July 2022 arXiv papers — page 130

Akira Taniguchi, Maoko Muro, Hiroshi Yamakawa, Tadahiro Taniguchi

The human brain, among its several functions, analyzes the double articulation structure in spoken language, i.e., double articulation analysis (DAA). A hierarchical structure in which words are connected to form a sentence and words are composed of phonemes or syllables is called a double articulation structure. Where and how DAA is performed in the human b

Jishnu Jaykumar P, Yu-Wei Chao, Yu Xiang

We introduce the Few-Shot Object Learning (FewSOL) dataset for object recognition with a few images per object. We captured 336 real-world objects with 9 RGB-D images per object from different views. Object segmentation masks, object poses and object attributes are provided. In addition, synthetic images generated using 330 3D object models are used to augme

Ankush Sharma, Alka Upadhyay

Tetraquark states are classified using the $SU(6)_{sf}$ spin-flavor symmetry and Young tableau technique. Further, by using the extension of Gursey-Radicati mass formula, masses of tetraquark states are predicted upto good level of accuracy. Also, Decay channels and decay widths of tetraquark states are calculated and found to be in good agreement with the e

Mehdi Dehghan, Zeinab Gharibi, Ricardo Ruiz-Baier

In this article, we propose and analyze a fully coupled, nonlinear, and energy-stable virtual element method (VEM) for solving the coupled Poisson-Nernst-Planck (PNP) and Navier--Stokes (NS) equations modeling microfluidic and electrochemical systems (diffuse transport of charged species within incompressible fluids coupled through electrostatic forces). A m

Bin Su, Shaoguang Mao, Frank Soong, Zhiyong Wu

Ordinal regression with anchored reference samples (ORARS) has been proposed for predicting the subjective Mean Opinion Score (MOS) of input stimuli automatically. The ORARS addresses the MOS prediction problem by pairing a test sample with each of the pre-scored anchored reference samples. A trained binary classifier is then used to predict which sample, te

Jose M. Lopez

Recent experiments have reported a novel transition to elasto-inertial turbulence in the Taylor--Couette flow of a dilute polymer solution. Unlike previously reported transitions, this newly discovered scenario, dubbed vortex merging and splitting (VMS) transition, occurs in the centrifugally unstable regime and the mechanisms underlying it are two-dimension

Umberto Guarnotta, Roberto Livrea, Salvatore A. Marano

Some recent existence, multiplicity, and uniqueness results for singular p-Laplacian systems either in bounded domains or in the whole space are presented, with a special attention to the case of convective reactions. A extensive bibliography is also provided.

Antara Dey, Arnab Paul, Supratik Pal

In this article, we have done a thorough investigation of the possible effects of interaction between dark matter (DM) and neutrinos on reionization history. We have constrained the interaction strength using 21 cm Cosmology and found out possible deviations from standard, non-interacting $\Lambda$CDM scenario. Comparing the results with the existing constra

Shintaro Akamine, Hiroki Fujino

Zero mean curvature surfaces in the simply isotropic 3-space $\mathbb{I}^3$ naturally appear as intermediate geometry between geometry of minimal surfaces in $\mathbb{E}^3$ and that of maximal surfaces in $\mathbb{L}^3$. In this paper, we investigate reflection principles for zero mean curvature surfaces in $\mathbb{I}^3$ as with the above surfaces in $\math

Naoya Fujita, Hiroshi Watanabe

We approximated the evaluation function for the game Tic-Tac-Toe by singular value decomposition (SVD) and investigated the effect of approximation accuracy on winning rate. We first prepared the perfect evaluation function of Tic-Tac-Toe and performed low-rank approximation by considering the evaluation function as a ninth-order tensor. We found that we can

Kyungtaek Jun

Computed tomography (CT) has been developed as a non-destructive technique for observing minute internal images of samples. It has been difficult to obtain photo-realistic (clean or clear) CT images due to various unwanted artifacts generated during the CT scanning process, along with limitations of back projection algorithms. Recently, an iterative optimiza

Huaying Wei, Katsuhiko Matsuzaki

We consider a univalent analytic function $f$ on the half-plane satisfying the condition that the supremum norm of its (pre-)Schwarzian derivative vanishes on the boundary. Under certain extra assumptions on $f$, we show that there exists a chordal Loewner chain initiated from $f$ until some finite time, and this Loewner chain defines a quasiconformal extens

Zheng Wang, Shandian Zhe

Tensor decomposition is a fundamental framework to analyze data that can be represented by multi-dimensional arrays. In practice, tensor data is often accompanied by temporal information, namely the time points when the entry values were generated. This information implies abundant, complex temporal variation patterns. However, current methods always assume

