January 2022 arXiv papers — page 83

Shan Zou, Sebastian Grossenbach, Denis Konstantinov

We report the first observation of the microwave-induced intersubband (Rydberg) resonance in the surface-bound electrons on superfluid helium confined in a single 4-$\mu$m deep channel. The resonance signal from a few thousand of surface electrons comprising the Wigner Solid (WS) is detected by observing WS melting due to the microwave absorption. The observ

Alper Odabaş, Elis Soylu Yılmaz

In this paper, we define the notion of crossed modules of groups with action and investigate related structures. Functions for computing of these structures have been written using the GAP computational discrete algebra programming language.

Zhiyuan Liu, Yixin Cao, Fuli Feng, Xiang Wang

We present a framework of Training Free Graph Matching (TFGM) to boost the performance of Graph Neural Networks (GNNs) based graph matching, providing a fast promising solution without training (training-free). TFGM provides four widely applicable principles for designing training-free GNNs and is generalizable to supervised, semi-supervised, and unsupervise

George Barmpalias, Lu Liu

Muchnik's paradox says that enumerable betting strategies are not always reducible to enumerable strategies whose bets are restricted to either even rounds or odd rounds. In other words, there are outcome sequences x where an effectively enumerable strategy succeeds, but no such parity-restricted effectively enumerable strategy does. We characterize the effe

Brittany Reid, Markus Wagner, Marcelo d'Amorim, Christoph Treude

Online participant recruitment platforms such as Prolific have been gaining popularity in research, as they enable researchers to easily access large pools of participants. However, participant quality can be an issue; participants may give incorrect information to gain access to more studies, adding unwanted noise to results. This paper details our experien

Muhittin Evren Aydin, Rafael López

A $K^\alpha$-translator is a surface in Euclidean space $\r^3$ that moves by translations in a spatial direction and under the $K^\alpha$-flow, where $K$ is the Gauss curvature and $\alpha$ is a constant. We classify all $K^\alpha$-translators that are rotationally symmetric. In particular, we prove that for each $\alpha$ there is a $K^\alpha$-translator int

Kai Yang, Xiaoman Liang, Huihuang Zhao

This paper proposed a method to imitate handwriting style by style transfer. We proposed an neural network model based on conditional generative adversarial networks (cGAN) for handwriting style transfer. This paper improved the loss function on the basis of the GAN. Compared with other handwriting imitation methods, the handwriting style transfer's effect a

A Gijón, FJ Gálvez, F. Arias de Saavedra, E Buendía

Multicluster models consider that the nucleons can be moving around different centers in the nuclei. These models have been widely used to describe light nuclei but always considering that the mean field is composed of isotropic harmonic oscillators with different centers. In this work, we propose an extension of these models by using anisotropic harmonic os

Kai-Ni Wang, Xin Yang, Juzheng Miao, Lei Li

Multi-sequence cardiac magnetic resonance (CMR) provides essential pathology information (scar and edema) to diagnose myocardial infarction. However, automatic pathology segmentation can be challenging due to the difficulty of effectively exploring the underlying information from the multi-sequence CMR data. This paper aims to tackle the scar and edema segme

Przemek Mroz, Angel Otarola, Thomas A. Prince, Richard Dekany

There is a growing concern about an impact of low-Earth-orbit (LEO) satellite constellations on ground-based astronomical observations, in particular, on wide-field surveys in the optical and infrared. The Zwicky Transient Facility (ZTF), thanks to the large field of view of its camera, provides an ideal setup to study the effects of LEO megaconstellations -

Zhaorong Zhang, Juanjuan Xu, Xun Li

This paper studies a discrete-time stochastic control problem with linear quadratic criteria over an infinite-time horizon. We focus on a class of control systems whose system matrices are associated with random parameters involving unknown statistical properties. In particular, we design a distributed Q-learning algorithm to tackle the Riccati equation and

Christoph Fleckenstein, Alberto Zorzato, Daniel Varjas, Emil J. Bergholtz

We demonstrate that genuinely non-Hermitian topological phases and corresponding topological phase transitions can be naturally realized in monitored quantum circuits, exemplified by the paradigmatic non-Hermitian Su-Schrieffer-Heeger model. We emulate this model by a 1D chain of spinless electrons evolving under unitary dynamics and subject to periodic meas

Lena Schmid, Alexander Gerharz, Andreas Groll, Markus Pauly

Tree-based ensembles such as the Random Forest are modern classics among statistical learning methods. In particular, they are used for predicting univariate responses. In case of multiple outputs the question arises whether we separately fit univariate models or directly follow a multivariate approach. For the latter, several possibilities exist that are, e

