December 2020 arXiv papers — page 35

David Senjaya, Alejandro Saiz Rivera

The gravitational field is usually neglected in the calculation of atomic energy levels as its effect is much weaker than the electromagnetic field, but that is not the case for a particle orbiting a black hole. In this work, the canonical quantization of a massive and massless particles under gravitational field exerted by this tiny but very massive object,

Kush Kinra, Manil T. Mohan

This work is concerned about the asymptotic behavior of the solutions of the two and three dimensional stochastic convective Brinkman-Forchheimer (SCBF) equations driven by white noise with nonlinear diffusion terms. We prove the existence and uniqueness of weak pullback mean random attractors for the 2D SCBF equations (for $r\geq1$) as well as 3D SCBF equat

Nirabhra Mandal, Pavankumar Tallapragada

In this second part of our work, we study the steady state of the population and the social utility for the three dynamics SSD, NBRD and NRPM; which were introduced in the first part. We provide sufficient conditions on the network based on a maximum payoff density parameter of each node under which there exists a unique Nash equilibrium. We then utilize pos

Jens Van den Broeck, Bart Coppens, Bjorn De Sutter

To counter software reverse engineering or tampering, software obfuscation tools can be used. However, such tools to a large degree hard-code how the obfuscations are deployed. They hence lack resilience and stealth in the face of many attacks. To counter this problem, we propose the novel concept of flexible obfuscators, which implement protections in terms

Yan Liu, Xin-Meng Wu

We study an improved holographic model for the strongly coupled nodal line semimetal which satisfies the duality relation between the rank two tensor operators $\bar{\psi}\gamma^{\mu \nu}\psi$ and $\bar{\psi}\gamma^{\mu \nu}\gamma^5\psi$. We introduce a Chern-Simons term and a mass term in the bulk for a complex two form field which is dual to the above tens

Min Li, Zuojia Wang, Wenyan Yin, Erping Li

Metasurfaces based on subwavelength resonators enable novel ways to manipulate the flow of light at optical interfaces. In pursuit of multifunctional or reconfigurable metadevices, efficient tuning of macroscopic performance with little structural/material variation remains a challenge. Here, we put forward the concept of non-Hermitian doping in metasurfaces

David Jacob Kedziora, Katarzyna Musial, Bogdan Gabrys

Over the last decade, the long-running endeavour to automate high-level processes in machine learning (ML) has risen to mainstream prominence, stimulated by advances in optimisation techniques and their impact on selecting ML models/algorithms. Central to this drive is the appeal of engineering a computational system that both discovers and deploys high-perf

Nirabhra Mandal, Pavankumar Tallapragada

In this work, we consider a population composed of a continuum of agents that seek to maximize a payoff function by moving on a network. The nodes in the network may represent physical locations or abstract choices. The population is stratified and hence agents opting for the same choice may not get the same payoff. In particular, we assume payoff functions

Mariana Mar Lucas, Tiago Ramos, Peter S. Jørgensen, Stela Canulescu

We demonstrate a non-destructive approach to provide structural properties on the grain level for the absorber layer of kesterite solar cells. Kesterite solar cells are notoriously difficult to characterize structurally due to the co-existence of several phases with very similar lattice parameters. Specifically, we present a comprehensive study of 597 grains

Alexander Yu. Zyuzin

We consider the effect of strong electron-electron attraction on superconductivity in the dilute system of the negative U-centers. They couple to the conducting electronic states and mediate attraction between electrons. We predict the formation of the local Cooper pairs provided attraction exceeds a certain threshold value. The global coherent superconducti

Sajilesh K. P., R. P. Singh

We have investigated the ternary noncentrosymmetric superconductors TaXSi (X=Re, Ru) by magnetization, resistivity, and specific heat measurements. The samples crystallize in orthorhombic TiFeSi structure having superconducting transition Tc = 5.32 K and 3.91 K, for TaReSi and TaRuSi respectively. Specific heat measurements indicated an s-wave nature of both

Zakariae El Youbi, Sung Won Jung, Christine Richter, Karol Hricovini

We investigate the electronic structure of the 2H and 3R polytypes of NbS$_2$. The Fermi surfaces measured by angle-resolved photoemission spectroscopy show a remarkable difference in size, reflecting a significantly increased band filling in 3R-Nb$_{1+x}$S$_2$ compared to 2H-NbS$_2$, which we attribute to the presence of additional interstitial Nb which act

