May 2022 arXiv papers — page 115

Jing Song, A. Feijoo, E. Oset

We perform a theoretical study of the $D_s^{+}\to \pi^{+}\pi^{+}\pi^{-}\eta$ decay. We look first at the basic $D_s^{+}$ decay at the quark level from external and internal emission. Then hadronize a pair or two pairs of $q\bar{q}$ states to have mesons at the end. Posteriorly the pairs of mesons are allowed to undergo final state interaction, by means of wh

Johann A. Preuß, Daniel Groll, Robert Schmidt, Thilo Hahn

Single-photon emitters in solid-state systems are important building blocks for scalable quantum technologies. Recently, quantum light emitters have been discovered in the wide-gap van der Waals insulator hBN. These color centers have attracted considerable attention due to their quantum performance at elevated temperatures and wide range of transition energ

A. Beguinet, V. Ehrlacher, R. Flenghi, M. Fuente

Deep learning-based numerical schemes such as Physically Informed Neural Networks (PINNs) have recently emerged as an alternative to classical numerical schemes for solving Partial Differential Equations (PDEs). They are very appealing at first sight because implementing vanilla versions of PINNs based on strong residual forms is easy, and neural networks of

L. Bufaiçal, M. A. V. Heringer, J. R. Jesus, A. Caytuero

In this work, we report the synthesis and investigation of structural, electronic, and magnetic properties of La$_{1.5}$Ca$_{0.5}$(Co$_{0.5}$Fe$_{0.5}$)IrO$_6$. Our polycrystalline sample forms as a single-phase double perovskite in monoclinic $P2_{1}/n$ space group. Co and Ir are most likely in bivalent and tetravalent oxidation states, respectively, while

Hu Zhu, Minyong Guo

We revisit the innermost stable circular orbits (ISCOs) of charged particles and study the polarized images of synchrotron radiations emitted from such orbiting hotspots on the equatorial plane in Schwarzchild-Melvin black hole spacetime. We obtain a constraint on the magnetic field to retain ISCOs for charged particles. In particular, we identify a critical

Florian Frick, R. Amzi Jeffs

We give a complete combinatorial characterization of weakly $d$-Tverberg complexes. These complexes record which intersection combinatorics of convex hulls necessarily arise in any sufficiently large general position point set in $\mathbb R^d$. This strengthens the concept of $d$-representable complexes, which describe intersection combinatorics that arise i

Pascal Nasahl, Miguel Osorio, Pirmin Vogel, Michael Schaffner

Fault attacks are active, physical attacks that an adversary can leverage to alter the control-flow of embedded devices to gain access to sensitive information or bypass protection mechanisms. Due to the severity of these attacks, manufacturers deploy hardware-based fault defenses into security-critical systems, such as secure elements. The development of th

Kazumasa Shinagawa, Kengo Miyamoto

In card-based cryptography, a deck of physical cards is used to achieve secure computation. A shuffle, which randomly permutes a card-sequence along with some probability distribution, ensures the security of a card-based protocol. The authors proposed a new class of shuffles called graph shuffles, which randomly permutes a card-sequence by an automorphism o

Aditya Sharma, Kinjal Banerjee, Jishnu Bhattacharyya

Spatially homogeneous cosmological spacetimes, evolving in the presence of a positive cosmological constant and matter satisfying some reasonable energy conditions, typically approach the de Sitter geometry asymptotically (at least locally). In this work, we propose an alternate way to characterize this phenomena. We focus on a subset of such models admittin

J. R. Jesus, L. Bufaiçal, E. M. Bittar

Structural, electronic and magnetic properties of polycrystalline La2-xCaxCoMnO6 (0 $\leq$ x $\leq$ 0.75) compounds are investigated by x-ray diffraction and magnetometry. All the samples have an orthorhombic structure and show a slight decrease in the unit cell with Ca-doping. Temperature-dependent magnetization measurements reveal a complex magnetic behavi

Haiyang Yang, Meilin Chen, Yizhou Wang, Shixiang Tang

Generalizing learned representations across significantly different visual domains is a fundamental yet crucial ability of the human visual system. While recent self-supervised learning methods have achieved good performances with evaluation set on the same domain as the training set, they will have an undesirable performance decrease when tested on a differ

Nicolás Abadía, Frank Bello, Chuan Zhong, Patrick Flanigan

We investigate a tapered, hybrid plasmonic waveguide which has previously been proposed as an optically efficient near-field transducer (NFT), or component thereof, in several devices which aim to exploit nanofocused light. We numerically analyze how light is transported through the waveguide and ultimately focused via effective-mode coupling and taper optim

