March 2024 arXiv papers — page 148

Jianping Li, Thien-Minh Nguyen, Shenghai Yuan, Lihua Xie

Accurate and consistent construction of point clouds from LiDAR scanning data is fundamental for 3D modeling applications. Current solutions, such as multiview point cloud registration and LiDAR bundle adjustment, predominantly depend on the local plane assumption, which may be inadequate in complex environments lacking of planar geometries or substantial in

Soumya Sasidharan, Naveen Surendran

We study the dynamics of a three-dimensional generalization of Kitaev's honeycomb lattice spin model (defined on the hyperhoneycomb lattice) subjected to a harmonic driving of $J_z$, one of the three types of spin-couplings in the Hamiltonian. Using numerical solutions supported by analytical calculations based on a rotating wave approximation, we find that

Woo-Jin Ahn, Geun-Yeong Yang, Hyun-Duck Choi, Myo-Taeg Lim

Deep learning models for semantic segmentation often experience performance degradation when deployed to unseen target domains unidentified during the training phase. This is mainly due to variations in image texture (\ie style) from different data sources. To tackle this challenge, existing domain generalized semantic segmentation (DGSS) methods attempt to

Shuangjian Guo, Bibhash Mondal, Ripan Saha

In this paper, we investigate the mathematical structure of Nijenhuis Lie triple systems, an extension of classical Lie triple systems augmented with the Nijenhuis operator. Our study focuses on the cohomology of Nijenhuis Lie triple systems and demonstrates how abelian extensions of Nijenhuis Lie triple systems are related to cohomology groups. Additionally

Qing Xu, Qisheng Jiang, Chundong Wang

Byte-addressable non-volatile memory (NVM) sitting on the memory bus is employed to make persistent memory (PMem) in general-purpose computing systems and embedded systems for data storage. Researchers develop software drivers such as the block translation table (BTT) to build block devices on PMem, so programmers can keep using mature and reliable conventio

Doanh C. Bui, Thinh V. Le, Ba Hung Ngo, Tae Jong Choi

Person attribute recognition and attribute-based retrieval are two core human-centric tasks. In the recognition task, the challenge is specifying attributes depending on a person's appearance, while the retrieval task involves searching for matching persons based on attribute queries. There is a significant relationship between recognition and retrieval task

Ryota Yambe, Satoru Hayami

A skyrmion crystal (SkX) has attracted much attention in condensed matter physics, since topologically nontrivial structures induce fascinating physical phenomena. The SkXs have been experimentally observed in a variety of materials, where the Zeeman coupling to the static magnetic field plays an important role in the formation of the SkXs. In this study, we

Emile S. Medvedev, Aleksander Yu. Ermilov, Vladimir G. Ushakov

The latest experimental [Noll et al. Atmos. Chem. Phys. 20(2020)5269] and theoretical [Brooke et al. J. Quant. Spectr. Rad. Transfer 168(2016)142] data on the OH emission intensities are analyzed with use of the NIDL. It is found that the calculated intensities of the $\Delta v>6$ transitions should not be trusted. The analysis of the OH data revealed that t

Kohei Kobayashi

We investigate the effect of analog control errors which deterministically occurs on isolated quantum dynamics. Quantum information technologies require careful control for preparing a desired quantum state used as an information resource. However, in realistic experiment systems, it is difficult to implement the driving Hamiltonian without analog errors and

Yayue Deng, Mohan Xu, Yao Tang

The effectiveness of central bank communication is a crucial aspect of monetary policy transmission. While recent research has examined the influence of policy communication by the chairs of the Federal Reserve on various financial variables, much of the literature relies on rule-based or dictionary-based methods in parsing the language of the chairs, leavin

Raj Shah, Gobinda Majumder

The India-Based Neutrino Observatory (INO) collaboration houses the miniICAL detector, at the transit campus of IICHEP, Madurai, India, which serves as a prototype-detector of the larger Iron-Calorimeter detector (ICAL). The purpose of miniICAL lies in unraveling the intricate engineering challenges inherent in constructing a substantial ICAL-type detector.