Maria Cabrera Calvo

In this paper we present a novel class of asymptotic consistent exponential-type integrators for Klein-Gordon-Schr\"odinger systems that capture all regimes from the slowly varying classical regime up to the highly oscillatory non-relativistic limit regime. We achieve convergence of order one and two that is uniform in $c$ without any time step size restrict

Lasse Bonn, Aleksandra Ardaseva, Romain Mueller, Tyler N. Shendruk

Topological defects are increasingly being identified in various biological systems, where their characteristic flow fields and stress patterns are associated with continuous active stress generation by biological entities. Here, using numerical simulations of continuum fluctuating nematohydrodynamics we show that even in the absence of any specific form of

Hanqing Zhang, Haolin Song, Shaoyu Li, Ming Zhou

Controllable Text Generation (CTG) is emerging area in the field of natural language generation (NLG). It is regarded as crucial for the development of advanced text generation technologies that better meet the specific constraints in practical applications. In recent years, methods using large-scale pre-trained language models (PLMs), in particular the wide

Mengyue Zha, Kani Chen, Tong Zhang

We enhance the accuracy and generalization of univariate time series point prediction by an explainable ensemble on the fly. We propose an Interpretable Dynamic Ensemble Architecture (IDEA), in which interpretable base learners give predictions independently with sparse communication as a group. The model is composed of several sequentially stacked groups co

Erjian Cheng, Xianbiao Shi, Limin Yan, Tianheng Huang

The study on quantum spin Hall effect and topological insulators formed the prologue to the surge of research activities in topological materials in the past decade. Compared to intricately engineered quantum wells, three-dimensional weak topological insulators provide a natural route to the quantum spin Hall effect, due to the adiabatic connection between t

Abraham Israeli, Alexander Kremiansky, Oren Tsur

Understanding collective decision making at a large-scale, and elucidating how community organization and community dynamics shape collective behavior are at the heart of social science research. In this work we study the behavior of thousands of communities with millions of active members. We define a novel task: predicting which community will undertake an

Fan Meng, Tao Song, Danya Xu

Tropical cyclones (TC) generally carry large amounts of water vapor and can cause large-scale extreme rainfall. Passive microwave rainfall (PMR) estimation of TC with high spatial and temporal resolution is crucial for disaster warning of TC, but remains a challenging problem due to the low temporal resolution of microwave sensors. This study attempts to sol

Zhuoyi Lin, Sheng Zang, Rundong Wang, Zhu Sun

Re-ranking models refine item recommendation lists generated by the prior global ranking model, which have demonstrated their effectiveness in improving the recommendation quality. However, most existing re-ranking solutions only learn from implicit feedback with a shared prediction model, which regrettably ignore inter-item relationships under diverse user

Chaojie Zhu, Yingli Ran, Zhao Zhang, Ding-Zhu Du

A connected dominating set is a widely adopted model for the virtual backbone of a wireless sensor network. In this paper, we design an evolutionary algorithm for the minimum connected dominating set problem (MinCDS), whose performance is theoretically guaranteed in terms of both computation time and approximation ratio. Given a connected graph $G=(V,E)$, a

Masahiro Oda, Tomoaki Suito, Yuichiro Hayashi, Takayuki Kitasaka

CT image-based diagnosis of the stomach is developed as a new way of diagnostic method. A virtual unfolded (VU) view is suitable for displaying its wall. In this paper, we propose a semi-automated method for generating VU views of the stomach. Our method requires minimum manual operations. The determination of the unfolding forces and the termination of the

Gan Liu, Xinran Ma, Kuanyu He, Qing Li

The charge-density wave (CDW) phase is often accompanied by the condensation of a soft acoustic phonon mode, giving rise to lattice distortion and charge density modulation. This picture was challenged for the recently discovered kagome metal CsV$_3$Sb$_5$, based on the evidence of absence of soft phonons. Here we report the observation of Raman-active CDW a

Haizi Yu, Igor Mineyev, Lav R. Varshney, James A. Evans

Humans can generalize from only a few examples and from little pretraining on similar tasks. Yet, machine learning (ML) typically requires large data to learn or pre-learn to transfer. Motivated by nativism and artificial general intelligence, we directly model human-innate priors in abstract visual tasks such as character and doodle recognition. This yields

Mengyue Zha, SiuTim Wong, Mengqi Liu, Tong Zhang

This paper shows that masked autoencoder with extrapolator (ExtraMAE) is a scalable self-supervised model for time series generation. ExtraMAE randomly masks some patches of the original time series and learns temporal dynamics by recovering the masked patches. Our approach has two core designs. First, ExtraMAE is self-supervised. Supervision allows ExtraMAE

