May 2020 arXiv papers — page 3

Nikolaos S. Papageorgiou, Vicenţiu D. Rădulescu, Dušan D. Repovš

We consider a nonlinear elliptic equation driven by a nonhomogeneous differential operator plus an indefinite potential. On the reaction term we impose conditions only near zero. Using variational methods, together with truncation and perturbation techniques and critical groups, we produce three nontrivial solutions with sign information. In the semilinear c

Yura Zharkovsky, Ovadya Menadeva

Monitoring large areas is presently feasible with high resolution drone cameras, as opposed to time-consuming and expensive ground surveys. In this work we reveal for the first time, the potential of using a state-of-the-art change detection GAN based algorithm with high resolution drone images for infrastructure inspection. We demonstrate this concept on so

Ilya Gorshkov, Timur Nasybullov

A. Smoktunowicz and L. Vendramin conjectured that if $A$ is a finite skew brace with solvable additive group, then the multiplicative group of $A$ is solvable. In this short note we make a step towards positive solution of this conjecture proving that if $A$ is a minimal finite skew brace with solvable additive group and non-solvable multiplicative group, th

Girma Negashe, Adane Mamuye

Recognition of handwritten document aims at transforming document images into a machine understandable format. Handwritten document recognition is the most challenging area in the field of pattern recognition. It becomes more complex when a document was written on vellum before hundreds of years, like older Geez scripts. In this study, we introduced a modifi

Pin Tang, Pinli Yang, Yuang Shi, Yi Zhou

Named entity recognition (NER) plays an essential role in natural language processing systems. Judicial NER is a fundamental component of judicial information retrieval, entity relation extraction, and knowledge map building. However, Chinese judicial NER remains to be more challenging due to the characteristics of Chinese and high accuracy requirements in t

Chaman Kumar, Neelima, Christoph Reisinger, Wolfgang Stockinger

In this paper, we first establish well-posedness of McKean-Vlasov stochastic differential equations (McKean-Vlasov SDEs) with common noise, possibly with coefficients having super-linear growth in the state variable. Second, we present stable time-stepping schemes for this class of McKean-Vlasov SDEs. Specifically, we propose an explicit tamed Euler and tame

Ashkan Mohammadi, Boris Mordukhovich

This paper is devoted to developing and applications of a generalized differential theory of variational analysis that allows us to work in incomplete normed spaces, without employing conventional variational techniques based on completeness and limiting procedures. The main attention is paid to generalized derivatives and subdifferentials of the Dini-Hadama

Florian Pausinger

For coprime integers $N,a,b,c$, with $0<a<b<c<N$, we define the set $$ \{ (na \! \! \! \! \pmod{N}, nb \! \! \! \! \pmod{N}, nc \! \! \! \! \pmod{N}) : 0 \leq n < N\}. $$ We study which parameters $N,a,b,c$ generate point sets with long shortest distances between the points of the set in dependence of $N$ and relate such sets to lattices of a particular form

Eliav Buchnik, Edith Cohen

Classically, ML models trained with stochastic gradient descent (SGD) are designed to minimize the average loss per example and use a distribution of training examples that remains {\em static} in the course of training. Research in recent years demonstrated, empirically and theoretically, that significant acceleration is possible by methods that dynamically

Hichem Rahab, Abdelhafid Zitouni, Mahieddine Djoudi

It is very current in today life to seek for tracking the people opinion from their interaction with occurring events. A very common way to do that is comments in articles published in newspapers web sites dealing with contemporary events. Sentiment analysis or opinion mining is an emergent field who is the purpose is finding the behind phenomenon masked in

Xueyan Wang, Man Jiang, Zhangcan Yang

One of the key challenges to realize controlled fusion energy is tritium self-sufficiency. The application of hydrogen permeation barrier (HPB) is considered to be necessary for tritium self-sufficiency. {\alpha}-Al2O3 is currently a candidate material for HPB. However, a crucial issue for {\alpha}-Al2O3 is that its permeability reduction factor (PRF) will d

Weijun Fang, Shu-Tao Xia, Fang-Wei Fu

The parameters of a $q$-ary MDS Euclidean self-dual codes are completely determined by its length and the construction of MDS Euclidean self-dual codes with new length has been widely investigated in recent years. In this paper, we give a further study on the construction of MDS Euclidean self-dual codes via generalized Reed-Solomon (GRS) codes and their ext

