January 2022 arXiv papers — page 51

Adrien Richard

We review and discuss some results about the influence of positive and negative feedback cycles in asynchronous Boolean networks. These results merge several ideas of Thomas: positive and negative feedback cycles have been largely emphasized by Thomas, through the so called Thomas' rules, and asynchronous Boolean networks have been introduced by Thomas as a

Edvinas Goldštein, Andrius Grigutis

In this paper we investigate the positivity property of the real part of logarithmic derivative of the Riemann $\xi$-function for $1/2<\sigma<1$ and sufficiently large $t$. We give an explicit upper and lower bounds for $\Re\sum_{\rho} 1/(s-\rho)$, where the sum runs over the zeros of $\zeta(s)$ on the line $1/2+it$. We also check the positivity of $\Re \xi'

Irina Nikishina, Mikhail Tikhomirov, Varvara Logacheva, Yuriy Nazarov

Knowledge graphs such as DBpedia, Freebase or Wikidata always contain a taxonomic backbone that allows the arrangement and structuring of various concepts in accordance with the hypo-hypernym ("class-subclass") relationship. With the rapid growth of lexical resources for specific domains, the problem of automatic extension of the existing knowledge bases wit

Jian-Keng Yuan, Shuai A. Chen, Peng Ye

Symmetry defects, e.g., vortices in conventional superfluids, play a critical role in a complete description of symmetry-breaking phases. In this paper, we develop the theory of symmetry defects in fractonic superfluids, i.e., spontaneously higher-rank symmetry (HRS) breaking phases. By Noether's theorem, HRS is associated with the conservation law of higher

Adrien Richard

A finite dynamical system with $n$ components is a function $f:X\to X$ where $X=X_1\times\dots\times X_n$ is a product of $n$ finite intervals of integers. The structure of such a system $f$ is represented by a signed digraph $G$, called interaction graph: there are $n$ vertices, one per component, and the signed arcs describe the positive and negative influ

Liqiao Jing, Dashuang Liao, Jie Tao, Hongsheng Chen

Metasurface has recently emerged as a powerful platform to engineer wave packets of free electron radiation at the mesoscale. Here, we propose that accelerating waves can be generated when moving electrons interact with an array of bianisotropic meta-atoms. By changing the intrinsic coupling strength, we show full amplitude coverage and 0-to-{\pi} phase swit

J. von der Linden, G. Fiksel, J. Peebles, M. R. Edwards

Creating magnetized relativistic pair plasma in the laboratory would enable the exploration of unique plasma physics relevant to some of the most energetic events in the universe. As a step towards a laboratory pair plasma, we have demonstrated effective confinement of multi-$\mathrm{MeV}$ electrons inside a pulsed-power-driven $13$ $\mathrm{T}$ magnetic mir

Herve Hugonnet, Mahn Jae Lee, Yongkeun Park

Deconvolution phase microscopy enables high-contrast visualization of transparent samples through reconstructions of their transmitted phases or refractive indexes. Herein, we propose a method to extend 2D deconvolution phase microscopy to thick 3D samples. The refractive index distribution of a sample can be obtained at a specific axial plane by measuring o

Pierre-Antoine Guihéneuf, Emmanuel Militon

This paper states a definition of homotopic rotation set for higher genus surface homeomorphisms, as well as a collection of results that justify this definition. We first prove elementary results: we prove that this rotation set is star-shaped, we discuss the realisation of rotation vectors by orbits or periodic orbits and we prove the creation of new rotat

Thibault Bonnemain, Matteo Butano, Théophile Bonnet, Iñaki Echeverría-Huarte

The local navigation of pedestrians amid a crowd is generally believed to involve no anticipation beyond (at best) the avoidance of the most imminent collisions. We show that current models rooted in this belief fail to reproduce some key features experimentally evidenced when a dense static crowd is crossed by an intruder. We identify the missing ingredient

Ke Liu, Frank Stefani, Norbert Weber, Tom Weier

This study is a continuation of the combined experimental and numerical investigation [1] of the flow of the eutectic GaInSn alloy inside a cylindrical vessel exposed to a constant electrical current. The emerging electrovortex flow driven by the interaction of the current, which is applied through a tapered electrode, with its own magnetic field might have

Saeid Amjadian, Mahdi Esmaeilzadeh, Nezhat Pournaghavi

Using first principle calculations, we study the structural, optical and electronic properties of two-dimensional silicene-like structures of CSi7 (carbosilicene) and GeSi7 (germasilicene) monolayers. We show that both CSi7 and GeSi7 monolayers have different buckling that promises a new way to control the buckling in silicene-like structures. Carbon impurit

