December 2024 arXiv papers — page 126

Markus J. Stroppel

We construct a model of the Hermitian unital of order 3 (obtained from the non-degenerate hermitian form in three variables over the field of order 9) inside the octonion algebra over the field of order 2. This construction is invariant under the automorphism group of that algebra, and explains the known isomorphism from the finite group of exceptional Lie t

Borjan Geshkovski, Philippe Rigollet, Yihang Sun

We consider the Gaussian kernel density estimator with bandwidth $\beta^{-\frac12}$ of $n$ iid Gaussian samples. Using the Kac-Rice formula and an Edgeworth expansion, we prove that the expected number of modes on the real line scales as $\Theta(\sqrt{\beta\log\beta})$ as $\beta,n\to\infty$ provided $n^c\lesssim \beta\lesssim n^{2-c}$ for some constant $c>0$

Elisa Negrini, Almanzo Jiahe Gao, Abigail Bowering, Wei Zhu

We introduce two convolutional neural network (CNN) architectures, inspired by the Merriman-Bence-Osher (MBO) algorithm and by cellular automatons, to model and learn threshold dynamics for front evolution from video data. The first model, termed the (single-dynamics) MBO network, learns a specific kernel and threshold for each input video without adapting t

Zhenni Bi, Kai Han, Chuanjian Liu, Yehui Tang

Large Language Models (LLMs) have demonstrated remarkable abilities across various language tasks, but solving complex reasoning problems remains a significant challenge. While existing methods, such as Chain-of-Thought (CoT) and Tree-of-Thought (ToT), enhance reasoning by decomposing problems or structuring prompts, they typically perform a single pass of r

Ya-xiang Yuan, Yi Zhang

The proximal point algorithm plays a central role in non-smooth convex programming. The Augmented Lagrangian Method, one of the most famous optimization algorithms, has been found to be closely related to the proximal point algorithm. Due to its importance, accelerated variants of the proximal point algorithm have received considerable attention. In this pap

Qing Yan, Hailong Li, Hua Jiang, Qing-Feng Sun

Topological systems hosting gapless boundary states have attracted huge attention as promising components for next-generation information processing, attributed to their capacity for dissipationless electronics. Nevertheless, recent theoretical and experimental inquiries have revealed the emergence of energy dissipation in precisely quantized electrical tran

Emil Håkansson, Amelie Piveteau, Alban Seguinard, Muhammad Sadiq

We present a derivation and experimental implementation of a dimension-dependent contextuality inequality to certify both the quantumness and dimensionality of a given system. Existing methods for certification of the dimension of quantum system can be cheated by using larger classical systems, creating a potential loophole in these benchmarks. Our approach

Qingyang Guan

This note is to study Bourgain's slicing problem following the routes investigated in the last decade. We show that the slicing constant $L_n$ is bounded by $C\log(\log n) $, $n\geq 3$, for some universal constant $C$.

Jin-Seop Lee, Noo-ri Kim, Jee-Hyong Lee

Self-supervised learning (SSL) methods based on the instance discrimination tasks with InfoNCE have achieved remarkable success. Despite their success, SSL models often struggle to generate effective representations for unseen-domain data. To address this issue, research on unsupervised domain generalization (UDG), which aims to develop SSL models that can g

Wenqian Li, Pengfei Fang, Hui Xue

Cross-Domain Few-Shot Learning (CD-FSL) aims to transfer knowledge from seen source domains to unseen target domains, which is crucial for evaluating the generalization and robustness of models. Recent studies focus on utilizing visual styles to bridge the domain gap between different domains. However, the serious dilemma of gradient instability and local op

Honggyu An, Jinhyeon Kim, Seonghoon Park, Jaewoo Jung

In this work, we explore new perspectives on cross-view completion learning by drawing an analogy to self-supervised correspondence learning. Through our analysis, we demonstrate that the cross-attention map within cross-view completion models captures correspondence more effectively than other correlations derived from encoder or decoder features. We verify

Kabir S. Suraj, M. Hussein N. Assadi

We employ accurate density functional theory calculations to examine the electronic structure of three Ni/SrO$_2$ nanostructures containing single-layer, bilayer and four-layer Ni nanosheets. The single Ni layer interacts strongly with the topmost oxygen layer at the Ni/SrO$_2$ interface, resulting in significant surface reconstruction and strong hybridisati

Lin Zhang, Bing Xie, Bo Li

Certain unitary-invariants, known as Bargmann invariants or multivariate traces of quantum states, have recently gained attention due to their applications in quantum information theory. However, determining the boundaries of sets of Bargmann invariants remains a theoretical challenge. In this study, we address the problem by developing a unified, dimension-

