November 2022 arXiv papers — page 126

Nathan Lundblad, David C. Aveline, Antun Balaz, Elliot Bentine

Progress in understanding quantum systems has been driven by the exploration of the geometry, topology, and dimensionality of ultracold atomic systems. The NASA Cold Atom Laboratory (CAL) aboard the International Space Station has enabled the study of ultracold atomic bubbles, a terrestrially-inaccessible topology. Proof-of-principle bubble experiments have

Usman Khalil, Owais Ahmed Malik, Ong Wee Hong, Mueen Uddin

Smart city architecture brings all the underlying architectures, i.e., Internet of Things (IoT), Cyber-Physical Systems (CPSs), Internet of Cyber-Physical Things (IoCPT), and Internet of Everything (IoE), together to work as a system under its umbrella. The goal of smart city architecture is to come up with a solution that may integrate all the real-time res

H. Sebastian Scheid

For Run 4, ALICE is pioneering the construction of truly cylindrical tracking layers, which will improve the measurements of heavy-flavour hadrons and dielectrons. In addition, a Forward Calorimeter (FoCal) for the measurement of direct photons is being developed to access the gluon distributions of nucleons and nuclei at low $x$. For Run 5 and beyond, ALICE

Antoine Seguin, Yann Bertho, Baptiste Darbois Texier

We consider the penetration dynamics of a vertical cylinder into a dry granular medium subjected to successive impacts. The depth of the impactor below the free surface $z_N$ first evolves linearly with the impact number $N$ and then follows a power-law evolution $z_N \propto N^{1/3}$. The depth reached by the cylinder after a given number of impacts is obse

Chien-Yao Wang, Hong-Yuan Mark Liao, I-Hau Yeh

Designing a high-efficiency and high-quality expressive network architecture has always been the most important research topic in the field of deep learning. Most of today's network design strategies focus on how to integrate features extracted from different layers, and how to design computing units to effectively extract these features, thereby enhancing t

Nickolay Safonov, Dmitriy Vatolin

In recent years, display intensity and contrast have increased considerably. Many displays support high dynamic range (HDR) and 10-bit color depth. Since high bit-depth is an emerging technology, video content is still largely shot and transmitted with a bit depth of 8 bits or less per color component. Insufficient bit-depths produce distortions called false

Geri Skenderi, Christian Joppi, Matteo Denitto, Marco Cristani

The fashion industry is one of the most active and competitive markets in the world, manufacturing millions of products and reaching large audiences every year. A plethora of business processes are involved in this large-scale industry, but due to the generally short life-cycle of clothing items, supply-chain management and retailing strategies are crucial f

Fedor V. Fomin, Petr A. Golovach, Tuukka Korhonen, Daniel Lokshtanov

We introduce the following submodular generalization of the Shortest Cycle problem. For a nonnegative monotone submodular cost function $f$ defined on the edges (or the vertices) of an undirected graph $G$, we seek for a cycle $C$ in $G$ of minimum cost $\textsf{OPT}=f(C)$. We give an algorithm that given an $n$-vertex graph $G$, parameter $\varepsilon > 0$,

Tobias Gergs, Thomas Mussenbrock, Jan Trieschmann

Simulations of Al thin film sputter depositions rely on accurate plasma and surface interaction models. Establishing the latter commonly requires a higher level of abstraction and means to dismiss the fundamental atomic fidelity. Previous works on sputtering processes addressed this issue by establishing machine learning surrogate models, which include a bas

Walter Smilga

It is common practice to describe elementary particles by irreducible unitary representations of the Poincar\'e group. In the same way, multi-particle systems can be described by irreducible unitary representations of the Poincar\'e group. Representations of the Poincar\'e group are characterised by fixed eigenvalues of two Casimir operators corresponding to

Tero Mäkinen, Agata Zaborowska, Małgorzata Frelek-Kozak, Iwona Jóźwik

There are a multitude of applications in which structural materials would be desired to be nondestructively evaluated, while in a component, for plasticity and failure characteristics. In this way, safety and resilience features can be significantly improved. Nevertheless, while failure can be visible through cracks, plasticity is commonly invisible and high

Soumen Basu, Mayank Gupta, Pratyaksha Rana, Pankaj Gupta

We propose a novel deep neural network architecture to learn interpretable representation for medical image analysis. Our architecture generates a global attention for region of interest, and then learns bag of words style deep feature embeddings with local attention. The global, and local feature maps are combined using a contemporary transformer architectu