J. Lacalle, L. M. Pozo-Coronado, A. L. Fonseca de Oliveira, R. Martin-Cuevas

Given an $m-$qubit $\Phi_0$ and an $(n,m)-$quantum code $\mathcal{C}$, let $\Phi$ be the $n-$qubit that results from the $\mathcal{C}-$encoding of $\Phi_0$. Suppose that the state $\Phi$ is affected by an isotropic error (decoherence), becoming $\Psi$, and that the corrector circuit of $\mathcal{C}$ is applied to $\Psi$, obtaining the quantum state $\tilde\P

M. Z. Feng, L. D. Kong, P. J. Wang, S. N. Zhang

We execute a detailed spectral-timing study of Cygnus X-1 in the low/hard, intermediate and high/soft states with the Hard X-ray Modulation Telescope observations. The broad band energy spectra fit well with the "truncated disk model" with the inner boundary of the accretion disk stays within $\sim$10 \textit R$_{\rm g}$ and moves inward as the source soften

Xiaoliang Li, Cong Wang

We consider the optimization problem of minimizing $\int_{\mathbb{R}^n}|\nabla u|^2\,\mathrm{d}x$ with double obstacles $\phi\leq u\leq\psi$ a.e. in $D$ and a constraint on the volume of $\{u>0\}\setminus\overline{D}$, where $D\subset\mathbb{R}^n$ is a bounded domain. By studying a penalization problem that achieves the constrained volume for small values of

Charl Maree, Christian Omlin

The increased complexity of state-of-the-art reinforcement learning (RL) algorithms have resulted in an opacity that inhibits explainability and understanding. This has led to the development of several post-hoc explainability methods that aim to extract information from learned policies thus aiding explainability. These methods rely on empirical observation

Jitendra Bajpai, Sandip Singh, Shashank Vikram Singh

We show that the hypergeometric groups associated to the pairs of the parameters $\left(0,0,\frac{1}{3}, \frac{2}{3}\right)$, $\left(\frac{1}{2},\frac{1}{2},\frac{1}{4},\frac{3}{4}\right)$; and $\left(0,\frac{1}{12}, \frac{5}{12},\frac{7}{12},\frac{11}{12}\right), \left(\frac{1}{2},\frac{1}{3},\frac{1}{3},\frac{2}{3},\frac{2}{3}\right)$ are arithmetic.

Patrick Sieberer, Thomas Bergauer, Klemens Floeckner, Christian Irmler

The RD50-CMOS group aims to design and study High Voltage CMOS (HVCMOS) chips for use in a high radiation environment. Currently, measurements are performed on RD50-MPW2 chip, the second prototype developed by this group. The active matrix of the prototype consists of 8x8 pixels with analog front end. Details of the analog front end and simulations have been

Benjamin Poignard, Manabu Asai

Although multivariate stochastic volatility models usually produce more accurate forecasts compared to the MGARCH models, their estimation techniques such as Bayesian MCMC typically suffer from the curse of dimensionality. We propose a fast and efficient estimation approach for MSV based on a penalized OLS framework. Specifying the MSV model as a multivariat

Lei Cheng, Xingyu Ji, Hangfang Zhao, Jianlong Li

Basis function learning is the stepping stone towards effective three-dimensional (3D) sound speed field (SSF) inversion for various acoustic signal processing tasks, including ocean acoustic tomography, underwater target localization/tracking, and underwater communications. Classical basis functions include the empirical orthogonal functions (EOFs), Fourier

Quan-Dung Pham, Hai Nguyen-Truong, Nam Nguyen Phuong, Khoa N. A. Nguyen

Current research on deep learning for medical image segmentation exposes their limitations in learning either global semantic information or local contextual information. To tackle these issues, a novel network named SegTransVAE is proposed in this paper. SegTransVAE is built upon encoder-decoder architecture, exploiting transformer with the variational auto

Kenta Takeda, Akito Noiri, Takashi Nakajima, Takashi Kobayashi

Large-scale quantum computers rely on quantum error correction to protect the fragile quantum information. Among the possible candidates of quantum computing devices, silicon-based spin qubits hold a great promise due to their compatibility to mature nanofabrication technologies for scaling up. Recent advances in silicon-based qubits have enabled the impleme

Jiacheng Huang, Yao Zhao, Wei Hu, Zhen Ning

Knowledge graphs (KGs) have become a valuable asset for many AI applications. Although some KGs contain plenty of facts, they are widely acknowledged as incomplete. To address this issue, many KG completion methods are proposed. Among them, open KG completion methods leverage the Web to find missing facts. However, noisy data collected from diverse sources m