Faisal Abu-Khzam, Sebastian Lamm, Matthias Mnich, Alexander Noe

Over the last two decades, significant advances have been made in the design and analysis of fixed-parameter algorithms for a wide variety of graph-theoretic problems. This has resulted in an algorithmic toolbox that is by now well-established. However, these theoretical algorithmic ideas have received very little attention from the practical perspective. We

Zaid Amin, Nazlena Mohamad Ali, Alan F. Smeaton

The behaviour of sharing information on social media should be fulfilled only when a user is exhibiting attentive behaviour. So that the useful information can be consumed constructively, and misinformation can be identified and ignored. Attentive behaviour is related to users' cognitive abilities in their processing of set information. The work described in

Edouard B. Sonin

The paper develops the theory of the ballistic SNS sandwich, in which the Josephson effect exists without the proximity effect. The theory takes into account restrictions imposed by the charge conservation law and the incommensurability of the superconducting gap with the Andreev level energy spacing. This resulted in revisions of some conclusions of previou

Manish Gawali, Arvind C S, Shriya Suryavanshi, Harshit Madaan

In this paper, we compare three privacy-preserving distributed learning techniques: federated learning, split learning, and SplitFed. We use these techniques to develop binary classification models for detecting tuberculosis from chest X-rays and compare them in terms of classification performance, communication and computational costs, and training time. We

José Pereira dos Reis, Fernando Brito e Abreu, Glauco de Figueiredo Carneiro

Code smells are seen as major source of technical debt and, as such, should be detected and removed. However, researchers argue that the subjectiveness of the code smells detection process is a major hindrance to mitigate the problem of smells-infected code. We proposed the crowdsmelling approach based on supervised machine learning techniques, where the wis

Yangyang Liu, Yuan Sun, Zhuo Fu, Peng Xu

For neutral atom qubits, the two-qubit gate is typically realized via the Rydberg blockade effect, which hints about the special status of the Rydberg level besides the regular qubit register states. Here, we carry out experimental and theoretical studies to reveal how the ground-Rydberg coherence of the control qubit atom affects the process of two-qubit Co

Jean-Guy Mailly, Julien Rossit

The notion of stability in a structured argumentation setup characterizes situations where the acceptance status associated with a given literal will not be impacted by any future evolution of this setup. In this paper, we abstract away from the logical structure of arguments, and we transpose this notion of stability to the context of Dungean argumentation

Oguz Savk

A central problem in low-dimensional topology asks which homology $3$-spheres bound contractible $4$-manifolds or homology $4$-balls. In this paper, we address this question for plumbed $3$-manifolds and we present two new infinite families. We consider most of the classical examples from around the nineteen eighties by reproving that they all bound Mazur ma

Oscar Guerrero-Rosado, Paul Verschure

In the past decade, society has experienced notable growth in a variety of technological areas. However, the Fourth Industrial Revolution has not been embraced yet. Industry 4.0 imposes several challenges which include the necessity of new architectural models to tackle the uncertainty that open environments represent to cyber-physical systems (CPS). Waste E

Anna-Karin Tornberg

A non-local slender body approximation for slender flexible fibers in Stokes flow can be derived, yielding an integral equation along the center lines of the fibers that involves a slenderness parameter. The formulation contains a so-called finite part singular integral, and can in the case of several fibers or evaluation of the flow field require the evalua

Lukas Franken, Bogdan Georgiev, Sascha Mücke, Moritz Wolter

Variational quantum circuits build the foundation for various classes of quantum algorithms. In a nutshell, the weights of a parametrized quantum circuit are varied until the empirical sampling distribution of the circuit is sufficiently close to a desired outcome. Numerical first-order methods are applied frequently to fit the parameters of the circuit, but

Zhi-Cheng Ren, Zi-Mo Cheng, Xi-Lin Wang, Jianping Ding

Optical vortex and vector field are two important types of structured optical fields. Due to their wide applications and unique features in many scientific realms, the generation, manipulation and measurement of such fields have attracted significant interest and become very important topics. However, most ways to generate vector fields have a trade-off amon

Aurélien Defossez, Morteza Ansarinia, Brice Clocher, Emmanuel Schmück

For more than a century, scientists have been collecting behavioral data--an increasing fraction of which is now being publicly shared so other researchers can reuse them to replicate, integrate or extend past results. Although behavioral data is fundamental to many scientific fields, there is currently no widely adopted standard for formatting, naming, orga