Takashi Yamazoe

Let $\mathcal{E}$ denote the $\sigma$-ideal generated by closed null sets on the reals. We show that the uniformity and the covering of $\mathcal{E}$ can be added to Cicho\'n's maximum with distinct values. More specifically, it is consistent that $\aleph_1<\mathrm{add}(\mathcal{N})<\mathrm{cov}(\mathcal{N})<\mathfrak{b}<\mathrm{non}(\mathcal{E})<\mathrm{non

Shachar Shayovitz, Doron Ezri, Yoav Levinbook

MIMO systems can simultaneously transmit multiple data streams within the same frequency band, thus exploiting the spatial dimension to enhance performance. MIMO detection poses considerable challenges due to the interference and noise introduced by the concurrent transmission of multiple streams. Efficient Uplink (UL) MIMO detection algorithms are crucial f

Vedran Krčadinac, Mario Osvin Pavčević

We study two kinds of generalizations of symmetric block designs to higher dimensions, the so-called $\mathcal{C}$-cubes and $\mathcal{P}$-cubes. For small parameters, all examples up to equivalence are determined by computer calculations. Known properties of automorphisms of symmetric designs are extended to autotopies of $\mathcal{P}$-cubes, while countere

Yusuke Inoue, Keiichi Maeda

Type Ibn/Icn supernovae (SNe Ibn/Icn), which are characterized by narrow helium or carbon lines originated in hydrogen-poor dense circumstellar medium (CSM), provide new insights into the final evolution of massive stars. While SNe Ibn/Icn are expected to emit strong X-rays through the strong SN-CSM interaction, the X-ray emission modeling effort has been li

Chenxing Jia, Mingjie Cai, Hamido Fujita

Multi-view clustering has become increasingly important due to the multi-source character of real-world data. Among existing multi-view clustering methods, multi-kernel clustering and matrix factorization-based multi-view clustering have gained widespread attention as mainstream approaches. However, multi-kernel clustering tends to learn an optimal kernel an

Jun-Xian Li, Shuang Wang

Holographic dark energy (HDE), which arises from a theoretical attempt to apply the holographic principle (HP) to the dark energy (DE) problem, has attracted significant attention over the past two decades. We perform a comprehensive numerical study on HDE models that can be classified into four categories: 1) HDE models with other characteristic length scal

Chunxiao Li, Xiaoxiao Wang, Boming Miao, Chuanlong Xie

Image classification serves as the cornerstone of computer vision, traditionally achieved through discriminative models based on deep neural networks. Recent advancements have introduced classification methods derived from generative models, which offer the advantage of zero-shot classification. However, these methods suffer from two main drawbacks: high com

Lucas Invernizzi, Jean-françois Lemaître, Mathieu Douhard

In its initial form, the expensive son hypothesis postulates that sons from male-biased sexually dimorphic species require more food during growth than daughters, which ultimately incur fitness costs for mothers predominantly producing and rearing sons. We first dissect the evolutionary framework in which the expensive son hypothesis is rooted, and we provid

Shuangshuang Chen, Zijun Wan, Xiaohua Yao

This paper is devoted to the time decay estimates for the following beam equation with a potential on the line: $$ \partial_t^2 u + \left( \Delta^2 + m^2 + V(x) \right) u = 0, \ \ u(0, x) = f(x),\quad \partial_t u(0, x) = g(x), $$ where $V$ is a real-valued decaying potential on $\mathbb{R}$, and $m \in \mathbb{R}$. Let $H = \Delta^2 + V$ and $P_{ac}(H)$ den

George Rapakoulias, Ali Reza Pedram, Fengjiao Liu, Lingjiong Zhu

Schrodinger Bridges (SBs) are diffusion processes that steer, in finite time, a given initial distribution to another final one while minimizing a suitable cost functional. Although various methods for computing SBs have recently been proposed in the literature, most of these approaches require computationally expensive training schemes, even for solving low

Huanqi Yang, Mingzhe Li, Mingda Han, Zhenjiang Li

Embedded IoT system development is crucial for enabling seamless connectivity and functionality across a wide range of applications. However, such a complex process requires cross-domain knowledge of hardware and software and hence often necessitates direct developer involvement, making it labor-intensive, time-consuming, and error-prone. To address this cha

Yuexin Li, Hiok Kuek Tan, Qiaoran Meng, Mei Lin Lock

Phishing is a critical cyber threat, exploiting deceptive tactics to compromise victims and cause significant financial losses. While reference-based phishing detectors (RBPDs) have achieved notable advancements in detection accuracy, their real-world deployment is hindered by challenges such as high latency and inefficiency in URL analysis. To address these