Min Du, Luis C. Ho, Hao-Ran Yu, Victor P. Debattista

The IllustrisTNG simulations reproduce the observed scaling relation between stellar specific angular momentum (sAM) $j_{\rm s}$ and mass $M_{\rm s}$ of central galaxies. We show that the local $j_{\rm s}$-$M_{\rm s}$ relation ${\rm log}\ j_{\rm s} = 0.55 \ {\rm log}\ M_{\rm s} + 2.77$ develops at $z\lesssim 1$ in disk-dominated galaxies. We provide a simple

Elham Nazari, Mahmood Roshan, Ivan De Martino

In this paper, we introduce the post-Minkowskian approximation of Energy-Momentum-Squared Gravity (EMSG). This approximation is used to study the gravitational energy flux in the context of EMSG. As an application of our results, we investigate the EMSG effect on the first time derivative of the orbital period of the binary pulsars. Utilizing this post-Keple

Wei-Jie Xu, Tian-Xing Bai, Chun-Gui Duan

By means of the nuclear parton distributions determined without the fixed-target Drell-Yan experimental data and the analytic expression of quenching weight based on BDMPS formalism, a next-to-leading order analyses are performed on the Drell-Yan differential cross section ratios from Fermilab E906 and E866 Collaborations. It is found that the calculated res

Feng Ren, Xiao Ding, Min Zheng, Mikhail Korzinkin

The AlphaFold computer program predicted protein structures for the whole human genome, which has been considered as a remarkable breakthrough both in artificial intelligence (AI) application and structural biology. Despite the varying confidence level, these predicted structures still could significantly contribute to structure-based drug design of novel ta

Gudrun Szewieczek

The induced metrics of Dupin cyclidic systems, that is, orthogonal coordinate systems with Dupin cyclides and spheres as coordinate surfaces, were provided by Darboux. Here we take a more geometric point of view and discuss how Dupin cyclides and Lam\'e families of Dupin cyclidic systems can be obtained by suitably evolving an initial circle or a Dupin cycli

Yu-Rong Shu, Shuai Yin

Emergent symmetry is one of the characteristic phenomena in deconfined quantum critical point (DQCP). As its nonequilibrium generalization, the dual dynamic scaling was recently discovered in the nonequilibrium imaginary-time relaxation dynamics in the DQCP of the $J$-$Q_3$ model. In this work, we study the nonequilibrium imaginary-time relaxation dynamics i

Jobin K. V., Ajoy Mondal, C. V. Jawahar

Slide presentations are an effective and efficient tool used by the teaching community for classroom communication. However, this teaching model can be challenging for blind and visually impaired (VI) students. The VI student required personal human assistance for understand the presented slide. This shortcoming motivates us to design a Classroom Slide Narra

Leyla Majidi, Reza Asgari

The broad tunability of the proximity exchange effect between transition-metal dichalcogenides (TMDCs) and chromium iodide (CrI$_3$) heterostructures offers intriguing possibilities for the use of TMDCs in two-dimensional magnetoelectrics. In this work, the influence of the twist angle and the gate electric field on the electric and thermal transport in a TM

Bin Duan, Zihao Bai, Yulong Zhang, Qingyuan Zhang

Based on the double pendulum and Lagrange equation, the moving particles are captured by a binocular three-dimensional capture camera. Two trajectory models of Astrojax and the relationship between trajectory empirical formula and parameters are established. Through research, the calculated trajectory of this formula and related parameters fit well with the

Boqian Shen, Beatrice Riviere

We formulate a numerical method for solving the two-phase flow poroelasticity equations. The scheme employs the interior penalty discontinuous Galerkin method and a sequential time-stepping method. The unknowns are the phase pressures and the displacement. Existence of the solution is proved. Three-dimensional numerical results show the accuracy and robustne

Weidong Wang, Yujian Kang, Dian Li

A large-scale evaluation for current naming approaches substantiates that such approaches are accurate. However, it is less known about which categories of method names work well via such naming approaches and how's the performance of naming approaches. To point out the superiority of the current naming approach, in this paper, we conduct an empirical study

Wolswijk L., Mordini C., Farolfi A., Trypogeorgos D.