Zhenjun Liu, Leroy Chew, Marijn Heule

Ramsey Theory deals with avoiding certain patterns. When constructing an instance that avoids one pattern, it is observed that other patterns emerge. For example, repetition emerges when avoiding arithmetic progression (Van der Waerden numbers), while reflection emerges when avoiding monochromatic solutions of $a+b=c$ (Schur numbers). We exploit observed pat

Wei Qiu, Yangsibo Huang, Quanzheng Li

Missing value imputation is a challenging and well-researched topic in data mining. In this paper, we propose IFGAN, a missing value imputation algorithm based on Feature-specific Generative Adversarial Networks (GAN). Our idea is intuitive yet effective: a feature-specific generator is trained to impute missing values, while a discriminator is expected to d

Charles M. Elliott, Luke Hatcher, Björn Stinner

We consider sharp interface asymptotics for a phase field model of two phase near spherical biomembranes involving a coupling between the local mean curvature and the local composition proposed by the first and second authors. The model is motivated by lipid raft formation. We introduce a reduced diffuse interface energy depending only on the membrane compos

A. Gallo Rosso

When the next galactic core-collapse supernova occurs, we must be ready to obtain as much information as possible. Although many present and future detectors are well equipped to detect $\overline{\nu}_{\mathrm{e}}$ and $\nu_x$ neutrinos, the detection of the $\nu_{\mathrm{e}}$ species presents the biggest challenges. We assess the impact that a 1 ktonne lea

Ali Baba Dauda, Mohammed Sani Adam, Muhammad Ahmad Mustapha, Audu Musa Mabu

Although serialization improves the transmission of data through utilization of bandwidth, but its impact at the communication systems is not fully accounted. This research used Simple Object Access Protocol (SOAP) Web services to exchange serialized and normal messages via Hypertext Transfer Protocol (HTTP) and Java Messaging System (JMS). We implemented tw

Shen-Song Wan, Marco Ruggieri

We study the thermodynamics of helical matter, namely quark matter in which a net helicity, $n_H$, is in equilibrium. Interactions are modeled by the renormalized quark-meson model with two flavors of quarks. Helical density is described within the grand-canonical ensemble formalism via a chemical potential, $\mu_H$. We study the transitions from the normal

Marvin Dippell, Felix Menke, Stefan Waldmann

Coisotropic algebras are used to formalize coisotropic reduction in Poisson geometry as well as in deformation quantization and find applications in various other fields as well. In this paper we prove a Serre-Swan Theorem relating the regular projective modules over the coisotropic algebra built out of a manifold $M$, a submanifold $C$ and an integrable smo

David Cimasoni, Adrien Kassel

Given a graph and a representation of its fundamental group, there is a naturally associated twisted adjacency operator. The main result of this article is the fact that these operators behave in a controlled way under graph covering maps. When such an operator can be used to enumerate objects, or compute a partition function, this has concrete implications

Martin Donati, Dragos Iftimie

In this paper we consider the incompressible Euler equation in a simply-connected bounded planar domain. We study the confinement of the vorticity around a stationary point vortex. We show that the power law confinement around the center of the unit disk obtained in [2] remains true in the case of a stationary point vortex in a simply-connected bounded domai

Yue Guo, Wei Qiu, Yizhong Wang, Trevor Cohen

Health literacy has emerged as a crucial factor in making appropriate health decisions and ensuring treatment outcomes. However, medical jargon and the complex structure of professional language in this domain make health information especially hard to interpret. Thus, there is an urgent unmet need for automated methods to enhance the accessibility of the bi

Sewook Oh, Sanghyuk Lee

In this paper, we consider the uniform estimate for the oscillatory integral with stationary phase, which was previously studied by Alazard-Burq-Zuily. We significantly reduce the order of required regularity condition on the phase and amplitude functions for the uniform estimate. We also study estimates for the oscillatory integrals of which phase and ampli

Sakurako Tanida

Elevators can be regarded as oscillators driven by the calls of passengers who arrive randomly. We study the dynamic behavior of elevators during the down peak period numerically and analytically. We assume that new passengers arrive at each floor according to a Poisson process and call the elevators to go down to the ground floor. We numerically examine how

Guodong Zeng, Till D. Lerch, Florian Schmaranzer, Guoyan Zheng

Unsupervised domain adaptation (UDA) for cross-modality medical image segmentation has shown great progress by domain-invariant feature learning or image appearance translation. Adapted feature learning usually cannot detect domain shifts at the pixel level and is not able to achieve good results in dense semantic segmentation tasks. Image appearance transla