Shuo Shi, Chao Peng, Chenyang Xu, Zhengfeng Yang

The paper studies sequential reasoning over graph-structured data, which stands as a fundamental task in various trending fields like automated math problem solving and neural graph algorithm learning, attracting a lot of research interest. Simultaneously managing both sequential and graph-structured information in such tasks presents a notable challenge. Ov

Wenrui Li, Zhe Yang, Wei Han, Hengyu Man

Reconstructing desired objects and scenes has long been a primary goal in 3D computer vision. Single-view point cloud reconstruction has become a popular technique due to its low cost and accurate results. However, single-view reconstruction methods often rely on expensive CAD models and complex geometric priors. Effectively utilizing prior knowledge about t

Jiayi Zhao, Dongdong Weng, Qiuxin Du, Zeyu Tian

Human motion generation involves creating natural sequences of human body poses, widely used in gaming, virtual reality, and human-computer interaction. It aims to produce lifelike virtual characters with realistic movements, enhancing virtual agents and immersive experiences. While previous work has focused on motion generation based on signals like movemen

Tingting Yu

GB migration plays a central role in microstructural evolution. Many experiments and simulations have been conducted to clarify the relationships between GB velocity and various parameters to tailor GB networks. However, the complexity of GB migration has surpassed initial expectations. In this study, the ramped Energy Conserving Orientational (r-ECO) Drivin

Leon Glass, Katharina Ensinger, Christoph Zimmer

Gaussian Process differential equations (GPODE) have recently gained momentum due to their ability to capture dynamics behavior of systems and also represent uncertainty in predictions. Prior work has described the process of training the hyperparameters and, thereby, calibrating GPODE to data. How to design efficient algorithms to collect data for training

Cheng Mei, Hao He, Yahui Liu, Zhenhua Guo

In the technical report, we present a novel transformer-based framework for nuScenes lidar-based object detection task, termed Spatial Expansion Group Transformer (SEGT). To efficiently handle the irregular and sparse nature of point cloud, we propose migrating the voxels into distinct specialized ordered fields with the general spatial expansion strategies,

András Sasfi, Alberto Padoan, Ivan Markovsky, Florian Dörfler

This paper introduces an online approach for identifying time-varying subspaces defined by linear dynamical systems. The approach of representing linear systems by non-parametric subspace models has received significant interest in the field of data-driven control recently. This system representation enables us to provide rigorous guarantees for linear time-

Philipp Spelten, Dominik Wilde, Mario Christopher Bedrunka, Dirk Reith

Lattice Boltzmann method (LBM) simulations of incompressible flows are nowadays common and well-established. However, for compressible turbulent flows with strong variable density and intrinsic compressibility effects, results are relatively scarce. Only recently, progress was made regarding compressible LBM, usually applied to simple one and two-dimensional

Mingda Jia, Liming Zhao, Ge Li, Yun Zheng

Spatial contexts, such as the backgrounds and surroundings, are considered critical in Human-Object Interaction (HOI) recognition, especially when the instance-centric foreground is blurred or occluded. Recent advancements in HOI detectors are usually built upon detection transformer pipelines. While such an object-detection-oriented paradigm shows promise i

Mengze Hong, Wailing Ng, Chen Jason Zhang, Yuanfeng Song

Discovering customer intentions is crucial for automated service agents, yet existing intent clustering methods often fall short due to their reliance on embedding distance metrics and neglect of underlying semantic structures. To address these limitations, we propose an LLM-in-the-loop (LLM-ITL) intent clustering framework, integrating the language understa

Shuying Qiao, Paul Denny, Nasser Giacaman

The integration of large language models (LLMs) into computing education offers many potential benefits to student learning, and several novel pedagogical approaches have been reported in the literature. However LLMs also present challenges, one of the most commonly cited being that of student over-reliance. This challenge is compounded by the fact that LLMs

Shohei Yuasa, Yohei Kono, Yuta Ozaki, Minoru Yamashita

The Gr\"{u}neisen ratio $\Gamma$ and its magnetic analog, the magnetic Gr\"{u}neisen ratio $\Gamma_H$, are powerful probes to study the nature of quantum phase transitions. Here, we propose a Gr\"{u}neisen parameter, the rotational Gr\"{u}neisen ratio $\Gamma_\phi$, by introducing the orientation of the external field as a control parameter. We investigate $