We investigate the strong out-of-equilibrium dynamics occurring when a harmonically trapped ultracold bosonic gas is evaporatively cooled across the Bose--Einstein condensation transition. By imaging the cloud after free expansion, we study how the cooling rate affects the timescales for the growth of the condensate order parameter and the relaxation dynamic

Rémi Chan--Renous-Legoubin, Clément W. Royer

Nonlinear conjugate gradients are among the most popular techniques for solving continuous optimization problems. Although these schemes have long been studied from a global convergence standpoint, their worst-case complexity properties have yet to be fully understood, especially in the nonconvex setting. In particular, it is unclear whether nonlinear conjug

Yu-Jie Cen, Chang-chun He, Shao-Bin Qiu, Yu-Jun Zhao

Reducing the number of candidate structures is crucial to improve the efficiency of global optimization. Herein, we demonstrate that the generalized Hamiltonian can be described by the atom classification model (ACM) based on symmetry, generating competent candidates for the first-principles calculations to determine ground states of alloy directly. The cand

Qi-Jun Ye, Xin-Zheng Li

Thermodynamic conventions suffer from describing dynamical distinctions, especially when the structural and energetic changes induced by localized rare events are insignificant. By using the ensemble theory in the trajectory space, we present a statistical approach to address this problem.Rather than spatial particle-particle interaction which dominates ther

Rushit Dave, Naeem Seliya, Mounika Vanamala, Wei Tee

Human activity recognition has grown in popularity with its increase of applications within daily lifestyles and medical environments. The goal of having efficient and reliable human activity recognition brings benefits such as accessible use and better allocation of resources; especially in the medical industry. Activity recognition and classification can b

Rushit Dave, Naeem Seliya, Laura Pryor, Mounika Vanamala

In recent years the amount of secure information being stored on mobile devices has grown exponentially. However, current security schemas for mobile devices such as physiological biometrics and passwords are not secure enough to protect this information. Behavioral biometrics have been heavily researched as a possible solution to this security deficiency fo

Haibo Wang, Zaichen Zhang, Bingcheng Zhu, Yidi Zhang

Optical reconfigurable intelligent surface (ORIS) is an emerging technology that can achieve reconfigurable optical propagation environments by precisely adjusting signal's reflection and shape through a large number of passive reflecting elements. In this paper, we investigate the performance of an ORIS-assisted dual-hop hybrid radio frequency (RF) and free

I. Y. Dodin

This paper presents quasilinear theory (QLT) for classical plasma interacting with inhomogeneous turbulence. The particle Hamiltonian is kept general; for example, relativistic, electromagnetic, and gravitational effects are subsumed. A Fokker--Planck equation for the dressed 'oscillation-center' distribution is derived from the Klimontovich equation and cap

Bhargav Kumar Kakumani, Suman Kumar Tumuluri

In this paper, a numerical scheme for a nonlinear McKendrick-von Foerster equation with diffusion in age (MV-D) with the Dirichlet boundary condition is proposed. The main idea to derive the scheme is to use the discretization based on the method of characteristics to the convection part, and the finite difference method to the rest of the terms. The nonloca

Jou-An Chen, Hsin-Hsuan Sung, Xipeng Shen, Nathan Tallent

In a general graph data structure like an adjacency matrix, when edges are homogeneous, the connectivity of two nodes can be sufficiently represented using a single bit. This insight has, however, not yet been adequately exploited by the existing matrix-centric graph processing frameworks. This work fills the void by systematically exploring the bit-level re

Naveen Nair, Karthik S. Gurumoorthy, Dinesh Mandalapu

We develop a Causal-Deep Neural Network (CDNN) model trained in two stages to infer causal impact estimates at an individual unit level. Using only the pre-treatment features in stage 1 in the absence of any treatment information, we learn an encoding for the covariates that best represents the outcome. In the $2^{nd}$ stage we further seek to predict the un

Hao Chen, Junzhi Zhang, Chen Lv

Featuring the fast response and flexibility in control allocation, an electric vehicle with in-wheel motors is a good platform for implementing advanced vehicle dynamics control. Among many active safety functions of an in-wheel motor driven vehicle (IMDV), lateral stability control is a key technology, which can be realized through torque vectoring. To furt

Jihong Wang, Minnan Luo, Jundong Li, Ziqi Liu

Recent studies have revealed that GNNs are vulnerable to adversarial attacks. Most existing robust graph learning methods measure model robustness based on label information, rendering them infeasible when label information is not available. A straightforward direction is to employ the widely used Infomax technique from typical Unsupervised Graph Representat

Fumio Hirata

In the past decade, a literary phrase "No man's land" has been flooded in the scientific papers. The expression is used to describe a meta-stable region in the phase-diagram that cannot be accessed by experiments. It has been claimed based on the molecular dynamics (MD) simulation that there is a critical point, or the second critical point (SCP), in the "no

Zhouhang Xie, Jonathan Brophy, Adam Noack, Wencong You

The landscape of adversarial attacks against text classifiers continues to grow, with new attacks developed every year and many of them available in standard toolkits, such as TextAttack and OpenAttack. In response, there is a growing body of work on robust learning, which reduces vulnerability to these attacks, though sometimes at a high cost in compute tim