S. L. Feng, W. Z. Jia

We investigate coherent single-photon transport in a waveguide-QED structure containing two giant atoms. The unified analytical expressions of the single-photon scattering amplitudes applicable for different topological configurations are derived. The spectroscopic characteristics in different parameter regimes, especially the asymmetric Fano line shapes and

Shoya Motonaga, Kazuyuki Yagasaki

In recent papers by the authors (S.~Motonaga and K.~Yagasaki, Obstructions to integrability of nearly integrable dynamical systems near regular level sets, submitted for publication, and K.~Yagasaki, Nonintegrability of nearly integrable dynamical systems near resonant periodic orbits, submitted for publication), two different techniques which allow us to pr

Michael Kaufmann

Entity relationship extraction envisions the automatic generation of semantic data models from collections of text, by automatic recognition of entities, by association of entities to form relationships, and by classifying these instances to assign them to entity sets (or classes) and relationship sets (or associations). As a first step in this direction, th

Upendra Bartwal, Subhasis Mukhopadhyay, Rohit Negi, Sandeep Shukla

Cyber Security is a critical topic for organizations with IT/OT networks as they are always susceptible to attack, whether insider or outsider. Since the cyber landscape is an ever-evolving scenario, one must keep upgrading its security systems to enhance the security of the infrastructure. Tools like Security Information and Event Management (SIEM), Endpoin

Masahiro Oda, Hiroaki Kondo, Takayuki Kitasaka, Kazuhiro Furukawa

This paper reports a detailed evaluation results of a colonoscope tracking method. A colonoscope tracking method utilizing electromagnetic sensors and a CT volume has been proposed. Tracking accuracy of this method was evaluated by using a colon phantom. In the previously proposed paper, tracking errors were measured only at six points on the colon phantom f

Florian Luca, Bertrand Teguia Tabuguia

Let $(u(n))_{n\in\mathbb{N}}$ be an arithmetic progression of natural integers in base $b\in\mathbb{N}\setminus \{0,1\}$. We consider the following sequences: $s(n)=\overline{u(0)u(1)\cdots u(n) }^b$ formed by concatenating the first $n+1$ terms of $(u(n))_{n\in\mathbb{N}}$ in base $b$ from the right; $s_g(n) = \overline{u(n)u(n-1)\cdots u(0)}^b$; and $(s_*(

Milutin Obradović, Nikola Tuneski

Let $\mathcal{S}$ denote the class of functions $f$ which are analytic and univalent in the unit disk ${\mathbb D}=\{z:|z|<1\}$ and normalized with $f(z)=z+\sum_{n=2}^{\infty} a_n z^n$. Using a method based on Grusky coefficients we study two problems over the class $\mathcal{S}$: estimate of the fourth logarithmic coefficient and upper bound of the coeffici

Hidefumi Ohsugi, Kohji Yanagawa

In this paper, we give the Gr\"obner fan and the state polytope of a Specht ideal $I_\lambda$ explicitly. In particular, we show that the state polytope of $I_\lambda$ for a partition $\lambda=(\lambda_1, \ldots, \lambda_m)$ is always a generalized permutohedron, and it is a (usual) permutohedron if and only if $\lambda_{i-1}=\lambda_i>0$ for some $i$.

Lujun Huang, Bin Jia, Yan Kei Chiang, Sibo Huang

Acoustic resonant cavities play a vital role in modern acoustical systems. They have led to many essential applications for noise control, biomedical ultrasonics, and underwater communications. The ultrahigh quality-factor resonances are highly desired for some applications like high-resolution acoustic sensors and acoustic lasers. Here, we theoretically pro

Eugenia Franco, Odo Diekmann, Mats Gyllenberg

We analyse the long term behaviour of the measure-valued solutions of a class of linear renewal equations modelling physiologically structured populations. The renewal equations that we consider are characterised by a regularisation property of the kernel. This regularisation property allows to deduce the large time behaviour of the measure-valued solutions

Romi Gideoni, Shanee Honig, Tal Oron-Gilad

In three laboratory experiments, we examine the impact of personally relevant failures (PeRFs) on perceptions of a collaborative robot. PeR is determined by how much a specific issue applies to a particular person, i.e., it affects one's own goals and values. We hypothesized that PeRFs would reduce trust in the robot and the robot's Likeability and Willingne

Yu Ichida

It is important to study the global behavior of solutions to systems of ordinary differential equations describing the transmission dynamics of infectious disease. In this paper, we present a different approach from the Lyapunov function used in most of them. This approach is based on the Poincar\'e compactification. We then apply the method to a SIR endemic