Giovanni Carlotti

Seventy-five years ago, Bernardo Dessau passed away. He had been the sole professor of Physics at the University of Perugia for thirty years. With this brief contribution we intend to repay a debt of gratitude, first of all because his scientific merits were probably underestimated, having spent the first fifteen years of his career under the shadow of the g

Masoud H. Nazari, Antar Kumar Biswas

This paper introduces peer to peer (P2P) trading mechanisms based on decentralized Blockchain to facilitate retail electricity market for ever-increasing distributed energy resources (DERs). The Blockchain network supports fast and secure retail trading among DERs and facilitates a sustainable local P2P trading platform. In this decentralized Blockchain arch

Hritwik Bora, Rukaiya Khatoon, Ranjeev Misra, Rupjyoti Gogoi

We consider the broadband spectral energy distribution of the high energy peaked (HBL) blazar Mkn 501 using $\textit{Swift}$-XRT/UVOT, NuSTAR and $\textit{Fermi}$-LAT observations taken between 2013 and 2022. The spectra were fitted with a one-zone leptonic model using synchrotron and synchrotron self-Compton emission from different particle energy distribut

Guohuan Qiu, Dekai Zhang

We study the Neumann problem for special Lagrangian type equations with critical and supercritical phases. These equations naturally generalize the special Lagrangian equation and the k-Hessian equation. By establishing uniform a priori estimates up to the second order, we obtain the existence result using the continuity method. The new technical aspect is o

Liancheng Zhou, Keyao Wu, Yunlu Gong, Jun Fang

We report a detection of GeV $\gamma$-ray emission potentially originating from the pulsar wind nebula in CTA 1 by analyzing about 15 yr of Fermi Large Area Telescope data. By selecting an energy range from 50 GeV to 1 TeV to remove contamination from the $\gamma$-ray pulsar PSR J0007+7303, we have discovered an extended $\gamma$-ray source with a TS value o

Gianluca Geraci, Kayla Clements, Aaron J Olson

In this contribution, we discuss the construction of Polynomial Chaos surrogates for Monte Carlo radiation transport applications via non-intrusive spectral projection. This contribution focuses on improvements with respect to the approach that we previously introduced in previous work. We focus on understanding the impact of re-sampling cost on the algorith

Kaipeng Wang, Zhi Jing, Yongye Su, Yikun Han

This paper delves into enhancing the classification performance on the GoEmotions dataset, a large, manually annotated dataset for emotion detection in text. The primary goal of this paper is to address the challenges of detecting subtle emotions in text, a complex issue in Natural Language Processing (NLP) with significant practical applications. The findin

Frincy Clement, Kirtan Shah, Dhara Pancholi, Gabriel Lugo Bustillo

Textureless object recognition has become a significant task in Computer Vision with the advent of Robotics and its applications in manufacturing sector. It has been challenging to obtain good accuracy in real time because of its lack of discriminative features and reflectance properties which makes the techniques for textured object recognition insufficient

Chunhui Gu, Ruosha Li, Guoqiang Zhang

Objectives: To provide an overall quality assessment of the methods used for COVID-19-related studies using propensity score matching (PSM). Study Design and Setting: A systematic search was conducted in June 2021 on PubMed to identify COVID-19-related studies that use the PSM analysis between 2020 and 2021. Key information about study design and PSM analysi

Kayla Clements, Gianluca Geraci, Aaron J Olson, Todd S Palmer

We consider Global Sensitivity Analysis for Monte Carlo radiation transport applications. GSA is usually combined with Uncertainty Quantification, where the latter quantifies the variability of a model output in the presence of uncertain inputs and the former attributes this variability to the inputs. The additional noise inherent to MC RT solvers due to the

Yong Lin, Shi Wan, Haohang Zhang

This paper presents a comprehensive analysis of the spectral properties of the connection Laplacian for both real and discrete tori. We introduce novel methods to examine these eigenvalues by employing parallel orthonormal basis in the pullback bundle on universal covering spaces. Our main results reveal that the eigenvalues of the connection Laplacian on a

Ruinan Jin, Wenlong Deng, Minghui Chen, Xiaoxiao Li

In the era of Foundation Models' (FMs) rising prominence in AI, our study addresses the challenge of biases in medical images while the model operates in black-box (e.g., using FM API), particularly spurious correlations between pixels and sensitive attributes. Traditional methods for bias mitigation face limitations due to the restricted access to web-hoste

Morgen Pronk

In the evolving field of maintenance and reliability engineering, the organization of equipment into hierarchical structures presents both a challenge and a necessity, directly impacting the operational integrity of industrial facilities. This paper introduces an innovative approach employing machine learning, specifically Long Short-Term Memory (LSTM) model