Steve Nyemba, Chao Yan, Ziqi Zhang, Amol Rajmane

Artificial intelligence, and particularly machine learning (ML), is increasingly developed and deployed to support healthcare in a variety of settings. However, clinical decision support (CDS) technologies based on ML need to be portable if they are to be adopted on a broad scale. In this respect, models developed at one institution should be reusable at ano

Alejandro Ríos-Herrejón

In this paper we prove that if $\kappa$ is a singular cardinal with uncountable cofinality, then every power of a given topological space with precaliber $\kappa$ has precaliber $\kappa$ as well. Furthermore, if $\{X_\alpha : \alpha<\lambda\}$ is a family of topological spaces with precaliber $\kappa$ and $\lambda<cf(\kappa)$, then $\kappa$ is a precaliber f

Pei-Zhe Li, Peter van Loock

A quantum repeater scheme based on cavity-QED and quantum error correction of channel loss via rotation-symmetric bosonic codes (RSBC) is proposed to distribute atomic entangled states over long distances without memories and at high clock rates. In this scheme, controlled rotation gates, i.e., phase shifts of the propagating light modes conditioned upon the

Vidhi Jain, Yixin Lin, Eric Undersander, Yonatan Bisk

Every home is different, and every person likes things done in their particular way. Therefore, home robots of the future need to both reason about the sequential nature of day-to-day tasks and generalize to user's preferences. To this end, we propose a Transformer Task Planner(TTP) that learns high-level actions from demonstrations by leveraging object attr

Yueyuan Gao, Natsuhiko Yoshinaga

We propose inverse problems of crack propagation using the phase-field models. First, we study the crack propagation in an inhomogeneous media in which fracture toughness varies in space. Using the two phase-field models based on different surface energy functionals, we perform simulations of the crack propagation and show that the $J$-integral reflects the

Sangdon Park, Edgar Dobriban, Insup Lee, Osbert Bastani

Uncertainty quantification is a key component of machine learning models targeted at safety-critical systems such as in healthcare or autonomous vehicles. We study this problem in the context of meta learning, where the goal is to quickly adapt a predictor to new tasks. In particular, we propose a novel algorithm to construct \emph{PAC prediction sets}, whic

Valentin Dallerit, Mayya Tokman, Ilon Joseph

The goal of this project is to compare the performance of exponential time integrators with traditional methods such as diagonally implicit Runge-Kutta methods in the context of solving the system of reduced magnetohydrodynamics (RMHD). In this report, we present initial results of a proof of concept study that shows that exponential integrators can be an ef

Wei Wu, Jun-Hong An

Setting the minimal-time bound for a quantum system to evolve between two distinguishable states, the quantum speed limit (QSL) characterizes the latent capability in speeding up of the system. It has found applications in determining the quantum superiority in many quantum technologies. However, previous results showed that such a speedup capability is gene

Zishuo Zheng, Chunyu Lin, Lang Nie, Kang Liao

Recently, horizontal representation-based panoramic semantic segmentation approaches outperform projection-based solutions, because the distortions can be effectively removed by compressing the spherical data in the vertical direction. However, these methods ignore the distortion distribution prior and are limited to unbalanced receptive fields, e.g., the re

Ryan Hechenberger, Daniel Harabor, Muhammad Aamir Cheema, Peter J Stuckey

The Euclidean shortest path problem (ESPP) is a well studied problem with many practical applications. Recently a new efficient online approach to this problem, RayScan, has been developed, based on ray shooting and polygon scanning. In this paper we show how we can improve RayScan by carefully reasoning about polygon scans. We also look into how RayScan cou

Huanchen Zhai, Garnet Kin-Lic Chan

The efficient and reliable treatment of both spin-orbit coupling (SOC) and electron correlation is essential for understanding f-element chemistry. We analyze two approaches to the problem, the one-step approach where both effects are treated simultaneously, and the two-step state interaction approach. We report an implementation of the ab initio density mat

Shahin Yousefi, Mohsen Hooshmand, Mohsen Afsharchi

Discovering emerging entities (EEs) is the problem of finding entities before their establishment. These entities can be critical for individuals, companies, and governments. Many of these entities can be discovered on social media platforms, e.g. Twitter. These identities have been the spot of research in academia and industry in recent years. Similar to an

