February 2021 arXiv papers — page 5

Yuya Tanaka

This paper deals with the quasilinear parabolic-elliptic Keller-Segel system with logistic source, \begin{align*} u_t=Δ(u+1)^m - χ\nabla \cdot (u(u+1)^{α- 1} \nabla v) + λ(|x|) u - μ(|x|) u^κ, \quad 0=Δv - v + u, \quad x\inΩ,\ t>0, \end{align*} where $Ω:=B_{R}(0)\subset\mathbb{R}^n\ (n\ge3)$ is a ball with some $R>0$; $m>0$, $χ>0$, $α>0$ and $κ\ge1$; $λ$ and

S. A. Kulagin, V. V. Barinov

We develop a "hybrid" model of the proton inelastic structure functions applicable in a wide region of invariant mass of produced states $W$ and invariant momentum transfer $Q$ including deep inelastic scattering (DIS), nucleon resonance production as well as the region close to inelastic threshold. DIS is described in terms of the parton distributio

Gianmassimo Tasinato, Alice Garoffolo, Daniele Bertacca, Sabino Matarrese

We analyze the propagation of high-frequency gravitational waves (GW) in scalar-tensor theories of gravity, with the aim of examining properties of cosmological distances as inferred from GW measurements. By using symmetry principles, we first determine the most general structure of the GW linearized equations and of the GW energy momentum tensor, assuming t

Arnav Arora, Preslav Nakov, Momchil Hardalov, Sheikh Muhammad Sarwar

The proliferation of harmful content on online platforms is a major societal problem, which comes in many different forms including hate speech, offensive language, bullying and harassment, misinformation, spam, violence, graphic content, sexual abuse, self harm, and many other. Online platforms seek to moderate such content to limit societal harm, to comply

Kangtai Sun, Zhibin Gao, Jian-Sheng Wang

Phonon Hall effect (PHE) has attracted a lot of attention in recent years with many theoretical and experimental explorations published. While experiments work on complicated materials, theoretical studies are still hovering around the phenomenon-based models. Moreover, previous microscopic theory was found unable to explain large thermal Hall conductivity o

Juan Gerardo Alcázar, Georg Muntingh

We introduce a characterization for affine equivalence of two surfaces of translation defined by either rational or meromorphic generators. In turn, this induces a similar characterization for minimal surfaces. In the rational case, our results provide algorithms for detecting affine equivalence of these surfaces, and therefore, in particular, the symmetries

Sambunath Das, Dayasindhu Dey, S. Ramasesha, Manoranjan Kumar

We study the quantum phase transitions of frustrated antiferromagnetic Heisenberg spin-1 systems on the 3/4 and 3/5 skewed two leg ladder geometries. These systems can be viewed as arising by periodically removing rung bonds from a zigzag ladder. We find that in large systems, the ground state (gs) of the 3/4 ladder switches from a singlet to a magnetic stat

Morgan Ekmefjord, Addi Ait-Mlouk, Sadi Alawadi, Mattias Åkesson

Federated machine learning has great promise to overcome the input privacy challenge in machine learning. The appearance of several projects capable of simulating federated learning has led to a corresponding rapid progress on algorithmic aspects of the problem. However, there is still a lack of federated machine learning frameworks that focus on fundamental

Vinu Sankar Sadasivan, Anirban Dasgupta

Curriculum learning is a training strategy that sorts the training examples by some measure of their difficulty and gradually exposes them to the learner to improve the network performance. Motivated by our insights from implicit curriculum ordering, we first introduce a simple curriculum learning strategy that uses statistical measures such as standard devi

Almudena Colacito, Nikolaos Galatos, George Metcalfe, Simon Santschi

We prove that an inverse-free equation is valid in the variety LG of lattice-ordered groups (l-groups) if and only if it is valid in the variety DLM of distributive lattice-ordered monoids (distributive l-monoids). This contrasts with the fact that, as proved by Repnitskii, there exist inverse-free equations that are valid in all Abelian l-groups but not in

M. V. Fedorov, S. S. Mernova, K. V. Sliporod

As known, the degree of entanglement of biphoton states with respect to the transverse components of photon wave vectors (momenta) or coordinates can be characterized either by the parameter K associated with the Schmidt decompositions, or by the parameter R, defined as the ratio of the widths of the unconditional and conditional single-particle distribution

Tristan Aumentado-Armstrong, Stavros Tsogkas, Sven Dickinson, Allan Jepson

A complete representation of 3D objects requires characterizing the space of deformations in an interpretable manner, from articulations of a single instance to changes in shape across categories. In this work, we improve on a prior generative model of geometric disentanglement for 3D shapes, wherein the space of object geometry is factorized into rigid orie