Alon Talmor, Ori Yoran, Ronan Le Bras, Chandra Bhagavatula

Constructing benchmarks that test the abilities of modern natural language understanding models is difficult - pre-trained language models exploit artifacts in benchmarks to achieve human parity, but still fail on adversarial examples and make errors that demonstrate a lack of common sense. In this work, we propose gamification as a framework for data constr

Yan Sun, Faming Liang

The deep neural network suffers from many fundamental issues in machine learning. For example, it often gets trapped into a local minimum in training, and its prediction uncertainty is hard to be assessed. To address these issues, we propose the so-called kernel-expanded stochastic neural network (K-StoNet) model, which incorporates support vector regression

Lihwai Lin, Sara L. Ellison, Hsi-An Pan, Mallory Thorp

We utilize the ALMA-MaNGA QUEnch and STar formation (ALMaQUEST) survey to investigate the kpc-scale scaling relations, presented as the resolved star forming main sequence (rSFMS: $\Sigma_{\rm SFR}$ vs. $\Sigma_{*}$), the resolved Schmidt-Kennicutt relation (rSK: $\Sigma_{\rm SFR}$ vs. $\Sigma_{\rm H_{2}}$), and the resolved molecular gas main sequence (rMGM

Gi-Sang Cheon, Bumtle Kang, Suh-Ryung Kim, Seyed Ahmad Mojallal

A Toeplitz graph $T_n \langle t_1,t_2,\ldots,t_k\rangle$ is a simple graph with the vertex set $[n]$ such that two vertices $v$ and $w$ are adjacent if and only if $|v-w| = t_i$ for some $i \in [k]$. In this paper, we investigate line Toeplitz graphs, which are Toeplitz graphs that happen to be line graphs. We first show that for a sufficiently large $n$, th

Ying Qin, Xinwen Shu, Shuangxi Yi, Yuan-Zhu Wang

Recent observations of AdLIGO and Virgo have shown that the spin measurements in binary black hole (BH) systems are typically small, which is consistent with the predictions by the classical isolated binary evolution channel. In this standard formation channel, the progenitor of the first-born BH is assumed to have efficient angular momentum transport. The B

Dejian Tian

A pricing principle is introduced for non-attainable $q$-exponential bounded contingent claims in an incomplete Brownian motion market setting. The buyer evaluates the contingent claim under the ``distorted Radon-Nikodym derivative'' and adjustment by Tsallis relative entropy over a family of equivalent martingale measures. The pricing principle is proved to

Xiuyang Xia, Peilin Rao, Juan Yang, Massimo Pica Ciamarra

Thermo-gelling polymers have been envisioned as promising smart biomaterials but limited to their weak mechanical and thermodynamic stabilities. Here we propose a new thermo-gelling vitrimer, which remains at a liquid state because of the addition of protector molecules preventing the crosslinking, and with increasing temperature, an entropy driven crosslink

Parham Hadikhani, Daphne Teck Ching Lai, Wee-Hong Ong

Human activity discovery aims to cluster the activities performed by humans without any prior information on what defines each activity. Most methods presented in human activity recognition are supervised, where there are labeled inputs to train the system. In reality, it is difficult to label activities data because of its huge volume and the variety of hum

Mengsay Loem, Sho Takase, Masahiro Kaneko, Naoaki Okazaki

Neural models trained with large amount of parallel data have achieved impressive performance in abstractive summarization tasks. However, large-scale parallel corpora are expensive and challenging to construct. In this work, we introduce a low-cost and effective strategy, ExtraPhrase, to augment training data for abstractive summarization tasks. ExtraPhrase

Dean Buckner, Kevin Dowd, Hardy Hulley

Contrary to the claims made by several authors, a financial market model in which the price of a risky security follows a reflected geometric Brownian motion is not arbitrage-free. In fact, such models violate even the weakest no-arbitrage condition considered in the literature. Consequently, they do not admit num\'eraire portfolios or equivalent risk-neutra

Michal Křížek, Vesselin G. Gueorguiev, André Maeder

Recently it was found from Cassini data that the mean recession speed of Titan from Saturn is $v=11.3\pm 2.0$ cm/yr which corresponds to a tidal quality factor of Saturn $Q\cong 100$ while the standard estimate yields $Q\ge 6\cdot 10^4$. It was assumed that such a large speed $v$ is due to a resonance locking mechanism of five inner mid-sized moons of Saturn

Vaibhav Wasnik

In this work we construct metrics corresponding to radiating black holes whose near horizon regions cannot be approximated by Rindler spacetime. We first construct infinite parameter coordinate transformations from Minkowski coordinates, such that an observer using these coordinates to describe spacetime events measures the Minkowski vacuum to be Planckian.