Guodong Ding, Hans Golong, Angela Yao

Data replay is a successful incremental learning technique for images. It prevents catastrophic forgetting by keeping a reservoir of previous data, original or synthesized, to ensure the model retains past knowledge while adapting to novel concepts. However, its application in the video domain is rudimentary, as it simply stores frame exemplars for action re

Fancong Zeng, Yi Jiao, Weihang Liu, Cheng-Ying Tsai

High peak current electron beams are essential for x-ray free-electron lasers (FELs), and generally realized through multi-stage compression with symmetric C-shape four-bend chicanes. However, the coherent synchrotron radiations (CSR), emitted for wavelengths longer than or comparable to the length of the electron bunch during the compression, may degrade th

Yusuke Yasuda, Tomoki Toda

A preference-based subjective evaluation is a key method for evaluating generative media reliably. However, its huge combinations of pairs prohibit it from being applied to large-scale evaluation using crowdsourcing. To address this issue, we propose an automatic optimization method for preference-based subjective evaluation in terms of pair combination sele

Chao Zhang, Liang Zheng, ShuSu Shi, Zi-Wei Lin

We study the production of $D^0$ meson in $p$+$p$ and $p-$Pb collisions using the improved AMPT model considering both coalescence and independent fragmentation of charm quarks after the Cronin broadening are included. After a detailed discussion of the improvements implemented in the AMPT model for heavy quark production, we show that the modified AMPT mode

Wenhao Wang, Yi Yang

The arrival of Sora marks a new era for text-to-video diffusion models, bringing significant advancements in video generation and potential applications. However, Sora, along with other text-to-video diffusion models, is highly reliant on prompts, and there is no publicly available dataset that features a study of text-to-video prompts. In this paper, we int

Yuxuan Yao, Sichun Luo, Haohan Zhao, Guanzhi Deng

We present CNER-UAV, a fine-grained \textbf{C}hinese \textbf{N}ame \textbf{E}ntity \textbf{R}ecognition dataset specifically designed for the task of address resolution in \textbf{U}nmanned \textbf{A}erial \textbf{V}ehicle delivery systems. The dataset encompasses a diverse range of five categories, enabling comprehensive training and evaluation of NER model

Si-Ao Xu, Huan Zhou, Xiang-Feng Pan

Let $G=(V(G),E(G))$ be a graph with vertex set $V(G)$ and edge set $E(G)$. The resistance distance $R_G(x,y)$ between two vertices $x,y$ of $G$ is defined to be the effective resistance between the two vertices in the corresponding electrical network in which each edge of $G$ is replaced by a unit resistor. The resistance spectrum $\mathrm{RS}(G)$ of a graph

Huy N. Phan, Hoang N. Phan, Tien N. Nguyen, Nghi D. Q. Bui

Code Large Language Models (CodeLLMs) have demonstrated impressive proficiency in code completion tasks. However, they often fall short of fully understanding the extensive context of a project repository, such as the intricacies of relevant files and class hierarchies, which can result in less precise completions. To overcome these limitations, we present \

Tiroshan Madushanka, Dhammika S. Kumara, Atheesh A. Rathnaweera

In the dynamic realm of digital content, safeguarding intellectual property rights poses critical challenges. This paper presents "SecureRights," an innovative Blockchain-based Trusted Digital Rights Management (DRM) framework. It strengthens the defence against unauthorized use and streamlines the claim of digital rights. Utilizing blockchain, digital water

Haoxuanye Ji, Pengpeng Liang, Erkang Cheng

Multi-camera-based 3D object detection has made notable progress in the past several years. However, we observe that there are cases (e.g. faraway regions) in which popular 2D object detectors are more reliable than state-of-the-art 3D detectors. In this paper, to improve the performance of query-based 3D object detectors, we present a novel query generating

Hanxin Zhu, Tianyu He, Xin Li, Bingchen Li

Neural Radiance Field (NeRF) has achieved superior performance for novel view synthesis by modeling the scene with a Multi-Layer Perception (MLP) and a volume rendering procedure, however, when fewer known views are given (i.e., few-shot view synthesis), the model is prone to overfit the given views. To handle this issue, previous efforts have been made towa

Peter Gunnarson, John O. Dabiri

Autonomous ocean-exploring vehicles have begun to take advantage of onboard sensor measurements of water properties such as salinity and temperature to locate oceanic features in real time. Such targeted sampling strategies enable more rapid study of ocean environments by actively steering towards areas of high scientific value. Inspired by the ability of aq