Hilmi E. Egilmez, Ankitesh K. Singh, Muhammed Coban, Marta Karczewicz

Most of the existing deep learning based end-to-end image/video coding (DLEC) architectures are designed for non-subsampled RGB color format. However, in order to achieve a superior coding performance, many state-of-the-art block-based compression standards such as High Efficiency Video Coding (HEVC/H.265) and Versatile Video Coding (VVC/H.266) are designed

Fei Li, Xiangxu Li, Jun Luo, Shiwei Fan

Intention prediction is a crucial task for Autonomous Driving (AD). Due to the variety of size and layout of intersections, it is challenging to predict intention of human driver at different intersections, especially unseen and irregular intersections. In this paper, we formulate the prediction of intention at intersections as an open-set prediction problem

Mohammad Ali Javidian, Om Pandey, Pooyan Jamshidi

One of the most critical problems in transfer learning is the task of domain adaptation, where the goal is to apply an algorithm trained in one or more source domains to a different (but related) target domain. This paper deals with domain adaptation in the presence of covariate shift while invariances exist across domains. One of the main limitations of exi

Debmalya Mandal, Sourav Medya, Brian Uzzi, Charu Aggarwal

Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems. However, GNNs on such applications are limited when there are few available samples. Meta-learning has been an important framework to address the lack of samples in machine learning

Takeshi Teshima, Masashi Sugiyama

Causal graphs (CGs) are compact representations of the knowledge of the data generating processes behind the data distributions. When a CG is available, e.g., from the domain knowledge, we can infer the conditional independence (CI) relations that should hold in the data distribution. However, it is not straightforward how to incorporate this knowledge into

Erfan Nozari, Robert Planas, Jorge Cortes

Among the versatile forms of dynamical patterns of activity exhibited by the brain, oscillations are one of the most salient and extensively studied, yet are still far from being well understood. In this paper, we provide various structural characterizations of the existence of oscillatory behavior in neural networks using a classical neural mass model of me

Suraj Kothawade, Vishal Kaushal, Ganesh Ramakrishnan, Jeff Bilmes

With ever-increasing dataset sizes, subset selection techniques are becoming increasingly important for a plethora of tasks. It is often necessary to guide the subset selection to achieve certain desiderata, which includes focusing or targeting certain data points, while avoiding others. Examples of such problems include: i)targeted learning, where the goal

Xuewan Zhang, Dalong Zhang, Liuqing Yang, Gangtao Han

Sparse code multiple access (SCMA), which helps improve spectrum efficiency (SE) and enhance connectivity, has been proposed as a non-orthogonal multiple access (NOMA) scheme for 5G systems. In SCMA, codebook design determines system overload ratio and detection performance at a receiver. In this paper, an SCMA codebook design approach is proposed based on u

Lucy Nwosu, Xiangfang Li, Lijun Qian, Seungchan Kim

Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic in over 200 countries and territories, which has resulted in a great public health concern across the international community. Analysis of X-ray imaging data can play a critical role in timely and accurate screening and fighting against COVID-19. Supervised deep learning has been successfully

Krishnateja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, Abir De

The great success of modern machine learning models on large datasets is contingent on extensive computational resources with high financial and environmental costs. One way to address this is by extracting subsets that generalize on par with the full data. In this work, we propose a general framework, GRAD-MATCH, which finds subsets that closely match the g

Ali Pourramezan Fard, Hojjat Abdollahi, Mohammad Mahoor

Active Shape Model (ASM) is a statistical model of object shapes that represents a target structure. ASM can guide machine learning algorithms to fit a set of points representing an object (e.g., face) onto an image. This paper presents a lightweight Convolutional Neural Network (CNN) architecture with a loss function being assisted by ASM for face alignment

Xiaojun Zheng, Simon Mak, Yao Xie

Topological Data Analysis (TDA) is a rapidly growing field, which studies methods for learning underlying topological structures present in complex data representations. TDA methods have found recent success in extracting useful geometric structures for a wide range of applications, including protein classification, neuroscience, and time-series analysis. Ho

Yixin Liu, Zhao Li, Shirui Pan, Chen Gong

Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems. Recently, the deep learning-based anomaly detection methods have shown promising results over shallow approaches, especially on networks with high-dimensional attributes and complex str

Kai Han, An Xiao, Enhua Wu, Jianyuan Guo

Transformer is a new kind of neural architecture which encodes the input data as powerful features via the attention mechanism. Basically, the visual transformers first divide the input images into several local patches and then calculate both representations and their relationship. Since natural images are of high complexity with abundant detail and color i