V. L. Katkov, V. A. Osipov

We have studied the influence of the transverse size of a magnetic tunnel nanojunction on the magnitude of the magnetoresistance. During modeling, the size of the right contact was fixed, while the size of the left one gradually changed until they coincided. We found a sharp drop in the tunneling magnetoresistance (TMR) in nanocontacts with mismatched cross

Joshua Bardwell, Dominic Searles

We construct modules of the $0$-Hecke algebra whose images under the quasisymmetric characteristic map are the Young row-strict quasisymmetric Schur functions. This provides a representation-theoretic interpretation of this basis of quasisymmetric functions, answering a question of Mason and Niese (2015). Additionally, we classify when these modules are inde

Lena Leitenmaier, Olof Runborg

In this paper, we consider homogenization of the Landau-Lifshitz equation with a highly oscillatory material coefficient with period $\varepsilon$ modeling a ferromagnetic composite. We derive equations for the homogenized solution to the problem and the corresponding correctors and obtain estimates for the difference between the exact and homogenized soluti

Palash B. Pal

The equations of electromagnetic fields in a medium is usually written in the rest frame of the medium. We outline a method of generalizing the discussion to arbitrary inertial frames. In the discussion, we also include the possibility that the medium is optically active, a possibility that is often overlooked in discussions of electromagnetic fields in a me

Sajad Aghapour, Lars Andersson, Kjell Rosquist

In this paper, a novel conserved Lorentz covariant tensor, termed the helicity tensor, is introduced in Maxwell theory. The conservation of the helicity tensor expresses the conservation laws contained in the helicity array, introduced by Cameron et al., including helicity, spin, and the spin-flux or infra-zilch. The Lorentz covariance of the helicity tensor

Oleg Aristov

We prove that every non-degenerate Banach space representation of the Drinfeld-Jimbo algebra $U_q(\mathfrak{g})$ of a semisimple complex Lie algebra $\mathfrak{g}$ is finite dimensional when $|q|\ne 1$. As a corollary, we find an explicit form of the Arens-Michael envelope of $U_q(\mathfrak{g})$, which is similar to that of $U(\mathfrak{g})$ obtained by Jose

Takehiko Asaka, Hiroyuki Ishida, Kazuki Tanaka

We present a possibility that the neutrinoless double beta decay can be hidden in the minimal seesaw mechanism where the standard model is extended by two right-handed neutrinos which have a hierarchical mass structure. In this framework, the lepton number is violated due to the massive Majorana neutrinos. Especially, we investigate the case that the heavier

Jan Moritz Joseph, Ananda Samajdar, Lingjun Zhu, Rainer Leupers

The everlasting demand for higher computing power for deep neural networks (DNNs) drives the development of parallel computing architectures. 3D integration, in which chips are integrated and connected vertically, can further increase performance because it introduces another level of spatial parallelism. Therefore, we analyze dataflows, performance, area, p

Tobias Lehmann, Max-K. von Renesse, Alexander Sambale, André Uschmajew

We derive an a priori parameter range for overrelaxation of the Sinkhorn algorithm, which guarantees global convergence and a strictly faster asymptotic local convergence. Guided by the spectral analysis of the linearized problem we pursue a zero cost procedure to choose a near optimal relaxation parameter.

Jiulou Zhang, Yuxia Tang, Shouju Wang

Intratumoral nanoparticles (NPs) distribution is critical for the success of nanomedicine in imaging and treatment, but computational models to describe the NPs distribution remain unavailable due to the complex tumor-nano interactions. Here, we develop a Generative Adversarial Network for Distribution Analysis (GANDA) to describe and conditionally generates

István Groma, Péter Dusán Ispánovity, Thomas Hochrainer

To develop a dislocation-based statistical continuum theory of crystal plasticity is a major challenge of materials science.During the last two decades such a theory has been developed for the time evolution of a system of parallel edge dislocations. The evolution equations were derived by a systematic coarse-graining of the equations of motion of the indivi

Roberto Alicandro, Andrea Braides, Marco Cicalese, Lucia De Luca

We describe the emergence of topological singularities in periodic media within the Ginzburg-Landau model and the core-radius approach. The energy functionals of both models are denoted by $E_{\varepsilon,\delta}$, where $\varepsilon$ represent the coherence length (in the Ginzburg-Landau model) or the core-radius size (in the core-radius approach) and $\del

Jin Liu, Jianqin Yin

Human motion prediction is an essential part for human-robot collaboration. Unlike most of the existing methods mainly focusing on improving the effectiveness of spatiotemporal modeling for accurate prediction, we take effectiveness and efficiency into consideration, aiming at the prediction quality, computational efficiency and the lightweight of the model.