Wenna Lai, Haoran Xie, Guandong Xu, Qing Li

Implicit sentiment analysis (ISA) presents significant challenges due to the absence of salient cue words. Previous methods have struggled with insufficient data and limited reasoning capabilities to infer underlying opinions. Integrating multi-task learning (MTL) with large language models (LLMs) offers the potential to enable models of varying sizes to rel

Xuebin Wang, Lei Zhang, Zhenghua Li, Shilin Zhou

Inspired by early research on exploring naturally annotated data for Chinese Word Segmentation (CWS), and also by recent research on integration of speech and text processing, this work for the first time proposes to explicitly mine word boundaries from speech-text parallel data. We employ the Montreal Forced Aligner (MFA) toolkit to perform character-level

Haocong Rao, Chunyan Miao

Person re-identification (re-ID) via 3D skeleton data is a challenging task with significant value in many scenarios. Existing skeleton-based methods typically assume virtual motion relations between all joints, and adopt average joint or sequence representations for learning. However, they rarely explore key body structure and motion such as gait to focus o

Hui Dou, Xinyu Mu, Mengjun Yi, Feng Han

Convolutional Neural Networks (CNNs) have demonstrated remarkable prowess in the field of computer vision. However, their opaque decision-making processes pose significant challenges for practical applications. In this study, we provide quantitative metrics for assessing CNN filters by clustering the feature maps corresponding to individual filters in the mo

Hao Lu, Tianshuo Xu, Wenzhao Zheng, Yunpeng Zhang

Photorealistic 4D reconstruction of street scenes is essential for developing real-world simulators in autonomous driving. However, most existing methods perform this task offline and rely on time-consuming iterative processes, limiting their practical applications. To this end, we introduce the Large 4D Gaussian Reconstruction Model (DrivingRecon), a genera

Stavros Melemenidis, Anna Stephanie Kim, Jenny Vo-Phamhi, Edward Graves

Background: This protocol introduces an ultrasound (US) configuration for whole-body 3D dynamic contrast-enhanced ultrasound (DCE-US) imaging in preclinical applications. The set-up relies on a clinical abdominal matrix US probe to enable mice imaging beyond current preclinical systems that are generally unable to capture whole-body volumetric and dynamic im

Zirui Chen, Zhaoyang Zhang, Chenyu Liu, Ziqing Xing

Research on leveraging big artificial intelligence model (BAIM) technology to drive the intelligent evolution of wireless networks is emerging. However, breakthroughs in generalization brought about by BAIM techniques mainly occur in natural language processing. There is a lack of a clear technical direction on how to efficiently apply BAIM techniques to wir

Aisha S. Almannaei, Daisuke Kawata, Ioana Ciuca, Connor Fallows

In the era of Gaia, the accurate determination of stellar ages is transforming Galactic archaeology. We demonstrate the feasibility of inferring stellar ages from Gaia's RVS spectra and the BP/RP (XP) spectrophotometric data, specifically for red giant branch and high-mass red clump stars. We successfully train two machine learning models, dubbed SIDRA: Stel

Jiahao Cheng, Zhuo Chen, Yu Qiao, Maosong Xiang

Consider a Lie subalgebra $\mathfrak{l} \subset \mathfrak{g}$ and an $\mathfrak{l}$-invariant open submanifold $V \subset \mathfrak{l}^{\ast}$. We demonstrate that any smooth dynamical twist on $V$, valued in $U(\mathfrak{g}) \otimes U(\mathfrak{g})\llbracket \hbar \rrbracket$, establishes a twistor on the associated quantum groupoid when combined with the G

Wenxuan Liu, Monde Duinkharjav, Qi Sun, Sai Qian Zhang

Leveraging real-time eye-tracking, foveated rendering optimizes hardware efficiency and enhances visual quality virtual reality (VR). This approach leverages eye-tracking techniques to determine where the user is looking, allowing the system to render high-resolution graphics only in the foveal region-the small area of the retina where visual acuity is highe

Lei Liu, Xiujuan Zhang, Ming-Hui Lu, Yan-Feng Chen

Multimode interference (MMI) is a fundamental physical principle that plays a crucial role in modern communication technologies for wave splitting, filtering, switching and multiplexing. Typically, the generation of multimodes is highly dependent on the waveguide's cross-section, particularly its width, by which the mode profiles and the interference pattern

Daniel Geissler, Dominique Nshimyimana, Vitor Fortes Rey, Sungho Suh

The research of machine learning (ML) algorithms for human activity recognition (HAR) has made significant progress with publicly available datasets. However, most research prioritizes statistical metrics over examining negative sample details. While recent models like transformers have been applied to HAR datasets with limited success from the benchmark met