R P Singh, B K Singh, R K Gupta, Shobhit Sachan

The Bardeen black hole solution is the first spherically symmetric regular black hole based on the Sakharov and Gliner proposal which is a modification of the Schwarzschild black hole. We present the Bardeen black hole solution in the presence of the de Rham, Gabadaadze, and Tolly (dRGT) massive gravity, which is regular everywhere in the presence of a nonli

Ru Zheng, Rong-Qiang He, Zhong-Yi Lu

Using the natural orbitals renormalization group (NORG) method, we have investigated the screening of the local spin of an Anderson impurity interacting with the helical edge states in a quantum spin Hall insulator. We find that there is a local spin formed at the impurity site and the local spin is completely screened by electrons in the quantum spin Hall i

Daizong Liu, Xiaoye Qu, Yinzhen Wang, Xing Di

Temporal video grounding (TVG) aims to localize a target segment in a video according to a given sentence query. Though respectable works have made decent achievements in this task, they severely rely on abundant video-query paired data, which is expensive and time-consuming to collect in real-world scenarios. In this paper, we explore whether a video ground

Naoto Kajiwara

We prove resolvent $L_p$ estimates and maximal $L_p$-$L_q$ regularity estimates for the Stokes equations with Dirichlet, Neumann and Robin boundary conditions in the half space. Each solution is constructed by a Fourier multiplier of $x'$-direction and an integral of $x_N$-direction. We decompose the solution such that the symbols of the Fourier multipliers

Yi-an Zhou, Jie Hong, Y. LI, M. D. Ding

In the optically thin regime, the intensity ratio of the two Si IV resonance lines (1394 and 1403 \AA\ ) are theoretically the same as the ratio of their oscillator strengths, which is exactly 2. Here, we study the ratio of the integrated intensity of the Si IV lines ($R=\int I_{1394}(\lambda)\mathrm{d}\lambda/\int I_{1403}(\lambda)\mathrm{d}\lambda$) and th

K. N. Khikmah, A. Sofro

Autoregressive moving average and generalized autoregressive moving average are often used in statistical modeling. This study uses this method because the method uses data from the previous period to model the data for the current period. In addition, the technique is often used in data prediction. The familiar data used is count data. Count data is the dat

Yuchen He, Guillaume Ducrozet, Norbert Hoffmann, John M. Dudley

The Galilean transformation is a universal operation connecting the coordinates of a dynamical system, which move relative to each other with a constant speed. In the context of exact solutions of the universal nonlinear Schr\"odinger equation (NLSE), inducing a Galilean velocity (GV) to the pulse involves a frequency shift to satisfy the symmetry of the wav

Ngoc Anh Minh Tran, Aditya Savitha Dutt, Nithin Bharadwaj Pulumati, Heiko Reith

Thermoelectric materials exhibit correlated transport of charge and heat. The Johnson-Nyquist noise formula $ 4 k_B T R $ for spectral density of voltage fluctuations accounts for fluctuations associated solely with Ohmic dissipation. Applying the fluctuation-dissipation theorem, we generalize the Johnson-Nyquist formula for thermoelectrics, finding an enhan

C. Y. Zhao, P. Y. Chen, P. Y. Li, C. M. Zhang

We propose a novel bio-sensor structure composed of slot dual-micro-ring resonators and mono-layer graphene.Based on the electromagnetically induced transparency (EIT)-like phenomenon and the light-absorption characteristics of graphene,we present a theoretical analysis of transmission by using the coupled mode theory and Kubo formula.The results demonstrate

Md Faisal Mahbub Chowdhury, Gaetano Rossiello, Michael Glass, Nandana Mihindukulasooriya

In recent years, a number of keyphrase generation (KPG) approaches were proposed consisting of complex model architectures, dedicated training paradigms and decoding strategies. In this work, we opt for simplicity and show how a commonly used seq2seq language model, BART, can be easily adapted to generate keyphrases from the text in a single batch computatio

Chao Ying Zhao, Wei Fan, Weihan Tan

Usually, it's difficult for us to observe the Compton Scattering in an atom. One way to overcome this difficult is using multi-photon collide with an atom, which will come into being multi-photon Compton Scattering (MCS) phenomenon. Thus, we can investigate the MCS process in visible light region. During the MCS process, the cluster atoms moving as a whole,