Jiahong Liu, Menglin Yang, Min Zhou, Shanshan Feng

Recently, hyperbolic space has risen as a promising alternative for semi-supervised graph representation learning. Many efforts have been made to design hyperbolic versions of neural network operations. However, the inspiring geometric properties of this unique geometry have not been fully explored yet. The potency of graph models powered by the hyperbolic s

Kangning Hou, Fangfang Zheng, Xiaobo Liu, Zhichen Fan

This paper presents a distributed cascade Proportional Integral Derivate (DCPID) control algorithm for the connected and automated vehicle (CAV) platoon considering the heterogeneity of CAVs in terms of the inertial lag. Furthermore, a real-time dynamic cooperative lane-changing model for CAVs, which can seamlessly combine the DCPID algorithm and the improve

Jan Cao

To examine the aesthetics of video games, this paper proposes to consider video games as a contemporary multi-media version of the so-called "Denkbild," or "thought-image," an experimental genre of philosophical writing employed by members of the Frankfurt School. A poetic mode of writing, the Denkbild takes literary snapshots of philosophical, political, an

Amir Afaq, Zeeshan Ahmed, Noman Haider, Muhammad Imran

Federated Learning (FL) provides privacy preservation by allowing the model training at edge devices without the need of sending the data from edge to a centralized server. FL has distributed the implementation of ML. Another variant of FL which is well suited for the Internet of Things (IoT) is known as Collaborated Federated Learning (CFL), which does not

Xiaoqi Li, Haoyuan Chen, Chen Li, Md Mamunur Rahaman

In the past ten years, the computing power of machine vision (MV) has been continuously improved, and image analysis algorithms have developed rapidly. At the same time, histopathological slices can be stored as digital images. Therefore, MV algorithms can provide doctors with diagnostic references. In particular, the continuous improvement of deep learning

O. Deniz Kose, Yanning Shen

Node representation learning has demonstrated its efficacy for various applications on graphs, which leads to increasing attention towards the area. However, fairness is a largely under-explored territory within the field, which may lead to biased results towards underrepresented groups in ensuing tasks. To this end, this work theoretically explains the sour

Ankan Shaw, Sanjit Bhowmick, Satya Bagchi

We establish a complete classification of binary group codes with complementary duals for a finite group and explicitly determine the number of linear complementary dual (LCD) cyclic group codes by using cyclotomic cosets. The dimension and the minimum distance for LCD group codes are explored. Finally, we find a connection between LCD MDS group codes and ma

Manish Gupta, Shalabh C. Maroo

Bubble nucleation in liquid confined in nanochannel is studied using molecular dynamics simulations and compared against nucleation in the liquid over smooth (i.e. without confinement). Nucleation is achieved by heating part of a surface to high temperatures using a surface-to-liquid heating algorithm implemented in LAMMPS. The surface hydrophilicity of nano

Kongtao Chen

Grain boundary (GB) kinetics is important for many applications in 2d materials and metal thin films. To study how the substrate shape affects GB mobility and kinetics, we develop a kinetic Monte Carlo (kMC) simulation method and an analytical model for GBs on the curved substrate by combining disconnection theory and by Foppl von Karman equations. Using sin

Sapna Mishra, Sowgat Muzahid

We built the first-ever statistically significant sample of ~ 80,000 background quasar - foreground cluster pairs to study the cool, metal-rich gas in the outskirts (> R500) of z ~ 0.5 clusters with a median mass of ~ 10^14.2 M_sun. The sample was obtained by cross-matching the SDSS cluster catalog of Wen & Han (2015) and SDSS quasar catalog of Lyke et al. (

Xueshan Fu, Seoung Dal Jung

In this paper, we study $(\mathcal F,\mathcal F')_{p}$-harmonic maps between foliated Riemannian manifolds $(M,g,\mathcal F)$ and $(M',g',\mathcal F')$. A $(\mathcal F,\mathcal F')_{p}$-harmonic map $\phi:(M,g,\mathcal F)\to (M', g',\mathcal F')$ is a critical point of the transversal $p$-energy functional $E_{B,p}$. Trivially, $(\mathcal F,\mathcal F')_2$-h

Hewei Tang, Pengcheng Fu, Honggeun Jo, Su Jiang

Fast forecasting of reservoir pressure distribution in geologic carbon storage (GCS) by assimilating monitoring data is a challenging problem. Due to high drilling cost, GCS projects usually have spatially sparse measurements from wells, leading to high uncertainties in reservoir pressure prediction. To address this challenge, we propose to use low-cost Inte

Guangxuan Xu, Qingyuan Hu

Model compression techniques are receiving increasing attention; however, the effect of compression on model fairness is still under explored. This is the first paper to examine the effect of distillation and pruning on the toxicity and bias of generative language models. We test Knowledge Distillation and Pruning methods on the GPT2 model and found a consis

