March 2023 arXiv papers — page 78

Shin Harase

A one-dimensional sequence $u_0, u_1, u_2, \ldots \in [0, 1)$ is said to be completely uniformly distributed (CUD) if overlapping $s$-blocks $(u_i, u_{i+1}, \ldots , u_{i+s-1})$, $i = 0, 1, 2, \ldots$, are uniformly distributed for every dimension $s \geq 1$. This concept naturally arises in Markov chain quasi-Monte Carlo (QMC). However, the definition of CU

Aloïs Castellano, J. P. Alvarinhas Batista, Matthieu J. Verstraete

The dynamical properties of nuclei, carried by the concept of phonon quasiparticles (QP), are central to the field of condensed matter. While the harmonic approximation can reproduce a number of properties observed in real crystals, the inclusion of anharmonicity in lattice dynamics is essential to accurately predict properties such as heat transport or ther

Noemi David, Tomasz Dębiec, Mainak Mandal, Markus Schmidtchen

In recent years, there has been a spike in the interest in multi-phase tissue growth models. Depending on the type of tissue, the velocity is linked to the pressure through Stoke's law, Brinkman's law or Darcy's law. While each of these velocity-pressure relations has been studied in the literature, little emphasis has been placed on the fine relationship be

Bonan Ni, Weiran Shen, Pingzhong Tang

Bayesian persuasion and its derived information design problem has been one of the main research agendas in the economics and computation literature over the past decade. However, when attempting to apply its model and theory, one is often limited by the fact that the sender can only implement very restricted information structures. Moreover, in this case, t

Nikolaos Papadatos, Dimitris Moustos

We consider a two-level atom that follows a wordline of constant velocity, while interacting with a massless scalar field in a thermal state through: (i) an Unruh-DeWitt coupling, and (ii) a coupling that involves the time derivative of the field. We treat the atom as an open quantum system, with the field playing the role of the environment, and employ a ma

Abhishek Ajayakumar, Soumyendu Raha

Graph sparsification is an area of interest in computer science and applied mathematics. Sparsification of a graph, in general, aims to reduce the number of edges in the network while preserving specific properties of the graph, like cuts and subgraph counts. Computing the sparsest cuts of a graph is known to be NP-hard, and sparsification routines exist for

Andrea T. Ricolfi

We provide a recursive formula for the motivic class of the noncommutative Quot scheme in the Grothendieck ring of stacks.

Zekun Liu, Siwei Yu

In this paper, we propose an alternating direction method of multipliers (ADMM)-based optimization algorithm to achieve better undersampling rate for multiple measurement vector (MMV) problem. The core is to introduce the $\ell_{2,0}$-norm sparsity constraint to describe the joint-sparsity of the MMV problem, which is different from the widely used $\ell_{2,

Radek Hušek, Robert Šámal

We study a counting version of Cycle Double Cover Conjecture. We discuss why it is more interesting to count circuits (i.e., graphs isomorphic to $C_k$ for some $k$) instead of cycles (graphs with all degrees even). We give an almost-exponential lower-bound for graphs with a surface embedding of representativity at least 4. We also prove an exponential lower

Xin Meng, Shuguan Ji

This paper is concerned with breather solutions of a radially symmetric curl-curl wave equation with double power nonlinearity. By considering the solutions with a special form, we obtain a family of ordinary differential equations (ODEs) parameterized by the radial variable. Then we characterize periodic behaviors and analyze the joint effects of the double

Pu Li, Jianwei Guo, Xiaopeng Zhang, Dong-ming Yan

Reverse engineering CAD models from raw geometry is a classic but strenuous research problem. Previous learning-based methods rely heavily on labels due to the supervised design patterns or reconstruct CAD shapes that are not easily editable. In this work, we introduce SECAD-Net, an end-to-end neural network aimed at reconstructing compact and easy-to-edit C

H. A. Z. Sameen Shahgir, Khondker Salman Sayeed

This paper presents a method for detecting grammatical errors in Bangla using a Text-to-Text Transfer Transformer (T5) Language Model, using the small variant of BanglaT5, fine-tuned on a corpus of 9385 sentences where errors were bracketed by the dedicated demarcation symbol. The T5 model was primarily designed for translation and is not specifically design

Xiaoyan Yao, Di Hu, Shuai Dong

The intrinsic magnetism observed in two-dimensional (2D) van der Waals (vdW) materials provides a unique opportunity for exploring the 2D topological magnetic textures, in particular skyrmionic magnetic textures (SMTs) including skyrmion and its topological equivalents. Since the experimental discovery of skyrmions in the 2D vdW materials and their heterostr