Kazunori Kohri, Toyokazu Sekiguchi, Sai Wang

In this paper, we study scenarios of the super-Eddington accretion onto black holes at high redshifts $z > 10$, which are expected to be seeds to evolve to supermassive black holes until redshift $z \sim 7$. For an initial mass, $M_{\rm BH, ini} \lesssim 2 \times 10^{3} M_{\odot}$ of a seed BH, we definitely need the super-Eddington accretion, which can be a

C. Y. Zhao, P. Y. Li, C. M. Zhang

We propose a novel ultrasonic sensor structure composed of Cantilever arm structure slot dual-micro-ring resonators(DMRR).We present a theoretical analysis of transmission by using the coupled mode theory.The mode field distributions and sound pressure distributions of transmission spectrum are obtained from 3D simulations based on Comsol Multi-physics(COMSO

Feng Gao, Qing Ping, Govind Thattai, Aishwarya Reganti

Outside-knowledge visual question answering (OK-VQA) requires the agent to comprehend the image, make use of relevant knowledge from the entire web, and digest all the information to answer the question. Most previous works address the problem by first fusing the image and question in the multi-modal space, which is inflexible for further fusion with a vast

Shiang-Chih Wang, H. -Y. Karen Yang

Feedback from active galactic nuclei (AGN) is believed to be the most promising solution to the cooling flow problem in cool-core clusters, though how exactly the jet energy is transformed into heat is a subject of debate. Dissipation of sound waves is considered as one of the possible heating mechanisms; however, its relative contribution to heating remains

Hanting Li, Mingzhe Sui, Zhaoqing Zhu, Feng Zhao

Facial micro-expressions (MEs) are involuntary facial motions revealing peoples real feelings and play an important role in the early intervention of mental illness, the national security, and many human-computer interaction systems. However, existing micro-expression datasets are limited and usually pose some challenges for training good classifiers. To mod

Bijan Bagchi, Rahul Ghosh

We solve the one-dimensional Dirac equation by taking into account the possibility of position-dependence in the mass function. We also take the Fermi velocity to act as a local variable and examine the combined effects of the two on the solvability of the Dirac equation with respect to the Morse potential. Our results for the wave functions and the energy l

Luke G. Bennetts, Malte A. Peter

Extensions of Rayleigh-Bloch waves above the cut-off frequency are studied via the discrete spectrum of a transfer operator for a generalised channel containing a single cylinder. Their wavenumbers are shown to become complex-valued and an additional pair of wavenumbers to appear. For small to intermediate radius values, the extended Rayleigh-Bloch waves are

Naman Bansal, Mousumi Akter, Shubhra Kanti Karmaker Santu

In this paper, we introduce an important yet relatively unexplored NLP task called Multi-Narrative Semantic Overlap (MNSO), which entails generating a Semantic Overlap of multiple alternate narratives. As no benchmark dataset is readily available for this task, we created one by crawling 2,925 narrative pairs from the web and then, went through the tedious p

Baole Ai, Zhou Qin, Wenting Shen, Yong Li

Graph Neural Networks (GNNs) have shown promising results in various tasks, among which link prediction is an important one. GNN models usually follow a node-centric message passing procedure that aggregates the neighborhood information to the central node recursively. Following this paradigm, features of nodes are passed through edges without caring about w

Xingzhi Zhan

We determine the possible maximum degrees of a minimally hamiltonian-connected graph with a given order. This answers a question posed by Modalleliyan and Omoomi in 2016. We also pose two unsolved problems.

Kaige Liu, Hengkang Zhang, Shanshan Du, Zeqi Liu

Optical tweezers can manipulate tiny particles. However, the distortion caused by the scattering medium restricts the applications of optical tweezers. Wavefront shaping techniques including the transmission matrix (TM) method are powerful tools to achieve light focusing behind the scattering medium. In this paper, we propose a new kind of TM, named intensit

Lijun Yu, Yijun Qian, Wenhe Liu, Alexander G. Hauptmann

Activity detection is one of the attractive computer vision tasks to exploit the video streams captured by widely installed cameras. Although achieving impressive performance, conventional activity detection algorithms are usually designed under certain constraints, such as using trimmed and/or object-centered video clips as inputs. Therefore, they failed to

Leying Guan

Multi-block CCA constructs linear relationships explaining coherent variations across multiple blocks of data. We view the multi-block CCA problem as finding leading generalized eigenvectors and propose to solve it via a proximal gradient descent algorithm with $\ell_1$ constraint for high dimensional data. In particular, we use a decaying sequence of constr

