October 2020 arXiv papers — page 91

S. M. Bilenky

We present a basics of the Einstein General Theory of Relativity. In the first part of this review we derive relations of Riemann geometry which are used in the General Relativity. In the second part we discuss Einstein Equations and some of its consequences (The Schwarzschild solution, gravitational waves, Friedman Equations etc). In the Appendix we briefly

Zilong Wang, Mingjie Zhan, Xuebo Liu, Ding Liang

Form understanding depends on both textual contents and organizational structure. Although modern OCR performs well, it is still challenging to realize general form understanding because forms are commonly used and of various formats. The table detection and handcrafted features in previous works cannot apply to all forms because of their requirements on for

Nikolaos N. Vlassis, WaiChing Sun

We introduce a deep learning framework designed to train smoothed elastoplasticity models with interpretable components, such as a smoothed stored elastic energy function, a yield surface, and a plastic flow that are evolved based on a set of deep neural network predictions. By recasting the yield function as an evolving level set, we introduce a machine lea

Ling Wang, Cheng Zhang, Zejian Luo, Chenguang Liu

Nowadays, Deep Learning as a service can be deployed in Internet of Things (IoT) to provide smart services and sensor data processing. However, recent research has revealed that some Deep Neural Networks (DNN) can be easily misled by adding relatively small but adversarial perturbations to the input (e.g., pixel mutation in input images). One challenge in de

M. Ya. Amusia, L. V. Chernysheva

In this Letter, we investigate the non-dipole effects in time delay of photoelectrons emitted by multi-electron atoms, negative ions, and respective endohedrals. We present the necessary general formulas in the frame of the random phase approximation with exchange (RPAE) applied to atoms, negative ions, and properly adjusted to endohedrals. We concentrate on

Zhuchao Ji

Let $f$ be a post-critically finite endomorphism (PCF map for short) on $\mathbb{P}^2$, let $J_1$ denote the Julia set and let $J_2$ denote the support of the measure of maximal entropy. In this paper we show that: 1. $J_1\setminus J_2$ is contained in the union of the (finitely many) basins of critical component cycles and stable manifolds of sporadic super

Ramya Hariharan

The spread of the novel coronavirus across various countries is wide and rapid. The number of confirmed cases and the reproduction number are some of the epidemiological parameters utilized in scientific studies for the analysis and prediction of the viral transmission. The positive rate, an indicator on the extent of testing the population, aids in understa

Hannah Knight

This is an English translation of "The Problem of Resolvents and Critical Manifolds" by Tschebotarow/Chebotarev. In this article, Chebotarev explains his work on resolvent problems using critical manifolds. The current ideas of resolvent degree and essential dimension arose out of the resolvent problems Chebotarev addresses here. Original abstract by

Yong Wang

For insight into the parallel composition for true concurrency, we recall the axiomatization of the parallel composition modulo truly concurrent behavioral equivalences as the sidelights of truly concurrent process algebra APTC. We prove that: (1) There is a finite sound and complete axiomatization of the parallel composition modulo pomset, step and hp-bisim

Bingqing Yu, Jacopo Tagliabue

We tackle tag-based query refinement as a mobile-friendly alternative to standard facet search. We approach the inference challenge with reinforcement learning, and propose a deep contextual bandit that can be efficiently scaled in a multi-tenant SaaS scenario.

Machiel van Frankenhuijsen

Ellenberg proved that the abc conjecture would follow if this conjecture were known for sums $a+b=c$ such that $D\mid abc$ for some integer~$D$. Mochizuki proved a theorem with an opposite restriction, that the full abc conjecture would follow if it were known for abc sums that are not highly divisible. We prove both theorems for general number fields.

Liwei Hu, Yu Xiang, Jun Zhan, Zifang Shi

The quality of datasets is one of the key factors that affect the accuracy of aerodynamic data models. For example, in the uniformly sampled Burgers' dataset, the insufficient high-speed data is overwhelmed by massive low-speed data. Predicting high-speed data is more difficult than predicting low-speed data, owing to that the number of high-speed data i

Djallel Bouneffouf, Raphaël Féraud, Sohini Upadhyay, Yasaman Khazaeni

In this paper, we analyze and extend an online learning framework known as Context-Attentive Bandit, motivated by various practical applications, from medical diagnosis to dialog systems, where due to observation costs only a small subset of a potentially large number of context variables can be observed at each iteration;however, the agent has a freedom to