Ziqi Gao, S. Kevin Zhou

Undersampled MRI reconstruction is crucial for accelerating clinical scanning. Dual-domain reconstruction network is performant among SoTA deep learning methods. In this paper, we rethink dual-domain model design from the perspective of the receptive field, which is needed for image recovery and K-space interpolation problems. Further, we introduce domain-sp

Yijun Yang, Huazhu Fu, Angelica I. Aviles-Rivero, Carola-Bibiane Schönlieb

Diffusion Probabilistic Models have recently shown remarkable performance in generative image modeling, attracting significant attention in the computer vision community. However, while a substantial amount of diffusion-based research has focused on generative tasks, few studies have applied diffusion models to general medical image classification. In this p

Nevo Fishbein

We prove $\times a$ $\times b$ measure rigidity for multiplicatively independent pairs when $a\in\mathbb{N}$ and $b>1$ is a ``specified'' real number (the $b$-expansion of $1$ has a tail or bounded runs of $0$'s) under a positive entropy condition. This is done by proving a mean decay of the Fourier series of the point masses average along $\times b$ orbits.

Thomas Dagès, Laurent D. Cohen, Alfred M. Bruckstein

Traditional signal processing methods relying on mathematical data generation models have been cast aside in favour of deep neural networks, which require vast amounts of data. Since the theoretical sample complexity is nearly impossible to evaluate, these amounts of examples are usually estimated with crude rules of thumb. However, these rules only suggest

Fatemeh Pouromran, Yingzi Lin, Sagar Kamarthi

Physiological responses to pain have received increasing attention among researchers for developing an automated pain recognition sensing system. Though less explored, Blood Volume Pulse (BVP) is one of the candidate physiological measures that could help objective pain assessment. In this study, we applied machine learning techniques on BVP signals to devic

Mehrdad Rafiepour, Javad Salimi Sartakhti

Intent-detection and slot-filling are the two main tasks in natural language understanding. In this study, we propose CTRAN, a novel encoder-decoder CNN-Transformer-based architecture for intent-detection and slot-filling. In the encoder, we use BERT, followed by several convolutional layers, and rearrange the output using window feature sequence. We use sta

Wenze Lan, Peng Fu, Chang-Yin Ji, Gang Wang

The band structures of the photonic crystal slabs play a significant role in manipulating the flow of light and pre-dicting exotic physics in photonics. In this letter, we show that the key features of photonic band structures can be achieved experimentally by the polarization- and momentum-resolved photoluminescence spectroscopy utilizing the light emission

S. Markham, T. Guillot, C. Li

Results from Juno's microwave radiometer indicate non-uniform mixing of ammonia vapor in Jupiter's atmosphere down to tens of bars, far beneath the cloud level. Helioseismic observations suggest solar convection may require narrow, concentrated downdrafts called entropy rain to accommodate the Sun's luminosity. Both observations suggest some mechanism of non

Richard Kerner

The lagrangian of the Kaluza-Klein theory, in its simplest five-dimensional version, should include not only the scalar curvature R, but also the quadratic Gauss-Bonnet invariant. The general lagrangian is computed and the resulting non-linear equations which generalize Maxwell's system in a quite unique way are investigated. The possibility of the existence

Shilin Wen, Junsheng Fang, Zhaolin Yao

Let $\M$ be a type ${\rm II_1}$ factor and let $\tau$ be the faithful normal tracial state on $\M$. In this paper, we prove that given finite elements $X_1,\cdots X_n \in \M$, there is a finite decomposition of the identity into $N \in \NNN$ mutually orthogonal nonzero projections $E_j\in\M$, $I=\sum_{j=1}^NE_j$, such that $E_jX_iE_j=\tau(X_i) E_j$ for all $

Kolesnikov Dmitry

The possibility of high-precision and rapid detection of pathologies on chest X-rays makes it possible to detect the development of pneumonia at an early stage and begin immediate treatment. Artificial intelligence can speed up and qualitatively improve the procedure of X-ray analysis and give recommendations to the doctor for additional consideration of sus

Luca Heltai, Paolo Zunino

Many physical problems involving heterogeneous spatial scales, such as the flow through fractured porous media, the study of fiber-reinforced materials, or the modeling of the small circulation in living tissues -- just to mention a few examples -- can be described as coupled partial differential equations defined in domains of heterogeneous dimensions that

Tianyou Li, Fan Chen, Huajie Chen, Zaiwen Wen

Understanding stochastic gradient descent (SGD) and its variants is essential for machine learning. However, most of the preceding analyses are conducted under amenable conditions such as unbiased gradient estimator and bounded objective functions, which does not encompass many sophisticated applications, such as variational Monte Carlo, entropy-regularized