Stefan Zitz, Andrea Scagliarini, Jens Harting

We study numerically the effect of thermal fluctuations and of variable fluid-substrate interactions on the spontaneous dewetting of thin liquid films. To this aim, we use a recently developed lattice Boltzmann method for thin liquid film flows, equipped with a properly devised stochastic term. While it is known that thermal fluctuations yield shorter ruptur

Kai Han, Yunhe Wang, Hanting Chen, Xinghao Chen

Transformer, first applied to the field of natural language processing, is a type of deep neural network mainly based on the self-attention mechanism. Thanks to its strong representation capabilities, researchers are looking at ways to apply transformer to computer vision tasks. In a variety of visual benchmarks, transformer-based models perform similar to o

Zhen Zhang, Dave Cliff

Financial markets populated by human traders often exhibit "market impact", where the traders' quote-prices move in the direction of anticipated change, before any transaction has taken place, as an immediate reaction to the arrival of a large (i.e., "block") buy or sell order in the market: e.g., traders in the market know that a block buy order will push t

Feng Tian, Jian Zou, Lei Li, Hai Li

In this paper we consider a bipartite system composed of two subsystems each coupled to its own thermal environment. Based on a collision model, we mainly study whether the approximation (i.e., the inter-system interaction is ignored when modeling the system-environment coupling) is valid or not. We also address the problem of heat transport unitedly for bot

Vinod Nair, Sergey Bartunov, Felix Gimeno, Ingrid von Glehn

Mixed Integer Programming (MIP) solvers rely on an array of sophisticated heuristics developed with decades of research to solve large-scale MIP instances encountered in practice. Machine learning offers to automatically construct better heuristics from data by exploiting shared structure among instances in the data. This paper applies learning to the two ke

A. Kuznetsova, A. Talati, Y. Luo, K. Simmons

We introduce a unified framework for generic video annotation with bounding boxes. Video annotation is a longstanding problem, as it is a tedious and time-consuming process. We tackle two important challenges of video annotation: (1) automatic temporal interpolation and extrapolation of bounding boxes provided by a human annotator on a subset of all frames,

T. Aumann, C. Barbieri, D. Bazin, C. A. Bertulani

In this review article we discuss the present status of direct nuclear reactions and the nuclear structure aspects one can study with them. We discuss the spectroscopic information we can assess in experiments involving transfer reactions, heavy-ion-induced knockout reactions and quasifree scattering with (p,2p), (p,pn), and (e,e'p) reactions. In particular,

Rosanna Campagna, Emma Perracchione

The data driven extrapolation requires the definition of a functional model depending on the available data and has the application scope of providing reliable predictions on the unknown dynamics. Since data might be scattered, we drive our attention towards kernel models that have the advantage of being meshfree. Precisely, the proposed numerical method mak

Niv Arad, Yair Noam

Downlink joint transmission by a cluster of remote radio heads (RRHs) is an essential technique for enhancing throughput in future cellular networks. This method requires global channel state information (CSI) at the processing unit that designs the joint precoder. To this end, a large amount of CSI must be shared between the RRHs and that unit. This paper p

Jiaying Gu, Roger Koenker

There is an innate human tendency, one might call it the "league table mentality," to construct rankings. Schools, hospitals, sports teams, movies, and myriad other objects are ranked even though their inherent multi-dimensionality would suggest that -- at best -- only partial orderings were possible. We consider a large class of elementary ranking problems

Jiri Silha

Space debris is a major threat to the satellite infrastructure. A collision with even small particle, e.g. 1 cm of size, can cause a catastrophic event when the parent body, spacecraft or upper stage, will break up into hundreds of trackable fragments. Space debris research helps to discover, monitor and characterize these objects, identify their origin and

Lauro Conti, Jürgen Barnstedt, Sebastian Buntrock, Sebastian Diebold

The Institute for Astronomy and Astrophysics in T\"ubingen (IAAT) has a long-term experience in developing and building space-qualified imaging and photon counting microchannel-plate (MCP) detectors, which are sensitive in the ultraviolet wavelength range. Our goal is to achieve high quantum efficiency and spatial resolution, while maintaining solar blindnes

Thomas Berger, Henk de Snoo, Carsten Trunk, Henrik Winkler

Singular chain spaces for linear relations in linear spaces play a fundamental role in the decomposition of linear relations in finite-dimensional spaces. In this paper singular chains and singular chain spaces are discussed in detail for not necessarily finite-dimensional linear spaces. This leads to an identity characterizing a singular chain space in term