S. Indrapriyadarsini, Shahrzad Mahboubi, Hiroshi Ninomiya, Takeshi Kamio

Deep Q-learning method is one of the most popularly used deep reinforcement learning algorithms which uses deep neural networks to approximate the estimation of the action-value function. Training of the deep Q-network (DQN) is usually restricted to first order gradient based methods. This paper attempts to accelerate the training of deep Q-networks by intro

Vanja Doskoč, Timo Kötzing

In language learning in the limit we investigate computable devices (learners) learning formal languages. Through the years, many natural restrictions have been imposed on the studied learners. As such, monotonic restrictions always enjoyed particular attention as, although being a natural requirement, monotonic learners show significantly diverse behaviour

Mohammad Bagher Heydari, Mohammad Hashem Vadjed Samiei

Graphene is an interesting debated topic between scientists because of its unique properties such as tunable conductivity. Graphene conductivity can be varied by either electrostatic or magnetostatic gating or via chemical doping, which leads to the design of various photonic and electronic devices. Among various graphene-based structures, plasmonic graphene

Vanja Doskoč, Timo Kötzing

We study learners (computable devices) inferring formal languages, a setting referred to as language learning in the limit or inductive inference. In particular, we require the learners we investigate to be witness-based, that is, to justify each of their mind changes. Besides being a natural requirement for a learning task, this restriction deserves special

Julian Berger, Maximilian Böther, Vanja Doskoč, Jonathan Gadea Harder

We study learning of indexed families from positive data where a learner can freely choose a hypothesis space (with uniformly decidable membership) comprising at least the languages to be learned. This abstracts a very universal learning task which can be found in many areas, for example learning of (subsets of) regular languages or learning of natural langu

Tomasz Szandała

The primary neural networks decision-making units are activation functions. Moreover, they evaluate the output of networks neural node; thus, they are essential for the performance of the whole network. Hence, it is critical to choose the most appropriate activation function in neural networks calculation. Acharya et al. (2018) suggest that numerous recipes

Md. Masum Mushfiq, Tarik Reza Toha, Saiful Islam Salim, Aaiyeesha Mostak

Even though the traffic systems in developed countries have been analyzed with rigor and operated efficiently, the same does not generally hold for developing countries due to inadequate planning, design, and operations of their transportation systems. Because of inherent differences between internal infrastructures, the systems deployed in developed countri

Linda Ghegal

We investigate Bhabha scattering with the Seiberg-Witten expended noncommutative standard model scenario to first order of the noncommutativity parameter $Θ_{μν}$. This study is based on the definition of the noncommutativity parameter that we have assumed. We explore the noncommutative scale $Λ_{_{NC}}\geq0.8$ TeV considering different machine energy rangin

Michel Feidt, Renaud Feidt

Thermostatics of CARNOT engines has been extended by more recent research based on endo-reversible model. Our model assumes exo-reversibility but endo-irreversibility to determine new upper-bound to thermomechanical conversion. We propose a functional expression of entropy production related to transformation cycle durations. This approach analyses the energ

Andrey Chugunov

I discuss elastic properties of neutron star crust in the framework of static Coulomb solid model when atomic nuclei are treated as non-vibrating point charges; electron screening is neglected. The results are also applicable for solidified white dwarf cores and other materials, which can be modeled as Coulomb solids (dusty plasma, trapped ions, etc.). I dem

Tao Luo, Zheng Ma, Zhi-Qin John Xu, Yaoyu Zhang

Recent works show an intriguing phenomenon of Frequency Principle (F-Principle) that deep neural networks (DNNs) fit the target function from low to high frequency during the training, which provides insight into the training and generalization behavior of DNNs in complex tasks. In this paper, through analysis of an infinite-width two-layer NN in the neural

Aristidis K. Nikoloulopoulos

There is an extensive literature on methods for meta-analysis of diagnostic studies, but it mainly focuses on a single test. However, the better understanding of a particular disease has led to the development of multiple tests. A multinomial generalized linear mixed model (GLMM) is recently proposed for the joint meta-analysis of studies comparing multiple

Antonio Márquez, Salim Meddahi

We introduce and analyze a stress-based formulation for Zener's model in linear viscoelasticity. The method is aimed to tackle efficiently heterogeneous materials that admit purely elastic and viscoelastic parts in their composition. We write the mixed variational formulation of the problem in terms of a class of tensorial wave equation and obtain an ene

V. A. Antonov, I. R. Khairulin, Olga Kocharovskaya

In this paper, we present the analytical theory of attosecond pulse formation via optical modulation of an active medium of the hydrogen-like C5+ plasma-based X-ray laser at 3.4 nm wavelength in the "water window" range, taking into account a variation of the population inversion caused by radiative decay of the upper lasing states. We derive an anal