Kwing-Lam Leung, Ming-Chung Chu, Lap-Ming Lin

The tidal properties of a neutron star are measurable in the gravitational waves emitted from inspiraling binary neutron stars, and they have been used to constrain the neutron star equation of state. In the same spirit, we study the dimensionless tidal deformability of dark matter admixed neutron stars. The tidal Love number is computed in a two-fluid frame

Elnaz Banan Sadeghian

The advent of multiple readers in magnetic recording opens the possibility of replacing the current industry's single-track detection with the more promising multitrack detection architectures. We have proposed a first solution, a generalized partial-response maximum-likelihood (GPRML) architecture, that extends the conventional PRML paradigm to jointly dete

Shruti Vyas, Chen Chen, Mubarak Shah

The existing work in cross-view geo-localization is based on images where a ground panorama is matched to an aerial image. In this work, we focus on ground videos instead of images which provides additional contextual cues which are important for this task. There are no existing datasets for this problem, therefore we propose GAMa dataset, a large-scale data

C. Huerta Alderete, Alaina M. Green, Nhung H. Nguyen, Yingyue Zhu

Deformed oscillators allow for a generalization of the standard fermions and bosons, namely, for the description of para-particles. Such particles, while indiscernible in nature, can represent good candidates for descriptions of physical phenomena like topological phases of matter. Here, we report the digital quantum simulation of para-particle oscillators b

Xiang Bai, Qianyun Miao, Changhui Tan, Liutang Xue

In this paper, we study the Cauchy problem of the compressible Euler system with strongly singular velocity alignment. We prove the existence and uniqueness of global solutions in critical Besov spaces to the considered system with small initial data. The local-in-time solvability is also addressed. Moreover, we show the large-time asymptotic behavior and op

Ali Menati, Kiyeob Lee, Le Xie

The electricity sector is facing the dual challenge of supporting increasing level of demand electrification while substantially reducing its carbon footprint. Among electricity demands, the energy consumption of cryptocurrency mining data centers has witnessed significant growth worldwide. If well-coordinated, these data centers could be tailor-designed to

Kyle A. Miller

The arrow polynomial is an invariant of framed oriented virtual links that generalizes the virtual Kauffman bracket. In this paper we define the homological arrow polynomial, which generalizes the arrow polynomial to framed oriented virtual links with labeled components. The key observation is that, given a link in a thickened surface, the homology class of

Yifang Men, Yuan Yao, Miaomiao Cui, Zhouhui Lian

This paper introduces DCT-Net, a novel image translation architecture for few-shot portrait stylization. Given limited style exemplars ($\sim$100), the new architecture can produce high-quality style transfer results with advanced ability to synthesize high-fidelity contents and strong generality to handle complicated scenes (e.g., occlusions and accessories

Eric C. Wong

A fundamental feature of human intelligence is that we accumulate and transfer knowledge as a society and across generations. We describe here a network architecture for the human brain that may support this feature and suggest that two key innovations were the ability to consider an offline model of the world, and the use of language to record and communica

Zhehan Kan, Shuoshuo Chen, Zeng Li, Zhihai He

We observe that human poses exhibit strong group-wise structural correlation and spatial coupling between keypoints due to the biological constraints of different body parts. This group-wise structural correlation can be explored to improve the accuracy and robustness of human pose estimation. In this work, we develop a self-constrained prediction-verificati

Tianyu Zhao, Junping Du, Zhe Xue, Ang Li

Text sentiment analysis, also known as opinion mining, is research on the calculation of people's views, evaluations, attitude and emotions expressed by entities. Text sentiment analysis can be divided into text-level sentiment analysis, sen-tence-level sentiment analysis and aspect-level sentiment analysis. Aspect-Based Sentiment Analysis (ABSA) is a fine-g

Yaoyao Ding, Chenghao Wu, Xinyu Liu, Yyuntao Zou

The mature development and the extension of the industry chain make the income structure of the film industry. The income of the traditional film industry depends on the box office and also includes movie merchandising, advertisement, home entertainment, book sales etc. Movie merchandising can even become more profitable than the box office. Therefore, marke

Lantian Zhang, Lei Guo

This paper investigates the adaptive identification and prediction problems for stochastic dynamical systems with saturated observations, which arise from various fields in engineering and social systems, but up to now still lack comprehensive theoretical studies including performance guarantees needed in practical applications. With this impetus, the paper

Javier A. Almonacid, Sebastián A. Domínguez-Rivera, Ryan N. Konno, Nilima Nigam

Skeletal muscles are living tissues that can undergo large deformations in short periods of time and that can be activated to produce force. In this paper we use the principles of continuum mechanics to propose a dynamic, fully non-linear, and three-dimensional model to describe the deformation of these tissues. We model muscles as a fibre-reinforced composi