Alberto Cabada, Lucía López-Somoza, Mouhcine Yousfi

In this paper we will deduce several properties of the Green's functions related to the Hill's equation coupled to various boundary value conditions. In particular, the idea is to study the Green's functions of the second order differential operator coupled to Neumann, Dirichlet, Periodic and Mixed boundary conditions, by expressing the Green's function of a

M. Barbisan, R. Agnello, G. Casati, R. Pasqualotto

The SPIDER experiment, operated at the Neutral Beam Test Facility of Consorzio RFX, Padua, hosts the prototype of the H-/D- ion source for the ITER neutral beam injectors. The maximization of the ion current extracted from the source and the minimization of the amount of co-extracted electrons are among the most relevant targets to accomplish. The Cavity Rin

M. Barbisan, R. Pasqualotto, R. Agnello, M. Pilieci

The Neutral Beam Injectors of the ITER experiment will rely on negative ion sources to produce 16.7 MW beams of H/D particles accelerated at 1 MeV. The prototype of these sources was built and is currently operated in the SPIDER experiment (Source for the Production of Ions o Deuterium Extracted from an RF plasma), part of the Neutral Beam Test Facility of C

Vittoria Sposini, Diego Krapf, Enzo Marinari, Raimon Sunyer

Anomalous-diffusion, the departure of the spreading dynamics of diffusing particles from the traditional law of Brownian-motion, is a signature feature of a large number of complex soft-matter and biological systems. Anomalous-diffusion emerges due to a variety of physical mechanisms, e.g., trapping interactions or the viscoelasticity of the environment. How

Dinakar Muthiah, Alex Weekes

Braverman, Finkelberg, and Nakajima define Kac-Moody affine Grassmannian slices as Coulomb branches of $3d$ $\mathcal{N}=4$ quiver gauge theories and prove that their Coulomb branch construction agrees with the usual loop group definition in finite ADE types. The Coulomb branch construction has good algebraic properties, but its geometry is hard to understan

Tero Mäkinen, Alisa Halonen, Juha Koivisto, Mikko J. Alava

Wood deformation, in particular when subject to compression, exhibits scale-free avalanche-like behavior as well as structure-dependent localization of deformation. We have taken three-dimensional (3D) x-ray tomographs during compression with constant stress rate loading. Using digital volume correlation, we obtain the local total strain during the experimen

Alexandre Chenu, Olivier Serris, Olivier Sigaud, Nicolas Perrin-Gilbert

Deep Reinforcement Learning has been successfully applied to learn robotic control. However, the corresponding algorithms struggle when applied to problems where the agent is only rewarded after achieving a complex task. In this context, using demonstrations can significantly speed up the learning process, but demonstrations can be costly to acquire. In this

Jie Wu, Ying Peng, Shengming Zhang, Weigang Qi

Scene text recognition (STR) enables computers to recognize and read the text in various real-world scenes. Recent STR models benefit from taking linguistic information in addition to visual cues into consideration. We propose a novel Masked Vision-Language Transformers (MVLT) to capture both the explicit and the implicit linguistic information. Our encoder

Shiyuan He, Hanxuan Ye, Kejun He

Functional principal component analysis has become the most important dimension reduction technique in functional data analysis. Based on B-spline approximation, functional principal components (FPCs) can be efficiently estimated by the expectation-maximization (EM) and the geometric restricted maximum likelihood (REML) algorithms under the strong assumption

Apurba Tiwari, Sudhir Kumar Gupta

In this study, we explore the effects of $\mathcal CP$-violating anomalous interactions of the top-quark through the semileptonic decay modes of the top-quark arising due to pair-production of $t\bar{t}$ at the Large Hardon Collider. Predictions on the LHC sensitivities of the coupling strength to such $\mathcal CP$-violating interactions would be discussed

Kristian Bredies, Enis Chenchene, Emanuele Naldi

In this paper, we propose several graph-based extensions of the Douglas-Rachford splitting (DRS) method to solve monotone inclusion problems involving the sum of $N$ maximal monotone operators. Our construction is based on a two-layer architecture that we refer to as bilevel graphs, to which we associate a generalization of the DRS algorithm that presents th

Usman Khalil, Owais Ahmed Malik, Daphne Teck Ching Lai, Ong Sok King

National Health and Nutritional Status Survey (NHANSS) is conducted annually by the Ministry of Health in Negara Brunei Darussalam to assess the population health and nutritional patterns and characteristics. The main aim of this study was to discover meaningful patterns (groups) from the obese sample of NHANSS data by applying data reduction and interpretat