J. Shejeelammal, Aruna Goswami

Understanding the nucleosynthesis and evolution of Asymptotic Giant Branch (AGB) stars is of primary importance as they are the main producers of some of the key elements in the Universe. They are the predominant sites for the slow neutron-capture nucleosynthesis. The exact physical conditions and nucleosynthetic processes occuring at the interior of AGB sta

O. D. Petrenko, A. M. Gangapshev, Yu. M. Garilyuk, V. V. Kazalov

The study of low-yield effects requires not only good quality of the original data but also puts high requirements for their processing procedures to increase the efficiency of the selection of useful events. The exploiting of the large cylindrical proportional counter's electrostatic topology allows improving the extrapolation of information about the p

R. Spiwoks, A. Armbruster, P. Czodrowski, N. Ellis

A new Muon-to-Central-Trigger Processor Interface (MUCTPI) was built as part of the upgrade of the ATLAS Level-1 trigger system for the next Run of the Large Hadron Collider at CERN. The MUCTPI has 208 high-speed optical serial links for receiving muon candidates from the muon trigger detectors. Three high-end FPGAs are used for real-time processing of the m

Jaeseong Oh, Brendon Rhoades

Orbit harmonics is a tool in combinatorial representation theory which promotes the (ungraded) action of a linear group $G$ on a finite set $X$ to a graded action of $G$ on a polynomial ring quotient by viewing $X$ as a $G$-stable point locus in $\mathbb{C}^n$. The cyclic sieving phenomenon is a notion in enumerative combinatorics which encapsulates the fixe

Xiao Ma, Qingyuan Qi, Xun Li, Huanshui Zhang

This paper studies optimal control and stabilization problems for continuous-time mean-field systems with input delay, which are the fundamental development of control and stabilization problems for mean-field systems. There are two main contributions: 1) To the best of our knowledge, the present paper is first to establish the necessary and sufficient solva

Giuliana Materzanini, Leonid Kahle, Aris Marcolongo, Nicola Marzari

In this work we address Li-ion diffusion in thio-LISICON materials and in their oxide counterparts, exploring both the orthorhombic and tetragonal phases of Li10GeP2S12(LGPS) and Li10GeP2O12(LGPO) through extended Car-Parrinello molecular dynamics in the canonical and isobaric-isothermal ensemble. The (quasi-)orthorhombic and tetragonal phases are studied bo

Gene Louis Kim, Aaron Steven White

We propose a computational modeling framework for inducing combinatory categorial grammars from arbitrary behavioral data. This framework provides the analyst fine-grained control over the assumptions that the induced grammar should conform to: (i) what the primitive types are; (ii) how complex types are constructed; (iii) what set of combinators can be used

Abrar Hasin Kamal, Md. Asifuzzaman Jishan, Nafees Mansoor

Neural Networks and Deep Learning have seen an upsurge of research in the past decade due to the improved results. Generates text from the given image is a crucial task that requires the combination of both sectors which are computer vision and natural language processing in order to understand an image and represent it using a natural language. However exis

Omar Mohamed Awad, Mostafa Mahmoud, Isak Edo, Ali Hadi Zadeh

We present FPRaker, a processing element for composing training accelerators. FPRaker processes several floating-point multiply-accumulation operations concurrently and accumulates their result into a higher precision accumulator. FPRaker boosts performance and energy efficiency during training by taking advantage of the values that naturally appear during t

Stefan Pillwein, Johanna Kübert, Florian Rist, Przemyslaw Musialski

Elastic geodesic grids (EGG) are lightweight structures that can be easily deployed to approximate designer provided free-form surfaces. In the initial configuration the grids are perfectly flat, during deployment, though, curvature is induced to the structure, as grid elements bend and twist. Their layout is found geometrically, it is based on networks of g

Xuedong Shang, Han Shao, Jian Qian

Multi-armed bandits are widely applied in scenarios like recommender systems, for which the goal is to maximize the click rate. However, more factors should be considered, e.g., user stickiness, user growth rate, user experience assessment, etc. In this paper, we model this situation as a problem of $K$-armed bandit with multiple losses. We define relative l

Jie Wang

Let $(N, g)$ be a complete noncompact Riemannian manifold with Ricci curvature bounded from below. In this paper, we study the gradient estimates of positive solutions to a class of nonlinear elliptic equations $$Δu(x)+a(x)u(x)\log u(x)+b(x)u(x)=0$$ on $N$ where $a(x)$ is $C^{2}$-smooth while $b(x)$ is $C^{1}$ and its parabolic counterparts $$(Δ-\frac{\parti