Michael S. Fuhrer, Mark T. Edmonds, Dimitrie Culcer, Muhammad Nadeem

A topological quantum field effect transistor (TQFET) uses electric field to switch a material from topological insulator ("on", with conducting edge states) to a conventional insulator ("off"), and can have low subthreshold swing due to strong Rashba spin-orbit interaction. Numerous materials have been proposed, and electric field switching has been demonst

Valerio Faraoni, Geneviève Vachon, Robert Vanderwee, Sonia Jose

Friedmann-Lema\^itre-Robertson-Walker cosmology is examined from the point of view of gravitoelectromagnetism, in the approximation of spacetime regions small in comparison with the Hubble radius. The usual Lorentz gauge is not appropriate for this situation, while the Painlev\'e-Gullstrand gauge is rather natural. Several non-trivial features and difference

Jialiang Han, Yun Ma, Yudong Han

Federated learning (FL) is an emerging promising privacy-preserving machine learning paradigm and has raised more and more attention from researchers and developers. FL keeps users' private data on devices and exchanges the gradients of local models to cooperatively train a shared Deep Learning (DL) model on central custodians. However, the security and faul

Katherine Zine, Samir Salim

Derivation of physical properties of galaxies using spectral energy distribution (SED) fitting is a powerful method, but can suffer from various systematics arising from model assumptions. Previously, such biases were mostly studied in the context of individual galaxies. In this study, we investigate potential biases arising from performing the SED fitting o

James Walsh

It is well-known that natural axiomatic theories are pre-well-ordered by logical strength, according to various characterizations of logical strength such as consistency strength and inclusion of $\Pi^0_1$ theorems. Though these notions of logical strength coincide for natural theories, they are not generally equivalent. We study analogues of these notions -

S. V. Malik, E. T. Dias, A. K. Nigam, K. R. Priolkar

A systematic study of crystal structure, local structure, magnetic and transport properties in quenched and temper annealed Ni$_{2-x}$Mn$_{1+x}$Sn alloys indicate the formation of Mn$_3$Sn type structural defects caused by an antisite disorder between Mn and Sn occupying the Y and Z sublattices of X$_2$YZ Heusler structure. The antisite disorder is caused by

Roberto Vega, Russell Greiner

A predictor, $f_A : X \to Y$, learned with data from a source domain (A) might not be accurate on a target domain (B) when their distributions are different. Domain adaptation aims to reduce the negative effects of this distribution mismatch. Here, we analyze the case where $P_A(Y\ |\ X) \neq P_B(Y\ |\ X)$, $P_A(X) \neq P_B(X)$ but $P_A(Y) = P_B(Y)$; where t

Yaxiong Xie, Kyle Jamieson

Accurate and highly-granular channel capacity telemetry of the cellular last hop is crucial for the effective operation of transport layer protocols and cutting-edge applications, such as video on demand and videotelephony. This paper presents the design, implementation, and experimental performance evaluation of NG-Scope, the first such telemetry tool able

Praveen Kumar Kolluru, Mohammad Atif, Santosh Ansumali

Kinetic models of polyatomic gas typically account for the internal degrees of freedom at the level of the two-particle distribution function. However, close to the hydrodynamic limit, the internal (rotational) degrees of freedom tend to be well represented just by rotational kinetic energy density. We account for the rotational energy by augmenting the Elli

Dayang Wang, Feng-Lei Fan, Bo-Jian Hou, Hao Zhang

A neural network with the widely-used ReLU activation has been shown to partition the sample space into many convex polytopes for prediction. However, the parameterized way a neural network and other machine learning models use to partition the space has imperfections, \textit{e}.\textit{g}., the compromised interpretability for complex models, the inflexibi

Jaime Freire de Souza, João Baptista Dias Moreira, Keith Jared Roberts, Roussian di Ramos Alves Gaioso

${\tt simwave}$ is an open-source Python package to perform wave simulations in 2D or 3D domains. It solves the constant and variable density acoustic wave equation with the finite difference method and has support for domain truncation techniques, several boundary conditions, and the modeling of sources and receivers given a user-defined acquisition geometr

Jonghwan Mun, Minchul Shin, Gunsoo Han, Sangho Lee

Self-supervised learning has drawn attention through its effectiveness in learning in-domain representations with no ground-truth annotations; in particular, it is shown that properly designed pretext tasks (e.g., contrastive prediction task) bring significant performance gains for downstream tasks (e.g., classification task). Inspired from this, we tackle v

Mitchell Schiworski, Vladimir Bossilkov, Carl Blair, Daniel Brown

Parametric Instability (PI) is a phenomenon that results from resonant interactions between optical and acoustic modes of a laser cavity. This is problematic in gravitational wave interferometers where the high intra-cavity power and low mechanical loss mirror suspension systems create an environment where three mode PI will occur without intervention. We de