Amlan Jyoti, Karthik Balaji Ganesh, Manoj Gayala, Nandita Lakshmi Tunuguntla

Explainability has been widely stated as a cornerstone of the responsible and trustworthy use of machine learning models. With the ubiquitous use of Deep Neural Network (DNN) models expanding to risk-sensitive and safety-critical domains, many methods have been proposed to explain the decisions of these models. Recent years have also seen concerted efforts t

Henrik Seckler, Ralf Metzler

Modern single-particle-tracking techniques produce extensive time-series of diffusive motion in a wide variety of systems, from single-molecule motion in living-cells to movement ecology. The quest is to decipher the physical mechanisms encoded in the data and thus to better understand the probed systems. We here augment recently proposed machine-learning te

Tingyan Ma, Guoyan Ao, Ruifang Liu, Ligong Wang

For integer $k\geq2,$ a graph $G$ is called $k$-leaf-connected if $|V(G)|\geq k+1$ and given any subset $S\subseteq V(G)$ with $|S|=k,$ $G$ always has a spanning tree $T$ such that $S$ is precisely the set of leaves of $T.$ Thus a graph is $2$-leaf-connected if and only if it is Hamilton-connected. In this paper, we present a best possible condition based up

Xianhuan Lei, Hui Zhu, Haiyan Zhang, Wenwu Tian

The study on the dynamic evolution of young supernova remnants (SNRs) is an important way to understand the density structure of the progenitor's circumstellar medium. We have reported the acceleration or deceleration, proper motion and brightness changes of 260 compact radio features in the second youngest known SNR Cas A at 5\,GHz based on the VLA data of

Thomas Guilmeau, Emilie Chouzenoux, Víctor Elvira

We study the variational inference problem of minimizing a regularized R\'enyi divergence over an exponential family. We propose to solve this problem with a Bregman proximal gradient algorithm. We propose a sampling-based algorithm to cover the black-box setting, corresponding to a stochastic Bregman proximal gradient algorithm with biased gradient estimato

Daniel Kang, Tatsunori Hashimoto, Ion Stoica, Yi Sun

Over the past few years, AI methods of generating images have been increasing in capabilities, with recent breakthroughs enabling high-resolution, photorealistic "deepfakes" (artificially generated images with the purpose of misinformation or harm). The rise of deepfakes has potential for social disruption. Recent work has proposed using ZK-SNARKs (zero-know

Zheng Lian, Mingyu Xu, Lan Chen, Licai Sun

Partial label learning (PLL) is a typical weakly supervised learning, where each sample is associated with a set of candidate labels. Its basic assumption is that the ground-truth label must be in the candidate set, but this assumption may not be satisfied due to the unprofessional judgment of annotators. Therefore, we relax this assumption and focus on a mo

Anh Duc Bui, Soyeon Caren Han, Josiah Poon

Scene Graph Generation (SGG) serves a comprehensive representation of the images for human understanding as well as visual understanding tasks. Due to the long tail bias problem of the object and predicate labels in the available annotated data, the scene graph generated from current methodologies can be biased toward common, non-informative relationship lab

Florian Schmid, Khaled Koutini, Gerhard Widmer

Audio Spectrogram Transformer models rule the field of Audio Tagging, outrunning previously dominating Convolutional Neural Networks (CNNs). Their superiority is based on the ability to scale up and exploit large-scale datasets such as AudioSet. However, Transformers are demanding in terms of model size and computational requirements compared to CNNs. We pro

Yuxing Zhou, Stephen R. Elliott, Volker L. Deringer

Amorphous red phosphorus (a-P) is one of the remaining puzzling cases in the structural chemistry of the elements. Here, we elucidate the structure, stability, and chemical bond-ing in a-P from first principles, combining machine-learning and density-functional theo-ry (DFT) methods. We show that a-P structures exist with a range of energies slightly higher

Patrick Stiller, Varun Makdani, Franz Pöschel, Richard Pausch

The upcoming exascale era will provide a new generation of physics simulations. These simulations will have a high spatiotemporal resolution, which will impact the training of machine learning models since storing a high amount of simulation data on disk is nearly impossible. Therefore, we need to rethink the training of machine learning models for simulatio

Krist Shingjergji, Deniz Iren, Felix Bottger, Corrie Urlings

Training facial emotion recognition models requires large sets of data and costly annotation processes. To alleviate this problem, we developed a gamified method of acquiring annotated facial emotion data without an explicit labeling effort by humans. The game, which we named Facegame, challenges the players to imitate a displayed image of a face that portra