N. Wall Wennerdal, A. Ask, P. Holmvall, T. Löfwander

We report results of a microscopic calculation of a second-order phase transition into a state breaking time-reversal and translational invariance along pair-breaking edges of $d$-wave superconductors. By solving a tight-binding model through exact diagonalization with the Bogoliubov-de~Gennes method, we find that such a state with current loops having a dia

Anze Bozic, Antonio Siber

When pollen grains become exposed to the environment, they rapidly desiccate. To protect themselves until rehydration, the grains undergo characteristic infolding with the help of special structures in the grain wall---apertures---where the otherwise thick exine shell is absent or reduced in thickness. Recent theoretical studies have highlighted the importan

M. Chaudhary, T. V. Krishna, Sowmya R. Nanda, S. K. Karthick

The present work aims to study the fluidic behavior on lateral confinement by placing side-walls on the planar plug nozzle through experiments. The study involves two cases of nozzle pressure ratio (NPR=3, 6), which correspond to over-expanded nozzle operating conditions. Steady-state pressure measurements, together with schlieren and surface oil flow visual

Igor Molybog, Javad Lavaei

The paper studies the complexity of the optimization problem behind the Model-Agnostic Meta-Learning (MAML) algorithm. The goal of the study is to determine the global convergence of MAML on sequential decision-making tasks possessing a common structure. We are curious to know when, if at all, the benign landscape of the underlying tasks results in a benign

Liang Chen, Yanchun Liang, Xiaohu Shi, You Zhou

Time Delay Neural Network (TDNN) is a well-performing structure for DNN-based speaker recognition systems. In this paper we introduce a novel structure Crossed-Time Delay Neural Network (CTDNN) to enhance the performance of current TDNN. Inspired by the multi-filters setting of convolution layer from convolution neural network, we set multiple time delay uni

Michael Finkelberg, David Kazhdan, Yakov Varshavsky

We apply the dimension theory developed in [BKV] to establish some of Lusztig's conjectures [Lu].

Shaobo Gan, Yi Shi, Disheng Xu, Jinhua Zhang

In this paper, we study the centralizer of a partially hyperbolic diffeomorphism on $\mathbb{T}^3$ which is homotopic to an Anosov automorphism, and we show that either its centralizer is virtually trivial or such diffeomorphism is smoothly conjugate to its linear part.

C. Lidman, B. E. Tucker, T. M. Davis, S. A. Uddin

We present a description of the Australian Dark Energy Survey (OzDES) and summarise the results from its six years of operations. Using the 2dF fibre positioner and AAOmega spectrograph on the 3.9-metre Anglo-Australian Telescope, OzDES has monitored 771 AGN, classified hundreds of supernovae, and obtained redshifts for thousands of galaxies that hosted a tr

Takeshi Saito

For a regular scheme and a prime number $p$, we define the FW-cotangent bundle as a vector bundle on the closed subscheme defined by $p=0$, under a certain finiteness condition. For a constructible complex on the etale site of the scheme, we introduce the condition to be micro-supported on a closed conical subset in the FW-cotangent bundle. At the end of the

Álvaro Gajardo, Hans-Georg Müller

Point processes in time have a wide range of applications that include the claims arrival process in insurance or the analysis of queues in operations research. Due to advances in technology, such samples of point processes are increasingly encountered. A key object of interest is the local intensity function. It has a straightforward interpretation that all

Ehsan Haghighat, Ali Can Bekar, Erdogan Madenci, Ruben Juanes

The Physics-Informed Neural Network (PINN) framework introduced recently incorporates physics into deep learning, and offers a promising avenue for the solution of partial differential equations (PDEs) as well as identification of the equation parameters. The performance of existing PINN approaches, however, may degrade in the presence of sharp gradients, as

Ping Guo, Qian Yin

Drawing on the idea that brain development is a Darwinian process of ``evolution + selection'' and the idea that the current state is a local equilibrium state of many bodies with self-organization and evolution processes driven by the temperature and gravity in our universe, in this work, we describe an artificial intelligence system called the ``Synergetic

Yuanyuan Chen, Wuhong Zhang, Dongkai Zhang, Xiaodong Qiu

The Bell basis, a set of maximally entangled biphoton state, is a critical prerequisite towards quantum information processing, and many quantum applications have highlighted the requirement for the manipulation of high-dimensional Bell basis. While the Bell states can be created by using ingenious single-photon quantum gates, its implementation complexity i

Xueqi Yang, Jianfeng Chen, Rahul Yedida, Zhe Yu

Static code warning tools often generate warnings that programmers ignore. Such tools can be made more useful via data mining algorithms that select the "actionable" warnings; i.e. the warnings that are usually not ignored. In this paper, we look for actionable warnings within a sample of 5,675 actionable warnings seen in 31,058 static code warnings from Fin