Kunhao Liu, Fangneng Zhan, Yiwen Chen, Jiahui Zhang

3D style transfer aims to render stylized novel views of a 3D scene with multi-view consistency. However, most existing work suffers from a three-way dilemma over accurate geometry reconstruction, high-quality stylization, and being generalizable to arbitrary new styles. We propose StyleRF (Style Radiance Fields), an innovative 3D style transfer technique th

Jingwen Ye, Songhua Liu, Xinchao Wang

In this paper, we study a novel task that enables partial knowledge transfer from pre-trained models, which we term as Partial Network Cloning (PNC). Unlike prior methods that update all or at least part of the parameters in the target network throughout the knowledge transfer process, PNC conducts partial parametric "cloning" from a source network and then

Jiangbin Zheng, Stan Z. Li

Effective protein representation learning is crucial for predicting protein functions. Traditional methods often pretrain protein language models on large, unlabeled amino acid sequences, followed by finetuning on labeled data. While effective, these methods underutilize the potential of protein structures, which are vital for function determination. Common

L. A. Leppin, T. Görler, M. Cavedon, M. G. Dunne

The theoretical investigation of relevant turbulent transport mechanisms in H-mode pedestals is a great scientific and numerical challenge. In this study, we address this challenge by global, nonlinear gyrokinetic simulations of a full pedestal up to the separatrix, supported by a detailed characterisation of gyrokinetic instabilities from just inside the pe

Shoya Ogawa, Shin Watanabe, Takuma Matsumoto, Kazuyuki Ogata

Systematic measurement of $t$ and $^3$He knockout processes is planned. The weakly-bound nature of these nuclei may affect the interpretation of forthcoming knockout reaction data. Purpose: We aim at clarifying breakup properties of $t$ and $^3$He by investigating their elastic and breakup cross sections. We employ the four-body continuum-discretized coupled

Lijun Sheng, Jian Liang, Ran He, Zilei Wang

Model adaptation aims at solving the domain transfer problem under the constraint of only accessing the pretrained source models. With the increasing considerations of data privacy and transmission efficiency, this paradigm has been gaining recent popularity. This paper studies the vulnerability to universal attacks transferred from the source domain during

Fangxing Zhang, Shengnan HuangFu, Jialve Sun, Shengqiang Ji

Whispering gallery mode (WGM) microcavities can confine photons within a microscale volume for long periods of time, strongly enhancing light-matter interactions, and making it a crucial platform in optical science and applications. Current research on microcavity coupling system relays on precise mechanical coupling with microscope monitoring, and its reson

Honghui Liu, Jiachen Jiang, Zuobin Zhang, Cosimo Bambi

We present a high-density relativistic reflection analysis of 21 spectra of six black hole X-ray binaries in the hard state with data from \textit{NuSTAR} and \textit{Swift}. We find that 76\% of the observations in our sample require a disk density higher than the 10$^{15}$~cm$^{-3}$ assumed in the previous reflection analysis. Compared with the measurement

Jinfeng Zhou, Xian-Ming Gu, Yong-Liang Zhao, Hu Li

The Black-Scholes (B-S) equation has been recently extended as a kind of tempered time-fractional B-S equations, which becomes an interesting mathematical model in option pricing. In this study, we provide a fast numerical method to approximate the solution of the tempered time-fractional B-S model. To achieve high-order accuracy in space and overcome the we

Jiarui Fei

We propose a mutation formula for the general rank from a principal component ${\rm PC}(\delta)$ of representations to another one ${\rm PC}(\epsilon)$ for a quiver with potential. We give sufficient conditions for the formula to hold. In particular, the formula holds when any of $\delta$ and $\epsilon$ is reachable. We discover several related mutation inva

Yufeng Yin, Minh Tran, Di Chang, Xinrui Wang

Facial action unit detection has emerged as an important task within facial expression analysis, aimed at detecting specific pre-defined, objective facial expressions, such as lip tightening and cheek raising. This paper presents our submission to the Affective Behavior Analysis in-the-wild (ABAW) 2023 Competition for AU detection. We propose a multi-modal m

Xujun Zhang

Apply the $L^2$ technique developed by Deng-Ning-Wang-Zhou,we give a new characterization of Nakano $q$-positivity for Riemannian flat vector bundle in the sense of $L^2 $ estimate for the $d$ operator. We also give a new characterization of the positivity of the curvature operator associated to the Riemannian metric on manifold in a local version in the sen