Yixin Liu, Ming Jin, Shirui Pan, Chuan Zhou

Deep learning on graphs has attracted significant interests recently. However, most of the works have focused on (semi-) supervised learning, resulting in shortcomings including heavy label reliance, poor generalization, and weak robustness. To address these issues, self-supervised learning (SSL), which extracts informative knowledge through well-designed pr

Ye Zhang, Yuan Cao, Mahdis Mahdieh, Jeffrey Zhao

Dialogue state tracking (DST) is a pivotal component in task-oriented dialogue systems. While it is relatively easy for a DST model to capture belief states in short conversations, the task of DST becomes more challenging as the length of a dialogue increases due to the injection of more distracting contexts. In this paper, we aim to improve the overall perf

Ian Convy, William Huggins, Haoran Liao, K. Birgitta Whaley

Tensor networks have emerged as promising tools for machine learning, inspired by their widespread use as variational ansatze in quantum many-body physics. It is well known that the success of a given tensor network ansatz depends in part on how well it can reproduce the underlying entanglement structure of the target state, with different network designs fa

Rodrigo Hernández-Gutiérrez, Alfredo Zaragoza

Erdős space $\mathfrak{E}$ and complete Erdős space $\mathfrak{E}_c$ have been previously shown to have topological characterizations. In this paper, we provide a topological characterization of the topological space $\mathbb{Q}\times\mathfrak{E}_c$, where $\mathbb{Q}$ is the space of rational numbers. As a corollary, we show that the Vietoris hyperspace of

Y. Kishimoto, Y. Suzuki, I. Ogawa, Y. Mori

Two cavities in different size with the photonic crystal structure have been developed for axion searches. In the cavities, the dispersion relation in the photonic crystal is utilised, and so was named to "DRiPC cavities". The size of the smaller one is 100 mm x 100 mm x 10 mm, where 16 cylindrical metal poles with a diameter of 4 mm are introduced i

Kazuyuki Ogata, Tokuro Fukui, Yuki Kamiya, Akira Ohnishi

The hadron-deuteron correlation function has attracted many interests as a potential method to access the three-hadron interactions. However, the weakly-bound nature of deuteron has not been considered in the preceding studies. In this study, the breakup effect of deuteron on the deuteron-$Ξ^-$ ($d$-$Ξ^-$) correlation function $C_{dΞ^-}$ is investigated. The

Kevin Coulembier, Pavel Etingof, Victor Ostrik, Bregje Pauwels

We study abelian envelopes for pseudo-tensor categories with the property that every object in the envelope is a quotient of an object in the pseudo-tensor category. We establish an intrinsic criterion on pseudo-tensor categories for the existence of an abelian envelope satisfying this quotient property. This allows us to interpret the extension of scalars a

Md Kamran Chowdhury Shisher, Heyang Qin, Lei Yang, Feng Yan

In this paper, we analyze the impact of information freshness on supervised learning based forecasting. In these applications, a neural network is trained to predict a time-varying target (e.g., solar power), based on multiple correlated features (e.g., temperature, humidity, and cloud coverage). The features are collected from different data sources and are

Omar A. Ashour

NonlinearSchrodinger.jl is a Julia package with a simple interface for studying solutions of nonlinear Schrödinger equations (NLSEs). In approximately ten lines of code, one can perform a simulation of the cubic NLSE using one of 32 algorithms, including symplectic and Runge-Kutta-Nyström integrators up to eighth order. Furthermore, it is possible to compute

Dharam Vir Ahluwalia

This editorial presents the quantum field theoretic ambiance in which Elko and mass dimension one fermions come to exist. This may serve not only to introduce Elko, and the associated quantum field, but it may also open a door to a new class of spin one half fermions and bosons. These new particles, as it will be argued in this volume, are first-principle ca

Hardi M. Mohammed, Zrar Kh. Abdul, Tarik A. Rashid, Abeer Alsadoon

Purpose: The development of metaheuristic algorithms has increased by researchers to use them extensively in the field of business, science, and engineering. One of the common metaheuristic optimization algorithms is called Grey Wolf Optimization (GWO). The algorithm works based on imitation of the wolves' searching and the process of attacking grey wolv

Biplav Srivastava

Artificial Intelligence (AI) technologies have long been positioned as a tool to provide crucial data-driven decision support to people. In this survey paper, we look at how AI in general, and collaboration assistants (CAs or chatbots for short) in particular, have been used during a true global exigency - the COVID-19 pandemic. The key observation is that c