Siavash Valafar

Molecular testing is rapidly becoming integral to the global tuberculosis (TB) control effort. Uncommon mechanisms of resistance can escape detection by these platforms and lead to the development of Multi-Drug Resistant (MDR) strains. This article is a systematic review of published articles that reported isoniazid (INH) resistance-conferring mutations betw

Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Ravikumar

Feature based explanations, that provide importance of each feature towards the model prediction, is arguably one of the most intuitive ways to explain a model. In this paper, we establish a novel set of evaluation criteria for such feature based explanations by robustness analysis. In contrast to existing evaluations which require us to specify some way to

Qinggang Zhou, Yawen Zhang, Pengcheng Li, Xiaoyong Liu

The state-of-the-art deep learning algorithms rely on distributed training systems to tackle the increasing sizes of models and training data sets. Minibatch stochastic gradient descent (SGD) algorithm requires workers to halt forward/back propagations, to wait for gradients aggregated from all workers, and to receive weight updates before the next batch of

Eiichi Bannai, Manabu Oura, Da Zhao

Nebe, Rains and Sloane studied the polynomial invariants for real and complex Clifford groups and they relate the invariants to the space of complete weight enumerators of certain self-dual codes. The purpose of this paper is to show that very similar results can be obtained for the invariants of the complex Clifford group $\mathcal{X}_m$ acting on the space

Feifan Lv, Bo Liu, Feng Lu

This paper proposes a new light-weight convolutional neural network (5k parameters) for non-uniform illumination image enhancement to handle color, exposure, contrast, noise and artifacts, etc., simultaneously and effectively. More concretely, the input image is first enhanced using Retinex model from dual different aspects (enhancing under-exposure and supp

Zimu Khakhaleva-Li, Craig J. Hogan

Rapid advancement in the observation of cosmic strings has been made in recent years placing increasingly stringent constraints on their properties, with $G\mu\lesssim 10^{-11}$ from Pulsar Timing Array (PTA). Cosmic string loops with low string tension clump in the Galaxy due to slow loop decay and low gravitational recoil, resulting in great enhancement to

Henrik Parsamyan, Hovhannes Haroyan, Khachatur Nerkararyan

We have shown that a structure composed of a semiconductor hemicylinder and a dielectric waveguide separated by a thin metal layer permits efficient and easy light control. In such a system, it is possible to ensure matching between TE and TM modes of a low refractive index waveguide with the corresponding whispering gallery modes of the hemicylinder of a hi

Zheng Tracy Ke, Yucong Ma, Xihong Lin

The spiked covariance model has gained increasing popularity in high-dimensional data analysis. A fundamental problem is determination of the number of spiked eigenvalues, $K$. For estimation of $K$, most attention has focused on the use of $top$ eigenvalues of sample covariance matrix, and there is little investigation into proper ways of utilizing $bulk$ e

Aikaterini Aretaki, Efthymios N. Karatzas, Georgios Katsouleas

In this work, we analyze an unfitted discontinuous Galerkin discretization for the numerical solution of the Stokes system based on equal higher-order discontinuous velocities and pressures. This approach combines the best from both worlds, firstly the advantages of a piece-wise discontinuous high-order accurate approximation and secondly the advantages of a

Hui Hu, Jia Wang, Xia-Ji Liu

Ultradilute quantum droplets are intriguing new state of matter, in which the attractive mean-field force can be balanced by the repulsive force from quantum fluctuations to avoid collapse. Here, we present a microscopic theory of ultradilute quantum droplets in three-, one- and two-dimensional two-component Bose-Bose mixtures, by generalizing the convention

He Wang, Jin Wang

We investigate the nonequilibrium back reaction on the Schwarzschild black hole from the radiation field. The back reactions are characterized by the membrane close to the black hole. When the membrane is thin, we found that larger temperature difference can lead to more significant negative surface tension, larger thermodynamic dissipation cost and back rea

Abhishek Gupta, Tania De Gasperis

With the push for contact- and proximity-tracing solutions as a means to manage the spread of the pandemic, there is a distrust between the citizens and authorities that are deploying these solutions. The efficacy of the solutions relies on meeting a minimum uptake threshold which is hitting a barrier because of a lack of trust and transparency in how these