Massoud Amini, Mahdi Moosazadeh

We introduce and study the notion of continuous orbit equivalence of actions of countable discrete groups on Cartan pairs in (twisted) groupoid context. We characterize orbit equivalence of actions in terms of the corresponding C$^*$-algebraic crossed products using Kumjian-Renault theory. We relate our notion to the classical notion of orbit equivalence of

Paolo Aceto, Nickolas A. Castro, Maggie Miller, JungHwan Park

We discuss an obstruction to a knot being smoothly slice that comes from minimum-genus bounds on smoothly embedded surfaces in definite 4-manifolds. As an example, we provide an alternate proof of the fact that the (2,1)-cable of the figure eight knot is not smoothly slice, as shown by Dai--Kang--Mallick--Park--Stoffregen in 2022. The main technical input of

Jean-Simon Pacaud Lemay, Jean-Baptiste Vienney

In Linear Logic ($\mathsf{LL}$), the exponential modality $!$ brings forth a distinction between non-linear proofs and linear proofs, where linear means using an argument exactly once. Differential Linear Logic ($\mathsf{DiLL}$) is an extension of Linear Logic which includes additional rules for $!$ which encode differentiation and the ability of linearizing

Yixun Liang, Hao He, Shishi Xiao, Hao Lu

Point cloud segmentation is a fundamental task in 3D vision that serves a wide range of applications. Although great progresses have been made these years, its practical usability is still limited by the availability of training data. Existing approaches cannot make full use of multiple datasets on hand due to the label mismatch among different datasets. In

Z. M. Song, N. Zhao, H. Ge, T. T. Li

We report a detailed study of the magnetic properties of KBaGd(BO$_3$)$_2$, in which magnetic Gd$^{3+}$ ($S=7/2$) ions form into two-dimensional triangular layers. Magnetization, specific heat and magnetocaloric effect (MCE) measurements have been performed on KBaGd(BO$_3$)$_2$ single crystals. The results show that a long-range antiferromagnetic state is es

Qihao Zhu, Jianxi Luo

In the early stages of the design process, designers explore opportunities by discovering unmet needs and developing innovative concepts as potential solutions. From a human-centered design perspective, designers must develop empathy with people to truly understand their needs. However, developing empathy is a complex and subjective process that relies heavi

Guanhua Chen, Yao Yao

We construct a dipole-facilitated kinetic constraint to partition the Hilbert space into three disconnected subspaces, two of which are nonthermal and the other acts as an intrinsic thermal bath. The resulting glassy system freely oscillates in nonthermal subspaces, making the quantum entanglement perform like a substantial qubit. The spatially spreading ent

Riccardo Fantoni

In our previous work [R. Fantoni, Int. J. Mod. Phys. C, {\bf 29}, 1850064 (2018)] we studied, through a computer experiment, a one-component fermion plasma on a sphere at finite, non-zero, temperature. We extracted thermodynamic properties like the kinetic and internal energy per particle and structural properties like the radial distribution function, and p

Roberto Carrasco, Héctor Ferrada, Cristóbal A. Navarro, Nancy Hitschfeld

The Convex Hull algorithm is one of the most important algorithms in computational geometry, with many applications such as in computer graphics, robotics, and data mining. Despite the advances in the new algorithms in this area, it is often needed to improve the performance to solve more significant problems quickly or in real-time processing. This work pre

Chaoqun You, Kun Guo, Howard H. Yang, Tony Q. S. Quek

Personalized Federated Learning (PFL) is a new Federated Learning (FL) paradigm, particularly tackling the heterogeneity issues brought by various mobile user equipments (UEs) in mobile edge computing (MEC) networks. However, due to the ever-increasing number of UEs and the complicated administrative work it brings, it is desirable to switch the PFL algorith

Nicolai Reshetikhin

Quantum Calogero-Moser spin system is a superintegable system with the spectrum of commuting Hamiltonians that can be described entirely in terms of representation theory of corresponding simple Lie group. In this paper the underlying Lie group G is a compact connected, simply connected simple Lie group. It has a natural generalization known as quantum Calog

Nan Zhao, Jieming Sheng, Jinchen Wang, Han Ge

The rare-earth perovskite TbScO$_3$ has been widely used as a substrate for the growth of epitaxial ferroelectric and multiferroic thin films, while its detailed low-temperature magnetic properties were rarely reported. In this paper, we performed detailed magnetization, specific heat and single crystal neutron scattering measurements, along with the crystal