Youngki Kwon, Hee-Soo Heo, Bong-Jin Lee, You Jin Kim

Our focus lies in developing an online speaker diarisation framework which demonstrates robust performance across diverse domains. In online speaker diarisation, outputs generated in real-time are irreversible, and a few misjudgements in the early phase of an input session can lead to catastrophic results. We hypothesise that cautiously increasing the number

Sophie Prendergast, Cadence Payne, Miles Lifson, Christian Haughwout

AEROS is a 3U CubeSat pathfinder toward a future ocean-observing constellation, targeting the Portuguese Atlantic region. AEROS features a miniaturized, high-resolution Hyperspectral Imager (HSI), a 5MP RGB camera, and a Software Defined Radio (SDR). The sensor generated data will be processed and aggregated for end-users in a new web-based Data Analysis Cen

Ning Li, Yuxuan Li, Jichao jiao

In recent years, due to the wide application of multi-sensor vision systems, multimodal image acquisition technology has continued to develop, and the registration problem based on multimodal images has gradually emerged. Most of the existing multimodal image registration methods are only suitable for two modalities, and cannot uniformly register multiple mo

Kyle Acheson, Adam Kirrander

An inversion method for time-resolved data from ultrafast experiments is introduced, based on forward-optimisation in a trajectory basis. The method is applied to experimental data from x-ray scattering of the photochemical ring-opening reaction of 1,3-cyclohexadiene and electron diffraction of the photodissociation of CS2. In each case, inversion yields a m

Hannah Sanderson, Moira Jardine, Andrew Collier Cameron, Julien Morin

One of the puzzles to have emerged from the Kepler and TESS missions is the existence of unexplained dips in the lightcurves of a small fraction of rapidly-rotating M dwarfs in young open clusters and star-forming regions. We present a theoretical investigation of one possible explanation - that these are caused by dust clouds trapped in the stellar magnetic

Sixing Yu, J. Pablo Muñoz, Ali Jannesari

Federated Learning (FL) is extensively used to train AI/ML models in distributed and privacy-preserving settings. Participant edge devices in FL systems typically contain non-independent and identically distributed (Non-IID) private data and unevenly distributed computational resources. Preserving user data privacy while optimizing AI/ML models in a heteroge

Shradha Verma, Tripti Goel, M Tanveer

In the human brain, essential iron molecules for proper neurological functioning exist in transferrin (tf) and ferritin (Fe3) forms. However, its unusual increment manifests iron overload, which reacts with hydrogen peroxide. This reaction will generate hydroxyl radicals, and irons higher oxidation states. Further, this reaction causes tissue damage or cogni

Alexander Küchler, Christian Banse

In the past years, a number of static application security testing tools have been proposed which make use of so-called code property graphs, a graph model which keeps rich information about the source code while enabling its user to write language-agnostic analyses. However, they suffer from several shortcomings. They work mostly on source code and exclude

Masahiro Kubo, Shunsuke Tsuda

This paper investigates the origins of Islamist insurgencies as a form of cultural revival in West Africa. Exploiting variation in access to ancient water sources, which have largely disappeared, as an instrument, we show that the decline of trans-Saharan cities -- once-prosperous under pre-colonial Islamic states -- led to contemporary hotspots of Islamist

Jan Butora, Patrick Bas

Side-informed steganography has always been among the most secure approaches in the field. However, a majority of existing methods for JPEG images use the side information, here the rounding error, in a heuristic way. For the first time, we show that the usefulness of the rounding error comes from its covariance with the embedding changes. Unfortunately, thi

Kerstin Awiszus, Yannick Bell, Jan Lüttringhaus, Gregor Svindland

Based on classical contagion models we introduce an artificial cyber lab: the digital twin of a complex cyber system in which possible cyber resilience measures may be implemented and tested. Using the lab, in numerical case studies, we identify two classes of measures to control systemic cyber risks: security- and topology-based interventions. We discuss th

Phung Van Dong, Duong Van Loi, Do Thi Huong

We argue that if an electroweak Higgs field possesses a dark gauge charge responsible for dark matter stability, the $W$-boson mass deviation is properly induced, besides appropriately generated neutrino masses. We examine a simple model in which the usual Higgs doublet plays the role but dark matter candidates are somewhat input by ad hoc. We look for a rea

Giuseppe Buttazzo

Starting from the Brock's construction of Continuous Steiner Symmetrization of sets, the problem of modifying continuously a given domain up to obtain a ball, preserving its measure and with decreasing first eigenvalue of the Laplace operator, is considered. For a large class of cases it is shown this is possible, while the general question remains still ope