Naoki Akai

Reliability is a key factor for realizing safety guarantee of full autonomous robot systems. In this paper, we focus on reliability in mobile robot localization. Monte Carlo localization (MCL) is widely used for mobile robot localization. However, it is still difficult to guarantee its safety because there are no methods determining reliability for MCL estim

Benjamin Audoux, Jean-Baptiste Meilhan, Akira Yasuhara

We characterize, in an algebraic and in a diagrammatic way, Milnor string link invariants indexed by sequences where any index appears at most $k$ times, for any fixed $k\ge 1$. The algebraic characterization is given in terms of an Artin-like action on the so-called $k$-reduced free groups; the diagrammatic characterization uses the langage of welded knot t

Vincenzo Antonelli, Gianfranco Casnati

A $h$-instanton sheaf on a closed subscheme $X$ of some projective space endowed with an ample and globally generated line bundle $\mathcal{O}_X(h)$ is a coherent sheaf whose cohomology table has a certain prescribed shape. In this paper we deal with $h$-instanton sheaves relating them to Ulrich sheaves. Moreover, we study $h$-instanton sheaves on smooth cur

Yuan Bi, Zhongliang Jiang, Yuan Gao, Thomas Wendler

Ultrasound (US) is one of the most common medical imaging modalities since it is radiation-free, low-cost, and real-time. In freehand US examinations, sonographers often navigate a US probe to visualize standard examination planes with rich diagnostic information. However, reproducibility and stability of the resulting images often suffer from intra- and int

Cristiano Patrício, João C. Neves, Luís F. Teixeira

The remarkable success of deep learning has prompted interest in its application to medical imaging diagnosis. Even though state-of-the-art deep learning models have achieved human-level accuracy on the classification of different types of medical data, these models are hardly adopted in clinical workflows, mainly due to their lack of interpretability. The b

Hanyu Jiang, Li You, Jue Wang, Wenjin Wang

In the fifth-generation and beyond era, reconfigurable intelligent surface (RIS) and dynamic metasurface antennas (DMAs) are emerging metamaterials keeping up with the demand for high-quality wireless communication services, which promote the diversification of portable wireless terminals. However, along with the rapid expansion of wireless devices, the elec

Marek Rogatko

The uniqueness theorem for static, spherically symmetric, asymptotically flat, higher dimensional phantom black holes, with non-degenerate event horizon , being the solutions of Einstein phantom/dilaton Maxwell/anti-Maxwell gravity systems is considered. Conformal positive energy theorem and conformal transformations authorize the crucial tools for exploitin

Aidan Crilly, Brian Appelbe, Owen Mannion, William Taitano

Recent inertial confinement fusion experiments have shown primary fusion spectral moments which are incompatible with a Maxwellian velocity distribution description. These results show that an ion kinetic description of the reacting ions is necessary. We develop a theoretical classification of non-Maxwellian ion velocity distributions using the spectral mome

Zhijun Chen, Zhe Lu, Qiushi Chen, Hongliang Zhong

Short-term traffic flow prediction is a vital branch of the Intelligent Traffic System (ITS) and plays an important role in traffic management. Graph convolution network (GCN) is widely used in traffic prediction models to better deal with the graphical structure data of road networks. However, the influence weights among different road sections are usually

Xuan Ye, Yang Zhang, Bo Wang

We perform the point-splitting regularization on the vacuum stress tensor of a coupling scalar field in de Sitter space under the guidance from the adiabatically regularized Green's function. For the massive scalar field with the minimal coupling $\xi=0$, the 2nd order point-splitting regularization yields a finite vacuum stress tensor with a positive, const

Melissa Pesce-Rollins, Nicola Omodei, Sam Krucker, Niccol`o Di Lalla

We present the Fermi-LAT observations of the behind-the-limb (BTL) flare of July 17, 2021 and the joint detection of this flare by STIX onboard Solar Orbiter. The separation between Earth and the Solar Orbiter was 99.2$^{\circ}$ at 05:00 UT, allowing STIX to have a front view of the flare. The location of the flare was ~S20E140 in Stonyhurst heliographic coo

Soonchan Park, Jinah Park

Studies of virtual try-on (VITON) have been shown their effectiveness in utilizing the generative neural network for virtually exploring fashion products, and some of recent researches of VITON attempted to synthesize human image wearing given multiple types of garments (e.g., top and bottom clothes). However, when replacing the top and bottom clothes of the