Guangkai Xu, Yongtao Ge, Mingyu Liu, Chengxiang Fan

Extensive pre-training with large data is indispensable for downstream geometry and semantic visual perception tasks. Thanks to large-scale text-to-image (T2I) pretraining, recent works show promising results by simply fine-tuning T2I diffusion models for dense perception tasks. However, several crucial design decisions in this process still lack comprehensi

Maddimsetti Srinivas, Debdoot Sheet

The interpretation of reasoning by Deep Neural Networks (DNN) is still challenging due to their perceived black-box nature. Therefore, deploying DNNs in several real-world tasks is restricted by the lack of transparency of these models. We propose a distillation approach by extracting features from the final layer of the convolutional neural network (CNN) to

Esmaeil Seraj, Walter Talamonti

In the burgeoning field of intelligent transportation systems, enhancing vehicle-driver interaction through facial attribute recognition, such as facial expression, eye gaze, age, etc., is of paramount importance for safety, personalization, and overall user experience. However, the scarcity of comprehensive large-scale, real-world datasets poses a significa

Yipei Wang, Bing He, Shannon Risacher, Andrew Saykin

Alzheimer's disease (AD) is a progressive and irreversible brain disorder that unfolds over the course of 30 years. Therefore, it is critical to capture the disease progression in an early stage such that intervention can be applied before the onset of symptoms. Machine learning (ML) models have been shown effective in predicting the onset of AD. Yet for sub

Juanwu Lu, Wei Zhan, Masayoshi Tomizuka, Yeping Hu

Estimating the potential behavior of the surrounding human-driven vehicles is crucial for the safety of autonomous vehicles in a mixed traffic flow. Recent state-of-the-art achieved accurate prediction using deep neural networks. However, these end-to-end models are usually black boxes with weak interpretability and generalizability. This paper proposes the

Hailan Luo, Lin Zhao, Zhen Zhao, Haitao Yang

The newly discovered Kagome superconductors AV$_3$Sb$_5$ (A=K, Rb and Cs) continue to bring surprises in generating unusual phenomena and physical properties, including anomalous Hall effect, unconventional charge density wave, electronic nematicity and time-reversal symmetry breaking. Here we report an unexpected emergence of multiple flat bands in the AV$_

Tunan Kao, He Zhang, Lei Zhang, Jin Zhao

We present the partial evolutionary tensor neural networks (pETNNs), a novel framework for solving time-dependent partial differential equations with high accuracy and capable of handling high-dimensional problems. Our architecture incorporates tensor neural networks and evolutionary parametric approximation. A posterior error bounded is proposed to support

Nathan J. Essner, Jeremiah Williams, Alexander B. Watson

We consider one-dimensional deterministic and random tight-binding Hamiltonians modeling electronic properties of twisted bilayer materials. When the twisted structure is incommensurate, we prove convergence of the density of states measure in the thermodynamic limit and Pastur's theorem on shift-invariance of the spectrum. Our results extend those of Massat

Harshavardhan Adepu, Zhanpeng Zeng, Li Zhang, Vikas Singh

Transformers are the backbone of powerful foundation models for many Vision and Natural Language Processing tasks. But their compute and memory/storage footprint is large, and so, serving such models is expensive often requiring high-end hardware. To mitigate this difficulty, Post-Training Quantization seeks to modify a pre-trained model and quantize it to e

Matheus Nunes Soares, Fábio Reis dos Santos

An integral inequality for the singular p-laplacian is established for 3/2<p<2. As consequence, lower bounds for the first eigenvalue of the p-laplacian are obtained for minimal submanifolds and prescribed scalar curvature submanifolds in the unit sphere.

Shouheng Li, Dongwoo Kim, Qing Wang

In recent years, there has been a significant amount of research focused on expanding the expressivity of Graph Neural Networks (GNNs) beyond the Weisfeiler-Lehman (1-WL) framework. While many of these studies have yielded advancements in expressivity, they have frequently come at the expense of decreased efficiency or have been restricted to specific types

Shouheng Li, Dongwoo Kim, Qing Wang

Despite the celebrated popularity of Graph Neural Networks (GNNs) across numerous applications, the ability of GNNs to generalize remains less explored. In this work, we propose to study the generalization of GNNs through a novel perspective - analyzing the entropy of graph homomorphism. By linking graph homomorphism with information-theoretic measures, we d

Peiqi Yang, Yingfeng Hu, Hao Wu

We study the relation between the persistent homology and the spectral sequence of a filtered chain complex over a field. Our method is based on a decomposition of the persistent homology. We demonstrate that, under fairly general assumptions, these two algebraic structures capture the same information from the filtered chain complex.