Yansong Li, Kai Hu, Kohei Nakajima, Yongping Pan

Echo state network (ESN), a kind of recurrent neural networks, consists of a fixed reservoir in which neurons are connected randomly and recursively and obtains the desired output only by training output connection weights. First-order reduced and controlled error (FORCE) learning is an online supervised training approach that can change the chaotic activity

Man Luo, Sharad Saxena, Swaroop Mishra, Mihir Parmar

Table Question Answering (TQA) is an important but under-explored task. Most of the existing QA datasets are in unstructured text format and only few of them use tables as the context. To the best of our knowledge, none of TQA datasets exist in the biomedical domain where tables are frequently used to present information. In this paper, we first curate a tab

Nicholas J. Moore, Jake Cherry, Shang-Huan Chiu, Bryan D. Quaife

Using a Cauchy integral formulation of the boundary integral equations, we simulate the erosion a porous medium comprised of up to 100 solid bodies embedded in a Stokes flow. The grains of the medium are resolved individually and erode under the action of surface shear stress. Through nonlinear feedback with the surrounding flow fields, microscopic changes i

Luis E. Herrera Rodriguez, Arif Ullah, Kennet J. Rueda Espinosa, Pavlo O. Dral

It has been recently shown that supervised machine learning (ML) algorithms can accurately and efficiently predict the long-time populations dynamics of dissipative quantum systems given only short-time population dynamics. In the present article we benchmaked 22 ML models on their ability to predict long-time dynamics of a two-level quantum system linearly

Mayank Tanwar, Sagar Udyavara, Hwanhui Yun, Supriya Ghosh

Layered black arsenic (b-As) has recently emerged as a new anisotropic two-dimensional (2D) semiconducting material with applications in electronic devices. Understanding factors affecting the ambient stability of this material remains crucial for its applications. Herein we use first-principles density functional theory (DFT) calculations to examine the sta

Sha Deng, Yichen Chu, Qizhong Zhu

Moir\'e exciton features tunable Dirac dispersion and spatially dependent optical selection rules. With the long lifetime due to its interlayer nature, it is promising to realize a Bose-Einstein condensation of moir\'e excitons. Here we study the properties of moir\'e exciton condensate within the mean-field theory, with special focus on exciton-exciton inte

Rowan Killip, Jason Murphy, Monica Visan

Using the two-dimensional nonlinear Schr\"odinger equation (NLS) as a model example, we present a general method for recovering the nonlinearity of a nonlinear dispersive equation from its small-data scattering behavior. We prove that under very mild assumptions on the nonlinearity, the wave operator uniquely determines the nonlinearity, as does the scatteri

Ruoyu Xiong, Yue Yu, Shuyi Chen, Maoyuan Li

Identifying overpotential components of electrochemical systems enables quantitative analysis of polarization contributions of kinetic processes under practical operating conditions. However, the inherently coupled kinetic processes lead to an enormous challenge in measuring individual overpotentials, particularly in composite electrodes of lithium-ion batte

Seokchang Hong

This paper is devoted to the investigation of long-time behaviour of solutions to wave equations with quadratic nonlinearity and cubic Dirac equations with Hartree-type nonlinearity. We consider the nonlinearity here with enough simplicity so that we can treat it as a toy model and simultaneously with enough generality so that we can apply our result to wave

Romulo Rougemont, Willians Barreto

We analyze the time evolution of several physical observables, namely the pressure anisotropy, the scalar condensate, the charge density, and also, for the first time, the non-equilibrium entropy for a Bjorken expanding strongly coupled $\mathcal{N}=4$ Supersymmetric Yang-Mills plasma charged under an Abelian $U(1)$ subgroup of the global $SU(4)$ R-symmetry.

Feng Sun, Ming-Kun Xie, Sheng-Jun Huang

In this paper, we study the partial multi-label (PML) image classification problem, where each image is annotated with a candidate label set consists of multiple relevant labels and other noisy labels. Existing PML methods typically design a disambiguation strategy to filter out noisy labels by utilizing prior knowledge with extra assumptions, which unfortun

C. Gong, X. Yang, S. J. Tang, Q. Q. Zhang

Biomarker detection is the key to identifying health risks. However, designing sensitive biosensors in a single-use mode for disease diagnosis remains a major challenge. Here, we report sub-monolayer biolasers with remarkable repeatability for ultrasensitive and disposable biomarker detection. The biolaser sensors are designed by employing the telecom optica