Junzhe Jiang, Mingyue Cheng, Qi Liu, Zhi Li

Recognizing useful named entities plays a vital role in medical information processing, which helps drive the development of medical area research. Deep learning methods have achieved good results in medical named entity recognition (NER). However, we find that existing methods face great challenges when dealing with the nested named entities. In this work,

Jie Han, Donglei Yang

Given integers $n\ge \Delta\ge 2$, let $\mathcal{T}(n, \Delta)$ be the collection of all $n$-vertex trees with maximum degree at most $\Delta$. A question of Alon, Krivelevich and Sudakov in 2007 asks for determining the best possible spectral gap condition forcing an $(n, d,\lambda)$-graph to be $\mathcal{T}(n, \Delta)$-universal, namely, it contains all me

Jeffrey Galkowski

We prove lower bounds on the error incurred when approximating any oscillating function using piecewise polynomial spaces. The estimates are explicit in the polynomial degree and have optimal dependence on the meshwidth and frequency when the polynomial degree is fixed. These lower bounds, for example, apply when approximating solutions to Helmholtz plane wa

Eike-Manuel Bansbach, Alexander von Bank, Laurent Schmalen

In the past years, artificial neural networks (ANNs) have become the de-facto standard to solve tasks in communications engineering that are difficult to solve with traditional methods. In parallel, the artificial intelligence community drives its research to biology-inspired, brain-like spiking neural networks (SNNs), which promise extremely energy-efficien

Hyun-Woo Jo, Alkiviadis Koukos, Vasileios Sitokonstantinou, Woo-Kyun Lee

The continuous increase in global population and the impact of climate change on crop production are expected to affect the food sector significantly. In this context, there is need for timely, large-scale and precise mapping of crops for evidence-based decision making. A key enabler towards this direction are new satellite missions that freely offer big rem

Eyal Rozenberg, Daniel Freedman

We propose an algorithm for learning a conditional generative model of a molecule given a target. Specifically, given a receptor molecule that one wishes to bind to, the conditional model generates candidate ligand molecules that may bind to it. The distribution should be invariant to rigid body transformations that act $\textit{jointly}$ on the ligand and t

Gyumin Shim, Minsoo Lee, Jaegul Choo

Single-image 3D human reconstruction aims to reconstruct the 3D textured surface of the human body given a single image. While implicit function-based methods recently achieved reasonable reconstruction performance, they still bear limitations showing degraded quality in both surface geometry and texture from an unobserved view. In response, to generate a re

Niko Hauzenberger, Florian Huber, Karin Klieber, Massimiliano Marcellino

Macroeconomic data is characterized by a limited number of observations (small T), many time series (big K) but also by featuring temporal dependence. Neural networks, by contrast, are designed for datasets with millions of observations and covariates. In this paper, we develop Bayesian neural networks (BNNs) that are well-suited for handling datasets common

Abhilasha Saini, N. K. Karn, Kapil Kumar, R. P. Aloysius

TiSe2 is a known Topological semimetal (TSM) having both the semi-metallic and topological characters simultaneously and the Charge density wave (CDW) at below 200K. In the current short article, we study the impact of Pd addition on the CDW character of TiSe2 and the possible induction of superconductivity at low temperatures. Bulk samples of TiSe2 and Pd0.

Jan Butora, Pauline Puteaux, Patrick Bas

Robust steganography is a technique of hiding secret messages in images so that the message can be recovered after additional image processing. One of the most popular processing operations is JPEG recompression. Unfortunately, most of today's steganographic methods addressing this issue only provide a probabilistic guarantee of recovering the secret and are

Kağan Kurşungöz, Mohammad Zadehdabbagh

Sang, Shi and Yee, in 2020, found overpartition analogs of Andrews' results involving parity in Rogers-Ramanujan-Gordon identities. Their result partially answered an open question of Andrews'. The open question was to involve parity in overpartition identities. We extend Sang, Shi, and Yee's work to arbitrary moduli, and also provide a missing case in their

Christian Halter, Alexander Miethke, Christian Sillus, Apoorva Hegde

We present an experimental study of the Rydberg $^1S_0$- and $^1P_1$- series of ytterbium for principal quantum numbers in the range of $n=70$ to $90$. The study is performed using trap loss spectroscopy in a magneto-optical trap operating on the $6^1S_0 \rightarrow 6^1P_1$ transition at $399\,\text{nm}$. Compared to the commonly used Rydberg spectroscopy me