Nguyen Quang Hieu, Dinh Thai Hoang, Diep N. Nguyen, Van-Dinh Nguyen

This article proposes a novel framework that utilizes an over-the-air Brain-Computer Interface (BCI) to learn Metaverse users' expectations. By interpreting users' brain activities, our framework can optimize physical resources and enhance Quality-of-Experience (QoE) for users. To achieve this, we leverage a Wireless Edge Server (WES) to process electroencep

Zuoyu Yan, Junru Zhou, Liangcai Gao, Zhi Tang

We investigate the enhancement of graph neural networks' (GNNs) representation power through their ability in substructure counting. Recent advances have seen the adoption of subgraph GNNs, which partition an input graph into numerous subgraphs, subsequently applying GNNs to each to augment the graph's overall representation. Despite their ability to identif

Qianang Zhou, JunLin Xiong, Youfu Li

The non-maximum suppression (NMS) is widely used in frame-based tasks as an essential post-processing algorithm. However, event-based NMS either has high computational complexity or leads to frequent discontinuities. As a result, the performance of event-based corner detectors is limited. This paper proposes a general-purpose asynchronous non-maximum suppres

Belle Collaboration, D. Bodrov, P. Pakhlov, I. Adachi

We present the first measurement of the Michel parameter $\xi^\prime$ in the $\tau^-\to\mu^-\bar{\nu}_\mu\nu_\tau$ decay using the full data sample of $988\,\text{fb}^{-1}$ collected by the Belle detector operating at the KEKB asymmetric energy $e^+ e^-$ collider. The method is based on the reconstruction of the $\mu^- \to e^- \bar{\nu}_e\nu_\mu$ decay-in-fl

Vaibhav Garg, Jiaqing Yuan, Rujie Xi, Munindar P. Singh

Survivors of sexual harassment frequently share their experiences on social media, revealing their feelings and emotions and seeking advice. We observed that on Reddit, survivors regularly share long posts that describe a combination of (i) a sexual harassment incident, (ii) its effect on the survivor, including their feelings and emotions, and (iii) the adv

Rajkumar Buyya, Shashikant Ilager, Patricia Arroba

Cloud computing has become a critical infrastructure for modern society, like electric power grids and roads. As the backbone of the modern economy, it offers subscription-based computing services anytime, anywhere, on a pay-as-you-go basis. Its use is growing exponentially with the continued development of new classes of applications driven by a huge number

Haobin Jiang, Junpeng Yue, Hao Luo, Ziluo Ding

One of the essential missions in the AI research community is to build an autonomous embodied agent that can achieve high-level performance across a wide spectrum of tasks. However, acquiring or manually designing rewards for all open-ended tasks is unrealistic. In this paper, we propose a novel cross-modal contrastive learning framework architecture, CLIP4M

Belle Collaboration, D. Bodrov, P. Pakhlov, I. Adachi

We report the first measurement of the Michel parameter $\xi^\prime$ in the $\tau^-\to\mu^-\bar{\nu}_\mu\nu_\tau$ decay with a new method proposed just recently. The measurement is based on the reconstruction of the $\tau^-\to\mu^-\bar{\nu}_\mu\nu_\tau$ events with subsequent muon decay-in-flight in the Belle central drift chamber. The analyzed data sample o

Hans Lindblad, Volker Schlue

We construct solutions with prescribed radiation fields for wave equations with polynomially decaying sources close to the lightcone. In this setting, which is motivated by semi-linear wave equations satisfying the weak null condition, solutions to the forward problem have a logarithmic leading order term on the lightcone and non-trivial homogeneous asymptot

I. A. Starkov, D. A. Pavlov, S. B. Tikhomirov, F. L. Bakharev

The paper presents a stochastic analysis of the growth rate of viscous fingers in miscible displacement in a heterogeneous porous medium. The statistical parameters characterizing the permeability distribution of a reservoir vary over a wide range. The formation of fingers is provided by the mixing of different-viscosity fluids -- water and polymer solution.

Jinyeong Jeong, Min Jun Kim

This paper proposes a decentralized passive impedance control scheme for collaborative grasping using under-actuated aerial manipulators (AMs). The AM system is formulated, using a proper coordinate transformation, as an inertially decoupled dynamics with which a passivity-based control design is conducted. Since the interaction for grasping can be interpret

Ricardo Augusto Borsoi, Tales Imbiriba, Pau Closas

Multitemporal hyperspectral unmixing (MTHU) is a fundamental tool in the analysis of hyperspectral image sequences. It reveals the dynamical evolution of the materials (endmembers) and of their proportions (abundances) in a given scene. However, adequately accounting for the spatial and temporal variability of the endmembers in MTHU is challenging, and has n