Young-Min Lee, Hyosun Kim, Hee-Won Lee

The accretion process in a typical S-type symbiotic star, targeting~AG Draconis, is investigated through 3D hydrodynamical simulations using the FLASH code. Regardless of the wind velocity of the giant star, an accretion disk surrounding the white dwarf is always formed. In the wind models faster than the orbital velocity of the white dwarf, the disk size an

A. Frasca, J. Molenda-Zakowicz, J. Alonso-Santiago, G. Catanzaro

In this work we present the results of our analysis of 16,300 medium-resolution LAMOST spectra of late-type stars in the Kepler field with the aim of determining the stellar parameters, activity level, lithium atmospheric content, and binarity. We have used a version of the code ROTFIT specifically developed for these spectra. We provide a catalog with the a

Siddhant Agrawal, Thomas Alazard

The classical Rellich inequalities imply that the $L^2$-norms of the normal and tangential derivatives of a harmonic function are equivalent. In this note, we prove several refined inequalities, which make sense even if the domain is not Lipschitz. For two-dimensional domains, we obtain a sharp $L^p$-estimate for $1<p\leq 2$ by using a Riemann mapping and in

Yaling Ke, Christoph Kaspar, André Erpenbeck, Uri Peskin

The study of chemical reactions in environments under nonequilibrium conditions has been of interest recently in a variety of contexts, including current-induced reactions in molecular junctions and scanning tunneling microscopy experiments. In this work, we outline a fully quantum mechanical, numerically exact approach to describe chemical reaction rates in

Dharam Vir Ahluwalia, Julio M. Hoff da Silva, Cheng-Yang Lee, Yu-Xiao Liu

Let $\Theta$ be the Wigner time reversal operator for spin half and let $\phi$ be a Weyl spinor. Then, for a left-transforming $\phi$, the construct $\zeta_\lambda \Theta \phi^\ast$ yields a right-transforming spinor. If instead, $\phi$ is a right-transforming spinor, then the construct $\zeta _\rho \Theta \phi^\ast$ results in a left-transforming spinor ($\

Huawen Xu, Tanjung Krisnanda, Ruiqi Bao, Timothy C. H. Liew

The emerging technology of quantum neural networks (QNNs) attracts great attention from both the fields of machine learning and quantum physics with the capability to gain quantum advantage from an artificial neural network (ANN) system. Comparing to the classical counterparts, QNNs have been proven to be able to speed up the information processing, enhance

Maximilian Bauer, Mario Bebendorf

This article deals with the adaptive and approximative computation of the Lam\'e equations. The equations of linear elasticity are considered as boundary integral equations and solved in the setting of the boundary element method (BEM). Using BEM, one is faced with the solution of a system of equations with a fully populated system matrix, which is in genera

Ziyang Gan, Ioannis Paradisanos, Ana Estrada-Real, Julian Picker

We report one-pot chemical vapor deposition (CVD) growth of large-area Janus SeMoS monolayers, with the asymmetric top (Se) and bottom (S) chalcogen atomic planes with respect to the central transition metal (Mo) atoms. The formation of these two-dimensional semiconductor monolayers takes place upon the thermodynamic equilibrium-driven exchange of the bottom

Grégory Baltus, Justin Janquart, Melissa Lopez, Harsh Narola

We present here the latest development of a machine-learning pipeline for pre-merger alerts from gravitational waves coming from binary neutron stars. This work starts from the convolutional neural networks introduced in our previous paper (PhysRevD.103.102003) that searched for three classes of early inspirals in simulated Gaussian noise colored with the de

Fuyan Ma, Bin Sun, Shutao Li

Previous methods for dynamic facial expression in the wild are mainly based on Convolutional Neural Networks (CNNs), whose local operations ignore the long-range dependencies in videos. To solve this problem, we propose the spatio-temporal Transformer (STT) to capture discriminative features within each frame and model contextual relationships among frames.

Tomoya Tsuchioka, Toshiki Sato, Shinya Yamada, Yasunobu Uchiyama

The central strong activities in core-collapse supernovae expect to produce the overturning of the Fe- and Si/O-rich ejecta during the supernova explosion based on multi-dimensional simulations. X-ray observations of the supernova remnant Cassiopeia A have indicated that the Fe-rich ejecta lies outside the Si-rich materials in the southeastern region, which

Sebastian T. Vincent, Loïc Barrault, Carolina Scarton

Unlike English, morphologically rich languages can reveal characteristics of speakers or their conversational partners, such as gender and number, via pronouns, morphological endings of words and syntax. When translating from English to such languages, a machine translation model needs to opt for a certain interpretation of textual context, which may lead to