Shira Faigenbaum-Golovin, David Levin

In this paper, we present a method for denoising and reconstruction of low-dimensional manifold in high-dimensional space. We suggest a multidimensional extension of the Locally Optimal Projection algorithm which was introduced by Lipman et al. in 2007 for surface reconstruction in 3D. The method bypasses the curse of dimensionality and avoids the need for c

Suhyeon Lee, Junhyuk Hyun, Hongje Seong, Euntai Kim

In this paper, we tackle the unsupervised domain adaptation (UDA) for semantic segmentation, which aims to segment the unlabeled real data using labeled synthetic data. The main problem of UDA for semantic segmentation relies on reducing the domain gap between the real image and synthetic image. To solve this problem, we focused on separating information in

Letian Zhao, Rui Xu, Tianqi Wang, Teng Tian

The size of deep neural networks (DNNs) grows rapidly as the complexity of the machine learning algorithm increases. To satisfy the requirement of computation and memory of DNN training, distributed deep learning based on model parallelism has been widely recognized. We propose a new pipeline parallelism training framework, BaPipe, which can automatically ex

Asad Ullah, Tauseef Ahmed

Training a code-switching (CS) language model using only monolingual data is still an ongoing research problem. In this paper, a CS language model is trained using only monolingual training data. As recurrent neural network (RNN) models are best suited for predicting sequential data. In this work, an RNN language model is trained using alternate batches from

Teppei Minoda, Kiyotomo Ichiki, Hiroyuki Tashiro

The primordial magnetic fields (PMFs) produced in the early universe are expected to be the origin of the large-scale cosmic magnetic fields. The PMFs are considered to leave a footprint on the cosmic microwave background (CMB) anisotropies due to both the electromagnetic force and gravitational interaction. In this paper, we investigate how the PMFs affect

Peng-Lu Zhao, Hai-Zhou Lu, X. C. Xie

Metal-insulator transitions driven by magnetic fields have been extensively studied in 2D, but a 3D theory is still lacking. Motivated by recent experiments, we develop a scaling theory for the metal-insulator transitions in the strong-magnetic-field quantum limit of a 3D system. By using a renormalization-group calculation to treat electron-electron interac

Nilotpal Sinha, Kuan-Wen Chen

Neural Architecture Search (NAS) is emerging as a new research direction which has the potential to replace the hand-crafted neural architectures designed for specific tasks. Previous evolution based architecture search requires high computational resources resulting in high search time. In this work, we propose a novel way of applying a simple genetic algor

H. Richter, W. Lang, M. Peruzzi, H. Hattmansdorfer

Thin films of the high-temperature superconductor HgBa$_2$CaCu$_2$O$_{6+\delta}$ have been prepared on SrTiO$_3$ substrates by pulsed-laser deposition of precursor films and subsequent annealing in mercury-vapor atmosphere. The microstructural properties of such films can vary considerably and have been analyzed by x-ray diffraction and atomic force microsco

Pradipta Bandyopadhyay, Jadav Ganesh, Deepak Gothwal

In this paper, we show that a Banach space $X$ has the Uniform Mazur Intersection Property (UMIP) if and only if every $f \in S(X^*)$ is uniformly w*-semidenting point of $B(X^*)$. We also prove analogous results for uniform w*-MIP.

Amit Giloni, Edita Grolman, Tanja Hagemann, Ronald Fromm

The need to detect bias in machine learning (ML) models has led to the development of multiple bias detection methods, yet utilizing them is challenging since each method: i) explores a different ethical aspect of bias, which may result in contradictory output among the different methods, ii) provides an output of a different range/scale and therefore, can't

A. Anikin, T. Zalialiutdinov, D. Solovyev

In the present paper, we consider nonresonant corrections to $ 2s-ns/nd $ transition frequencies in hydrogen for the experiments based on two-photon spectroscopy. A detailed study of angular correlations of quantum interference effects within the framework of rigorous quantum electrodynamics is given. Closed expressions for the resonant two-photon scattering

Hongtao Kang, Die Luo, Weihua Feng, Junbo Hu

Stain normalization often refers to transferring the color distribution of the source image to that of the target image and has been widely used in biomedical image analysis. The conventional stain normalization is regarded as constructing a pixel-by-pixel color mapping model, which only depends on one reference image, and can not accurately achieve the styl