Hei-Long Chan, Hoi-Man Yuen, Chun-Ting Au, Kate Ching-Ching Chan

Craniofacial profile is one of the anatomical causes of obstructive sleep apnea(OSA). By medical research, cephalometry provides information on patients' skeletal structures and soft tissues. In this work, a novel approach to cephalometric analysis using quasi-conformal geometry based local deformation information was proposed for OSA classification. Our stu

Noriko Akutsu

The crossover from a Berezinskii--Kosterlitz--Thouless (BKT) rough surface to a Kardar--Parisi--Zhang (KPZ) rough surface on a vicinal surface is studied using the Monte Carlo method in the non-equilibrium steady state in order to address discrepancies between theoretical results and experiments. The model used is a restricted solid-on-solid (RSOS) model wit

Nikolay M. Babayan, Mamikon S. Ginovyan, Murad S. Taqqu

One of the main problem in prediction theory of discrete-time second-order stationary processes $X(t)$ is to describe the asymptotic behavior of the best linear mean squared prediction error in predicting $X(0)$ given $ X(t),$ $-n\le t\le-1$, as $n$ goes to infinity. This behavior depends on the regularity (deterministic or non-deterministic) of the process

Song-Bo Yang, Tian-li Yu

In recent years, semi-supervised learning (SSL) has shown tremendous success in leveraging unlabeled data to improve the performance of deep learning models, which significantly reduces the demand for large amounts of labeled data. Many SSL techniques have been proposed and have shown promising performance on famous datasets such as ImageNet and CIFAR-10. Ho

Regina S. Burachik, Alexander C. Kalloniatis, C. Yalçın Kaya

We propose new mathematical optimization models for generating sparse dynamical graphs, or networks, that can achieve synchronization. The synchronization phenomenon is studied using the Kuramoto model, defined in terms of the adjacency matrix of the graph and the coupling strength of the network, modelling the so-called coupled oscillators. Besides sparsity

Santhosh Karnik, Justin Romberg, Mark A. Davenport

The discrete prolate spheroidal sequences (DPSSs) are a set of orthonormal sequences in $\ell_2(\mathbb{Z})$ which are strictly bandlimited to a frequency band $[-W,W]$ and maximally concentrated in a time interval $\{0,\ldots,N-1\}$. The timelimited DPSSs (sometimes referred to as the Slepian basis) are an orthonormal set of vectors in $\mathbb{C}^N$ whose

Xiufan Yu, Danning Li, Lingzhou Xue

Testing large covariance matrices is of fundamental importance in statistical analysis with high-dimensional data. In the past decade, three types of test statistics have been studied in the literature: quadratic form statistics, maximum form statistics, and their weighted combination. It is known that quadratic form statistics would suffer from low power ag

Yangyang Xu, Yibo Xu

Stochastic gradient methods (SGMs) have been extensively used for solving stochastic problems or large-scale machine learning problems. Recent works employ various techniques to improve the convergence rate of SGMs for both convex and nonconvex cases. Most of them require a large number of samples in some or all iterations of the improved SGMs. In this paper

Jae Sung Park, Dinesh Manocha

We present a novel approach to generate collision-free trajectories for a robot operating in close proximity with a human obstacle in an occluded environment. The self-occlusions of the robot can significantly reduce the accuracy of human motion prediction, and we present a novel deep learning-based prediction algorithm. Our formulation uses CNNs and LSTMs a

Liang Liu, Xiaopeng Luo

In this paper, we propose a novel accelerated stochastic gradient method with momentum, which momentum is the weighted average of previous gradients. The weights decays inverse proportionally with the iteration times. Stochastic gradient descent with momentum (Sgdm) use weights that decays exponentially with the iteration times to generate an momentum term.

Vivek Mohan, Deepak Singla, Tarun Pulluri, Andres Ussa

As an alternative sensing paradigm, dynamic vision sensors (DVS) have been recently explored to tackle scenarios where conventional sensors result in high data rate and processing time. This paper presents a hybrid event-frame approach for detecting and tracking objects recorded by a stationary neuromorphic sensor, thereby exploiting the sparse DVS output in

Nil-Jana Akpinar, Aaditya Ramdas, Umut Acar

Educational software data promises unique insights into students' study behaviors and drivers of success. While much work has been dedicated to performance prediction in massive open online courses, it is unclear if the same methods can be applied to blended courses and a deeper understanding of student strategies is often missing. We use pattern mining and

Yanjun Cao, Giovanni Beltrame

Monocular cameras coupled with inertial measurements generally give high performance visual inertial odometry. However, drift can be significant with long trajectories, especially when the environment is visually challenging. In this paper, we propose a system that leverages ultra-wideband ranging with one static anchor placed in the environment to correct t