Anqi Zhang, Xiaoyun He, Shengmei Zhao

In the paper, a gradient-free optimization algorithm for single-qubit quantum classifier is proposed to overcome the effects of barren plateau caused by quantum devices. A rotation gate RX({\phi}) is applied on a single-qubit binary quantum classifier, and the training data and parameters are loaded into {\phi} with the form of vector-multiplication. The cos

Florian Kurpicz, Hans-Peter Lehmann, Peter Sanders

We introduce PaCHash, a hash table that stores its objects contiguously in an array without intervening space, even if the objects have variable size. In particular, each object can be compressed using standard compression techniques. A small search data structure allows locating the objects in constant expected time. PaCHash is most naturally described as a

Shichuan Deng

In this paper, we study the fault-tolerant matroid median and fault-tolerant knapsack median problems. These two problems generalize many fundamental clustering and facility location problems, such as uniform fault-tolerant $k$-median, uniform fault-tolerant facility location, matroid median, knapsack median, etc. We present a versatile iterative rounding fr

Chenrui Zhang

We explore how to crawl financial forum data such as stock bars and combine them with deep learning models for sentiment analysis. In this paper, we will use the BERT model to train against the financial corpus and predict the SZSE Component Index, and find that applying the BERT model to the financial corpus through the maximum information coefficient compa

Gabriel Schmid

This thesis is devoted to the study of 3-dimensional quantum gravity as a spin foam model and group field theory. In the first part of this thesis, we review some general physical and mathematical aspects of 3-dimensional gravity, focusing on its topological nature. Afterwards, we review some important aspects of the Ponzano-Regge spin foam model for 3-dimen

Tomoyuki Hanawa, Nami Sakai, Satoshi Yamamoto

TMC-1A is a protostellar source harboring a young protostar, IRAS 04365+2353, and shows a highly asymmetric features of a few 100 au scale in the molecular emission lines. Blue-shifted emission is much stronger in the CS ($J=5$-4) line than red-shifted one. The asymmetry can be explained if the gas accretion is episodic and takes the form of cloudlet capture

Ziwei Wan, Zhen Gao, Marco Di Renzo, Lajos Hanzo

The fourth industrial revolution, i.e., Industry 4.0, is evolving all around the globe. In this article, we introduce the landscape of Industry 4.0 and beyond empowered by the seamless collaboration of communication technology (CT), information technology (IT), and operation technology (OT), i.e., CIOT collaboration. Specifically, CIOT collaboration is regar

Zilu Wang, Pingzhi Li, Yuxuan Yao, Youri L. W. Van Hees

Perpendicular synthetic antiferromagnets (SAFs) are of interest for the next generation ultrafast, high density spintronic memory and logic devices. However, to energy efficiently operate their magnetic order by current-induced spin orbit torques (SOTs), an unfavored high external field is conventionally required to break the symmetry. Here, we theoretically

Xiaodong He, Yinan Wang, Juan Li

This paper introduces a novel neural network - flow completion network (FCN) - to infer the fluid dynamics, includ-ing the flow field and the force acting on the body, from the incomplete data based on Graph Convolution AttentionNetwork. The FCN is composed of several graph convolution layers and spatial attention layers. It is designed to inferthe velocity

Andrew Darby, Catherine Nicole Coleman, Claudia Engel, Daniel van Strien

We take a snapshot of current resources available for teaching and learning AI with a focus on the Galleries, Libraries, Archives and Museums (GLAM) community. The review was carried out in 2021 and 2022. The review provides an overview of material we identified as being relevant, offers a description of this material and makes recommendations for future wor

Francesca Soldan, Alberto Maldarella, Gabriele Paludetto, Enea Bionda

Clustering analysis of daily load profiles represents an effective technique to classify and aggregate electric users based on their actual consumption patterns. Among other purposes, it may be exploited as a preliminary stage for load forecasting, which is applied in the same way to consumers in the same cluster. Several clustering algorithms have been prop

Mike Ludkovski, Glen Swindle, Eric Grannan

We develop a probabilistic framework for joint simulation of short-term electricity generation from renewable assets. In this paper we describe a method for producing hourly day-ahead scenarios of generated power at grid-scale across hundreds of assets. These scenarios are conditional on specified forecasts and yield a full uncertainty quantification both at

Florien Müller, Lukas Woiwode, Johann Gross, Maren Scheel

The present work addresses the experimental identification of amplitude-dependent modal parameters (modal frequency, damping ratio, Fourier coefficients of periodic modal oscillation). Phase-resonant testing has emerged as an important method for this task, as it substantially reduces the amount of data required for the identification compared to conventiona