Arnab Maiti, Kevin Jamieson, Lillian J. Ratliff

We study the sample complexity of identifying an approximate equilibrium for two-player zero-sum $n\times 2$ matrix games. That is, in a sequence of repeated game plays, how many rounds must the two players play before reaching an approximate equilibrium (e.g., Nash)? We derive instance-dependent bounds that define an ordering over game matrices that capture

Iman Nodozi, Abhishek Halder, Ion Matei

In micro-assembly applications, ensemble of chiplets immersed in a dielectric fluid are steered using dielectrophoretic forces induced by an array of electrode population. Generalizing the finite population deterministic models proposed in prior works for individual chiplet position dynamics, we derive a controlled mean field model for a continuum of chiplet

Xiumin Du, Yumeng Ou, Hong Wang, Ruixiang Zhang

We present a free Schr\"{o}dinger solution studied by Barcel\'{o}--Bennett--Carbery--Ruiz--Vilela and show why it can be viewed as a sharp example for the recently discovered refined decoupling theorem.

Vaibhav Garg, Ganning Xu, Munindar P. Singh

In India, people identify with a particular group based on certain attributes such as religion. The same religious groups are often provoked against each other. Previous studies show the role of provocation in increasing tensions between India's two prominent religious groups: Hindus and Muslims. With the advent of the Internet, such provocation also surface

Federica Pompa, Thomas Schwetz, Jing-Yu Zhu

Nuclear matrix elements (NME) are a crucial input for the interpretation of neutrinoless double beta decay data. We consider a representative set of recent NME calculations from different methods and investigate the impact on the present bound on the effective Majorana mass $m_{\beta\beta}$ by performing a combined analysis of the available data as well as o

Peng Zou, Rui Wang, Kehua Wen, Yasi Peng

The in-the-wild affective behavior analysis has been an important study. In this paper, we submit our solutions for the 5th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW), which includes V-A Estimation, Facial Expression Classification and AU Detection Sub-challenges. We propose a Transformer Encoder with Multi-Head Attention fram

Brandon Siyuan Loh, Raj Kumar Gupta, Ajay Vishwanath, Andrew Ortony

The COVID-19 pandemic has claimed millions of lives worldwide and elicited heightened emotions. This study examines the expression of various emotions pertaining to COVID-19 in the United States and India as manifested in over 54 million tweets, covering the fifteen-month period from February 2020 through April 2021, a period which includes the beginnings of

Kang Liao, Lang Nie, Shujuan Huang, Chunyu Lin

Camera calibration involves estimating camera parameters to infer geometric features from captured sequences, which is crucial for computer vision and robotics. However, conventional calibration is laborious and requires dedicated collection. Recent efforts show that learning-based solutions have the potential to be used in place of the repeatability works o

H. E. S. S. Collaboration, :, F. Aharonian, F. Ait Benkhali

GRB221009A is the brightest gamma-ray burst ever detected. To probe the very-high-energy (VHE, $>$\!100 GeV) emission, the High Energy Stereoscopic System (H.E.S.S.) began observations 53 hours after the triggering event, when the brightness of the moonlight no longer precluded observations. We derive differential and integral upper limits using H.E.S.S. dat

J. T. Zhu, C. Lin, H. B. Xiao, J. H. Fan

In this work, we make use of a supervised machine learning algorithm based on Logistic Regression (LR) to select TeV blazar candidates from the 4FGL-DR2 / 4LAC-DR2, 3FHL, 3HSP, and 2BIGB catalogs. LR constructs a hyperplane based on a selection of optimal parameters, named features, and hyper-parameters whose values control the learning process and determine

Zirui Ge, Haiyan Guo, Zhen Yang

Pre-trained wav2vec2.0 model has been proved its effectiveness for speaker recognition. However, current feature processing methods are focusing on classical pooling on the output features of the pre-trained wav2vec2.0 model, such as mean pooling, max pooling etc. That methods take the features as the independent and irrelevant units, ignoring the inter-rela

Takami Sato, Yuki Hayakawa, Ryo Suzuki, Yohsuke Shiiki

LiDAR (Light Detection And Ranging) is an indispensable sensor for precise long- and wide-range 3D sensing, which directly benefited the recent rapid deployment of autonomous driving (AD). Meanwhile, such a safety-critical application strongly motivates its security research. A recent line of research finds that one can manipulate the LiDAR point cloud and f

Mauricio S. Louzeiro, Gilson N. Silva, Jinyun Yuan, Daoping Zhang

The convergence of inexact Newton methods is studied for solving generalized equations on Riemannian manifolds by using the metric regularity property, which is also explored. Under appropriate conditions and without any additional geometric assumptions, local convergence results with linear and quadratic rates, as well as a semi-local convergence result, ar