Jim Pruyne, Valerie Hayot-Sasson, Weijian Zheng, Ryan Chard

Experimental science is increasingly driven by instruments that produce vast volumes of data and thus a need to manage, compute, describe, and index this data. High performance and distributed computing provide the means of addressing the computing needs; however, in practice, the variety of actions required and the distributed set of resources involved, req

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

The HIV/AIDS pandemic is a global issue that has unequally affected several countries. Due to the complexity of this condition and the human drama it represents to those most affected by it, several fields have contributed to solving or at least alleviating this situation, and the information systems (IS) field has not been absent from these efforts. With th

Yang He, Lingao Xiao, Joey Tianyi Zhou, Ivor Tsang

While dataset condensation effectively enhances training efficiency, its application in on-device scenarios brings unique challenges. 1) Due to the fluctuating computational resources of these devices, there's a demand for a flexible dataset size that diverges from a predefined size. 2) The limited computational power on devices often prevents additional con

Yuan Xu, Li Wei, Chongwen Huang, Chen Zhu

In this paper, we investigate the millimeter-wave (mmWave) near-field beam training problem to find the correct beam direction. In order to address the high complexity and low identification accuracy of existing beam training techniques, we propose an efficient hashing multi-arm beam (HMB) training scheme for the near-field scenario. Specifically, we first d

Yuan Xu, Li Wei, Chongwen Huang, Yongxu Zhu

Millimeter wave (mmWave) has attracted considerable attention due to its wide bandwidth and high frequency. However, it is highly susceptible to blockages, resulting in significant degradation of the coverage and the sum rate. A promising approach is deploying distributed reconfigurable intelligent surfaces (RISs), which can establish extra communication lin

Liu Junkai, Jiang Yi

The sixth-generation (6G) communication networks target peak data rates exceeding 1Tbps, necessitating base stations (BS) to support up to 100 simultaneous data streams. However, sparse pilot allocation to accommodate such streams poses challenges for users' channel estimation. This paper presents Channel Estimation Considerate Precoding (CECP), where BS pre

Liyang He, Zhenya Huang, Jiayu Liu, Enhong Chen

Unsupervised semantic hashing has emerged as an indispensable technique for fast image search, which aims to convert images into binary hash codes without relying on labels. Recent advancements in the field demonstrate that employing large-scale backbones (e.g., ViT) in unsupervised semantic hashing models can yield substantial improvements. However, the inf

Jiawang Cao, Yongliang Wu, Weiheng Chi, Wenbo Zhu

The proliferation of mobile devices and social media has revolutionized content dissemination, with short-form video becoming increasingly prevalent. This shift has introduced the challenge of video reframing to fit various screen aspect ratios, a process that highlights the most compelling parts of a video. Traditionally, video reframing is a manual, time-c

Yuang Wang, Siyeop Yoon, Pengfei Jin, Matthew Tivnan

Diffusion-based models have demonstrated remarkable effectiveness in image restoration tasks; however, their iterative denoising process, which starts from Gaussian noise, often leads to slow inference speeds. The Image-to-Image Schr\"odinger Bridge (I$^2$SB) offers a promising alternative by initializing the generative process from corrupted images while le

Kang Fu, Jianwei Hu, Meng Sun

The $\beta$-model has been extensively utilized to model degree heterogeneity in networks, wherein each node is assigned a unique parameter. In this article, we consider the hypothesis testing problem that two nodes $i$ and $j$ of a $\beta$-model have the same node parameter. We prove that the null distribution of the proposed statistic converges in distribu

Ghazaleh Shirvani, Saeid Ghasemshirazi, Behzad Beigzadeh

Using dispersed data and training, federated learning (FL) moves AI capabilities to edge devices or does tasks locally. Many consider FL the start of a new era in AI, yet it is still immature. FL has not garnered the community's trust since its security and privacy implications are controversial. FL's security and privacy concerns must be discovered, analyze

Dawei Fan, Yifan Gao, Jiaming Yu, Yanping Chen

Deep learning models have shown promising performance for cell nucleus segmentation in the field of pathology image analysis. However, training a robust model from multiple domains remains a great challenge for cell nucleus segmentation. Additionally, the shortcomings of background noise, highly overlapping between cell nucleus, and blurred edges often lead