Shingo Tagami, Tomotsugu Wakasa, Maya Takechi, Jun Matsui

In our previous paper, we determined $r_{\rm skin}^{208}({\rm exp})=0.278 \pm 0.035$~fm from $\sigma_{\rm R}$ for p+$^{208}$Pb scattering, using the Kyushu (chiral) $g$-matrix folding model with the densities calculated with D1S-GHFB with the angular momentum projection (AMP). The value agrees with that of PREX2. Reaction cross sections $\sigma_{\rm R}$ are

Muntasir Mahmud, Mohamed Younis, Gary Carter, Fow-Sen Choa

Localization of underwater networks is important in many military and civil applications. Because GPS receivers do not work below the water surface, traditional localization methods form a relative topology of underwater nodes (UWNs) and utilize either anchor nodes or floating gateways with dual transceivers in order to determine global coordinates. However,

Xiaofan Zhang, Zongwei Zhou, Deming Chen, Yu Emma Wang

Recently, large pre-trained models have significantly improved the performance of various Natural LanguageProcessing (NLP) tasks but they are expensive to serve due to long serving latency and large memory usage. To compress these models, knowledge distillation has attracted an increasing amount of interest as one of the most effective methods for model comp

Jason J. Choi, Ayush Agrawal, Koushil Sreenath, Claire J. Tomlin

Contact-rich robotic systems, such as legged robots and manipulators, are often represented as hybrid systems. However, the stability analysis and region-of-attraction computation for these systems are often challenging because of the discontinuous state changes upon contact (also referred to as state resets). In this work, we cast the computation of region-

Koulik Khamaru, Eric Xia, Martin J. Wainwright, Michael I. Jordan

Various algorithms for reinforcement learning (RL) exhibit dramatic variation in their convergence rates as a function of problem structure. Such problem-dependent behavior is not captured by worst-case analyses and has accordingly inspired a growing effort in obtaining instance-dependent guarantees and deriving instance-optimal algorithms for RL problems. T

Chengdong Han, Gang Xie, Wei Kou, Rong Wang

In this study, we try to extract the mass radii of the neutron and the proton from the differential cross section data of near-threshold $\omega$ and $\phi$ photoproductions on deuterium target, which is often approximated as a quasi-free neutron plus a quasi-free proton. The incoherent data of $\omega$ and $\phi$ photoproductions are provided by CBELSA/TAPS

S. Tori Ellison, Senthilkumar Duraivel, Vignesh Subramaniam, Kjell Fredrik Hugosson

In many tissues, cell type varies over single-cell length-scales, creating detailed spatial heterogeneities fundamental to physiological function. To gain understanding of this relationship between tissue function and detailed structure, and to one day engineer structurally and physiologically accurate tissues, we need the ability to assemble 3D cellular str

Wei-Jian Guo, Yan-Rong Li, Zhi-Xiang Zhang, Luis C. Ho

We compile a sample of 92 active galactic nuclei (AGNs) at z<0.75 with $gri$ photometric light curves from the archival data of the Zwicky Transient Facility and measure the accretion disk sizes via continuum reverberation mapping. We employ Monte Carlo simulation tests to assess the influences of data sampling and broad emission lines and select out the sam

S. Nishioka, S. Sasaki, S. Nakagawa, M. Yashima

In contrast to the "Pr-issue" that neither PrBa2Cu3O7 (Pr123) nor PrBa2Cu4O8 (Pr124) shows superconductivity (SC), we have observed 100%-fraction of SC in an oxygen-reduced Pr2Ba4Cu7O(15-delta) (Pr247) which has a hybrid structure that Pr247 = Pr123 + Pr124. It is found that Cu nuclear-spin signals from the CuO2 planes observed at 300K are completely wiped o

Shizhe Diao, Zhichao Huang, Ruijia Xu, Xuechun Li

The increasing scale of general-purpose Pre-trained Language Models (PLMs) necessitates the study of more efficient adaptation across different downstream tasks. In this paper, we establish a Black-box Discrete Prompt Learning (BDPL) to resonate with pragmatic interactions between the cloud infrastructure and edge devices. Particularly, instead of fine-tunin

Tal Shnitzer, Hau-Tieng Wu, Ronen Talmon

Multivariate time-series have become abundant in recent years, as many data-acquisition systems record information through multiple sensors simultaneously. In this paper, we assume the variables pertain to some geometry and present an operator-based approach for spatiotemporal analysis. Our approach combines three components that are often considered separat