Chuqi Chen, Yue Wu, Yang Xiang

In this paper, we propose a novel approach to generative modeling using a loss function based on elastic interaction energy (EIE), which is inspired by the elastic interaction between defects in crystals. The utilization of the EIE-based metric presents several advantages, including its long range property that enables consideration of global information in

Haibao Yu, Yingjuan Tang, Enze Xie, Jilei Mao

Cooperatively utilizing both ego-vehicle and infrastructure sensor data can significantly enhance autonomous driving perception abilities. However, temporal asynchrony and limited wireless communication in traffic environments can lead to fusion misalignment and impact detection performance. This paper proposes Feature Flow Net (FFNet), a novel cooperative d

James F. O'Brien, Victor B. Zordan, Jessica K. Hodgins

Objects that move in response to the actions of a main character often make an important contribution to the visual richness of an animated scene. We use the term "secondary motion" to refer to passive motions generated in response to the movements of characters and other objects or environmental forces. Secondary motions aren't normally the mail focus of an

Greeshma Balabhadra, El Mehdi Ainasse, Pawel Polak

We propose a method for constructing sparse high-frequency volatility estimators that are robust against change points in the spot volatility process. The estimators we propose are $\ell_1$-regularized versions of existing volatility estimators. We focus on power variation estimators as they represent a fundamental class of volatility estimators. We establis

Hongliang Zhang, Yeyang Sun, Junyi Huang, Bingjun Wu

Vortices in fluids and gases have piqued the interest of human for centuries. Development of classical-wave physics and quantum mechanics highlighted wave vortices characterized by phase singularities and topological charges. In particular, vortex beams have found numerous applications in modern optics and other areas. Recently, optical spatiotemporal vortex

Se-Ho Kim, Hosun Jun, Kyuseon Jang, Pyuck-Pa Choi

Proton exchange membrane fuel cells hold promise as energy conversion devices for hydrogen-based power generation and storage. However, the slow kinetics of the oxygen reduction at the cathode imposes the need for highly active catalysts, typically Pt or Pt-based, with a large available area. The scarcity of Pt increases deployment and operational cost, driv

Limei Liang, Ruiqi Ding, Shuai Liu

In this article, we investigate the distributed privacy preserving weighted consensus control problem for linear continuous-time multi-agent systems under the event-triggering communication mode. A novel event-triggered privacy preserving consensus scheme is proposed, which can be divided into three phases. First, for each agent, an event-triggered mechanism

Samantha Reig, Erica Principe Cruz, Melissa M. Powers, Jennifer He

Technology, especially the smartphone, is villainized for taking meaning and time away from in-person interactions and secluding people into "digital bubbles". We believe this is not an intrinsic property of digital gadgets, but evidence of a lack of imagination in technology design. Leveraging augmented reality (AR) toward this end allows us to create exper

Dali Cheng, Eran Lustig, Kai Wang, Shanhui Fan

The concept of synthetic dimensions in photonics provides a versatile platform in exploring multi-dimensional physics. Many of these physics are characterized by band structures in more than one dimensions. Existing efforts on band structure measurements in the photonic synthetic frequency dimension however are limited to either one-dimensional Brillouin zon

Xiangjing Liu, Yixian Qiu, Oscar Dahlsten, Vlatko Vedral

We give a causal inference scheme using quantum observations alone for a case with both temporal and spatial correlations: a bipartite quantum system with measurements at two times. The protocol determines compatibility with 5 causal structures distinguished by the direction of causal influence and whether there are initial correlations. We derive and exploi

Haotian Hu, Fanyi Wang, Jingwen Su, Hongtao Zhou

In point cloud analysis tasks, the existing local feature aggregation descriptors (LFAD) are unable to fully utilize information in the neighborhood of central points. Previous methods rely solely on Euclidean distance to constrain the local aggregation process, which can be easily affected by abnormal points and cannot adequately fit with the original geome

Hongyu Guo

Wheat is one of the most significant crop species with an annual worldwide grain production of 700 million tonnes. Assessing the production of wheat spikes can help us measure the grain production. Thus, detecting and characterizing spikes from images of wheat fields is an essential component in a wheat breeding process. In this study, we propose three wheat

Gary D. Yngve, James F. O'Brien, Jessica K. Hodgins

In this paper, we introduce techniques for animating explosions and their effects. The primary effect of an explosion is a disturbance that causes a shock wave to propagate through the surrounding medium. This disturbance determines the behavior of nearly all other secondary effects seen in explosions. We simulate the propagation of an explosion through the