Behnam Esmayli, Piotr Hajłasz

The aim of this paper is to provide a self-contained proof of a general case of the coarea inequality, also known as the Eilenberg inequality. The result is known, but we are not aware of any place that a proof would be written with all details. The known proof is based on a difficult result of Davies. Our proof is elementary and does not use Davies' theorem

Julia White, Jesse Mu, Noah D. Goodman

A hallmark of human language is the ability to effectively and efficiently convey contextually relevant information. One theory for how humans reason about language is presented in the Rational Speech Acts (RSA) framework, which captures pragmatic phenomena via a process of recursive social reasoning (Goodman & Frank, 2016). However, RSA represents ideal rea

Jeiyoon Park, Chanhee Lee, Kuekyeng Kim, Heuiseok Lim

Despite its notable success in adversarial learning approaches to multi-domain task-oriented dialog system, training the dialog policy via adversarial inverse reinforcement learning often fails to balance the performance of the policy generator and reward estimator. During optimization, the reward estimator often overwhelms the policy generator and produces

Ankit Goel, Abdulazeez Mohammed Salim, Ahmad Ansari, Sai Ravela

This paper develops an adaptive autopilot for quadcopters with unknown dynamics. To do this, the PX4 autopilot architecture is modified so that the feedback and feedforward controllers are replaced by adaptive control laws based on retrospective cost adaptive control (RCAC). The present paper provides a numerical investigation of the performance of the adapt

Brandon S. Zerbe, Phil M. Duxbury

This paper is the first in a series which develops the theory of emittance dynamics based on simple statistical reasoning. Emittance is a central quantity used to characterize the quality of electron microscopes, photon sources and particle beams. Emittance growth in high intensity charged particle beams is a particularly challenging non-equilibrium statisti

Ange Lou, Shuyue Guan, Murray Loew

Recently, deep learning has become much more popular in computer vision area. The Convolution Neural Network (CNN) has brought a breakthrough in images segmentation areas, especially, for medical images. In this regard, U-Net is the predominant approach to medical image segmentation task. The U-Net not only performs well in segmenting multimodal medical imag

Jiancheng Qin, Jin Yang, Ying Chen, Qiang Ye

Two-stage ensemble-based forecasting methods have been studied extensively in the wind power forecasting field. However, deep learning-based wind power forecasting studies have not investigated two aspects. In the first stage, different learning structures considering multiple inputs and multiple outputs have not been discussed. In the second stage, the mode

Hantao Yao, Shaobo Min, Yongdong Zhang, Changsheng Xu

Transductive Zero-shot learning (ZSL) targets to recognize the unseen categories by aligning the visual and semantic information in a joint embedding space. There exist four kinds of domain biases in Transductive ZSL, i.e., visual bias and semantic bias between two domains and two visual-semantic biases in respective seen and unseen domains, but existing wor

Lixing You

The superconducting nanowire single-photon detector (SNSPD) is a quantum-limit superconducting optical detector based on the Cooper-pair breaking effect by a single photon, which exhibits a higher detection efficiency, lower dark count rate, higher counting rate, and lower timing jitter when compared with those exhibited by its counterparts. SNSPDs have been

Zhu Wang, Anat Lubetzky, Charles Hendee, Marta Gospodarek

We developed a novel assessment platform with untethered virtual reality, 3-dimensional sounds, and pressure sensing floor mat to help assess the walking balance and negotiation of obstacles given diverse sensory load and/or cognitive load. The platform provides an immersive 3D city-like scene with anticipated/unanticipated virtual obstacles. Participants ne

Hongyuan You, Furkan Kocayusufoglu, Ambuj K. Singh

Network regularization is an effective tool for incorporating structural prior knowledge to learn coherent models over networks, and has yielded provably accurate estimates in applications ranging from spatial economics to neuroimaging studies. Recently, there has been an increasing interest in extending network regularization to the spatio-temporal case to

K. Tatar, D. Bisig, P. Pasquier

We present the Latent Timbre Synthesis (LTS), a new audio synthesis method using Deep Learning. The synthesis method allows composers and sound designers to interpolate and extrapolate between the timbre of multiple sounds using the latent space of audio frames. We provide the details of two Variational Autoencoder architectures for LTS, and compare their ad