Dimitri Pimenov, Andrey V. Chubukov

We review recent theoretical progress in understanding spatially uniform $s$-wave superconductivity which arises from a fermion-fermion interaction, which is repulsive on the Matsubara axis, where it is real, but does depend on the transferred frequency. Such a situation holds, e.g., for systems with a screened Coulomb and retarded electron-phonon interactio

Yigui Zhong, Shaozhi Li, Hongxiong Liu, Yuyang Dong

In crystalline materials, electron-phonon coupling (EPC) is a ubiquitous many-body interaction that drives conventional Bardeen-Cooper-Schrieffer superconductivity. Recently, in a new kagome metal $\rm{CsV_3Sb_5}$, superconductivity that possibly intertwines with time-reversal and spatial symmetry-breaking orders is observed. Density functional theory calcul

Madeline Howell, Simon W. Campbell, Dennis Stello, Gayandhi M. De Silva

Mass loss remains a major uncertainty in stellar modelling. In low-mass stars, mass loss is most significant on the red giant branch (RGB), and will impact the star's evolutionary path and final stellar remnant. Directly measuring the mass difference of stars in various phases of evolution represents one of the best ways to quantify integrated mass loss. Glo

Huagui Duan, Dong Xie

In this paper, we prove that for every Finsler $4$-dimensional sphere $(S^4,F)$ with reversibility $\lambda$ and flag curvature $K$ satisfying $\frac{25}{9}\left(\frac{\lambda}{1+\lambda}\right)^2<K\le 1$ with $\lambda<\frac{3}{2}$, either there exist at least four prime closed geodesics, or there exist exactly three prime non-hyperbolic closed geodesics and

Difei Cheng, Yunfeng Zhang, Ruinan Jin

K-medoids clustering is a popular variant of k-means clustering and widely used in pattern recognition and machine learning. A main drawback of k-medoids clustering is that an improper initialization can cause it to get trapped in local optima. An improved k-medoids clustering algorithm, called INCKM algorithm, which is the first to apply incremental initial

Adaora Nwosu, Samantha Boardman, Mustafa M. Husain, P. Murali Doraiswamy

Digit therapeutics are novel software devices that clinicians may utilize in delivering quality mental health care and ensuring positive outcomes. However, uptake of digital therapeutics and clinically tested software-based programs remains low. This article presents possible reasons for attrition and low engagement in clinical studies investigating digital

Yiwei Dong, Xiaobo Hou, Xueting Tian

In this article, we combine the perspectives of density, entropy, and multifractal analysis to investigate the structure of ergodic measures. We prove that for each transitive topologically Anosov system $(X,f)$, each continuous function $\varphi$ on $X$ and each $(a,h)\in \mathrm{Int}\{(\int \varphi d\mu, h_\mu(f)):\mu\in M_f(X)\},$ the set $\{\mu\in M_f^e(

Yuqian Chen, Fan Zhang, Chaoyi Zhang, Tengfei Xue

White matter tract microstructure has been shown to influence neuropsychological scores of cognitive performance. However, prediction of these scores from white matter tract data has not been attempted. In this paper, we propose a deep-learning-based framework for neuropsychological score prediction using microstructure measurements estimated from diffusion

Akram Shafie, Nan Yang, Chong Han, Josep M. Jornet

This paper presents a novel spectrum allocation strategy for multiuser terahertz (THz) band communication systems when the to-be-allocated spectrum is composed of multiple transmission windows (TWs). This strategy explores the benefits of (i) allowing users to occupy sub-bands with unequal bandwidths and (ii) optimally avoiding using some spectra that exist

Guangzhi Wang, Yangyang Guo, Yongkang Wong, Mohan Kankanhalli

Detecting Human-Object Interaction (HOI) in images is an important step towards high-level visual comprehension. Existing work often shed light on improving either human and object detection, or interaction recognition. However, due to the limitation of datasets, these methods tend to fit well on frequent interactions conditioned on the detected objects, yet

Yuemeng Li, Miguel Romanello Joaquim, Stephen Pickup, Hee Kwon Song

Purpose: To accelerate radially sampled diffusion weighted spin-echo (Rad-DW-SE) acquisition method for generating high quality apparent diffusion coefficient (ADC) maps. Methods: A deep learning method was developed to generate accurate ADC maps from accelerated DWI data acquired with the Rad-DW-SE method. The deep learning method integrates convolutional n

Bo Zhang, Yue Liu, Kaixin Lu, Li Niu

When using cut-and-paste to acquire a composite image, the geometry inconsistency between foreground and background may severely harm its fidelity. To address the geometry inconsistency in composite images, several existing works learned to warp the foreground object for geometric correction. However, the absence of annotated dataset results in unsatisfactor