Sadashige Matsuo, Takaya Imoto, Tomohiro Yokoyama, Yosuke Sato

The Josephson junction (JJ) is an essential element of superconducting (SC) devices for both fundamental and applied physics. The short-range coherent coupling of two adjacent JJs forms the Andreev molecule states (AMSs), which will provide a new ingredient to engineer the SC transport in JJs and control the Andreev qubits. However, no experimental evidence

SeungHeon Doh, Minz Won, Keunwoo Choi, Juhan Nam

We introduce a framework that recommends music based on the emotions of speech. In content creation and daily life, speech contains information about human emotions, which can be enhanced by music. Our framework focuses on a cross-domain retrieval system to bridge the gap between speech and music via emotion labels. We explore different speech representation

Yimeng Min, Yiwei Bai, Carla P. Gomes

We propose UTSP, an unsupervised learning (UL) framework for solving the Travelling Salesman Problem (TSP). We train a Graph Neural Network (GNN) using a surrogate loss. The GNN outputs a heat map representing the probability for each edge to be part of the optimal path. We then apply local search to generate our final prediction based on the heat map. Our l

Cheng Zhao, Shuo Yuan

In this paper, we will consider a class of continuous-time stochastic control systems with both unknown nonlinear structure and unknown disturbances, and investigate the capability of the classical proportional-integral-derivative(PID) controller in tracking time-varying reference signals. First, under some suitable conditions on system nonlinear functions,

Onur Cezmi Mutlu, Mohammadmahdi Honarmand, Saimourya Surabhi, Dennis P. Wall

We introduce Temporal consistency for Test-time adaptation (TempT) a novel method for test-time adaptation on videos through the use of temporal coherence of predictions across sequential frames as a self-supervision signal. TempT is an approach with broad potential applications in computer vision tasks including facial expression recognition (FER) in videos

Mina Jafari, Behnam Ghavami, Vahid Sattari Naeini

The problem of chemotherapy treatment optimization can be defined in order to minimize the size of the tumor without endangering the patient's health; therefore, chemotherapy requires to achieve a number of objectives, simultaneously. For this reason, the optimization problem turns to a multi-objective problem. In this paper, a multi-objective meta-heuristic

Kanade Nishikawa

In this paper, we Fourier transform the Wightman function concerning energy and angular momentum on the $S^{D-1}$ spatial slice in radial quantization in $D=2,3$ dimensions. In each case, we use the conformal Ward Identities to solve systematically for the Fourier components. We then use these Fourier components to build conformal blocks for the four-point f

Yang Chen, Zhenyu Yang, Jingtong Zhao, Justus Adamson

We developed a deep ensemble learning model with a radiomics spatial encoding execution for improved glioma segmentation accuracy using multi-parametric MRI (mp-MRI). This model was developed using 369 glioma patients with a 4-modality mp-MRI protocol: T1, contrast-enhanced T1 (T1-Ce), T2, and FLAIR. In each modality volume, a 3D sliding kernel was implement

James F. O'Brien, Robert E. Bodenheimer, Gabriel J. Brostow, Jessica K. Hodgins

This paper describes a technique for using magnetic motion capture data to determine the joint parameters of an articulated hierarchy. This technique makes it possible to determine limb lengths, joint locations, and sensor placement for a human subject without external measurements. Instead, the joint parameters are inferred with high accuracy from the motio

Nuno Costa Dias, João Nuno Prata

We address a recent conjecture stated by Z. Van Herstraeten and N.J. Cerf. They claim that the Shannon entropy for positive Wigner functions is bounded below by a positive constant, which can be attained only by Gaussian pure states. We introduce an alternative definition of entropy for all absolutely integrable Wigner functions, which is the Shannon entropy

József Balogh, Haoran Luo

Denote by $\mathcal{C}^-_{\ell}$ the $3$-uniform hypergraph obtained by removing one hyperedge from the tight cycle on $\ell$ vertices. It is conjectured that the Tur\'an density of $\mathcal{C}^-_{5}$ is $1/4$. In this paper, we make progress toward this conjecture by proving that the Tur\'an density of $\mathcal{C}^-_{\ell}$ is $1/4$, for every sufficientl

Tomohiro Oishi

Open-print version of dissertation thesis. This thesis focuses on the relation between the diproton correlation and the two-proton emission with quantum entanglement. For this purpose, I developed a time-dependent three-body (proton-proton-core) model, where one Cooper pair of protons move inside the mean-field potential. The time-dependent calculation based