Volker Kempf

The Brezzi--Douglas--Marini interpolation error on anisotropic elements has been analyzed in two recent publications, the first focusing on simplices with estimates in $L^2$, the other considering parallelotopes with estimates in terms of $L^p$-norms. This contribution provides generalized estimates for anisotropic simplices for the $L^p$ case, $1\leq p\leq\

Thibault Formal, Carlos Lassance, Benjamin Piwowarski, Stéphane Clinchant

Neural retrievers based on dense representations combined with Approximate Nearest Neighbors search have recently received a lot of attention, owing their success to distillation and/or better sampling of examples for training -- while still relying on the same backbone architecture. In the meantime, sparse representation learning fueled by traditional inver

Jacob Bernard John Chapman, Pui-Wai Ma

A machine-learned spin-lattice interatomic potential (MSLP) for magnetic iron is developed and applied to mesoscopic scale defects. It is achieved by augmenting a spin-lattice Hamiltonian with a neural network term trained to descriptors representing a mix of local atomic configuration and magnetic environments. It reproduces the cohesive energy of BCC and F

Sandeep Hans, Diptikalyan Saha, Aniya Aggarwal

Data values in a dataset can be missing or anomalous due to mishandling or human error. Analysing data with missing values can create bias and affect the inferences. Several analysis methods, such as principle components analysis or singular value decomposition, require complete data. Many approaches impute numeric data and some do not consider dependency of

Yuxuan Du, Zhuozhuo Tu, Bujiao Wu, Xiao Yuan

The intrinsic probabilistic nature of quantum mechanics invokes endeavors of designing quantum generative learning models (QGLMs). Despite the empirical achievements, the foundations and the potential advantages of QGLMs remain largely obscure. To narrow this knowledge gap, here we explore the generalization property of QGLMs, the capability to extend the mo

Robert Friedman, Radu Laza

We prove that the higher direct images $R^qf_*\Omega^p_{\mathcal Y/S}$ of the sheaves of relative K\"ahler differentials are locally free and compatible with arbitrary base change for flat proper families whose fibers have $k$-Du Bois local complete intersection singularities, for $p\leq k$ and all $q\geq 0$, generalizing a result of Du Bois (the case $k=0$)

Bhan Lam, Kenneth Ooi, Karn N. Watcharasupat, Zhen-Ting Ong

The introduction of ISO 12913-2:2018 has provided a framework for standardized data collection and reporting procedures for soundscape practitioners. A strong emphasis was placed on the use of calibrated head and torso simulators (HATS) for binaural audio capture to obtain an accurate subjective impression and acoustic measure of the soundscape under evaluat

Ta Tang, Brian Moritz, Thomas P. Devereaux

While a quantum spin liquid (QSL) phase has been identified in the $J_1$-$J_2$ Heisenberg model on a triangular lattice via numerical calculations, debate persists about whether or not such a QSL is gapped or gapless, with contradictory conclusions from different techniques. Moreover, information about excitations and dynamics is crucial for the experimental

Jun Nishimura

The IKKT matrix model (or the type IIB matrix model) is known as a promising candidate for a nonperturbative formulation of superstring theory in ten dimensions. As a most attractive feature, the model admits the emergence of (3+1)-dimensional space-time associated with the spontaneous breaking of the (9+1)-dimensional Lorentz symmetry. Numerical confirmatio

Robin Strudel, Ivan Laptev, Cordelia Schmid

Visual grounding localizes regions (boxes or segments) in the image corresponding to given referring expressions. In this work we address image segmentation from referring expressions, a problem that has so far only been addressed in a fully-supervised setting. A fully-supervised setup, however, requires pixel-wise supervision and is hard to scale given the

Ziheng Lu, Wenlei Shi, Lixin Sun, Haiguang Liu

Accessing structures of molecules, crystals, and complex interfaces with atomic level details is vital to the understanding and engineering of materials, chemical reactions, and biochemical processes. Currently, determination of accurate atomic positions heavily relies on advanced experimental techniques that are difficult to access or quantum chemical calcu

Cheng Xue, Lequan Yu, Pengfei Chen, Qi Dou

Deep neural networks have achieved remarkable success in a wide variety of natural image and medical image computing tasks. However, these achievements indispensably rely on accurately annotated training data. If encountering some noisy-labeled images, the network training procedure would suffer from difficulties, leading to a sub-optimal classifier. This pr

Luca Capizzi, Sara Murciano, Pasquale Calabrese

We investigate the ground state of a (1+1)-dimensional conformal field theory built with $M$ species of massless free Dirac fermions coupled at one boundary point via a conformal junction/interface. Each CFT represents a wire of finite length $L$. We develop a systematic strategy to compute the R\'enyi entropies for a generic bipartition between the wires an