Fernando Micena

We obtain smooth conjugacy between non-necessarily special Anosov endomorphisms in the conservative case. Among other results, we prove that a strongly special $C^{\infty}-$Anosov endomorphism of $\mathbb{T}^2$ and its linearization are smoothly conjugated since they have the same periodic data. Assuming that for a strongly special $C^{\infty}-$Anosov endomo

Fernando Micena, Rafael de la Llave

In the present work we obtain rigidity results analysing the set of regular points, in the sense of Oseledec's Theorem. It is presented a study on the possibility of an Anosov diffeomorphisms having all Lyapunov exponents defined everywhere. We prove that this condition implies local rigidity of an Anosov automorphism of the torus $\mathbb{T}^d, d \geq 3,$ $

Georg A. Gottwald

We propose a conceptual model which generates abrupt climate changes akin to Dansgaard-Oeschger events. In the model these abrupt climate changes are not triggered by external perturbations but rather emerge in a dynamic self-consistent model through complex interactions of the ocean, the atmosphere and an intermittent process. The abrupt climate changes are

Christopher Griffin, Justin Semonsen, Andrew Belmonte

We study the network replicator equation and characterize its fixed points on arbitrary graph structures for $2 \times 2$ symmetric games. We show a relationship between the asymptotic behavior of the network replicator and the existence of an independent vertex set in the graph and also show that complex behavior cannot emerge in $2 \times 2$ games. This li

Shanming Ji, Ming Mei

This paper is concerned with the multi-dimensional compressible Euler equations with time-dependent over-damping of the form $-\frac{\mu}{(1+t)^\lambda}\rho\boldsymbol u$ in $\mathbb R^n$, where $n\ge2$, $\mu>0$, and $\lambda\in[-1,0)$. This continues our previous work dealing with the under-damping case for $\lambda\in[0,1)$. We show the optimal decay estim

Boris Landa, Ronald R. Coifman, Yuval Kluger

A fundamental step in many data-analysis techniques is the construction of an affinity matrix describing similarities between data points. When the data points reside in Euclidean space, a widespread approach is to from an affinity matrix by the Gaussian kernel with pairwise distances, and to follow with a certain normalization (e.g. the row-stochastic norma

Shanming Ji, Ming Mei

This paper is concerned with the multi-dimensional compressible Euler equations with time-dependent damping of the form $-\frac{\mu}{(1+t)^\lambda}\rho\boldsymbol u$ in $\mathbb R^n$, where $n\ge2$, $\mu>0$, and $\lambda\in[0,1)$. When $\lambda>0$ is bigger, the damping effect time-asymptotically gets weaker, which is called under-damping. We show the optima

Arthur L. B. Yang

The Stern poset $\mathcal{S}$ is a graded infinite poset naturally associated to Stern's triangle, which was defined by Stanley analogously to Pascal's triangle. Let $P_n$ denote the interval of $\mathcal{S}$ from the unique element of row $0$ of Stern's triangle to the $n$-th element of row $r$ for sufficiently large $r$. For $n\geq 1$ let \begin{align*} L_

Ziyan Zhu, Stephen Carr, Daniel Massatt, Mitchell Luskin

We introduce twisted trilayer graphene (tTLG) with two independent twist angles as an ideal system for the precise tuning of the electronic interlayer coupling to maximize the effect of correlated behaviors. As established by experiment and theory in the related twisted bilayer graphene system, van Hove singularities (VHS) in the density of states can be use

Jacques Balayla

The relationship between a screening tests' positive predictive value, $\rho$, and its target prevalence, $\phi$, is proportional - though not linear in all but a special case. In consequence, there is a point of local extrema of curvature defined only as a function of the sensitivity $a$ and specificity $b$ beyond which the rate of change of a test's $\rho$

Christopher Griffin, Riley Mummah, Russ deForest

We derive both the finite and infinite population spatial replicator dynamics as the fluid limit of a stochastic cellular automaton. The infinite population spatial replicator is identical to the model used by Vickers and our derivation justifies the addition of a diffusion to the replicator. The finite population form generalizes the results by Durett and L

Benjamin Bode

A geometric braid $B$ can be interpreted as a loop in the space of monic complex polynomials with distinct roots. This loop defines a function $g:\mathbb{C}\times S^1\to\mathbb{C}$ that vanishes on $B$. We define the set of P-fibered braids as those braids that can be represented by loops of polynomials such that the corresponding function $g$ induces a fibr