Ann L. Greenaway, Celeste L. Melamed, M. Brooks Tellekamp, Rachel Woods-Robinson

Interest in inorganic ternary nitride materials has grown rapidly over the past few decades, as their diversity of chemistries and structures make them appealing for a variety of applications. Due to synthetic challenges posed by the stability of N2, the number of predicted nitride compounds dwarfs those that have been synthesized, offering a breadth of oppo

Andre R. R. Carvalho, Harrison Ball, Michael J. Biercuk, Michael R. Hush

We describe an experimental effort designing and deploying error-robust single-qubit operations using a cloud-based quantum computer and analog-layer programming access. We design numerically-optimized pulses that implement target operations and exhibit robustness to various error processes including dephasing noise, instabilities in control amplitudes, and

Keshav Bhandari, Mario A. DeLaGarza, Ziliang Zong, Hugo Latapie

Recently, there has been a growing interest in wearable sensors which provides new research perspectives for 360 ° video analysis. However, the lack of 360 ° datasets in literature hinders the research in this field. To bridge this gap, in this paper we propose a novel Egocentric (first-person) 360° Kinetic human activity video dataset (EgoK360). The EgoK360

Nivedita Dey, Mrityunjay Ghosh, Subhra Samir kundu, Amlan Chakrabarti

The magnificence grandeur of quantum computing lies in the inherent nature of quantum particles to exhibit true parallelism, which can be realized by indubitably fascinating theories of quantum physics. The possibilities opened by quantum computation (QC) is no where analogous to any classical simulation as quantum computers can efficiently simulate the comp

Joshua Clune, Vijay Ramamurthy, Ruben Martins, Umut A. Acar

In courses that involve programming assignments, giving meaningful feedback to students is an important challenge. Human beings can give useful feedback by manually grading the programs but this is a time-consuming, labor intensive, and usually boring process. Automatic graders can be fast and scale well but they usually provide poor feedback. Although there

Anna Trindade Falcao, S. B. Kraemer, T. C. Fischer, D. M. Crenshaw

We use Hubble Space Telescope (HST)/ Space Telescope Imaging Spectrograph (STIS) long-slit G430M and G750M spectra to analyse the extended [O~III] 5007A emission in a sample of twelve nearby (z < 0.12) luminous (L_bol > 1.6 x 10^45 erg s^-1) QSO2s. The purpose of the study is to determine the properties of the mass outflows of ionised gas and their role in A

Keshav Bhandari, Ziliang Zong, Yan Yan

Nowadays 360 video analysis has become a significant research topic in the field since the appearance of high-quality and low-cost 360 wearable devices. In this paper, we propose a novel LiteFlowNet360 architecture for 360 videos optical flow estimation. We design LiteFlowNet360 as a domain adaptation framework from perspective video domain to 360 video doma

Ram Sewak Dubey, Giorgio Laguzzi, Francesco Ruscitti

This paper examines the representation and explicit description of social welfare orders on infinite utility streams. It is assumed that the social welfare orders under investigation satisfy upper asymptotic Pareto and anonymity axioms. We prove that there exists no real-valued representation of such social welfare orders. In addition, we establish that the

Farzaneh Mousavi, Siavash Jafarzadeh, Florin Bobaru

We present a new ordinary state-based peridynamic model (OSB-PD) in 2D consistent with J2 plasticity using a novel decomposition for force and extension states. A new strategy for testing the consistency of an OSB-PD formulation for elastoplasticity is introduced. In contrast with other similar models, the new elasto-plastic OSB-PD model is objective and wor

Pierre Bourhis, Alejandro Grez, Louis Jachiet, Cristian Riveros

In the last years, enumeration algorithms with bounded delay have attracted a lot of attention for several data management tasks. Given a query and the data, the task is to preprocess the data and then enumerate all the answers to the query one by one and without repetitions. This enumeration scheme is typically useful when the solutions are treated on the f

József Kóbori, Zsolt Bagoly, Lajos G. Balázs

Detecting the thermal emission from double neutron star merger events is a challenging task because of the quick fading of the observed flux. In order to create an efficient observing strategy for their observing method it is crucial to know their intrinsic rate. Unfortunately, the numerous models existing today predict this rate on a vary wide range. Hence,