Zifan Lin, Sara Seager, Sukrit Ranjan, Thea Kozakis

Following the discovery of the first exoplanet candidate transiting a white dwarf (WD), a "white dwarf opportunity" for characterizing the atmospheres of terrestrial exoplanets around WDs is emerging. Large planet-to-star size ratios and hence large transit depths make transiting WD exoplanets favorable targets for transmission spectroscopy - conclusive dete

Yotam Elor, Hadar Averbuch-Elor

Balancing the data before training a classifier is a popular technique to address the challenges of imbalanced binary classification in tabular data. Balancing is commonly achieved by duplication of minority samples or by generation of synthetic minority samples. While it is well known that balancing affects each classifier differently, most prior empirical

Gonzalo D. Maso Talou, Pablo J. Blanco

Denoising is of utmost importance for the visualization and processing of images featuring low signal-to-noise ratio. Total variation methods are among the most popular techniques to perform this task improving the signal-to-noise ratio while preserving coherent intensity discontinuities. In this work, a novel method, termed maximum likelihood data, is propo

Zhejing Hu, Yan Liu, Gong Chen, Yongxu Liu

User preference music transfer (UPMT) is a new problem in music style transfer that can be applied to many scenarios but remains understudied.

Raul Puente, Zilin Chen, Herman Batelaan

Decoherence can be provided by a dissipative environment as described by the Caldeira-Leggett equation. This equation is foundational to the theory of quantum dissipation. However, no experimental test has been performed that measures for one physical system both the dissipation and the decoherence. Anglin and Zurek predicted that a resistive surface could p

Adhithiya Sivakumar, Jeffrey B. Weiss

Oceanic flows self-organize into coherent vortices which strongly influence their transport and mixing properties. Counter-rotating vortex pairs can travel long distances and carry trapped fluid as they move. These structures are often modeled as hetons, viz. counter-rotating quasigeostrophic point vortex pairs with equal circulations. Here, we investigate t

Yuuki Uesugi, Yuichi Kozawa, Shunichi Sato

The properties of electron round lenses produced by the ponderomotive potential are investigated in geometrical optics. The potential proportional to the intensity distribution of a focused first-order Bessel or Laguerre-Gaussian beam is exploited to produce an electron round lens and a third-order spherical aberration corrector. Several formulas for the foc

Neophytos Charalambides, Hessam Mahdavifar, Mert Pilanci, Alfred O. Hero

In this work, we propose a method for speeding up linear regression distributively, while ensuring security. We leverage randomized sketching techniques, and improve straggler resilience in asynchronous systems. Specifically, we apply a random orthonormal matrix and then subsample in \textit{blocks}, to simultaneously secure the information and reduce the di

Dibyayoti Dhananjay Jena

Innamorati and Zuanni have provided a combinatorial characterization of Baer and unital cones in PG(3,q). The current paper generalizes these results to arbitrary dimension. Furthermore, these results are extended to hyperoval and maximal arc cones.

Tongzhou Mu, Kaixiang Lin, Feiyang Niu, Govind Thattai

We present a two-step hybrid reinforcement learning (RL) policy that is designed to generate interpretable and robust hierarchical policies on the RL problem with graph-based input. Unlike prior deep reinforcement learning policies parameterized by an end-to-end black-box graph neural network, our approach disentangles the decision-making process into two st

Xiaofan Wu, Oleg Tchernyshyov

When a magnetic skyrmion is modeled as a point particle, its dynamics depends on the precise definition of the skyrmion center. The guiding-center position, defined as the first moment of the skyrmion density, exhibits Thiele's massless dynamics; position based on the first moment of magnetization component $m_z$ shows Larmor oscillations characteristic of a

Richard Swartzbaugh, Amil Khanzada, Praveen Govindan, Mert Pilanci

The COVID-19 pandemic has been a scourge upon humanity, claiming the lives of more than 5.1 million people worldwide; the global economy contracted by 3.5% in 2020. This paper presents a COVID-19 calculator, synthesizing existing published calculators and data points, to measure the positive U.S. socio-economic impact of a COVID-19 AI/ML pre-screening soluti

Wenlong Mou, Koulik Khamaru, Martin J. Wainwright, Peter L. Bartlett

We study the problem of estimating the fixed point of a contractive operator defined on a separable Banach space. Focusing on a stochastic query model that provides noisy evaluations of the operator, we analyze a variance-reduced stochastic approximation scheme, and establish non-asymptotic bounds for both the operator defect and the estimation error, measur

Wei Wu, Fuhui Zhou, Baoyun Wang, Qihui Wu

Unmanned aerial vehicle (UAV) swarm enabled edge computing is envisioned to be promising in the sixth generation wireless communication networks due to their wide application sensories and flexible deployment. However, most of the existing works focus on edge computing enabled by a single or a small scale UAVs, which are very different from UAV swarm-enabled