Davood Farrokhi, Rohollah Bakhshandeh-Chamazkoti, Mehdi Nadjafikhah

In the present paper, we try to investigate the Noether symmetries and Lie point symmetries of the Vaidya-Bonner geodesics. Classification of one-dimensional subalgebras of Lie point symmetries are considered. In fact, the collection of pairwise non-conjugate one-dimensional subalgebras that are called the optimal system of subalgebras is determined. Moreove

Benedikt T. Kleppmann

Many social sciences such as psychology and economics try to learn the behaviour of complex agents such as humans, organisations and countries. The current statistical methods used for learning this behaviour try to infer generally valid behaviour, but can only learn from one type of study at a time. Furthermore, only data from carefully designed studies can

Amol Kelkar

Human brain has been used as an inspiration for building autonomous agents, but it is not obvious what level of computational description of the brain one should use. This has led to overly opinionated symbolic approaches and overly unstructured connectionist approaches. We propose that using homeostasis as the computational description provides a good compr

Suwarno, Raditya Bayu Rahadian

This study aims to improve mathematics learning results in the second grade of elementary school, especially the matter of fractions. The background of the research is the low learning outcomes achieved by students due to the learning carried out does not pay attention to the level of student development. This research was classroom action research. The rese

Galamo Monkam

We consider formal power series $ f(x) = a_1 x + a_2 x^2 + \cdots $ $(a_1 \neq 0)$, with coefficients in a field. We revisit the classical subject of iteration of formal power series, the n-fold composition $f^{(n)}(x)=f(f(\cdots (f(x)\cdots))=f^{(n-1)}(f(x))=\sum\limits_{k=1}^{\infty}f_k^{(n)}x^k$ for $n=2,3,\ldots$, where $f^{(1)}(x)=f(x)$. The study of th

Saugat Kandel, S. Maddali, Youssef S G Nashed, Stephan O Hruszkewycz

The phase retrieval problem, where one aims to recover a complex-valued image from far-field intensity measurements, is a classic problem encountered in a range of imaging applications. Modern phase retrieval approaches usually rely on gradient descent methods in a nonlinear minimization framework. Calculating closed-form gradients for use in these methods i

Ana Fernández-Guillamón, Angel Molina-García

This paper describes variable speed wind turbine (Types 3 and 4, IEC 61400-27-1) simulations based on an open-source solution to be applied to Bachelor and Master Degrees. It is an attempt to improve the education quality of such sustainable energy by giving an open-source experimental environment for both undergraduate and graduate students. Indeed, among t

Kimitoshi Tsutaya, Yuta Wakasugi

Consider a nonlinear wave equation for a massless scalar field with self-interaction in the spatially flat Friedmann-Lemaître-Robertson-Walker spacetimes. For the case of accelerated expansion, we show that blow-up in a finite time occurs for the equation with arbitrary power nonlinearity as well as upper bounds of the lifespan of blow-up solutions. Comparin

Lei Gao, Lin Qi, Ling Guan

In this paper, we propose a novel discriminative model for online behavioral analysis with application to emotion state identification. The proposed model is able to extract more discriminative characteristics from behavioral data effectively and find the direction of optimal projection efficiently to satisfy requirements of online data analysis, leading to

Fabio Briscese, Leonardo Modesto

We show that Minkowskian non-local quantum field theories are not unitary. We consider a simple one loop diagram for a scalar non-local field and show that the imaginary part of the corresponding complex amplitude is not given by Cutkosky rules, indeed this diagram violates the unitarity condition. We compare this result with the case of an Euclidean non-loc

Kaylea Champion, Benjamin Mako Hill

The widespread adoption of Free/Libre and Open Source Software (FLOSS) means that the ongoing maintenance of many widely used software components relies on the collaborative effort of volunteers who set their own priorities and choose their own tasks. We argue that this has created a new form of risk that we call 'underproduction' which occurs when t

M. Ferrera, L. Razzari, D. Duchesne, R. Morandotti

Photonic integrated circuits (PICs) are a key component [1] for future telecommunication networks, where demands for greater bandwidth, network flexibility, low energy consumption and cost must all be met. The quest for all optical components has naturally targeted materials with extremely large nonlinearity, including chalcogenide glasses (ChG) [2] and semi

C. Acha, G. A. Sanca, M. Barella, M. Alurralde

10 MeV proton-irradiation effects on a YBCO-based test structure were analyzed by measuring its current-voltage (IV) characteristics for different cumulated fluences. For fluences of up to $\sim$80$\cdot$10$^9$~p/cm$^2$ no changes in the electrical behavior of the device were observed, while for a fluence of $\sim$~300$\cdot$10$^9~$ p/cm$^2$ it becomes less