Amita Malik, Neha Prabhu

We establish a joint distribution result concerning the fractional part of $\alpha p^\theta$ for $\theta \in (0,1), \ \alpha>0$, where $p$ is a prime satisfying a Chebotarev condition in a fixed finite Galois extension over $\mathbb{Q}$. As an application, for a fixed non-CM elliptic curve $E/\mathbb{Q}$, an asymptotic formula is given for the number of prim

Shichang Zhang, Ziniu Hu, Arjun Subramonian, Yizhou Sun

Pre-training Graph Neural Networks (GNN) via self-supervised contrastive learning has recently drawn lots of attention. However, most existing works focus on node-level contrastive learning, which cannot capture global graph structure. The key challenge to conducting subgraph-level contrastive learning is to sample informative subgraphs that are semantically

Charleston W. K. Chiang

There is an urgent and well-recognized need to extend genetic studies to diverse populations, but several obstacles continue to be prohibitive, including (but not limited to) the difficulty of recruiting individuals from diverse populations in large numbers and the lack of representation in available genomic references. These obstacles notwithstanding, study

Seokho Lee, Hideko Nomura, Kenji Furuya, Jeong-Eun Lee

Observations conducted using the Atacama Large Millimeter/submillimeter Array on the protoplanetary disk around TW Hya show the nitrogen fractionation of HCN molecules in HC$^{14}$N/HC$^{15}$N $\sim$120 at a radius of $\sim$20 AU. In this study, we investigated the physical and chemical conditions that control this nitrogen fractionation process. To this end

Shoki. Iwaguchi, Tomohiro. Ishikawa, Masaki. Ando, Yuta. Michimura

The DECi-hertz Interferometer Gravitational wave Observatory (DECIGO) is designed to detect gravitational waves at frequencies between 0.1 and 10 Hz. In this frequency band, one of the most important science targets is the detection of primordial gravitational waves. DECIGO plans to use a space interferometer with optical cavities to increase its sensitivity

Eyasu Getahun Chekole, Martin Ochoa, Sudipta Chattopadhyay

Cyber-Physical Systems (CPS) are being widely adopted in critical infrastructures, such as smart grids, nuclear plants, water systems, transportation systems, manufacturing and healthcare services, among others. However, the increasing prevalence of cyberattacks targeting them raises a growing security concern in the domain. In particular, memory-safety atta

Alon Zolfi, Moshe Kravchik, Yuval Elovici, Asaf Shabtai

Physical adversarial attacks against object detectors have seen increasing success in recent years. However, these attacks require direct access to the object of interest in order to apply a physical patch. Furthermore, to hide multiple objects, an adversarial patch must be applied to each object. In this paper, we propose a contactless translucent physical

Ramtin Hosseini, Pengtao Xie

Learning by self-explanation is an effective learning technique in human learning, where students explain a learned topic to themselves for deepening their understanding of this topic. It is interesting to investigate whether this explanation-driven learning methodology broadly used by humans is helpful for improving machine learning as well. Based on this i

Alim Kerem Erdogmuş, Didem Ozupek Tas, Mustafa Karaca, Ugur Yayan

The use of mobile robots has inevitably increased in recent years. The increase in the companies that produce products in this field, the popularity of the studies in the robotic field and the technological competence to serve many different areas have revealed this increase in usage. The importance of mobile robots used as health, education, pro-duction, lo

Alice Allen, Siddha Mavuram, Robert J. Nemiroff, Judy Schmidt

Software is the most used instrument in astronomy, and organizations such as NASA and the Heidelberg Institute for Theoretical Physics (HITS) fund, develop, and release research software. NASA, for example, has created sites such as code.nasa.gov to share its software with the world, but how easy is it to see what NASA has? Until recently, searching NASA's A

Yingying Guo, Zhaoyang Yin

This paper is devoted to the study of the existence and uniqueness of global admissible conservative weak solutions for the periodic single-cycle pulse equation. We first transform the equation into an equivalent semilinear system by introducing a new set of variables. Using the standard ordinary differential equation theory, we then obtain the global soluti

Piotr Jurkowski, Bartłomiej Szafran

We study a cluster of quantum dots defined within silicene that host confined electron states with spin and valley degrees of freedom. Atomistic tight-binding and continuum Dirac approximation are applied for few-electron system in quest for spontaneous valley polarization driven by inter-dot tunneling and electron-electron interaction, i.e. a valley counter