Xingfu Wu, Michael Kruse, Prasanna Balaprakash, Hal Finkel

An autotuning is an approach that explores a search space of possible implementations/configurations of a kernel or an application by selecting and evaluating a subset of implementations/configurations on a target platform and/or use models to identify a high performance implementation/configuration. In this paper, we develop an autotuning framework that lev

Wenchi Ma, Miao Yu, Kaidong Li, Guanghui Wang

Layer-wise learning, as an alternative to global back-propagation, is easy to interpret, analyze, and it is memory efficient. Recent studies demonstrate that layer-wise learning can achieve state-of-the-art performance in image classification on various datasets. However, previous studies of layer-wise learning are limited to networks with simple hierarchica

S. Nageeb Ali, Nima Haghpanah, Xiao Lin, Ron Siegel

The seller of an asset has the option to buy hard information about the value of the asset from an intermediary. The seller can then disclose the acquired information before selling the asset in a competitive market. We study how the intermediary designs and sells hard information to robustly maximize her revenue across all equilibria. Even though the interm

Daniel S. Farley, Bruce Hughes

Let $X$ be a set and let $S$ be an inverse semigroup of partial bijections of $X$. Thus, an element of $S$ is a bijection between two subsets of $X$, and the set $S$ is required to be closed under the operations of taking inverses and compositions of functions. We define $Γ_{S}$ to be the set of self-bijections of $X$ in which each $γ\in Γ_{S}$ is expressibl

Zichao Li, Liyuan Liu, Chengyu Dong, Jingbo Shang

Our goal is to understand why the robustness drops after conducting adversarial training for too long. Although this phenomenon is commonly explained as overfitting, our analysis suggest that its primary cause is perturbation underfitting. We observe that after training for too long, FGSM-generated perturbations deteriorate into random noise. Intuitively, si

Isaac Harris, Jacob D Rezac

We introduce the sparse direct sampling method (DSM) to estimate properties of a region from signals that probe the region. We demonstrate the sparse-DSM on two separate problems: estimating both the angle-of-arrival of a radio wave impinging on an array and the location and shape of an inhomogeneity from scattered acoustic waves. The sparse-DSM is qualitati

L. Parisi, D. Neagu, R. Ma, F. Campean

The ReLU activation function (AF) has been extensively applied in deep neural networks, in particular Convolutional Neural Networks (CNN), for image classification despite its unresolved dying ReLU problem, which poses challenges to reliable applications. This issue has obvious important implications for critical applications, such as those in healthcare. Re

Keyang He, Bikramjit Banerjee, Prashant Doshi

Consider a typical organization whose worker agents seek to collectively cooperate for its general betterment. However, each individual agent simultaneously seeks to act to secure a larger chunk than its co-workers of the annual increment in compensation, which usually comes from a {\em fixed} pot. As such, the individual agent in the organization must coope

Matt Shannon, Ben Poole, Soroosh Mariooryad, Tom Bagby

Non-saturating generative adversarial network (GAN) training is widely used and has continued to obtain groundbreaking results. However so far this approach has lacked strong theoretical justification, in contrast to alternatives such as f-GANs and Wasserstein GANs which are motivated in terms of approximate divergence minimization. In this paper we show tha

Roberto Baviera

Credit capital requirements in Internal Rating Based approaches require the calibration of two key parameters: the probability of default and the loss-given-default. This letter considers the uncertainty about these two parameters and models this uncertainty in an elementary way: it shows how this estimation risk can be computed and properly taken into accou

Timothy D Barfoot

Underlying many Bayesian inference techniques that seek to approximate the posterior as a Gaussian distribution is a fundamental linear algebra problem that must be solved for both the mean and key entries of the covariance. Even when the true posterior is not Gaussian (e.g., in the case of nonlinear measurement functions) we can use variational schemes that

Aman Khullar, Udit Arora

This paper presents MAST, a new model for Multimodal Abstractive Text Summarization that utilizes information from all three modalities -- text, audio and video -- in a multimodal video. Prior work on multimodal abstractive text summarization only utilized information from the text and video modalities. We examine the usefulness and challenges of deriving in

Wei Chen, Yu Liu, Weiping Wang, Tinne Tuytelaars

In this paper, we consider the problem of fine-grained image retrieval in an incremental setting, when new categories are added over time. On the one hand, repeatedly training the representation on the extended dataset is time-consuming. On the other hand, fine-tuning the learned representation only with the new classes leads to catastrophic forgetting. To t