Meizhi Zhong, Xikai Liu, Chen Zhang, Yikun Lei

Large Language models (LLMs) have become a research hotspot. To accelerate the inference of LLMs, storing computed caches in memory has become the standard technique. However, as the inference length increases, growing KV caches might lead to out-of-memory issues. Many existing methods address this issue through KV cache compression, primarily by preserving

Teddy Lazebnik

Data-driven models, in general, and machine learning (ML) models, in particular, have gained popularity over recent years with an increased usage of such models across the scientific and engineering domains. When using ML models in realistic and dynamic environments, users need to often handle the challenge of concept drift (CD). In this study, we explore th

Mengze Hong, Chen Jason Zhang, Chaotao Chen, Rongzhong Lian

Maintaining persona consistency is paramount in the application of open-domain dialogue systems, as exemplified by models like ChatGPT. Despite significant advancements, the limited scale and diversity of current persona dialogue datasets remain challenges to achieving robust persona-consistent dialogue models. In this study, drawing inspiration from the suc

Daowen Lin, Xi-Nan Ma

We derive an integral identity for a class $p$-Laplace equation, and then classify all positive finite energy cylindrically symmetric solutions of the equation (\ref{1.2}) for $3\leq k\leq n-1,$ with the help of some a prior estimates. Combining this with the result of Secchi-Smets-Willem{\cite{SSW03}}, as a consequence, we obtain the best constant and extre

Zhoulin Ji, Chenhao Lin, Hang Wang, Chao Shen

Detecting synthetic from real speech is increasingly crucial due to the risks of misinformation and identity impersonation. While various datasets for synthetic speech analysis have been developed, they often focus on specific areas, limiting their utility for comprehensive research. To fill this gap, we propose the Speech-Forensics dataset by extensively co

Thomas Hotz, Achim Ilchmann

Giovanni Battista Benedetti (1530--1590) derived two constructions of ovals given their minor and major axes. These were published in 1585 and seem to be the first solution to this problem. Therefore, the generally accepted view that ``the geometrical construction for drawing an oval for any given proportion was not known in the sixteenth century'' (Ana L\'o

Zhihao Ding, Ting Zhang, Yiran Li, Jieming Shi

Organic Solar Cells (OSCs) are a promising technology for sustainable energy production. However, the identification of molecules with desired OSC properties typically involves laborious experimental research. To accelerate progress in the field, it is crucial to develop machine learning models capable of accurately predicting the properties of OSC molecules

Wenjie Mei, Xiaorui Wang, Yanrong Lu, Ke Yu

Learning models for dynamical systems in continuous time is significant for understanding complex phenomena and making accurate predictions. This study presents a novel approach utilizing differential neural networks (DNNs) to model nonlinear systems, specifically permanent magnet synchronous motors (PMSMs), and to predict their current trajectories. The eff

Damiano F. G. Fiorillo, Manuel Goimil-García, Georg G. Raffelt

Even in the absence of neutrino masses, a neutrino gas can exhibit a homogeneous flavor instability that leads to a periodic motion known as the fast flavor pendulum. A well-known necessary condition is a crossing of the angular flavor lepton distribution. In an earlier work, some of us showed that homogeneous flavor instabilities also obey a Nyquist criteri

Hang Zhou, Jiale Cai, Yuteng Ye, Yonghui Feng

A recent endeavor in one class of video anomaly detection is to leverage diffusion models and posit the task as a generation problem, where the diffusion model is trained to recover normal patterns exclusively, thus reporting abnormal patterns as outliers. Yet, existing attempts neglect the various formations of anomaly and predict normal samples at the feat

Zhihan Chen, Xindi Zhang, Yuhang Qian, Shaowei Cai

In the application of IC design for microprocessors, there are often demands for optimizing the implementation of datapath circuits, on which various arithmetic operations are performed. Combinational equivalence checking (CEC) plays an essential role in ensuring the correctness of design optimization. The most prevalent CEC algorithms are based on SAT sweep

Advait Joglekar, Srinivasan Umesh

Neural Machine Translation (NMT) models are typically trained on datasets with limited exposure to Scientific, Technical and Educational domains. Translation models thus, in general, struggle with tasks that involve scientific understanding or technical jargon. Their performance is found to be even worse for low-resource Indian languages. Finding a translati

Robert Rugg, Shambel Sahlu, Amare Abebe

This work focuses on two linear interaction models between dark matter and dark energy, which are proposed as key factors in explaining cosmic history, specifically the latetime accelerating expansion of the universe. Both models are constrained using a Markov chain Monte Carlo analysis (MCMC) using different sets of observational data. The analysis was comp

Shanhe Su, Jincan Chen

By starting from the Euler chain rule of three thermodynamic quantities, it is proved that both the Nernst equation and the vanishing heat capacity at absolute zero temperature are mutually deducible and equivalent. Simultaneously, it is pointed out that the conclusions of the relevant literature are worthy of discussion.