Rulin Wang, Fuzhen Bi, Wencai Lu, Xiao Zheng

Visualizing and controlling electron dynamics over femtosecond timescale play a key role in the design of next-generation electronic devices. Using simulations, we demonstrate the electronic oscillation inside the naphthalene molecule can be tracked by means of the tuning of delay time between two identical femtosecond laser pulses. Both the frequency and de

Xiaobing Yu, Mike Stahr, Han Chen, Runming Yan

With the fact that the knowledge in each field in university is keeping increasing, the number of university courses is becoming larger, and the content and curriculum system is becoming much more complicated than it used to be, which bring many inconveniences to the course arrangement and analysis. In this paper, we aim to construct a method to visualize al

Mei Du, Shuang-Xi Yi, Tong Liu, Cui-Ying Song

Long-duration gamma-ray bursts (GRBs) are generally related to the core-collapse of massive stars. In the collapsar scenario, a rotating stellar-mass black hole (BH) surrounded by a hyperaccretion disk has been considered as one of the plausible candidates of GRB central engines. In this paper, we work on a sample including 146 long GRBs with significant jet

Liangyu Che, Chao Wei, Yulei Huang, Dafa Zhao

It is natural to measure the observables from the Hamiltonian-based quantum dynamics, and its inverse process that Hamiltonians are estimated from the measured data also is a vital topic. In this work, we propose a recurrent neural network to learn the parameters of the target Hamiltonians from the temporal records of single-qubit measurements. The method do

Zhijun Hu, Yong Xu, Jie Wen, Lilei Sun

Recently, deep learning has been widely used in the field of vehicle re-identification. When training a deep model, softmax loss is usually used as a supervision tool. However, the softmax loss performs well for closed-set tasks, but not very well for open-set tasks. In this paper, we sum up five shortcomings of center loss and solved all of them by proposin

Mahsa Saeidi, McKenzie Calvert, Audrey W. Au, Anita Sarma

End users are increasingly using trigger-action platforms like, If-This-Then-That (IFTTT) to create applets to connect smart home devices and services. However, there are inherent risks in using such applets -- even non-malicious ones -- as sensitive information may leak through their use in certain contexts (e.g., where the device is located, who can observ

Xiaohe Li, Lijie Wen, Chen Qian, Jianmin Wang

The real-world networks often compose of different types of nodes and edges with rich semantics, widely known as heterogeneous information network (HIN). Heterogeneous network embedding aims to embed nodes into low-dimensional vectors which capture rich intrinsic information of heterogeneous networks. However, existing models either depend on manually design

Uday Singh Saini, Evangelos E. Papalexakis

How can we discover and succinctly summarize the concepts that a neural network has learned? Such a task is of great importance in applications of networks in areas of inference that involve classification, like medical diagnosis based on fMRI/x-ray etc. In this work, we propose a framework to categorize the concepts a network learns based on the way it clus

Eman AbdelMaksoud, Sherif Barakat, Mohammed Elmogy

Much effort is being made by the researchers in order to detect and diagnose diabetic retinopathy (DR) accurately automatically. The disease is very dangerous as it can cause blindness suddenly if it is not continuously screened. Therefore, many computers aided diagnosis (CAD) systems have been developed to diagnose the various DR grades. Recently, many CAD

P. Castorina, A. Iorio, D. Lanteri, H. Satz

Strangeness enhancement and collective flow are considered signatures of the quark gluon plasma formation. These phenomena have been detected not only in relativistic heavy ion collisions but also in high energy, high multiplicity events of proton-proton and proton-nucleus (small systems) scatterings. A universal behavior emerges by considering the parton de

Zhao Li, Yefan Wang, Quan-feng Wu

The $e^+e^- \rightarrow ZH$ process is the dominant process for the Higgs boson production at the future Higgs factory. In order to match the analysis on the Higgs properties with the highly precise experiment data, it will be crucial to include the theoretical prediction to the full next-to-next-to-leading order electroweak effect in the production rate $\s

Davar Khoshnevisan, Kunwoo Kim, Carl Mueller, Shang-Yuan Shiu

We consider a reaction-diffusion equation of the type \[ \partial_t\psi = \partial^2_x\psi + V(\psi) + \lambda\sigma(\psi)\dot{W} \qquad\text{on $(0\,,\infty)\times\mathbb{T}$}, \] subject to a "nice" initial value and periodic boundary, where $\mathbb{T}=[-1\,,1]$ and $\dot{W}$ denotes space-time white noise. The reaction term $V:\mathbb{R}\to\mathbb{R}$ be