Roberto Ulloa, Mykola Makhortykh, Aleksandra Urman, Juhi Kulshrestha

The 2020 US elections news coverage was extensive, with new pieces of information generated rapidly. This evolving scenario presented an opportunity to study the performance of search engines in a context in which they had to quickly process information as it was published. We analyze novelty, a measurement of new items that emerge in the top news search res

Ioannis Rizos, Nikolaos Gkrekas

In this paper we discuss, from a historical and philosophical point of view, a variation of the meaning of the five postulates in Euclidean Geometry and we make a short reference to D. Hilberts formalism. We examine, throughout the ages, the question what is Geometry by studying the segmentation and the unification of various geometries, been introduced by F

Luca Schaller

The request for high-quality solutions continually grows in a world where more and more tasks are executed through computers. This also counts for fields such as engineering, computer graphics, etc., which use meshes to solve their problems. A mesh is a combination of some elementary elements, for which hexahedral elements are a good choice thanks to their s

Michael Peper, Martin Trautmann, Johannes Deiglmayr

We present a new mechanism contributing to the detection of photoassociated long-range Rydberg molecules via pulsed-field ionization: ionic products, created by the decay of a long-range Rydberg molecule, modify the excitation spectrum of surrounding ground-state atoms and facilitate the excitation of further atoms into Rydberg states by the photoassociation

Pietro Dona

Local flatness is a property shared by all the spin foam models. It ensures that the theory's fundamental building blocks are flat by requiring locally trivial parallel transport. In the context of simplicial Lorentzian spin foam theory, we show that local flatness is the main responsible for the emergence of geometry independently of the details of the spin

Alessio Mora, Irene Tenison, Paolo Bellavista, Irina Rish

Federated Learning (FL) enables the training of Deep Learning models without centrally collecting possibly sensitive raw data. The most used algorithms for FL are parameter-averaging based schemes (e.g., Federated Averaging) that, however, have well known limits, i.e., model homogeneity, high communication cost, poor performance in presence of heterogeneous

Mohit Bhasi Thazhath, Jan Michalak, Thang Hoang

The audit log is a crucial component to monitor fine-grained operations over sensitive data (e.g., personal, health) for security inspection and assurance. Since such data operations can be highly sensitive, it is vital to ensure that the audit log achieves not only validity and immutability, but also confidentiality against active threats to standard data r

B. Acar, G. Adamov, C. Adloff, S. Afanasiev

The upgrade of the CMS experiment for the high luminosity operation of the LHC comprises the replacement of the current endcap calorimeter by a high granularity sampling calorimeter (HGCAL). The electromagnetic section of the HGCAL is based on silicon sensors interspersed between lead and copper (or copper tungsten) absorbers. The hadronic section uses layer

Jin-Yi Zhang, Chang-Rui Yi, Long Zhang, Rui-Heng Jiao

The Floquet engineering opens the way to create new topological states without counterparts in static systems. Here, we report the experimental realization and characterization of new anomalous topological states with high-precision Floquet engineering for ultracold atoms trapped in a shaking optical Raman lattice. The Floquet band topology is manipulated by

Guoliang Zhang, Hongqiang Zhu, Tao Xiong

In this paper, uniformly unconditionally stable first and second order finite difference schemes are developed for kinetic transport equations in the diffusive scaling. We first derive an approximate evolution equation for the macroscopic density, from the formal solution of the distribution function, which is then discretized by following characteristics fo

Behnam Pishnamazi, Ehsan Koushki

As the advancements in the field of artificial intelligence and nonlinear optics continues new methods can be used to better describe and determine nonlinear optical phenomena. In this research we aimed to analyze the diffraction patterns of an organic material and determine the nonlinear refraction index of the material in question by utilizing ResNet 152 c

Yury Panov

The properties of the ground state of the simplest frustrated system, the dilute Ising chain in a magnetic field, are rigorously investigated over the entire range of concentrations of charged non-magnetic impurities. Analytical methods are proposed for calculating the residual entropy of frustrated states, including states at phase boundaries, which are bas

Min Li

By constructing a series of perturbation functions through localization in the Fourier domain and translation, we show that the data-to-solution map for the Euler-Poincar\'e equations is nowhere uniformly continuous in $B^s_{p,r}(\mathbb{R} ^d)$ with $s>\max\{1+\frac d2,\frac32\}$ and $(p,r)\in (1,\infty)\times [1,\infty)$. This improves our previous result

Pujian Mao, Weicheng Zhao

In this paper, we revisit the null boundary gravitational charge in the Newman-Penrose formalism with special emphasis on the charges from local Lorentz transformations. We find that there is one more charge derived from the local Lorentz transformation and the new charge is purely from the Holst term. This reveals a remarkable fact that trivial terms which