Shihao Xu, Yiyang Luo, Wei Shi

Geometry mathematics problems pose significant challenges for large language models (LLMs) because they involve visual elements and spatial reasoning. Current methods primarily rely on symbolic character awareness to address these problems. Considering geometry problem solving is a relatively nascent field with limited suitable datasets and currently almost

Tarik Sahin, Daniel Wolff, Alexander Popp

This paper explores the application of physics-informed neural networks (PINNs) to tackle forward problems in 3D contact mechanics, focusing on small deformation elasticity. We utilize a mixed-variable formulation, enhanced with output transformations, to enforce Dirichlet and Neumann boundary conditions as hard constraints. The inherent inequality constrain

Alexander F. Siegenfeld, Asier Piñeiro Orioli, Robin Na, Blake Elias

Although pandemics are often studied as if populations are well-mixed, disease transmission networks exhibit a multi-scale structure stretching from the individual all the way up to the entire globe. The COVID-19 pandemic has led to an intense debate about whether interventions should prioritize public health or the economy, leading to a surge of studies ana

Seongjun Kim, Seongah Jeong

This letter aims to provide sensing capabilities for a potential eavesdropper, while simultaneously enabling the secure communications with the legitimate users in a cell-free multipleinput multiple-output system with limited fronthaul links. In order to maximize the sensing performance, the joint design of fronthaul compression and beamforming is proposed c

Yihuai Zhang, Jean Auriol, Huan Yu

This paper addresses the problem of robust stabilization for linear hyperbolic Partial Differential Equations (PDEs) with Markov-jumping parameter uncertainty. We consider a 2 x 2 heterogeneous hyperbolic PDE and propose a control law using operator learning and the backstepping method. Specifically, the backstepping kernels used to construct the control law

Azuna Nishida

Kontsevich and Soibelman discussed homological mirror symmetry by using the SYZ torus fibrations, where they introduced the weighted version of Fukaya-Oh's Morse homotopy on the base space of the dual torus fibration in the intermediate step. Futaki and Kajiura applied Kontsevich-Soibelman's approach to the case when a complex manifold $X$ is a smooth compac

Sebastian Pape, Michael Moll, Marcos Fernández García, Moritz Wiehe

Device simulation plays a crucial role in complementing experimental device characterisation by enabling deeper understanding of internal physical processes. However, for simulations to be trusted, experimental validation is essential to confirm the accuracy of the conclusions drawn.In the framework of semiconductor detector characterisation, one powerful to

Xu-Lin Dong, Shu-Wei Ma, Yi-Qing Guo, Shu-Wang Cui

The electron spectrum exhibits a complex structure and has controversially proposed origins. This work reproduce the evolution of the electron spectrum based on a spatially dependent propagation (SDP) model. The key point is that our SPD model features two diffusion regions leading to two diffusion timescales, competing with the cooling timescale. This resul

Hamed Fayyaz, Mehak Gupta, Alejandra Perez Ramirez, Claudine Jurkovitz

Reliable prediction of pediatric obesity can offer a valuable resource to providers, helping them engage in timely preventive interventions before the disease is established. Many efforts have been made to develop ML-based predictive models of obesity, and some studies have reported high predictive performances. However, no commonly used clinical decision su

X. Chen, J. An, H. Wu, S. Li

Motor imagery (MI) is a classical paradigm in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Online accurate and fast decoding is very important to its successful applications. This paper proposes a simple yet effective front-end replication dynamic window (FRDW) algorithm for this purpose. Dynamic windows enable the classification based

Sihan Tan, Taro Miyazaki, Nabeela Khan, Kazuhiro Nakadai

Current sign language translation (SLT) approaches often rely on gloss-based supervision with Connectionist Temporal Classification (CTC), limiting their ability to handle non-monotonic alignments between sign language video and spoken text. In this work, we propose a novel method combining joint CTC/Attention and transfer learning. The joint CTC/Attention i

Zongsheng Yue, Kang Liao, Chen Change Loy

This study presents a new image super-resolution (SR) technique based on diffusion inversion, aiming at harnessing the rich image priors encapsulated in large pre-trained diffusion models to improve SR performance. We design a Partial noise Prediction strategy to construct an intermediate state of the diffusion model, which serves as the starting sampling po