Dasong Li, Yi Zhang, Ka Lung Law, Xiaogang Wang

With the growing popularity of smartphones, capturing high-quality images is of vital importance to smartphones. The cameras of smartphones have small apertures and small sensor cells, which lead to the noisy images in low light environment. Denoising based on a burst of multiple frames generally outperforms single frame denoising but with the larger computu

Aarju Mathew Koshy, A Sudha, Satyesh Kumar Yadav, Parasuraman Swaminathan

Copper oxide thin films are deposited on quartz substrates by DC magnetron sputtering and the effect of deposition temperature on their optoelectronic properties is examined in detail. Scanning Electron Microscopy (SEM), X-ray diffraction (XRD) analysis, Raman spectroscopy, UV-Vis spectroscopy, and four-probe sheet resistance measurements are used to charact

Shravan Kumar Akula, Hossein Salehfar, Shayan Behzadirafi

Reliability Assessment is an indispensable technology for identifying, interpreting, and lessening the potential failures in safety-critical systems like smart grids. Failure modes and effects analysis (FMEA) is one of the well documented techniques for risk analysis to study the impact of failure modes on safety critical systems like smart grid. In traditio

Sushanth Reddy Kamaram, Raj Prince, Suman Pramanick, Debanjan Bose

We have presented a multiwavelength temporal and spectral study of the Blazar PKS 0346-27 for the period 2019 January-2021 December (MJD 58484-59575) using data from Fermi-LAT (gamma-rays), Swift-XRT (X-rays) and Swift-UVOT (ultra-violet and optical). We identified multiple flaring episodes by analyzing the gamma-ray light curve generated from the Fermi-LAT

Fabian Fehn, Roman Engelhardt, Florian Dandl, Klaus Bogenberger

This paper examines the integration of freight delivery into the passenger transport of an on-demand ride-pooling service. The goal of this research is to use existing passenger trips for logistics services and thus reduce additional vehicle kilometers for freight delivery and the total number of vehicles on the road network. This is achieved by merging the

Srijith Balakrishnan, Beatrice Cassottana

Integrated simulation models are emerging as an alternative for analyzing large-scale interdependent infrastructure networks due to their modeling advantages over traditional interdependency models. This paper presents an open-source integrated simulation package for the asset-level analysis of interdependent infrastructure systems. The simulation platform,

Jing Xia, Xichao Zhang, Xiaoxi Liu, Yan Zhou

Merons and skyrmions are classical topological solitons. However, they will become quantum mechanical objects when their sizes are of the order of nanometers. Recently, quantum computation based on nanoscale skyrmions was proposed. Here, we propose to use a nanoscale meron in a magnetic nanodisk as a qubit, where the up and down directions of the core spin a

Keith L Chambers, Michael G Watson, Mary R Myerscough

We extend the lipid-structured model for atherosclerotic plaque development of Ford et al. (2019) to account for macrophage proliferation. Proliferation is modelled as a non-local decrease in the lipid structural variable that is similar to the treatment of cell division in size-structured models (e.g. Efendiev et al. (2018)). Steady state analysis indicates

Zhi Hu, Siqi Xu, Chandan Mondal, Xingbo Zhao

We obtain the leading-twist valence quark transverse-momentum-dependent parton distribution functions (TMD PDFs) for the proton within the basis light-front quantization (BLFQ) framework. Our results are consistent with lattice QCD calculations and our previous results for the collinear limit. We also obtain consistency with the Soffer-type bounds. Within ou

Yongji Wu, Matthew Lentz, Danyang Zhuo, Yao Lu

With the advent of ubiquitous deployment of smart devices and the Internet of Things, data sources for machine learning inference have increasingly moved to the edge of the network. Existing machine learning inference platforms typically assume a homogeneous infrastructure and do not take into account the more complex and tiered computing infrastructure that

Julian Wörmann, Daniel Bogdoll, Christian Brunner, Etienne Bührle

The availability of representative datasets is an essential prerequisite for many successful artificial intelligence and machine learning models. However, in real life applications these models often encounter scenarios that are inadequately represented in the data used for training. There are various reasons for the absence of sufficient data, ranging from

Yunjae Lee, Jinha Chung, Minsoo Rhu

Graph neural networks (GNNs) can extract features by learning both the representation of each objects (i.e., graph nodes) and the relationship across different objects (i.e., the edges that connect nodes), achieving state-of-the-art performance in various graph-based tasks. Despite its strengths, utilizing these algorithms in a production environment faces s

Krishna Kishore, Anupam Singh

We prove that for all integers $k \geq 1$, there exists a constant $C_k$ depending only on $k$ such that for all $q > C_k$ and for all $n \geq 1$ every matrix in $M_n(\mathbb F_q)$ is a sum of two $k$th powers.