Aleksandr Rodin

A single-piston quantum engine based on a harmonic oscillator acting as the working fluid is proposed. Using the fact that the interaction between the piston and the oscillator depends on the extent of the oscillator wavefunction, one can control this interaction by modifying the oscillator temperature. By retracting the piston when the interaction is weak (

Liyue Chen, Jiangyi Fang, Tengfei Liu, Shaosheng Cao

Spatio-Temporal (ST) prediction is crucial for making informed decisions in urban location-based applications like ride-sharing. However, existing ST models often require region partition as a prerequisite, resulting in two main pitfalls. Firstly, location-based services necessitate ad-hoc regions for various purposes, requiring multiple ST models with varyi

Qiuyu Liang, Weihua Wang, Feilong Bao, Guanglai Gao

Linear Graph Convolutional Networks (GCNs) are used to classify the node in the graph data. However, we note that most existing linear GCN models perform neural network operations in Euclidean space, which do not explicitly capture the tree-like hierarchical structure exhibited in real-world datasets that modeled as graphs. In this paper, we attempt to intro

Jian Wang, Dongding Lin, Wenjie Li

Target-oriented proactive dialogue systems aim to lead conversations from a dialogue context toward a pre-determined target, such as making recommendations on designated items or introducing new specific topics. To this end, it is critical for such dialogue systems to plan reasonable actions to drive the conversation proactively, and meanwhile, to plan appro

Guo-Qiang Zhang, Si-Yan Lin, Wei Feng, Yu Wang

Compared with an isolated exceptional point, exceptional surfaces in non-Hermitian systems are more robust against environment noises, fabrication errors, and experimental uncertainties. Thanks to this, exceptional surfaces can be applied to enhance the sensitivity of sensors and develop new quantum techniques. Over the past few years, several works have bee

Amit Acharya

A continuum mechanical model of coupled dislocation based plasticity and fracture at finite deformation is proposed. Motivating questions and target applications of the model are sketched.

Md Arid Hasan

The rapid advancement of social media enables us to analyze user opinions. In recent times, sentiment analysis has shown a prominent research gap in understanding human sentiment based on the content shared on social media. Although sentiment analysis for commonly spoken languages has advanced significantly, low-resource languages like Arabic continue to get

Yi Zhang, Ce Zhang

Vision-Language Pre-Trained (VLP) models, such as CLIP, have demonstrated remarkable effectiveness in learning generic visual representations. Several approaches aim to efficiently adapt VLP models to downstream tasks with limited supervision, aiming to leverage the acquired knowledge from VLP models. However, these methods suffer from either introducing bia

Ende Pan, Ce Xu

In this paper, we establish some expressions of Mneimneh-type binomial sums involving multiple harmonic-type sums in terms of finite sums of Stirling numbers, Bell numbers and some related variables. In particular, we present some new formulas of Mneimneh-type binomial sums involving generalized (alternating) harmonic numbers. Further, we establish a new ide

Jason DeBlois, Peter B. Shalen

Let $N$ be a compact, orientable hyperbolic 3-manifold whose boundary is a connected totally geodesic surface of genus $2$. If $N$ has Heegaard genus at least $5$, then its volume is greater than $2V_{\rm oct}$, where $V_{\rm oct}=3.66\ldots$ denotes the volume of a regular ideal hyperbolic octahedron in $\mathbb{H}^3$. This improves the lower bound given in

Mathieu Beau, Lionel Martellini

The uncertainty principle is one of the characteristic properties of quantum theory, where it signals the incompatibility of two types of measurements. In this paper, we argue that measurements of time-of-arrival $T_x$ at position $x$ and position $X_t$ at time $t$ are mutually exclusive for a quantum system, each providing complementary information about th

Ziye Ma, Ying Chen, Javad Lavaei, Somayeh Sojoudi

Matrix sensing problems exhibit pervasive non-convexity, plaguing optimization with a proliferation of suboptimal spurious solutions. Avoiding convergence to these critical points poses a major challenge. This work provides new theoretical insights that help demystify the intricacies of the non-convex landscape. In this work, we prove that under certain cond

Chenxing Gao, Hang Zhou, Junqing Yu, YuTeng Ye

Understanding the mechanisms behind Vision Transformer (ViT), particularly its vulnerability to adversarial perturba tions, is crucial for addressing challenges in its real-world applications. Existing ViT adversarial attackers rely on la bels to calculate the gradient for perturbation, and exhibit low transferability to other structures and tasks. In this p