Abdelrahman Sadallah, Daria Kotova, Ekaterina Kochmar

Cryptic crosswords are puzzles that rely on general knowledge and the solver's ability to manipulate language on different levels, dealing with various types of wordplay. Previous research suggests that solving such puzzles is challenging even for modern NLP models, including Large Language Models (LLMs). However, there is little to no research on the reason

Sean Nian, Angela Huang, Ben Reed

ActivityPub is a decentralized social networking protocol that has gained significant attention from the media for its ability to communicate through the Fediverse, short for the federated web. Servers such as Mastodon implement the ActivityPub protocol to communicate over the Fediverse. In this paper, we deconstruct the core protocols used to build the dist

Yusuke Sakemi, Yuji Okamoto, Takashi Morie, Sou Nobukawa

Large-scale deep learning models are increasingly constrained by their immense energy consumption, limiting their scalability and applicability for edge intelligence. In-memory computing (IMC) offers a promising solution by addressing the von Neumann bottleneck inherent in traditional deep learning accelerators, significantly reducing energy consumption. How

Sumanth Kumar Boya, Deepak Subramani

Initial boundary value problems arise commonly in applications with engineering and natural systems governed by nonlinear partial differential equations (PDEs). Operator learning is an emerging field for solving these equations by using a neural network to learn a map between infinite dimensional input and output function spaces. These neural operators are t

Yuqi Tong, Yue Qiu, Ruiyang Li, Shi Qiu

We present MS2Mesh-XR, a novel multi-modal sketch-to-mesh generation pipeline that enables users to create realistic 3D objects in extended reality (XR) environments using hand-drawn sketches assisted by voice inputs. In specific, users can intuitively sketch objects using natural hand movements in mid-air within a virtual environment. By integrating voice i

Inga Ivanova, John S. Torday

We study analysis of complex systems using a Quantitative Theory of Meaning developed as an extention of Shannon's Communication Theory. The approach consideres complexity not in terms of the manifestation of its effects which are manifestation of the dynamics of the system, but in terms of primary causes and taking into account the topology of the system. H

Soumita Roy

This document explores the potential of employing Artificial Intelligence (AI), specifically Natural Language Processing (NLP), to strengthen the detection and prevention of financial crimes within the Mobile Financial Services(MFS) of Bangladesh with multilingual scenario. The analysis focuses on the utilization of NLP for adverse media screening, a vital a

Huanyu Wu, Siyang Li, Dongrui Wu

Motor imagery (MI) based brain-computer interfaces (BCIs) enable the direct control of external devices through the imagined movements of various body parts. Unlike previous systems that used fixed-length EEG trials for MI decoding, asynchronous BCIs aim to detect the user's MI without explicit triggers. They are challenging to implement, because the algorit

Georgios Amanatidis, Michael Lampis, Evangelos Markakis, Georgios Papasotiropoulos

This work examines the Conditional Approval Framework for elections involving multiple interdependent issues, specifically focusing on the Conditional Minisum Approval Voting Rule. We first conduct a detailed analysis of the computational complexity of this rule, demonstrating that no approach can significantly outperform the brute-force algorithm under comm

Na Xiang, Jingtao Shi

This paper is concerned with a three-level multi-leader-follower incentive Stackelberg game with $H_\infty$ constraint. Based on $H_2/H_\infty$ control theory, we firstly obtain the worst-case disturbance and the team-optimal strategy by dealing with a nonzero-sum stochastic differential game. The main objective is to establish an incentive Stackelberg strat

K. W. Chen, X. Q. Liu, Y. Wang, Z. Y. Zhu

Here we report the positive muon spin relaxation study on Pr-doped La$_{1.9}$Pr$_{1.1}$Ni$_2$O$_{6.97}$ and oxygen-deficient La$_3$Ni$_2$O$_{6.63}$ polycrystalline under ambient pressure. Zero-field $\mu^+$SR experiments reveal the existence of bulk long-range magnetic order in La$_{1.9}$Pr$_{1.1}$Ni$_2$O$_{6.97}$ with $T_{N}=161\ \rm{K}$, while La$_3$Ni$_2$

Vojtěch Cvrček, Martino Romaniello, Radim Šára, Wolfram Freudling

We applied machine learning to the entire data history of ESO's High Accuracy Radial Velocity Planet Searcher (HARPS) instrument. Our primary goal was to recover the physical properties of the observed objects, with a secondary emphasis on simulating spectra. We systematically investigated the impact of various factors on the accuracy and fidelity of the res

Yunnong Chen, Shuhong Xiao, Yaxuan Song, Zejian Li

Programming has become an essential component of K-12 education and serves as a pathway for developing computational thinking skills. Given the complexity of programming and the advanced skills it requires, previous research has introduced user-friendly tools to support young learners. However, our interviews with six programming educators revealed that curr

