Corcoll

Observational data is often readily available in large quantities, but can lead to biased causal effect estimates due to the presence of unobserved confounding. Recent works attempt to remove this bias by supplementing observational data with experimental data, which, when available, is typically on a smaller scale due to the time and cost involved in runnin

Dougal Davis, Lucas Mason-Brown

We study the Hodge filtrations of Schmid and Vilonen on unipotent representations of real reductive groups. We show that for various well-defined classes of unipotent representations (including, for example, the oscillator representations of metaplectic groups, the minimal representations of all simple groups, and all unipotent representations of complex gro

M. Rejmund, A. Lemasson

The VAMOS++ magnetic spectrometer is characterized by a large angular and momentum acceptance and highly non-linear ion optics properties requiring the use of software ion trajectory reconstruction methods to measure the ion magnetic rigidity and the trajectory length between the beam interaction point and the focal plane of the spectrometer. Standard measur

Hannah Lin, Martin Maas, Maximilian Roquemore, Arman Hasanzadeh

With the end of Moore's Law, optimizing code for performance has become paramount for meeting ever-increasing compute demands, particularly in hyperscale data centers where even small efficiency gains translate to significant resource and energy savings. Traditionally, this process requires significant programmer effort to identify optimization opportuni

Daniel Tinoco, Raquel Menezes, Carlos Baquero

This paper presents a novel approach to classical linear regression, enabling model computation from data streams or in a distributed setting while preserving data privacy in federated environments. We extend this framework to generalized linear models (GLMs), ensuring scalability and adaptability to diverse data distributions while maintaining privacy-prese

Samuel Klein, Matthew Leigh, Stephen Mulligan, Tobias Golling

Supervised deep learning methods have been successful in the field of high energy physics, and the trend within the field is to move away from high level reconstructed variables to lower level, higher dimensional features. Supervised methods require labelled data, which is typically provided by a simulator. As the number of features increases, simulation acc

Dominik Krengel

Despite the simple impact of a rigid particle being a centuries old problem, a conclusive treatment of the general case is still outstanding. The influences of particle shape as well as elastic and plastic deformation of the particle upon contact significantly complicate the problem. Experiments have shown the possibilities of backward movement as well as fo

Jining Tang, Yang Huang, Hongsheng Zhang

Gravitational collapse and bubble evolution in the asymptotical Friedmann-Lemaitre-Robertson-Walker (FLRW) Universe is an intriguing and intricate problem. We systematically analyze dynamics for contact \sch-FLRW (McVittie) spacetimes, focusing on their general junction conditions and introducing a novel function to simplify the extrinsic curvature and surfa

Belle II Collaboration, I. Adachi, L. Aggarwal, H. Ahmed

We present measurements of $B \to K{}^{*}(892)γ$ decays using $365\,{\rm fb}^{-1}$ of data collected from 2019 to 2022 by the Belle~II experiment at the SuperKEKB asymmetric-energy $e^+e^-$ collider. The data sample contains $(387 \pm 6) \times 10^6$ $Υ(4S)$ events. We measure branching fractions ($\mathcal{B}$) and $C\!P$ asymmetries ($\mathcal{A}_{C\!P}$)

Sun Woo Park

Fix a prime number $p$. Let $\mathbb{F}_q$ be a finite field of characteristic coprime to 2, 3, and $p$, which also contains the primitive $p$-th root of unity $μ_p$. Based on the works by Swinnerton-Dyer and Klagsbrun, Mazur, and Rubin, we prove that the probability distribution of the sizes of prime Selmer groups over a family of cyclic prime twists of non