Jie Ding, Shuai Ma, Xin-Shan Zhu

Critical transmission ranges (or radii) in wireless ad-hoc and sensor networks have been extensively investigated for various performance metrics such as connectivity, coverage, power assignment and energy consumption. However, the regions on which the networks are distributed are typically either squares or disks in existing works, which seriously limits th

Shangrong Yu, Yuxin Chen, Hejun Wu

Low-rank inductive matrix completion (IMC) is currently widely used in IoT data completion, recommendation systems, and so on, as the side information in IMC has demonstrated great potential in reducing sample point remains a major obstacle for the convergence of the nonconvex solutions to IMC. What's more, carefully choosing the initial solution alone does

JC Olivier, E. Barnard

Minimum-phase finite impulse response filters are widely used in practice, and much research has been devoted to the design of such filters. However, for the important case of Chebyshev filters there is a curious mismatch between current best practice and well-established theoretical principles. The paper shows that this difference can be understood through

Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen

Self-training, a semi-supervised learning algorithm, leverages a large amount of unlabeled data to improve learning when the labeled data are limited. Despite empirical successes, its theoretical characterization remains elusive. To the best of our knowledge, this work establishes the first theoretical analysis for the known iterative self-training paradigm

Akira Kageyama, Nobuaki Ohno

When the Rayleigh number is low, Rayleigh-B\'enard convection in a nonrotating spherical shell with central gravity has symmetric solutions in terms of three-dimensional discrete rotation. All the known patterns with the regular polyhedral symmetries accompany reflection symmetry. We found a new type of steady convection in a nonrotating spherical shell by c

Peixi Liu, Guangxu Zhu, Wei Jiang, Wu Luo

This letter studies a vertical federated edge learning (FEEL) system for collaborative objects/human motion recognition by exploiting the distributed integrated sensing and communication (ISAC). In this system, distributed edge devices first send wireless signals to sense targeted objects/human, and then exchange intermediate computed vectors (instead of raw

Lei Ying, Michael S. Mattei, Boyuan Liu, Shi-Yao Zhu

Enhancing the radiative interaction between quantum electronic transitions is of general interest. There are two important properties of radiative interaction: the range and the strength. There has been a trade-off between the range and the strength observed in the literature. Such apparent trade-off arises from the dispersion relation of photonic environmen

Theodore Andronikos, Michael Stefanidakis

This paper introduces the first functional model of a quantum parliament that is dominated by two parties or coalitions, and may or may not contain independent legislators. We identify a single crucial parameter, aptly named \emph{free will radius}, that can be used as a practical measure of the quantumness of the parties and the parliament as a whole. The f

Hengdi Zhao, Yifei Ni, Bing Hu, Sabastian Selter

Cr2Ge2Te6 is a van der Waals ferromagnet with a Curie temperature at 66 K. Here we report a swift change in the magnetic ground state upon application of small DC electric current, a giant yet anisotropic magnetoelectric effect, and a sharp, lattice-driven quantum switching manifested in the I-V characteristic of the bulk single-crystal Cr2Ge2Te6. At the hea

Henrique Reggiani, Kevin C. Schlaufman, Brian F. Healy, Joshua D. Lothringer

Idealized protoplanetary disk and giant planet formation models have been interpreted to suggest that a giant planet's atmospheric abundances can be used to infer its formation location in its parent protoplanetary disk. It has recently been reported that the hot Jupiter WASP-77 A b has sub-solar atmospheric carbon and oxygen abundances with a solar C/O abun

Marie Maros, Gesualdo Scutari, Ying Sun, Guang Cheng

We study sparse linear regression over a network of agents, modeled as an undirected graph and no server node. The estimation of the $s$-sparse parameter is formulated as a constrained LASSO problem wherein each agent owns a subset of the $N$ total observations. We analyze the convergence rate and statistical guarantees of a distributed projected gradient tr

He Sun, Katherine L. Bouman, Paul Tiede, Jason J. Wang

Inference is crucial in modern astronomical research, where hidden astrophysical features and patterns are often estimated from indirect and noisy measurements. Inferring the posterior of hidden features, conditioned on the observed measurements, is essential for understanding the uncertainty of results and downstream scientific interpretations. Traditional

Eve Armstrong, Amol V. Patwardhan, A. A. Ahmetaj, M. Margarette Sanchez

Neutrinos in compact-object environments, such as core-collapse supernovae, can experience various kinds of collective effects in flavor space, engendered by neutrino-neutrino interactions. These include "bipolar" collective oscillations, which are exhibited by neutrino ensembles where different flavors dominate at different energies. Considering the importa