Yun Ji, Zhoubin Kou, Xiaoxiong Zhong, Sheng Zhang

Federated learning (FL) can train a global model from clients' local data set, which can make full use of the computing resources of clients and performs more extensive and efficient machine learning on clients with protecting user information requirements. Many existing works have focused on optimizing FL accuracy within the resource constrained in each ind

Dev R Sadaula, Manuel A Bautista, Javier A Garcia, Timothy R Kallman

Warm absorber spectra contain bound-bound and bound-free absorption features seen in the X-ray and UV spectra from many active galactic nuclei (AGN). The widths and centroid energies of these features indicate they occur in outflowing gas, and the outflow can affect the gas within the host galaxy. Thus the warm absorber mass and energy budgets are of great i

Ali H. Chamseddine, Ola Malaeb, Sara Najem

We focus on studying, numerically, the scalar curvature tensor in a two-dimensional discrete space. The continuous metric of a two-sphere is transformed into that of a lattice using two possible slicings. In the first, we use two integers, while in the second we consider the case where one of the coordinates is ignorable. The numerical results of both cases

Aurélien Drezet

In 1927 Louis de Broglie proposed an alternative approach to standard quantum mechanics known as the double solution program (DSP) where particles are represented as bunched fields or solitons guided by a base (weaker) wave. DSP evolved as the famous de Broglie-Bohm pilot wave interpretation (PWI) also known as Bohmian mechanics but the general idea to use s

Antoine Comby, Debobrata Rajak, Dominique Descamps, Stéphane Petit

We report on the development of a high-order harmonic generation (HHG)-based ultrafast high-repetition-rate (250 kHz) monochromatic extreme ultraviolet (21.6 eV) source with polarization tunability, specifically designed for multi-modal dichroism in time- and angle-resolved photoemission spectroscopy. Driving HHG using an annular beam allows us to spatially

Arturo Jaime, Rufus Willett

Complexity rank for $C^*$-algebras was introduced by the second author and Yu for applications towards the UCT: very roughly, this rank is at most $n$ if you can repeatedly cut the $C^*$-algebra in half at most $n$ times, and end up with something finite dimensional. In this paper, we study complexity rank, and also a weak complexity rank that we introduce;

Emil Mottola

Gravity and general relativity are considered as an Effective Field Theory at low energies and macroscopic distances. The effective action of the conformal anomaly of light or massless quantum fields has significant effects on macroscopic scales, due to associated light cone singularities that are not captured by an expansion in local curvature invariants. A

Youngeun Kwon, Minsoo Rhu

Personalized recommendation models (RecSys) are one of the most popular machine learning workload serviced by hyperscalers. A critical challenge of training RecSys is its high memory capacity requirements, reaching hundreds of GBs to TBs of model size. In RecSys, the so-called embedding layers account for the majority of memory usage so current systems emplo

Haoxuan Li, Chunyuan Zheng, Peng Wu

In recommender systems, users always choose the favorite items to rate, which leads to data missing not at random and poses a great challenge for unbiased evaluation and learning of prediction models. Currently, the doubly robust (DR) methods have been widely studied and demonstrate superior performance. However, in this paper, we show that DR methods are un

Vasily Krylov, Leonid Rybnikov

The loop group $G((z^{-1}))$ of a simple complex Lie group $G$ has a natural Poisson structure. We introduce a natural family of Poisson commutative subalgebras $\overline{{\mathbf{B}}}(C) \subset \mathcal{O}(G((z^{-1}))$ depending on the parameter $C\in G$ called classical universal Bethe subalgebras. To every antidominant cocharacter $\mu$ of the maximal t

G. A. Grigorian

The Riccati equation method is used to establish oscillation and non-oscillation criteria for second order linear nonhomogeneous functional-differential equations.We show that the obtained oscillation criterion is a generalization of J. S. W. Wong's oscillation criterion for second order linear nonhomogeneous ordinary differential equations. Two examples, de

Alexander Roth, Klemens Hammerer, Kirill S. Tikhonov

Spin-polarised atomic ensembles probed by light based on the Faraday interaction are a versatile platform for numerous applications in quantum metrology and quantum information processing. Here we consider an ensemble of Alkali atoms that are continuously optically pumped and probed. Due to the collective scattering of photons at large optical depth, the ste