Tamaghna Hazra, Piers Coleman

We show how Oshikawa's theorem for the Fermi surface volume of the Kondo lattice can be extended to the SU$(N)$ symmetric case. By extending the theorem, we are able to show that the mechanism of Fermi surface expansion seen in the large $N$ mean-field theory is directly linked to the expansion of the Fermi surface in a spin-$1/2$ Kondo lattice. This lin

Ruochen Jiao, Hengyi Liang, Takami Sato, Junjie Shen

In the development of advanced driver-assistance systems (ADAS) and autonomous vehicles, machine learning techniques that are based on deep neural networks (DNNs) have been widely used for vehicle perception. These techniques offer significant improvement on average perception accuracy over traditional methods, however, have been shown to be susceptible to a

Virginia Cuomo, Yun Hee Lee, Chiara Buttitta, José Alfonso Lopez Aguerri

More than 10% of the barred galaxies with a direct measurement of the bar pattern speed host an ultrafast bar. These bars extend beyond the corotation radius and challenge our understanding of the orbital structure of barred galaxies. Most of them are found in spiral galaxies, rather than in lenticular ones. We analysed the properties of the ultrafast bars d

Raouf Kerkouche, Gergely Ács, Claude Castelluccia, Pierre Genevès

Federated learning becomes a prominent approach when different entities want to learn collaboratively a common model without sharing their training data. However, Federated learning has two main drawbacks. First, it is quite bandwidth inefficient as it involves a lot of message exchanges between the aggregating server and the participating entities. This ban

Mengying Bian, Aleksandr N. Kamenskii, Mengjiao Han, Wenjie Li

To broaden the scope of van der Waals 2D magnets, we report the synthesis and magnetism of covalent 2D magnetic Cr$_2$Te$_3$ with a thickness down to one-unit-cell. The 2D Cr$_2$Te$_3$ crystals exhibit robust ferromagnetism with a Curie temperature of 180 K, a large perpendicular anisotropy of 7*105 J m-3, and a high coercivity of ~ 4.6 kG at 20 K. First-pri

Deniz Bilman, Peter D. Miller

We study fundamental rogue-wave solutions of the focusing nonlinear Schrödinger equation in the limit that the order of the rogue wave is large and the independent variables $(x,t)$ are proportional to the order (the far-field limit). We first formulate a Riemann-Hilbert representation of these solutions that allows the order to vary continuously rather than

Alex Lamb, Di He, Anirudh Goyal, Guolin Ke

An important development in deep learning from the earliest MLPs has been a move towards architectures with structural inductive biases which enable the model to keep distinct sources of information and routes of processing well-separated. This structure is linked to the notion of independent mechanisms from the causality literature, in which a mechanism is

Alan R. Rogers, Stephen P. Wooding

The site frequency spectrum describes variation among a set of n DNA sequences. Its i'th entry (i=1,2,...,n-1) is the number of nucleotide sites at which the mutant allele is present in i copies. Under selective neutrality, random mating, and constant population size, the expected value of the spectrum is well known but somewhat puzzling. Each additional

Manuel Sam Ribeiro, Aciel Eshky, Korin Richmond, Steve Renals

We investigate multi-speaker speech recognition from ultrasound images of the tongue and video images of the lips. We train our systems on imaging data from modal speech, and evaluate on matched test sets of two speaking modes: silent and modal speech. We observe that silent speech recognition from imaging data underperforms compared to modal speech recognit

Vadim Abramkin, Yuriy Shibanov, Roberto P. Mignani, George G. Pavlov

We present results of optical-UV observations of the 200 Myr old rotation-powered radio pulsar J0108$-$1431 with the Hubble Space Telescope. We found a putative candidate for the far-UV (FUV) pulsar counterpart, with the flux density $f_ν= 9.0\pm 3.2$ nJy at $λ= 1528$ Å. The pulsar was not detected, however, at longer wavelengths, with $3σ$ upper limits of 5

Daniela Kuinchtner, Afonso Sales, Felipe Meneguzzi

Markov Decision Process (MDP) is the underlying model for optimal planning for decision-theoretic agents in stochastic environments. Although much research focuses on solving MDP problems both in tabular form or using factored representations, none focused on tensor decomposition methods. Solving MDPs using tensor algebra offers the prospect of leveraging ad