Fahim Rahim, Nicholas J. Cavenagh

The $q^k$ (full) factorial design with replication $\lambda$ is the multi-set consisting of $\lambda$ occurrences of each element of each $q$-ary vector of length $k$; we denote this by $\lambda\times [q]^k$. An $m\times n$ row-column factorial design $q^k$ of strength $t$ is an arrangement of the elements of $\lambda \times [q]^k$ into an $m\times n$ array

Axel Garcia-Vega, Ricardo Espinosa, Gilberto Ochoa-Ruiz, Thomas Bazin

Endoscopy is the most widely used medical technique for cancer and polyp detection inside hollow organs. However, images acquired by an endoscope are frequently affected by illumination artefacts due to the enlightenment source orientation. There exist two major issues when the endoscope's light source pose suddenly changes: overexposed and underexposed tiss

Ruixi Liu, Kriti Gupta, Zhe Yuan, Paul J. Kelly

The role played by interfaces in metallic multilayers is not only to change the momenta of incident electrons; their symmetry lowering also results in an enhancement of the effects of spin-orbit coupling, in particular the flipping of the spins of conduction electrons. This leads to a significant reduction of a spin current through a metallic interface that

Mauro Fontana, Pablo D. Mininni, Pablo Dmitruk

We present a high-order numerical study of the dependence of the dynamo onset on different electromagnetic boundary conditions, in convecting Boussinesq flows forced by a temperature gradient. Perfectly conducting boundaries, vacuum, and mixed electromagnetic boundary conditions are considered, using a method that treats fields and boundary conditions with c

Yigon Kim, Rory Smith, Jihye Shin

A recent study conducted using CALIFA survey data (Lee et al, 2019b) has found that the orbital motions of neighbor galaxies are coherent with the spin direction of a target galaxy on scales of many Megaparsecs. We study this so called `large-scale coherence' phenomena using N-body cosmological simulations. We confirm a strong coherence signal within 1 Mpc/h

Ahmed Al-Shafei, Hamidreza Zareipour, Yankai Cao

The accelerating technological landscape and drive towards net-zero emission made the power system grow in scale and complexity. Serial computational approaches for grid planning and operation struggle to execute necessary calculations within reasonable times. Resorting to high-performance and parallel computing approaches has become paramount. Moreover, the

Ichiro Hasuo, Clovis Eberhart, James Haydon, Jérémy Dubut

We introduce a goal-aware extension of responsibility-sensitive safety (RSS), a recent methodology for rule-based safety guarantee for automated driving systems (ADS). Making RSS rules guarantee goal achievement -- in addition to collision avoidance as in the original RSS -- requires complex planning over long sequences of manoeuvres. To deal with the comple

Tianyi Pan, Shijie Shang, Tusheng Zhang

In this paper, we establish a large deviation principle for the solutions to the stochastic heat equations with logarithmic nonlinearity driven by Brownian motion, which is neither locally Lipschitz nor locally monotone. Nonlinear versions of Gronwall's inequalities and Log-Sobolev inequalities play an important role.

Bingqing Hu, Bin Nan

Most work in neural networks focuses on estimating the conditional mean of a continuous response variable given a set of covariates.In this article, we consider estimating the conditional distribution function using neural networks for both censored and uncensored data. The algorithm is built upon the data structure particularly constructed for the Cox regre

Zheng Zhang, Yanzhen Cai, Jing Kang, Zhongwen Ouyang

Rare-earth chalcohalide REChX (RE = rare earth; Ch = O, S, Se, Te; X = F, Cl, Br, I) is a newly reported family of Kitaev spin liquid candidates. The family offers a platform where a strong spin-orbit coupling meets a van der Waals layered and undistorted honeycomb spin lattice, which outputs highly anisotropic exchange couplings required by the Kitaev model

Pengfei Zhang

In this letter, we propose a quantized topological response in trapped 1D quantum gases. The experimental protocol for the response requires the application of an instant optical pulse to a half-infinite region in an asymptotically harmonic trap and measuring the density distribution. We show that the corresponding linear response is described by a universal

David W. Kanaar, Utkan Güngördü, J. P. Kestner

In semiconductor spin qubits which typically interact through short-range exchange coupling, shuttling of spin is a practical way to generate quantum operations between distant qubits. Although the exchange is often tunable through voltages applied to gate electrodes, its minimal value can be significantly large, which hinders the applicability of existing s