Renyu Zhu, Lei Yuan, Xiang Li, Ming Gao

Program understanding is a fundamental task in program language processing. Despite the success, existing works fail to take human behaviors as reference in understanding programs. In this paper, we consider human behaviors and propose the PGNN-EK model that consists of two main components. On the one hand, inspired by the "divide-and-conquer" reading behavi

Prajval Kumar Murali, Ravinder Dahiya, Mohsen Kaboli

Accurate object pose estimation using multi-modal perception such as visual and tactile sensing have been used for autonomous robotic manipulators in literature. Due to variation in density of visual and tactile data, we previously proposed a novel probabilistic Bayesian filter-based approach termed translation-invariant Quaternion filter (TIQF) for pose est

Kyudong Choi, In-Jee Jeong, Deokwoo Lim

We consider the incompressible Euler equations in the half cylinder $ \mathbb{R}_{>0}\times\mathbb{T}$. In this domain, any vorticity which is independent of $x_2$ defines a stationary solution. We prove that such a stationary solution is nonlinearly stable in a weighted $L^{1}$ norm involving the horizontal impulse, if the vorticity is non-negative and non-

Elahe Sadat Kazemi Nasab, Ramin Almasi, Bijan Shoushtarian, Ehsan Golkar

Diabetic Retinopathy (DR) caused by diabetes occurs as a result of changes in the retinal vessels and causes visual impairment. Microaneurysms (MAs) are the early clinical signs of DR, whose timely diagnosis can help detecting DR in the early stages of its development. It has been observed that MAs are more common in the inner retinal layers compared to the

Mikhael Carmona, Victor Chepoi, Guyslain Naves, Pascal Préa

Recently, Armstrong, Guzm\'an, and Sing Long (2021), presented an optimal $O(n^2)$ time algorithm for strict circular seriation (called also the recognition of strict quasi-circular Robinson spaces). In this paper, we give a very simple $O(n\log n)$ time algorithm for computing a compatible circular order for strict circular seriation. When the input space i

Adam Hibberd

The first interstellar object to be discovered, 1I/'Oumuamua, exhibited various unusual properties as it was tracked on its passage through the inner solar system in 2017/2018. In terms of the potential scientific return, a spacecraft mission to intercept and study it in situ would be invaluable. As an extension to previous Project Lyra studies, this paper e

Mingyang Chen, Wen Zhang, Zhen Yao, Xiangnan Chen

We study the knowledge extrapolation problem to embed new components (i.e., entities and relations) that come with emerging knowledge graphs (KGs) in the federated setting. In this problem, a model trained on an existing KG needs to embed an emerging KG with unseen entities and relations. To solve this problem, we introduce the meta-learning setting, where a

David El-Chai Ben-Ezra, Ron Arad, Ayelet Padowicz, Israel Tugendhaft

Being inspired by the biological eye, event camera is a novel asynchronous technology that pose a paradigm shift in acquisition of visual information. This paradigm enables event cameras to capture pixel-size fast motions much more naturally compared to classical cameras. In this paper we present a new asynchronous event-driven algorithm for detection of hig

S. G. Kobelkov, A. A. Mishchenko

This paper considers random graph approach to simulate irreversible step-growth polymerization. We study generalization of approach developed by Kryven~I. by introducing different types of bonds each with its own weight representing corresponding reaction rate.

Gholamreza Fardipour Raki, Maryam Ghahremani Gol, Mohammad Sahraei, Mohsen Khakzad

The signals received from the optical receivers like as SiPM and PMT due to the collision of energetic particles with the scintillators attached to these optical receivers, are weak and fast. To measure the number of signals as well as their peak height with electronic circuits, it is necessary to optimize the signals. An example of SiPM driver circuit, sign

Takashi Hiramatsu, Tsutomu Kobayashi

We provide a cosmological test of modified gravity with two tensorial degrees of freedom and no extra propagating scalar mode. The theory of gravity we consider admits a cosmological model that is indistinguishable from the $\Lambda$CDM model at the level of the background evolution. The model has a single modified-gravity parameter $\beta$, the effect of wh

Shao-Heng Ko, Kamesh Munagala

We study the power of price discrimination via an intermediary in bilateral trade, when there is a revenue-maximizing seller selling an item to a buyer with a private value drawn from a prior. Between the seller and the buyer, there is an intermediary that can segment the market by releasing information about the true values to the seller. This is termed sig

Chang Jin, Shigui Qiu, Nini Xiao, Hao Jia

In Neural Machine Translation (NMT), data augmentation methods such as back-translation have proven their effectiveness in improving translation performance. In this paper, we propose a novel data augmentation approach for NMT, which is independent of any additional training data. Our approach, AdMix, consists of two parts: 1) introduce faint discrete noise