Milutin Blagojević, Branislav Cvetković

A Hamiltonian approach to black hole entropy is used to study Riemannian Kerr-AdS solutions in the general, parity-violating Poincaré gauge theory. Entropy and the asymptotic charges are entirely determined by the parity-even sector of the theory, whereas the parity-odd contributions vanish. Entropy is found to be proportional to the horizon area, and the fi

Michele Buzzicotti, Luca Biferale, Fabio Bonaccorso, Patricio Clark di Leoni

We present theoretical and numerical results concerning the problem to find the path that minimizes the time to navigate between two given points in a complex fluid under realistic navigation constraints. We contrast deterministic Optimal Navigation (ON) control with stochastic policies obtained by Reinforcement Learning (RL) algorithms. We show that Actor-C

Simón Gutiérrez Brida, Germán Regis, Guolong Zhengz, Hamid Bagheriz

The rising popularity of declarative languages and the hard to debug nature thereof have motivated the need for applicable, automated repair techniques for such languages. However, despite significant advances in the program repair of imperative languages, there is a dearth of repair techniques for declarative languages. This paper presents BeAFix, an automa

George Avalos, Pelin G. Geredeli

We consider a multilayer hyperbolic-parabolic PDE system which constitutes a coupling of 3D thermal - 2D elastic - 3D elastic dynamics, in which the boundary interface coupling between 3D fluid and 3D structure is realized via a 2D elastic equation. Our main result here is one of strong decay for the given multilayered - heat system. That is, the solution to

Ansh Patel, Santosh P. Pandya

During disruptions in fusion-grade tokamaks like ITER, large electric fields are induced following the thermal quench (TQ) period which can generate a substantial amount of Runaway Electrons (REs) that can carry up to 10 MA current with energies as high as several tens of MeV [1-3] in current quench phase (CQ). These runaway electrons can cause significant d

Manuel Sam Ribeiro, Joanne Cleland, Aciel Eshky, Korin Richmond

Speech sound disorders are a common communication impairment in childhood. Because speech disorders can negatively affect the lives and the development of children, clinical intervention is often recommended. To help with diagnosis and treatment, clinicians use instrumented methods such as spectrograms or ultrasound tongue imaging to analyse speech articulat

Vaclav Macha

We use Feireisl-Lions theory to deduce the existence of weak solutions to a system describing the dynamics of a linear oscillator containing a Newtonian compressible fluid. The appropriate Navier-Stokes equation is considered on a domain whose movement has one degree of freedom. The equation is paired with the Newton law and we assume a no-slip boundary cond

Aleksandr Beznosikov, Vasilii Novitskii, Alexander Gasnikov

In this paper, we analyze gradient-free methods with one-point feedback for stochastic saddle point problems $\min_{x}\max_{y} φ(x, y)$. For non-smooth and smooth cases, we present analysis in a general geometric setup with arbitrary Bregman divergence. For problems with higher-order smoothness, the analysis is carried out only in the Euclidean case. The est

Markian Petkov, Marc Chevalier, David Dean, Alan C. F. Cocks

A micromechanical model at the microscale within a crystal plasticity self-consistent model, is used to analyse loading histories in Type 316H stainless steel, common to structural components in high-temperature power plants. The study compares the SCM predictions on changes in mechanical behaviour and creep properties to analyses via the UK R5 structural as

Lucia Minah Yang, Ian Grooms

Using generative models from the machine learning literature to create artificial ensemble members for use within data assimilation schemes has been introduced in [Grooms QJRMS, 2020] as constructed analog ensemble optimal interpolation (cAnEnOI). Specifically, we study general and variational autoencoders for the machine learning component of this method, a

D. I. Tetelbaum, A. A. Nikolskaya, D. S. Korolev, A. I. Belov

Gallium oxide with a corundum structure (α-Ga2O3) has recently attracted great attention in view of electronic and photonic applications due to its unique properties including a wide band gap exceeding that of the most stable beta phase (\b{eta}-Ga2O3). However, the lower thermal stability of the α-phase at ambient conditions in comparison with the \b{eta}-p

Juan Sosa, Lina Buitrago

In this paper we estimate the dynamic parameters of a time-varying coefficient model through radial kernel functions in the context of a longitudinal study. Our proposal is based on a linear combination of weighted kernel functions involving a bandwidth, centered around a given set of time points. In addition, we study different alternatives of estimation an

Peter K. F. Kuhfittig

It is well known that phantom energy, which is characterized by the equation of state $p=ωρ$, $ω<-1$, can support Morris-Thorne wormholes since it leads to a violation of the null energy condition. The purpose of this note is to show that the converse is also true in the following sense: for a typical shape function, the equation of state of exotic matter in