Shangshuai Li, Da-jun Zhang

The paper establishes a direct linearization scheme for the SU(2) anti-self-dual Yang-Mills (ASDYM) equation.The scheme starts from a set of linear integral equations with general measures and plane wave factors. After introducing infinite-dimensional matrices as master functions, we are able to investigate evolution relations and recurrence relations of the

Xiang Li, Soo Min Kwon, Shijun Liang, Ismail R. Alkhouri

Diffusion models have recently gained traction as a powerful class of deep generative priors, excelling in a wide range of image restoration tasks due to their exceptional ability to model data distributions. To solve image restoration problems, many existing techniques achieve data consistency by incorporating additional likelihood gradient steps into the r

Eugenio Bianchi, Monica Rincon-Ramirez

The Barbero-Immirzi parameter $\gamma$ appears as a coupling constant in the spinfoam dynamics of loop quantum gravity. In this work, we highlight that $\gamma$ can be understood as a measure of gravitational parity violation via a duality rotation for the EPRL spinfoam model. We call this property $\gamma$-duality, and we investigate an effective field theo

Fernando G. Gardim, Andre V. Giannini, Jean-Yves Ollitrault

It has been argued that the speed of sound of the strong interaction at high temperature can be measured using the variation of the mean transverse momentum with the particle multiplicity in ultracentral heavy-ion collisions. We test this correspondence by running hydrodynamic simulations at zero impact parameter with several equations of state, at several c

Pengfei Ding, Yan Wang, Guanfeng Liu

Few-shot learning on heterogeneous graphs (FLHG) is attracting more attention from both academia and industry because prevailing studies on heterogeneous graphs often suffer from label sparsity. FLHG aims to tackle the performance degradation in the face of limited annotated data and there have been numerous recent studies proposing various methods and appli

Zhujing Xu, Peng Ju, Kunhong Shen, Yuanbin Jin

Quantum mechanics predicts the occurrence of random electromagnetic field fluctuations, or virtual photons, in vacuum. The exchange of virtual photons between two bodies in relative motion could lead to non-contact quantum vacuum friction or Casimir friction. Despite its theoretical significance, the non-contact Casimir frictional force has not been observed

Paul Denny, David H. Smith, Max Fowler, James Prather

Reading, understanding and explaining code have traditionally been important skills for novices learning programming. As large language models (LLMs) become prevalent, these foundational skills are more important than ever given the increasing need to understand and evaluate model-generated code. Brand new skills are also needed, such as the ability to formu

Anubhav Wadehra, Rajni Chahal, Shubhojit Banerjee, Alexander Levy

The local atomic structure of NaF-ZrF$_4$ (53-47 mol%) molten system and its evolution with temperature are examined with x-ray scattering measurements and compared with $ab-initio$ and Neural Network-based molecular dynamics (NNMD) simulations in the temperature range 515-700 {\deg}C. The machine-learning enhanced NNMD calculations offer improved efficiency

Asal Rouhafzay, Nadia Baaziz, Mohand Said Allili

In this paper, we propose a new framework for improving Content Based Image Retrieval (CBIR) for texture images. This is achieved by using a new image representation based on the RCT-Plus transform which is a novel variant of the Redundant Contourlet transform that extracts a richer directional information in the image. Moreover, the process of image search

Tudor Ciurca, Sho Tanimoto, Yuri Tschinkel

We develop an equivariant version of the formalism of intermediate Jacobian torsor obstructions, and apply it to conic bundles over rational surfaces, quadric surface bundles over $\mathbb P^1$, and Fano threefolds.

Yaofeng Xie, Nathan Chalus, Zhiwei Wang, Weiliang Yao

A hallmark of unconventional superconductors is their complex electronic phase diagrams where "intertwined orders" of charge-spin-lattice degrees of freedom compete and coexist as in copper oxides and iron pnictides. While the electronic phase diagram of kagome lattice superconductor such as CsV3Sb5 also exhibits complex behavior involving coexisting and com

Ritabrata Ray, Yorie Nakahira, Soummya Kar

In this paper, we study the problem of ensuring safety with a few shots of samples for partially unknown systems. We first characterize a fundamental limit when producing safe actions is not possible due to insufficient information or samples. Then, we develop a technique that can generate provably safe actions and recovery behaviors using a minimum number o

Fabrizio Catanese

In this paper we characterize the quotients $ X = T/G$ of a complex torus $T$ by the action of a finite group $G$ as the K\"ahler orbifold classifying spaces of the even Euclidean cristallographic groups $\Gamma$, and we prove other similar and stronger characterizations.