Nick Doudchenko, Evgeni Drynkin

In this paper we propose a new method of estimation for discrete choice demand models when individual level data are available. The method employs a two-step procedure. Step 1 predicts the choice probabilities as functions of the observed individual level characteristics. Step 2 estimates the structural parameters of the model using the estimated choice prob

Alex Lamb, Anirudh Goyal, Agnieszka Słowik, Michael Mozer

Feed-forward neural networks consist of a sequence of layers, in which each layer performs some processing on the information from the previous layer. A downside to this approach is that each layer (or module, as multiple modules can operate in parallel) is tasked with processing the entire hidden state, rather than a particular part of the state which is mo

Francesco Cellarosi

We consider autocorrelation functions for supersymmetric quantum mechanical systems (consisting of a fermion and a boson) confined in trigonometric Pöschl-Teller partner potentials. We study the limit of rescaled autocorrelation functions (at random time) as the localization of the initial state goes to infinity. The limiting distribution can be described us

Zahra Nourbakhsh, Nicolas Tancogne-Dejean, Hamed Merdji, Angel Rubio

On the basis of real-time ab initio calculations, we study the non-perturbative interaction of two-color laser pulses with MgO crystal in the strong field regime to generate isolated attosecond pulse from high-harmonic emissions from MgO crystal. In this regard, we examine the impact of incident pulse characteristics such as its shape, intensity, and ellipti

Hafiz Anwar Ullah Khan, Jip Kim, Yury Dvorkin

Power producers can exhibit strategic behavior in electricity markets to maximize their profits. This behavior is more pronounced with the deregulation of distribution markets, which offers an opportunity for profit arbitrage between transmission and distribution (T&D) markets. However, the temporally distinct nature of these two markets introduces a signifi

Siyi Tang, Amirata Ghorbani, Rikiya Yamashita, Sameer Rehman

The reliability of machine learning models can be compromised when trained on low quality data. Many large-scale medical imaging datasets contain low quality labels extracted from sources such as medical reports. Moreover, images within a dataset may have heterogeneous quality due to artifacts and biases arising from equipment or measurement errors. Therefor

Paul Hriljac

Homomorphic encryption is a method used in cryptopgraphy to create programs that can interact with encrypted data without ever leaving the data in the clear. This has many potential applications in cybersecurity. This paper uses automorphisms of affine space to create a form of homomorphic encryption for straight line programs. The encryption method used for

Felipe Alarcon, Richard Teague, Ke Zhang, Edwin Bergin

Recent surveys of protoplanetary disks show that substructure in dust thermal continuum emission maps is common in protoplanetary disks. These substructures, most prominently rings and gaps, shape and change the chemical and physical conditions of the disk, along with the dust size distributions. In this work, we use a thermochemical code to focus on the che

Jie Lei, Licheng Yu, Tamara L. Berg, Mohit Bansal

Given a video with aligned dialogue, people can often infer what is more likely to happen next. Making such predictions requires not only a deep understanding of the rich dynamics underlying the video and dialogue, but also a significant amount of commonsense knowledge. In this work, we explore whether AI models are able to learn to make such multimodal comm

Nancy Remage Evans, H. Moritz Guenther, Howard E. Bond, Gail H. Schaefer

Cepheids in multiple systems provide information on the outcome of the formation of massive stars. They can also lead to exotic end-stage objects. This study concludes our survey of 70 galactic Cepheids using the {\it Hubble Space Telescope\} (\HST) Wide Field Camera~3 (WFC3) with images at two wavelengths to identify companions closer than $5\arcsec$. In th

Yilun Wu

Meta-learning or few-shot learning, has been successfully applied in a wide range of domains from computer vision to reinforcement learning. Among the many frameworks proposed for meta-learning, bayesian methods are particularly favoured when accurate and calibrated uncertainty estimate is required. In this paper, we investigate the similarities and disparit

Muhammad Waseem Akhtar, Syed Ali Hassan, Rizwan Ghaffar, Haejoon Jung

The sixth-generation (6G) wireless communication network is expected to integrate the terrestrial, aerial, and maritime communications into a robust network which would be more reliable, fast, and can support a massive number of devices with ultra-low latency requirements. The researchers around the globe are proposing cutting edge technologies such as artif

Pavel Loskot

The paper investigates the problem of performing correlation analysis when the number of observations is very large. In such a case, it is often necessary to combine the random observations to achieve dimensionality reduction of the problem. A novel class of statistical measures is obtained by approximating the Taylor expansion of a general multivariate scal