Annette Spooner, Gelareh Mohammadi, Perminder S. Sachdev, Henry Brodaty

Healthcare datasets often contain groups of highly correlated features, such as features from the same biological system. When feature selection is applied to these datasets to identify the most important features, the biases inherent in some multivariate feature selectors due to correlated features make it difficult for these methods to distinguish between

Kyle R. Myers

When funding public goods, resources are often allocated via mechanisms that resemble contests, especially in the case of research grants. A common critique of these contests is that they induce ``too much'' effort from participants. This need not be true if the effort in the contest is itself directed towards the public good. This papers analyzes survey dat

Wei Zhang

By involving some exponential sums related to $\Lambda(n)$ in arithmetic progression, we can obtain some new results for von Mangoldt function over {\bf nonhomogeneous} Beatty sequences in arithmetic progressions, which improve some recent results of Banks-Yeager unconditionally. On the other hand, we also considered the primes over Piatetski-Shapiro sequenc

Chanyong Jung, Joonhyung Lee, Sunkyoung You, Jong Chul Ye

The acquisition conditions for low-dose and high-dose CT images are usually different, so that the shifts in the CT numbers often occur. Accordingly, unsupervised deep learning-based approaches, which learn the target image distribution, often introduce CT number distortions and result in detrimental effects in diagnostic performance. To address this, here w

Renjie Li, Xinyi Wang, Guan Huang, Wenli Yang

Deep supervision, or known as 'intermediate supervision' or 'auxiliary supervision', is to add supervision at hidden layers of a neural network. This technique has been increasingly applied in deep neural network learning systems for various computer vision applications recently. There is a consensus that deep supervision helps improve neural network perform

Runyu Mao, Chen Bai, Yatong An, Fengqing Zhu

We tackle the essential task of finding dense visual correspondences between a pair of images. This is a challenging problem due to various factors such as poor texture, repetitive patterns, illumination variation, and motion blur in practical scenarios. In contrast to methods that use dense correspondence ground-truths as direct supervision for local featur

Yi-Jian Du, Konglong Wu

Graph-based Bern-Carasso-Johansson (BCJ) relation for Berends-Giele currents in bi-adjoint scalar (BS) theory, which is characterized by connected tree graphs, was proposed in an earlier work. In this note, we provide a systematic study of this relation. We first prove the relations based on two special types of graphs: simple chains and star graphs. The gen

Zhongxu Zhai, Will J. Percival

Recent local measurements of the Hubble constant made using supernovae have delivered a value that differs by $\sim$5$\sigma$ (statistical error) from predictions using the Cosmic Microwave Background (CMB), or using Baryon Acoustic Oscillations (BAO) and Big-Bang Nucleosynthesis (BBN) constraints, which are themselves consistent. The effective volume covere

Yun Xing, Dayan Guan, Jiaxing Huang, Shijian Lu

Video semantic segmentation has achieved great progress under the supervision of large amounts of labelled training data. However, domain adaptive video segmentation, which can mitigate data labelling constraints by adapting from a labelled source domain toward an unlabelled target domain, is largely neglected. We design temporal pseudo supervision (TPS), a

Shuai Xu, Haibo Yuan, Ruoyi Zhang, Haining Li

We employ Gaia, 2MASS, and ALLWISE photometry, as well as astrometric data from Gaia, to search for relatively bright very metal-poor ([Fe/H] $< -2.0$; VMP) giant star candidates using three different criteria: 1) our derived Gaia photometric metallicities, 2) the lack of stellar molecular absorption near 4.6 microns, and 3) their high tangential velocities.

Tianhong Li, Lijie Fan, Yuan Yuan, Dina Katabi

There is a growing literature demonstrating the feasibility of using Radio Frequency (RF) signals to enable key computer vision tasks in the presence of occlusions and poor lighting. It leverages that RF signals traverse walls and occlusions to deliver through-wall pose estimation, action recognition, scene captioning, and human re-identification. However, u

Lorenzo Caprini, Umberto Marini Bettolo Marconi, Andrea Puglisi, Hartmut Löwen

The vibrational dynamics of solids is described by phonons constituting basic collective excitations in equilibrium crystals. Here we consider an active crystal composed of self-propelled particles which bring the system into a non-equilibrium steady-state governed by entropy production. Calculating the entropy production spectrum, we put forward the picture

Nurendra Choudhary, Nikhil Rao, Karthik Subbian, Chandan K. Reddy

Heterogeneous networks, which connect informative nodes containing text with different edge types, are routinely used to store and process information in various real-world applications. Graph Neural Networks (GNNs) and their hyperbolic variants provide a promising approach to encode such networks in a low-dimensional latent space through neighborhood aggreg