Jonathan H. Brown, Adam H. Fuller, David R. Pitts, Sarah A. Reznikoff

Let $C^*(E)$ be the graph C$^*$-algebra of a row-finite graph $E$. We give a complete description of the vertex sets of the gauge-invariant regular ideals of $C^*(E)$. It is shown that when $E$ satisfies Condition (L) the regular ideals $C^*(E)$ are a class of gauge-invariant ideals which preserve Condition (L) under quotients. That is, we show that if $E$ s

Francesco Petocchi, Viktor Christiansson, Fredrik Nilsson, Ferdi Aryasetiawan

The recent discovery of superconductivity in hole-doped NdNiO$_2$ thin films has captivated the condensed matter physics community. Such compounds with a formal Ni$^+$ valence have been theoretically proposed as possible analogues of the cuprates, and the exploration of their electronic structure and pairing mechanism may provide important insights into the

Xiaocong He, Xiaobo He

The first Zagreb index $M_{1}$ of a graph is defined as the sum of the square of every vertex degree, and the second Zagreb index $M_{2}$ of a graph is defined as the sum of the product of vertex degrees of each pair of adjacent vertices. In this paper, we study the Zagreb indices of bipartite graphs of order $n$ with $\kappa(G)=k$ (resp. $\kappa'(G)=s$) and

Zhifeng Kong, Kamalika Chaudhuri

Normalizing flows have received a great deal of recent attention as they allow flexible generative modeling as well as easy likelihood computation. While a wide variety of flow models have been proposed, there is little formal understanding of the representation power of these models. In this work, we study some basic normalizing flows and rigorously establi

Arjumand Seemab, Jehad Alzabut, Mujeeb ur Rehman, Yacine Adjabi

This paper studies Langevin equation with nonlocal boundary conditions involving a $\psi$--Caputo fractional derivatives operator. By the aide of fixed point techniques of Krasnoselskii and Banach, we derive new results on existence and uniqueness of the problem at hand. Further, the $\psi $-fractional Gronwall inequality and $\psi $--fractional integration

Sourav Mondal, Goutam Das, Elaine Wong

Edge computing servers like cloudlets from different service providers compensate scarce computational, memory, and energy resources of mobile devices, are distributed across access networks. However, depending on the mobility pattern and dynamically varying computational requirements of associated mobile devices, cloudlets at different parts of the network

Emmanuel Jordy Menvouta, Sven Serneels, Tim Verdonck

The direpack package aims to establish a set of modern statistical dimension reduction techniques into the Python universe as a single, consistent package. The dimension reduction methods included resort into three categories: projection pursuit based dimension reduction, sufficient dimension reduction, and robust M estimators for dimension reduction. As a c

A. Aguilar-Arevalo, M. Aoki, M. Blecher, D. I. Britton

The rare pion decays ${\pi}^+{\rightarrow}{\mu}^+{\nu}_{\mu}{\nu}\bar{\nu}$ and ${\pi}^+{\rightarrow}e^+{\nu}_{e}{\nu}\bar{\nu}$ are allowed in the Standard Model but highly suppressed. These decays were searched for using data from the PIENU experiment. A first result for ${\Gamma}({\pi}^+{\rightarrow}{\mu}^+{\nu}_{\mu}{\nu}\bar{\nu})/{\Gamma}({\pi}^+{\righ

Qiunan Zhang, Ingo Deppner, Nobert Herrmann, Yi Wang

The future Facility for Anti-proton and Ion Research (FAIR), currently in construction in Darmstadt, Germany, is one of the largest research projects worldwide. The Compressed Baryonic Matter (CBM) experiment is one of the main pillars at FAIR, studying the quantum chromodynamics (QCD) phase diagram at high baryon densities with unprecedented interaction rat

Zheng Xu, Ali Shafahi, Tom Goldstein

Adversarial training has proven to be effective in hardening networks against adversarial examples. However, the gained robustness is limited by network capacity and number of training samples. Consequently, to build more robust models, it is common practice to train on widened networks with more parameters. To boost robustness, we propose a conditional norm

Susanne Albers, Maximilian Janke

Makespan minimization on identical machines is a fundamental problem in online scheduling. The goal is to assign a sequence of jobs to $m$ identical parallel machines so as to minimize the maximum completion time of any job. Already in the 1960s, Graham showed that Greedy is $(2-1/m)$-competitive. The best deterministic online algorithm currently known achie

Foyzul Hassan, Chetan Bansal, Nachiappan Nagappan, Thomas Zimmermann

Software engineers spend a substantial amount of time using Web search to accomplish software engineering tasks. Such search tasks include finding code snippets, API documentation, seeking help with debugging, etc. While debugging a bug or crash, one of the common practices of software engineers is to search for information about the associated error or exce