Ali Bereyhi, Saba Asaad, Ralf R. Müller, Rafael F. Schaefer

This work studies secure transmission in intelligent reflecting surfaces (IRS)-assisted MIMO systems when an active eavesdropper is available in the network. We consider a scenario in which the eavesdropper performs an active pilot attack to contaminate the channel estimation at the base station. Invoking the method of secure regularized zero forcing, we dev

Calum Perrio, Harish Tayyar Madabushi

This paper presents our submission to Task 2 of the Workshop on Noisy User-generated Text. We explore improving the performance of a pre-trained transformer-based language model fine-tuned for text classification through an ensemble implementation that makes use of corpus level information and a handcrafted feature. We test the effectiveness of including the

A. Yu. Polyarush, V. A. Duk, S. N. Filippov, E. N. Guschin

Results of a study of the $K^+ \rightarrow π^{0} e^{+} νγ$ decay at OKA setup are presented. More than 32000 events of this decay are observed. The differential spectra over the photon energy and the photon-electron opening angle in kaon rest frame are presented. The branching ratios, normalized to that of $K_{e3}$ decay are calculated for different cuts in

Saurabh Hinduja, Shaun Canavan, Saandeep Aathreya

Detecting action units is an important task in face analysis, especially in facial expression recognition. This is due, in part, to the idea that expressions can be decomposed into multiple action units. In this paper we investigate the impact of action unit occurrence patterns on detection of action units. To facilitate this investigation, we review state o

S. Mianowski, K. Brylew, A. Dziedzic, K. Grzenda

This paper presents the results of the fast neutron irradiation (E$_n$ > 0.5MeV) of an EJ-276 scintillator performed in the MARIA research reactor with fluence up to 5.3$\times$10$^{15}$ particles/cm$^2$. In our work, four samples with size $ϕ$25.4~mm$\times$5~mm were tested. The changes in the light yield, emission and absorption spectrum and neutron/gamma

John J. Howard, Yevgeniy B. Sirotin, Jerry L. Tipton, Arun R. Vemury

Human face features can be used to determine individual identity as well as demographic information like gender and race. However, the extent to which black-box commercial face recognition algorithms (CFRAs) use gender and race features to determine identity is poorly understood despite increasing deployments by government and industry. In this study, we qua

S. B. Soltau, L. C. L. Botti

We apply a machine learning algorithm called XGBoost to explore the periodicity of two radio sources: PKS~1921-293 (OV~236) and PKS~2200+420 (BL~Lac), both radio frequency dataset obtained from University of Michigan Radio Astronomy Observatory (UMRAO), at 4.8 GHz, 8.0 GHz, and 14.5 GHz, between 1969 to 2012. From this methods, we find that the XGBoost provi

Sandra Di Rocco, David Eklund, Oliver Gäfvert

In this paper we present an efficient algorithm to produce a provably dense sample of a smooth compact variety. The procedure is partly based on computing $\textit{bottlenecks}$ of the variety. Using geometric information such as the bottlenecks and the $\textit{local reach}$ we also provide bounds on the density of the sample needed in order to guarantee th

S. Das, N. Kennedy, A. Cacciuto

We perform numerical simulations of an active fully flexible self-avoiding polymer as a function of the quality of the embedding solvent described in terms of an effective monomer-monomer interaction. Specifically, by extracting the Flory exponent of the active polymer under different conditions, we are able to pin down the location of the coil-globule trans

T. Kobayashi, Q. -P. Ding, H. Taniguchi, K. Satoh

The spin-liquid candidate $κ$-(ET)$_2$Cu$_2$(CN)$_3$ [ET: bis(ethylenedithio)tetrathiafulvalene] does not exhibit magnetic ordering down to a very low temperature, but shows a mysterious anomaly at 6 K. The origin of the so-called 6 K anomaly is still under debate. We carried out nuclear quadrupole resonance (NQR) measurements on the copper sites of the insu

Nir Avni, Chen Meiri

We show that there is a sentence $φ$ in the first order language of groups such that a finitely generated group $Γ$ satisfies $φ$ if and only if $Γ$ is isomorphic to a group of the form $\mathrm{PSL}_n(O)$, where $n \geq 3$ and $O$ is a ring of $S$-integers in a number field.