Dante DeBlassie, Adina Oprisan, Robert G. Smits

We study the effect of a power law drift on Brownian motion in the positive half-line, where the order of the drift at 0 and infinity is different.

Ryan Gibara, Nageswari Shanmugalingam

In this note, we construct a Dirichlet-to-Neumann map, from a Besov space of functions, to the dual of this class. The Besov spaces are of functions on the boundary of a bounded, locally compact uniform domain equipped with a doubling measure supporting a $p$-Poincar\'e inequality so that this boundary is also equipped with a Radon measure that has a codimen

Zhuo Xu, Rui Zhou, Yida Yin, Huidong Gao

Data-driven methods have great advantages in modeling complicated human behavioral dynamics and dealing with many human-robot interaction applications. However, collecting massive and annotated real-world human datasets has been a laborious task, especially for highly interactive scenarios. On the other hand, algorithmic data generation methods are usually l

Yuchen Du, Tho V. Le

This article presents a comprehensive sentiment analysis (SA) of comments on YouTube videos related to Sidewalk Delivery Robots (SDRs). We manually annotated the collected YouTube comments with three sentiment labels: negative (0), positive (1), and neutral (2). We then constructed models for text sentiment classification and tested the models' performance o

Camillo Tassi, Dario Bercioux

Materials featuring touching points, localized states, and flat bands are of great interest in condensed matter and artificial systems due to their implications in topology, quantum geometry, superconductivity, and interactions. In this theoretical study, we propose the experimental realization of the dice lattice with adjustable parameters by arranging carb

Yao Lyu, He Zhang, Shuo Niu, Jie Cai

Content creators increasingly utilize generative artificial intelligence (Gen-AI) on platforms such as YouTube, TikTok, Instagram, and various blogging sites to produce imaginative images, AI-generated videos, and articles using Large Language Models (LLMs). Despite its growing popularity, there remains an underexplored area concerning the specific domains w

Sargam Yadav, Abhishek Kaushik, Kevin McDaid

The problems of online hate speech and cyberbullying have significantly worsened since the increase in popularity of social media platforms such as YouTube and Twitter (X). Natural Language Processing (NLP) techniques have proven to provide a great advantage in automatic filtering such toxic content. Women are disproportionately more likely to be victims of

Frank Wilczek

The bulk of this paper centers around the tension between confinement and freedom in QCD. I discuss how it can be understood heuristically as a manifestation of self-adhesive glue and how it fits within the larger contexts of energy-time uncertainty and $\textit{real virtuality}$. I discuss the possible emergence of $\textit{treeons}$ as a tangible ingredien

William D. Warke, J. Humberto Ramos, Prashant Ganesh, Kevin M. Brink

It is common in pose graph optimization (PGO) algorithms to assume that noise in the translations and rotations of relative pose measurements is uncorrelated. However, existing work shows that in practice these measurements can be highly correlated, which leads to degradation in the accuracy of PGO solutions that rely on this assumption. Therefore, in this p

Rohith R. Gangam, Naveen Garg, Parnian Shahkar, Vijay V. Vazirani

We study fair allocation of profit (or cost) for three central problems from combinatorial optimization: Max-Flow, MST and $b$-matching. The essentially unequivocal choice of solution concept for this purpose would be the core, because of its highly desirable properties. However, recent work [Vaz24] observed that for the assignment game, an arbitrary core im

Inwon Kang, Maruf Ahmed Mridul, Abraham Sanders, Yao Ma

Cryptocurrency is a fast-moving space, with a continuous influx of new projects every year. However, an increasing number of incidents in the space, such as hacks and security breaches, threaten the growth of the community and the development of technology. This dynamic and often tumultuous landscape is vividly mirrored and shaped by discussions within Crypt

Masoud Taherinia, Mehrdad Matin, Jamal Valipour, Kavian Abdolahi

Shareholders' expectations of stock returns and fluctuations are constantly changing due to restrictions in financial status and undesirable capital structure, which constrain managers to limit the changes in price trends in order to cover the risk instigated and infused by the unfavorable situation. The present research examines the moderating impact of inf

Jie Cai, Aashka Patel, Azadeh Naderi, Donghee Yvette Wohn

Social media users may perceive moderation decisions by the platform differently, which can lead to frustration and dropout. This study investigates users' perceived justice and fairness of online moderation decisions when they are exposed to various illegal versus legal scenarios, retributive versus restorative moderation strategies, and user-moderated vers