Kailas PS, Selvakumaran R, Palani Murugan, Ramesh Kumar

In recent years, significant advancements have been made in deep learning-based object detection algorithms, revolutionizing basic computer vision tasks, notably in object detection, tracking, and segmentation. This paper delves into the intricate domain of Small-Object-Detection (SOD) within satellite imagery, highlighting the unique challenges stemming fro

Nikhil Kasyap Puranam, E. Harikumar

We study the operator formulation of classical mechanics by explicitly applying it to two central potentials in 2 dimensions. After constructing the classical Hamiltonian operators and corresponding Schr\"odinger like equations, we solve for the corresponding classical wave functions associated with these two potentials, viz; Kepler and harmonic potentials.

J. E. S. Terhune, R. Elwell, H. B. Tran Tan, U. C. Perera

The population dynamics of the 229Th isomeric state is studied in a solid-state host under laser illumination. A photoquenching process is observed, where off-resonant vacuum-ultraviolet (VUV) radiation leads to relaxation of the isomeric state. The cross-section for this photoquenching process is measured and a model for the decay process, where photoexcita

William Q. Erickson, Nicholas B. Jones

Two subsets of $\mathbb{Z}_n$ are said to be homometric if they have the same multiset of pairwise cyclic (i.e., Lee) distances. Homometric subsets necessarily have the same cardinality, say $k$. In this paper, for all positive integers $n$, we classify the homometric subsets of $\mathbb{Z}_n$ with cardinality $k=5$ (modulo cyclic shifts and reflections). Ou

Amal Manoharan, Sai Subramanian, Ashwin Joy

Deformation of material lines drives transport and dissipation in many industrial and natural flows. Here we report an exact Eulerian formula for the stretching rate of a material line, also known as the topological entropy, in a prototype two-dimensional fluid. The only requirement is a distribution of eigenvalues of the strain rate tensor and their decorre

Fangyou Gao, Tao Wang, Yijun Wang

Radio observations provide a powerful tool to constrain the assembly of galaxies over cosmic time. Recent deep and wide radio continuum surveys have improved significantly our understanding on radio emission properties of AGNs and SFGs across $0 < z < 4$. This allows us to derive an empirical model of the radio continuum emission of galaxies based on their S

J. C. Jiao, K. W. Chen, A. D. Hillier, T. U. Ito

We report a muon spin relaxation ($\mu$SR) study on polycrystalline noncentrosymmetric superconductor NbGe$_2$~with the superconducting transition temperature $T_c=2.0\sim2.1$~K. Zero-field $\mu$SR~experiment indicates the absence of spontaneous magnetic field in the superconducting state, showing the preservation of time-reversal symmetry in the superconduc

Kandula Eswara Sai Kumar, Supreeth B S, Rajas Dalvi, Aman Mittal

This article presents a comparative analysis of GPU-parallelized implementations of the quantum-inspired evolutionary optimization (QIEO) approach and one of the well-known classical metaheuristic techniques, the genetic algorithm (GA). The study assesses the performance of both algorithms on highly non-linear, non-convex, and non-separable function optimiza

J. C. Jiao, K. W. Chen, O. O. Bernal, P. -C. Ho

We have performed magnetization, specific heat, and muon spin relaxation ($\mu$SR) measurements on single crystals of the noncentrosymmetric superconductor Ir$_{2}$Ga$_{9}$. The isothermal magnetization measurements show that there is a crossover from Type-I to Type-II superconductivity with decreasing temperature. Potential multi-band superconductivity of I

Brett McInnes

We study the intrinsic and extrinsic torsions (defined by analogy with the intrinsic and extrinsic curvatures) of the spatial sections of torsional spacetimes. We consider two possibilities. First, that the intrinsic torsion might prove to be directly observable. Second, that it is not observable, having been ``inflated away'' in the early Universe. We argue

Nikolai Beluhov

Which polygons admit two (or more) distinct lattice tilings of the plane? We call such polygons double tiles. It is well-known that a lattice tiling is always combinatorially isomorphic either to a grid of squares or to a grid of regular hexagons. We focus on the special case of the double tile problem where both tilings are in the square class. For this spe

Mayuri Mathur, Anav Chaudhary, Saurabh Kumar Gupta, Ojaswa Sharma

Medical image colorization can greatly enhance the interpretability of the underlying imaging modality and provide insights into human anatomy. The objective of medical image colorization is to transfer a diverse spectrum of colors distributed across human anatomy from Cryosection data to source MRI data while retaining the structures of the MRI. To achieve