Balduzzi Giuseppe, Bonizzoni Francesca, Tamellini Lorenzo

When dealing with timber structures, the characteristic strength and stiffness of the material are made highly variable and uncertain by the unavoidable, yet hardly predictable, presence of knots and other defects. In this work we apply the sparse grids stochastic collocation method to perform uncertainty quantification for structural engineering in the scen

Hang Yi, Tongxuan Bie, Tongjiang Yan

As the Internet grows in popularity, more and more classification jobs, such as IoT, finance industry and healthcare field, rely on mobile edge computing to advance machine learning. In the medical industry, however, good diagnostic accuracy necessitates the combination of large amounts of labeled data to train the model, which is difficult and expensive to

Nitsan Soffair

WQMIX, QMIX, QTRAN, and VDN are SOTA algorithms for Dec-POMDP. All of them cannot solve complex agents' cooperation domains. We give an algorithm to solve such problems. In the first stage, we solve a single-agent problem and get a policy. In the second stage, we solve the multi-agent problem with the single-agent policy. SA2MA has a clear advantage over all

M. A. Masharin, V. A. Shahnazaryan, I. V. Iorsh, S. V. Makarov

Systems supporting exciton-polaritons represent solid-state optical platforms with a strong built-in optical nonlinearity provided by exciton-exciton interactions. In conventional semiconductors with hydrogen-like excitons the nonlinearity rate demonstrates the inverse scaling with the binding energy. This makes excitons stable at room temperatures weakly in

Paul Jungeblut, Laura Merker, Torsten Ueckerdt

The stack number of a directed acyclic graph $G$ is the minimum $k$ for which there is a topological ordering of $G$ and a $k$-coloring of the edges such that no two edges of the same color cross, i.e., have alternating endpoints along the topological ordering. We prove that the stack number of directed acyclic outerplanar graphs is bounded by a constant, wh

Graham Cox, Mitchell Curran, Yuri Latushkin, Robert Marangell

We use the Maslov index to study the spectrum of a class of linear Hamiltonian differential operators. We provide a lower bound on the number of positive real eigenvalues, which includes a contribution to the Maslov index from a non-regular crossing. A close study of the eigenvalue curves, which represent the evolution of the eigenvalues as the domain is shr

Hirotaka Ijima, Carolina Minte-Vera, Yi-Jay Chang, Daisuke Ochi

Catch-and-effort data are among the primary sources of information for assessing the status of terrestrial wildlife and fish. In fishery science, elaborate stock-assessment models are fitted to such data in order to estimate fish-population sizes and guide management decisions. Given the importance of catch-and-effort data, we scoured a comprehensive dataset

Paul Kabaila

The theory and computational methods for custom-made Gauss quadrature have been described in Gautschi's 2004 monograph. Gautschi has also provided Fortran and MATLAB code for the implementation and illustration of these methods. We have written an R package, implemented in the high-precision arithmetic provided by the R package Rmpfr, that uses a moment-base

Kevin C. Miao, Matt McEwen, Juan Atalaya, Dvir Kafri

Leakage of quantum information out of computational states into higher energy states represents a major challenge in the pursuit of quantum error correction (QEC). In a QEC circuit, leakage builds over time and spreads through multi-qubit interactions. This leads to correlated errors that degrade the exponential suppression of logical error with scale, chall

T Curtright, H Alshal

We consider some elementary features of Newtonian gravity, or electrostatics, as defined on an N-sphere. In particular, we present and discuss "the shell theorem" for this system.

Fan Xu, Yong-Feng Huang, Jin-Jun Geng, Xue-Feng Wu

Empirical correlations between various key parameters have been extensively explored ever since the discovery of gamma-ray bursts (GRBs) and have been widely used as standard candles to probe the Universe. The Amati relation and the Yonetoku relation are two good examples that enjoyed special attention. The former reflects the connection between the peak pho

Hui Hu, Jia Wang, Riley Lalor, Xia-Ji Liu

We present a microscopic many-body calculation of the nonlinear two-dimensional coherent spectroscopy (2DCS) of trion-polaritons and exciton-polaritons in charge-tunable transition-metal-dichalcogenides monolayers placed in an optical microcavity. The charge tunability leads to an electron gas with nonzero density that brings brightness to the trion - a pola

Yanmei Shi, QiZhang

This paper infers a single parameter in non-sparse logistic regression models. By transforming the null hypothesis into a moment condition, we construct the test statistic and obtain the asymptotic null distribution. Numerical experiments show that our method performs well.