Chen Wang, Zhongcai Pei, Shuang Qiu, Zhiyong Tang

Staircases are some of the most common building structures in urban environments. Stair detection is an important task for various applications, including the environmental perception of exoskeleton robots, humanoid robots, and rescue robots and the navigation of visually impaired people. Most existing stair detection algorithms have difficulty dealing with

Martin Molina-Fructuoso, Ryan Murray

Statistical depths provide a fundamental generalization of quantiles and medians to data in higher dimensions. This paper proposes a new type of globally defined statistical depth, based upon control theory and eikonal equations, which measures the smallest amount of probability density that has to be passed through in a path to points outside the support of

Junyi Li, Tianyi Tang, Wayne Xin Zhao, Jian-Yun Nie

Text Generation aims to produce plausible and readable text in a human language from input data. The resurgence of deep learning has greatly advanced this field, in particular, with the help of neural generation models based on pre-trained language models (PLMs). Text generation based on PLMs is viewed as a promising approach in both academia and industry. I

Ryosuke Tsumura, Yoshihiko Koseki, Naotaka Nitta, Kiyoshi Yoshinaka

Since most developed countries are facing an increase in the number of patients per healthcare worker due to a declining birth rate and an aging population, relatively simple and safe diagnosis tasks may need to be performed using robotics and automation technologies, without specialists and hospitals. This study presents an automated robotic platform for re

Zhi Ji, Wendong Yang, Xinrong Guan, Xiao Zhao

In this letter, we investigate an unmanned aerial vehicle (UAV) communication system, where an intelligent reflecting surface (IRS) is deployed to assist in the transmission from a ground node (GN) to the UAV in the presence of a jammer. We aim to maximize the average rate of the UAV communication by jointly optimizing the GN's transmit power, the IRS's pass

Venkatesh Chebolu, Sadananda Behera, Goutam Das

Survivability is mission-critical for elastic optical networks (EONs) as they are expected to carry an enormous amount of data. In this paper, we consider the problem of designing shared backup path protection (SBPP) based EON that facilitates the minimum quality-of-transmission (QoT) assured allocation against physical layer impairments (PLIs) under any sin

Dmitrii V. Prokhorov

We study weighted altered Ces\`aro and Copson spaces, which is non-ideal enlargement of the usual spaces. We give full characterization of dual spaces for the spaces.

Masahiro Kojima

We consider applying multi-armed bandits to model-assisted designs for dose-finding clinical trials. Multi-armed bandits are very simple and powerful methods to determine actions to maximize a reward in a limited number of trials. Among the multi-armed bandits, we first consider the use of Thompson sampling which determines actions based on random samples fr

Yao Long, Daniel S. Kirschen

Optimal Volt/VAR control (VVC) in distribution networks relies on an effective coordination between the conventional utility-owned mechanical devices and the smart residential photovoltaic (PV) inverters. Typically, a central controller carries out a periodic optimization and sends setpoints to the local controller of each device. However, instead of trackin

Andy J. Goldschmidt, Jonathan L. DuBois, Steven L. Brunton, J. Nathan Kutz

A critical engineering challenge in quantum technology is the accurate control of quantum dynamics. Model-based methods for optimal control have been shown to be highly effective when theory and experiment closely match. Consequently, realizing high-fidelity quantum processes with model-based control requires careful device characterization. In quantum proce

Yuxi Zhang, Rubem Mondaini, Richard T. Scalettar

Real-time dynamics techniques have proven increasingly useful in understanding strongly correlated systems both theoretically and experimentally. By employing unbiased time-resolved exact diagonalization, we study pump dynamics in the two-dimensional plaquette Hubbard model, where distinct hopping integrals $t_h$ and $t_h^\prime$ are present within and betwe

Guojun Xu, Jingjun Zhang, Chenzi Liao, Ying Zhang

The Fermion flavor structure is investigated by bilinear decomposition of the mass matrix after EW symmetry breaking, and the roles of factorized matrices in flavor mixing and mass generation are explored. It is shown that flavor mixing can be addressed as an independent issue. On a new Yukawa basis, the minimal parameterization of flavor mixing is realized

Caitlin A. Rose, Vinaya Valsan, Patrick R. Brady, Sinead Walsh

In the age of multi-messenger astrophysics, low-latency parameter estimation of gravitational-wave signals is essential for electromagnetic follow-up observations. In this paper, we present a new edition of the Bayesian parameter estimation scheme for compact binaries known as Rapid PE. Rapid PE parallelizes parameter estimation by fixing the intrinsic param