Tibor Rakovszky, Frank Pollmann, C. W. von Keyserlingk

In a recent paper (Commun. Phys. 3, 100) Znidaric studies the growth of higher Renyi entropies in diffusive systems and claims that they generically grow ballistically in time, except for spin-1/2 models in d=1 dimension. Here, we point out that the necessary conditions for sub-ballistic growth of Renyi entropies are in fact much more general, and apply to a

Simeon Hellerman, Ian Swanson

We consider the large-charge expansion of the charged ground state of a Schrodinger-invariant, nonrelativistic conformal field theory in a harmonic trap, in general dimension d. In the existing literature, the energy in the trap has been computed to next-to-leading order (NLO) at large charge Q, which comes from the classical contribution of two higher-deriv

Frank Arute, Kunal Arya, Ryan Babbush, Dave Bacon

Strongly correlated quantum systems give rise to many exotic physical phenomena, including high-temperature superconductivity. Simulating these systems on quantum computers may avoid the prohibitively high computational cost incurred in classical approaches. However, systematic errors and decoherence effects presented in current quantum devices make it diffi

Santiago Mazuelas, Andrea Zanoni, Aritz Perez

Supervised classification techniques use training samples to find classification rules with small expected 0-1 loss. Conventional methods achieve efficient learning and out-of-sample generalization by minimizing surrogate losses over specific families of rules. This paper presents minimax risk classifiers (MRCs) that do not rely on a choice of surrogate loss

Siyuan Lu, Pengzi Miao

Motivated by the rigidity case in the localized Riemannian Penrose inequality, we show that suitable singular metrics attaining the optimal value in the Riemannian Penrose inequality is necessarily smooth in properly specified coordinates. If applied to hypersurfaces enclosing the horizon in a spatial Schwarzschild manifold, the result gives the rigidity of

Alexander Chemey, Ashley Pica, Liangyu Yao, Walter Loveland

The total kinetic energy (TKE) release in fission is an important observable, constituting over 80% of the energy released in fission (E$_{f}$ $\approx$ 200 MeV). While the TKE release in the $^{239}$Pu(n,f) reaction was previously measured up to 50 MeV incident neutron energy (E$_{n}$), there were features in TKE release at the highest values of E$_{n}$ tha

Paul Wrede, Mariana Medina-Sánchez, Vladimir M. Fomin, Oliver G. Schmidt

Different propulsion mechanisms have been suggested for describing the motion of a variety of chemical micromotors, including the bubble-recoil mechanism, which has attracted great attention in the last decades due to its high efficiency and thrust force, enabling several applications in the fields of environmental remediation and biomedicine. Bubble-induced

Yongqing Liang, Xin Li, Navid Jafari, Qin Chen

We propose a new matching-based framework for semi-supervised video object segmentation (VOS). Recently, state-of-the-art VOS performance has been achieved by matching-based algorithms, in which feature banks are created to store features for region matching and classification. However, how to effectively organize information in the continuously growing feat

Jamie Haddock, Lara Kassab, Sixian Li, Alona Kryshchenko

We propose several new models for semi-supervised nonnegative matrix factorization (SSNMF) and provide motivation for SSNMF models as maximum likelihood estimators given specific distributions of uncertainty. We present multiplicative updates training methods for each new model, and demonstrate the application of these models to classification, although they

T. Bonnaire, A. Decelle, N. Aghanim

We present a novel framework exploiting the cascade of phase transitions occurring during a simulated annealing of the Expectation-Maximisation algorithm to cluster datasets with multi-scale structures. Using the weighted local covariance, we can extract, a posteriori and without any prior knowledge, information on the number of clusters at different scales

Alexander Ku, Peter Anderson, Roma Patel, Eugene Ie

We introduce Room-Across-Room (RxR), a new Vision-and-Language Navigation (VLN) dataset. RxR is multilingual (English, Hindi, and Telugu) and larger (more paths and instructions) than other VLN datasets. It emphasizes the role of language in VLN by addressing known biases in paths and eliciting more references to visible entities. Furthermore, each word in a

Rakibur Rahman, Fahima Nowrin, M. Shahnoor Rahman, Jonathan A. D. Wattis

We consider the binary fragmentation problem in which, at any breakup event, one of the daughter segments either survives with probability $p$ or disappears with probability $1\!-\!p$. It describes a stochastic dyadic Cantor set that evolves in time, and eventually becomes a fractal. We investigate this phenomenon, through analytical methods and Monte Carlo

Gui-Jun Ding, Ferruccio Feruglio, Xiang-Gan Liu

We extend the framework of modular invariant supersymmetric theories to encompass invariance under more general discrete groups $Γ$, that allow the presence of several moduli and make connection with the theory of automorphic forms. Moduli span a coset space $G/K$, where $G$ is a Lie group and $K$ is a compact subgroup of $G$, modded out by $Γ$. For a genera