Jia-Yang Chen, Chao Tang, Mingwei Jin, Zhan Li

Thin-film lithium niobate (TFLN) is superior for integrated nanophotonics due to its outstanding properties in nearly all aspects: strong second-order nonlinearity, fast and efficient electro-optic effects, wide transparency window, and little two photon absorption and free carrier scattering. Together, they permit highly integrated nanophotonic circuits cap

Markian Petkov, Elsiddig Elmukashfi, Edmund Tarleton, Alan C. F. Cocks

Previous studies demonstrate that grain-boundary sliding could accelerate creep rate and give rise to large internal stresses that can lead to damage development, e.g. formation of wedge cracks. The present study provides more insight into the effects of grain-boundary sliding (GBS) on the deformation behaviour of realistic polycrystalline aggregates during

Carla Marin Benito

The coupling of the electroweak gauge bosons of the Standard Model (SM) to leptons is flavour universal. Extensions of the SM do not necessarily have this property. Rare decays of heavy flavour are suppressed in the SM and new particles may give sizeable contributions to these processes, therefore, their precise study allows for sensitive tests of lepton fla

Andrew Giffin, Brian J. Reich, Shu Yang, Ana G. Rappold

Unobserved spatial confounding variables are prevalent in environmental and ecological applications where the system under study is complex and the data are often observational. Instrumental variables (IVs) are a common way to address unobserved confounding; however, the efficacy of using IVs on spatial confounding is largely unknown. This paper explores the

Stefanie Günther, Will Pazner, Dongping Qi

Based on the continuous interpretation of deep learning cast as an optimal control problem, this paper investigates the benefits of employing B-spline basis functions to parameterize neural network controls across the layers. Rather than equipping each layer of a discretized ODE-network with a set of trainable weights, we choose a fixed number of B-spline ba

Shihan Sajeed, Thomas Jennewein

Quantum channels in free-space, an essential prerequisite for fundamental tests of quantum mechanics and quantum technologies in open space, have so far been based on direct line-of-sight because the predominant approaches for photon-encoding, including polarization and spatial modes, are not compatible with randomly scattered photons. Here we demonstrate a

Shahar Maoz, Rafi Shalom

One of the main challenges of reactive synthesis, an automated procedure to obtain a correct-by-construction reactive system, is to deal with unrealizable specifications. One means to deal with unrealizability, in the context of GR(1), an expressive assume-guarantee fragment of LTL that enables efficient synthesis, is the computation of an unrealizable core,

Naomi Oppenheimer, David B. Stein, Matan Yah Ben Zion, Michael J. Shelley

Ensembles of particles rotating in a two-dimensional fluid can exhibit chaotic dynamics yet develop signatures of hidden order. Such &#34;rotors&#34; are found in the natural world spanning vastly disparate length scales - from the rotor proteins in cellular membranes to models of atmospheric dynamics. Here we show that an initially random distribution of ei

Silke Britzen, Michal Zajaček, Luka Č. Popović, Christian Fendt

On 2019/07/30.86853 UT, IceCube detected a high-energy astrophysical neutrino candidate. The Flat Spectrum Radio Quasar PKS 1502+106 is located within the 50 percent uncertainty region of the event. Our analysis of 15 GHz Very Long Baseline Array (VLBA) and astrometric 8 GHz VLBA data, in a time span prior and after the IceCube event, reveals evidence for a

E. A. Feoktistova, S. I. Ipatov

We present a study on the relationship between the ratio of the depth of a crater to its diameter and the diameter for lunar craters both on the maria and on the highlands. We consider craters younger than 1.1 billion years in age, i.e. of Copernican period. The aim of this work is to improve our understanding of such relationships based on our new estimates

Kirill F. Sheberstov, Vitaly P. Kozinenko, Alexey S. Kiryutin, Hans-Martin Vieth

Development of the methods to exploit nuclear hyperpolarization and search for molecules whose nuclear spins can be efficiently hyperpolarized is an active area in nuclear magnetic resonance. Of particular interest are those molecules that have long nuclear relaxation times, making them to be suitable candidates as contrast agents in magnetic resonance imagi

Daniel Deutch, Ariel Frankenthal, Amir Gilad, Yuval Moskovitch

Organizations that collect and analyze data may wish or be mandated by regulation to justify and explain their analysis results. At the same time, the logic that they have followed to analyze the data, i.e., their queries, may be proprietary and confidential. Data provenance, a record of the transformations that data underwent, was extensively studied as mea