Francisco M. Fernández

The purpose of this comment is to present the perturbation approach proposed by Maiz J. Math. Phys. 60, 012103 (2019) in a clearer way. The results of our straightforward procedure agree with those obtained by that author except for one case in which we obtain the exact result while he obtained an approximate one. In addition to it, we show that for sufficie

Victor Freguglia, Nancy Lopes Garcia

Markov random fields on two-dimensional lattices are behind many image analysis methodologies. mrf2d provides tools for statistical inference on a class of discrete stationary Markov random field models with pairwise interaction, which includes many of the popular models such as the Potts model and texture image models. The package introduces representations

Santanu Nandi

This article discusses some topological properties of the dynamical plane ($z$-plane) of the holomorphic family of meromorphic maps $\lambda \tan z^2$ for $ \lambda \in \mathbb C^*$. In the dynamical plane, I prove that there is no Herman ring and the Julia set is a Cantor set for the maps when the parameter is in the hyperbolic component containing the orig

Shreyashi Chakdar, P. Q Hung

Motivated by the null results of the BSM searches in the post-Higgs era of the LHC, our current approach is to look for new physics shifting from theory driven search strategies to signature driven ones. One possible direction might come from investigating the long-lived particles (LLPs) present in various theoretical scenarios through the newly formed Lifet

Boris Teabe, Peterson Yuhala, Alain Tchana, Fabien Hermenier

The utilization of paging for virtual machine (VM) memory management is the root cause of memory virtualization overhead. This paper shows that paging is not necessary in the hypervisor. In fact, memory fragmentation, which explains paging utilization, is not an issue in virtualized datacenters thanks to VM memory demand patterns. Our solution Compromis, a n

A. S. Dosaev, M. I. Shishina, Yu. I. Troitskaya

We present a numerical study of essentially nonlinear dynamics of surface gravity waves on deep water with constant vorticity using governing equations in conformal coordinates. The dispersion relation of surface gravity waves on shear flow is known to have two branches, one of which is weakly dispersive for long waves. Weakly nonlinear evolution of the wave

Andre A. Marinho, G. M. Viswanathan, Francisco A. Brito, C. G. Bezerra

In literature one can find many generalizations of the usual Leibniz derivative, such as Jackson derivative, Tsallis derivative and Hausdorff derivative. In this article we present a connection between Jackson derivative and recently proposed Hausdorff derivative. On one hand, the Hausdorff derivative has been previously associated with non-extensivity in sy

Tovly Deutsch, Masoud Jasbi, Stuart Shieber

Readability assessment aims to automatically classify text by the level appropriate for learning readers. Traditional approaches to this task utilize a variety of linguistically motivated features paired with simple machine learning models. More recent methods have improved performance by discarding these features and utilizing deep learning models. However,

Peter Manohar, Jalani Williams

Caches are a fundamental component of latency-sensitive computer systems. Recent work of [ASWB20] has initiated the study of delayed hits: a phenomenon in caches that occurs when the latency between the cache and backing store is much larger than the time between new requests. We present two results for the delayed hits caching model. (1) Competitive ratio l

Jonas C. Kiemel, Robin Weitemeyer, Pascal Meißner, Torsten Kröger

We present True{\AE}dapt, a model-free method to learn online adaptations of robot trajectories based on their effects on the environment. Given sensory feedback and future waypoints of the original trajectory, a neural network is trained to predict joint accelerations at regular intervals. The adapted trajectory is generated by linear interpolation of the p

Michael Freedman

Classical results of Milnor, Wood, Mather, and Thurston produce flat connections in surprising places. The Milnor-Wood inequality is for circle bundles over surfaces, whereas the Mather-Thurston Theorem is about cobording general manifold bundles to ones admitting a flat connection. The surprise comes from the close encounter with obstructions from Chern-Wey

Yifei Huang

With today's savvy and empowered customers, sales requires more judgment and becomes more cognitively intense than ever before. We argue that Situation Awareness (SA) is at the center of effective sales and customer engagement in this new era, and Information Fusion (IF) is the key for developing the next generation of decision support systems for digital an

Nava Haghighi, Nathalie Vladis, Yuanbo Liu, Arvind Satyanarayan

With the rise of wearables, haptic interfaces are increasingly favored to communicate information in an ambient manner. Despite this expectation, existing guidelines are developed in studies where the participant's focus is entirely on the haptic task. In this work, we systematically study the cognitive load imposed by properties of a haptic signal. Particip