Atsuhide Ishida, Masaki Kawamoto

Sub-quadratic repulsive potentials accelerate quantum particles and can relax the decay rate in the $x$ of the external potentials $V$ that guarantee the existence of the quantum wave operators. In the case where the sub-quadratic potential is $- |x|^{\alpha} $ with $0< \alpha < 2$ and the external potential satisfies $|V(x) | \leq C (1+|x|) ^{-(1- \alpha /2

Mikhail Chebunin, Artyom Kovalevskii

We are developing a new method for the analysis of queuing systems with heterogeneous in time and space compound (marked) Poisson input flow. The state space of the input flow is embedded in a higher-dimensional space with a homogeneous marked Poisson field on it. We prove limit theorems for partial sums of marks under the ordering of field points by coordin

Utkarsh Patel, Lekhika Malhotra, Sudhanwa Patra, Urjit A. Yajnik

Recently there is a renewed interest in exploring the Dark sector of the universe in a more constrained way. Particularly in [1], the FIMP ( Feebly Interacting Massive Particle) scenario was shown to be realized with a minimal extension of the SM with three sterile neutrinos in the spirit of {\nu}MSM. In this paper, we show that without invoking any addition

Mikhail Chebunin, Artyom Kovalevskii

We study the joint asymptotics of forward and backward processes of numbers of non-empty urns in an infinite urn scheme. The probabilities of balls hitting the urns are assumed to satisfy the conditions of regular decrease. We prove weak convergence to a two-dimensional Gaussian process. Its covariance function depends only on exponent of regular decrease of

Chunyu Jia, Zhaoxin Liang

Exploring the dynamics of a mobile impurity immersed in the field excitations is challenging, as it requires to account for the entanglement between the impurity and the surrounding excitations. To this end, the impurity's effective mass has to be considered as finite, rather than infinite. Here, we theoretically investigate the interaction between a finite-

Sukanta Bhattacharjee, Ansuman Banerjee, Krishnendu Chakrabarty, Bhargab B. Bhattacharya

Recent advances in digital microfluidic (DMF) technologies offer a promising platform for a wide variety of biochemical applications, such as DNA analysis, automated drug discovery, and toxicity monitoring. For on-chip implementation of complex bioassays, automated synthesis tools have been developed to meet the design challenges. Currently, the synthesis to

Christopher J. Holder, Muhammad Shafique

Global localisation from visual data is a challenging problem applicable to many robotics domains. Prior works have shown that neural networks can be trained to map images of an environment to absolute camera pose within that environment, learning an implicit neural mapping in the process. In this work we evaluate the applicability of such an approach to rea

Yu Chen, Wen Ding, Junjie Lai

Noisy Student Training (NST) has recently demonstrated extremely strong performance in Automatic Speech Recognition(ASR). In this paper, we propose a data selection strategy named LM Filter to improve the performance of NST on non-target domain data in ASR tasks. Hypotheses with and without a Language Model are generated and the CER differences between them

J. Z. Kamiński, K. Krajewska

General formulation in the realm of strong-field quantum electrodynamics is provided for a process that occurs in the presence of a train of laser pulses and, in the tree level, is represented by a two-vertex Feynman diagram with exchange of a virtual photon. A scheme of retrieving resonances in the corresponding probability distributions is also formulated

Siddharth Sagar Nijhawan, Leo Hoshikawa, Atsushi Irie, Masakazu Yoshimura

We propose a light-weight and highly efficient Joint Detection and Tracking pipeline for the task of Multi-Object Tracking using a fully-transformer architecture. It is a modified version of TransTrack, which overcomes the computational bottleneck associated with its design, and at the same time, achieves state-of-the-art MOTA score of 73.20%. The model desi

Paul Denny, Sami Sarsa, Arto Hellas, Juho Leinonen

In this article, we introduce and evaluate the concept of robosourcing for creating educational content. Robosourcing lies in the intersection of crowdsourcing and large language models, where instead of a crowd of humans, requests to large language models replace some of the work traditionally performed by the crowd. Robosourcing includes a human-in-the-loo

Mingyang Chen, Yuguo Chen, Yanyan Zhu, Ying Jiang

We investigate the effects of chain flexibility on the self-assembly behavior of symmetric diblock copolymers (BCPs) when they are confined as a thin film between two surfaces. Employing worm-like chain (WLC) self-consistent field theory, we study the relative stability of parallel (L$_{\parallel}$) and perpendicular (L$_{\perp}$) orientations of BCP lamella