Yitong Li, Duoduo Liao, Jundong Li, Wenying Ji

When a disaster occurs, maintaining and restoring community lifelines subsequently require collective efforts from various stakeholders. Aiming at reducing the efforts associated with generating Stakeholder Collaboration Networks (SCNs), this paper proposes a systematic approach to reliable information extraction for stakeholder collaboration and automated n

A. Ziaee, R. Dehbozorgi, M. Döller

Building footprint segmentations for high resolution images are increasingly demanded for many remote sensing applications. By the emerging deep learning approaches, segmentation networks have made significant advances in the semantic segmentation of objects. However, these advances and the increased access to satellite images require the generation of accur

John Baillieul, Feiyang Kang

Borrowing terminology from fluid mechanics, the concepts of {\em Eulerian} and {\em Lagrangian optical flow sensing} are introduced. Eulerian optical flow sensing assumes that each photoreceptor in the camera or eye can instantaneously detect feature image points and their velocities on the retina. If this assumption is satisfied, even a two pixel imaging sy

Amarjit Budhiraja, Paul Dupuis, Pierre Nyquist, Guo-Jhen Wu

We introduce and study the basic properties of two ergodic stochastic control problems associated with the quasistationary distribution (QSD) of a diffusion process $X$ relative to a bounded domain. The two problems are in some sense dual, with one defined in terms of the generator associated with $X$ and the other in terms of its adjoint. Besides proving we

Jiewei Chen, Ting Zhang, Jingli Wang, Ning Zhang

Charge-based field-effect transistors (FETs) greatly suffer from unavoidable carrier scattering and heat dissipation. In analogy to valley degree of freedom in semiconductors, chiral anomaly current in Weyl/Dirac semimetals is theoretically predicted to be nearly non-dissipative over long distances, but still lacks experimental ways to efficiently control it

Dali Zangurashvili

A new protomodular analog of the classical criterion for the existence of a group term in the algebraic theory of a variety of universal algebras is given. To this end, the notion of a right-cancellable protomodular algebra is introduced. It is proved that the algebraic theory of a variety of universal algebras contains a group term if and only if it contain

Hossen Teimoorinia, Sara Shishehchi, Ahnaf Tazwar, Ping Lin

We have developed a method that maps large astronomical images onto a two-dimensional map and clusters them. A combination of various state-of-the-art machine learning (ML) algorithms is used to develop a fully unsupervised image quality assessment and clustering system. Our pipeline consists of a data pre-processing step where individual image objects are i

Yingying Xu, Ming Cai, Lanfen Lin, Yue Zhang

In this paper, we propose a phase attention residual network (PA-ResSeg) to model multi-phase features for accurate liver tumor segmentation, in which a phase attention (PA) is newly proposed to additionally exploit the images of arterial (ART) phase to facilitate the segmentation of portal venous (PV) phase. The PA block consists of an intra-phase attention

Philipp Steinbach, Daniel Otto Schulte, Bastian Welsch, Ingo Sass

Borehole heat exchanger arrays have become a common implement for the utilization of thermal energy in the soil. Building these facilities is expensive, especially the drilling of boreholes, into which closed-pipe heat exchangers are inserted. Therefore, cost-reducing drilling methods are common practice, which can produce inaccuracies of varying degree. Thi

Yi Li, Shaohua Wang, Tien N. Nguyen

In this paper, we propose DeepRL4FL, a deep learning fault localization (FL) approach that locates the buggy code at the statement and method levels by treating FL as an image pattern recognition problem. DeepRL4FL does so via novel code coverage representation learning (RL) and data dependencies RL for program statements. Those two types of RL on the dynami

Yi Li, Shaohua Wang, Tien N. Nguyen

Misleading method names in software projects can confuse developers, which may lead to software defects and affect code understandability. In this paper, we present DeepName, a context-based, deep learning approach to detect method name inconsistencies and suggest a proper name for a method. The key departure point is the philosophy of &#34;Show Me Your Frie

Nimish Shah, Laura I. Galindez Olascoaga, Wannes Meert, Marian Verhelst

Probabilistic reasoning is an essential tool for robust decision-making systems because of its ability to explicitly handle real-world uncertainty, constraints and causal relations. Consequently, researchers are developing hybrid models by combining Deep Learning with probabilistic reasoning for safety-critical applications like self-driving vehicles, autono

Alex Lamb

We introduce and motivate generative modeling as a central task for machine learning and provide a critical view of the algorithms which have been proposed for solving this task. We overview how generative modeling can be defined mathematically as trying to make an estimating distribution the same as an unknown ground truth distribution. This can then be qua