Akira Endo, Shingo Katsumoto, Yasuhiro Iye

We have calculated the thermoelectric conductivity tensor $\varepsilon_{ij}$ and the thermal conductivity tensor $\lambda_{ij}$ of a unidirectional lateral superlattice (ULSL) ($i,j = x,y$, with the $x$-axis aligned to the principal axis of the ULSL), %, given as the first- and the second-order moments, employing based on the asymptotic analytic formulas of

Ghaith A. Hiary, Dhir Patel, Andrew Yang

An explicit subconvex bound for the Riemann zeta function $\zeta(s)$ on the critical line $s=1/2+it$ is proved. Previous subconvex bounds relied on an incorrect version of the Kusmin-Landau lemma. After accounting for the needed correction in that lemma, we recover and improve the record explicit bound for $|\zeta(1/2 + it)|$.

Yongping Pan, Lin Pan, Haoyong Yu

Composite adaptive control (CAC) that integrates direct and indirect adaptive control techniques can achieve smaller tracking errors and faster parameter convergence compared with direct and indirect adaptive control techniques. However, the condition of persistent excitation (PE) still has to be satisfied to guarantee parameter convergence in CAC. This pape

Charles E. Thornton, R. Michael Buehrer

It has been shown (Amuru et al. 2015) that online learning algorithms can be effectively used to select optimal physical layer parameters for jamming against digital modulation schemes without a priori knowledge of the victim's transmission strategy. However, this learning problem involves solving a multi-armed bandit problem with a mixed action space that c

David Lambert, Luigi Palatella, Paolo Grigolini

We study a form of noise-induced intermittence originated by an out of equilibrium process yielding events in time with a survival probability that in the case of an infinitely aged condition coincides with the Mittag-Leffler function. In contrast with the Pomeau-Manneville intermittence, the aging process does not have any effect on the inverse power law of

Thu Nguyen-Phuoc, Feng Liu, Lei Xiao

This paper presents a stylized novel view synthesis method. Applying state-of-the-art stylization methods to novel views frame by frame often causes jittering artifacts due to the lack of cross-view consistency. Therefore, this paper investigates 3D scene stylization that provides a strong inductive bias for consistent novel view synthesis. Specifically, we

Garth Tarr, Ines Wilms

Faced with changing markets and evolving consumer demands, beef industries are investing in grading systems to maximise value extraction throughout their entire supply chain. The Meat Standards Australia (MSA) system is a customer-oriented total quality management system that stands out internationally by predicting quality grades of specific muscles process

Eliot Heinrich, Thore Posske, Benedetta Flebus

Recently, a colossal magnetoresistance (CMR) was observed in EuCd$_2$P$_2$ -- a compound that does not fit the conventional mixed-valence paradigm. Instead, experimental evidence points at a resistance driven by strong magnetic fluctuations within the two-dimensional ($2d$) ferromagnetic (FM) planes of the layered antiferromagnetic (AFM) structure. While the

Milad Pooladsanj, Ketan Savla, Petros A. Ioannou

We consider Ramp Metering (RM) at the microscopic level subject to vehicle following safety constraints for a freeway with arbitrary number of on- and off-ramps. The arrival times of vehicles to the on-ramps, as well as their destinations are modeled by exogenous stochastic processes. Once a vehicle is released from an on-ramp, it accelerates towards the fre

Svetlana Boyarchenko, Sergei Levendorskiĭ

In our previous publications (IJTAF 2019, Math. Finance 2020), we introduced a general class of SINH-regular processes and demonstrated that efficient numerical methods for the evaluation of the Wiener-Hopf factors and various probability distributions (prices of options of several types) in L\'evy models can be developed using only a few general properties

Giovanni Paolo Galdi

We study the motion of a rigid body $\mathscr B$ subject to an undamped elastic restoring force, in the stream of a viscous liquid $\mathscr L$. The motion of the coupled system $\mathscr B$-$\mathscr L\equiv\mathscr S$ is driven by a uniform flow of $\mathscr L$ at spatial infinity, characterized by a given, constant dimensionless velocity $\lambda\,\bfe_1$

Zhaoqi Li, Lillian Ratliff, Houssam Nassif, Kevin Jamieson

In the stochastic contextual bandit setting, regret-minimizing algorithms have been extensively researched, but their instance-minimizing best-arm identification counterparts remain seldom studied. In this work, we focus on the stochastic bandit problem in the $(\epsilon,\delta)$-$\textit{PAC}$ setting: given a policy class $\Pi$ the goal of the learner is t