June 2019 arXiv papers — page 15

Lin Sun, Jun Zhao, Xiaojun Ye

Clustering and analyzing on collected data can improve user experiences and quality of services in big data, IoT applications. However, directly releasing original data brings potential privacy concerns, which raises challenges and opportunities for privacy-preserving clustering. In this paper, we study the problem of non-interactive clustering in distribute

Zhongxin Zhou, Fan Zhang, Xuemin Lin, Wenjie Zhang

A popular model to measure the stability of a network is k-core - the maximal induced subgraph in which every vertex has at least k neighbors. Many studies maximize the number of vertices in k-core to improve the stability of a network. In this paper, we study the edge k-core problem: Given a graph G, an integer k and a budget b, add b edges to non-adjacent

Klaountia Pasmatsiou

We extend the study of the effect of static primordial black holes on vacuum decay. In particular, we compare the tunneling rates between vacua of different values of the cosmological constant and black hole mass by pointing out the dominant processes based on a numerical examination of the thin wall instanton. Three distinct cases are considered, namely the

Branko Malesevic, Marija Nenezic, Ling Zhu, Bojan Banjac

In this paper we present some new upper bounds of the Cusa-Huygens and the Huygens approximations. Bounds are obtained in the forms of some polynomial and some rational functions.

E. Marcellina, Pankaj Bhalla, A. R. Hamilton, Dimitrie Culcer

The Zeeman interaction is a quantum mechanical effect that underpins spin-based quantum devices such as spin qubits. Typically, identification of the Zeeman interaction needs a large out-of-plane magnetic field coupled with ultralow temperatures, which limits the practicality of spin-based devices. However, in two-dimensional (2D) semiconductor holes, the st

Hirotada Honda

In this paper, we discuss the sensitivity of quantum PageRank. By using the finite dimensional perturbation theory, we estimate the change of the quantum PageRank under a small analytical perturbation on the Google matrix. In addition, we will show the way to estimate the lower bound of the convergence radius as well as the error bound of the finite sum in t

Andy Hammerlindl, Bernd Krauskopf, Gemma Mason, Hinke M. Osinga

A heterodimensional cycle consists of a pair of heteroclinic connections between two saddle periodic orbits with unstable manifolds of different dimensions. Recent theoretical work on chaotic dynamics beyond the uniformly hyperbolic setting has shown that heterodimensional cycles may occur robustly in diffeomorphisms of dimension at least three. We study a c

Zhangxuan Gu, Li Niu, Haohua Zhao, Liqing Zhang

Semantic segmentation has achieved remarkable progress but remains challenging due to the complex scene, object occlusion, and so on. Some research works have attempted to use extra information such as a depth map to help RGB based semantic segmentation because the depth map could provide complementary geometric cues. However, due to the inaccessibility of d

Weifeng Qiu, Shun Zhang

This paper studies adaptive first-order least-squares finite element methods for second-order elliptic partial differential equations in non-divergence form. Unlike the classical finite element method which uses weak formulations of PDEs not applicable for the non-divergence equation, the first-order least-squares formulations naturally have stable weak form

Liming Han, Yimin Lin, Guoguang Du, Shiguo Lian

This paper presents an self-supervised deep learning network for monocular visual inertial odometry (named DeepVIO). DeepVIO provides absolute trajectory estimation by directly merging 2D optical flow feature (OFF) and Inertial Measurement Unit (IMU) data. Specifically, it firstly estimates the depth and dense 3D point cloud of each scene by using stereo seq

Linfeng Zhang, Mohan Chen, Xifan Wu, Han Wang

We introduce a deep neural network to model in a symmetry preserving way the environmental dependence of the centers of the electronic charge. The model learns from ab-initio density functional theory, wherein the electronic centers are uniquely assigned by the maximally localized Wannier functions. When combined with the Deep Potential model of the atomic p

Victor Alexandrov

We derive fundamentally new equations that are satisfied by first-order flexes of a flexible polyhedron. Moreover, we indicate two sources of such new equations. These sources are the Dehn invariants and rigidity matrix. The equations derived provide us with fundamentally new necessary conditions for the extendibility of a first-order flex of a polyhedron to

H. Villamizar, A. A. Neto, M. Kalinowski, A. Garcia

Defects in requirements specifications can have severe consequences during the software development lifecycle. Some of them result in overall project failure due to incorrect or missing quality characteristics such as security. There are several concerns that make security difficult to deal with; for instance, (1) when stakeholders discuss general requiremen

Li Chen, Ningxia Wang, Yonghua Yang, Keping Yang

Though it has been recognized that recommending serendipitous (i.e., surprising and relevant) items can be helpful for increasing users' satisfaction and behavioral intention, how to measure serendipity in the offline environment is still an open issue. In recent years, a number of metrics have been proposed, but most of them were based on researchers' assum

Yusuke Yamada, Jun'ichi Yokoyama

We construct the Horndeski Lagrangian within non-linearly realized supergravity. We will show that the bosonic part of the Horndeski Lagrangian can be realized. Gravitino naturally couples to Horndeski sector in a super-covariant way. Such gravitino couplings are also free from ghosts.

Zixi Liu, Qi Zhu, Mei Su

High-dimensional wireless power transfer (WPT) systems have received increasing attention for charging mobile devices. With the receivers have higher spatial freedom, the systems are more susceptible to the metal objects in the surroundings. However, conventional methods for metal object detection (MOD) can't satisfy the requirements of safety and stability

Junyong Eom, Manabu Machida, Gen Nakamura, Goro Nishimura

Light propagation through diffusive media can be described by the diffusion equation in a space-time domain. Further, fluorescence can be described by a system of coupled diffusion equations. This paper analyzes time-domain measurements, which measure the temporal point-spread function (TPSF), at a boundary of such diffusive media with a given source and det

Ushnish Ray, David T. Limmer

Molecular dynamics simulations and nonequilibrium importance sampling are used to study the heat transport of low dimensional carbon lattices. For both carbon nanotubes and graphene sheets heat transport is found to be anomalous, violating Fourier's law of conduction with a system size dependent thermal conductivity and concomitant nonlinear temperature prof

Xianwei Zheng, Linxi Huan, Hanjiang Xiong, Jianya Gong

Semantic segmentation has been a hot topic across diverse research fields. Along with the success of deep convolutional neural networks, semantic segmentation has made great achievements and improvements, in terms of both urban scene parsing and indoor semantic segmentation. However, most of the state-of-the-art models are still faced with a challenge in dis

Gaurav Choudhary, Jiyoon Kim, Vishal Sharma

The rapid involution of the mobile generation with incipient data networking capabilities and utilization has exponentially increased the data traffic volumes. Such traffic drains various key issues in 5G mobile backhaul networks. Security of mobile backhaul is of utmost importance; however, there are a limited number of articles, which have explored such a

Prashant Shekhar, Abani Patra

This paper describes a hierarchical learning strategy for generating sparse representations of multivariate datasets. The hierarchy arises from approximation spaces considered at successively finer scales. A detailed analysis of stability, convergence and behavior of error functionals associated with the approximations are presented, along with a well chosen

Patrik Jansson, Sólrún Halla Einarsdóttir, Cezar Ionescu

At the workshop on Trends in Functional Programming in Education (TFPIE) in 2015 Ionescu and Jansson presented the approach underlying the "Domain Specific Languages of Mathematics" (DSLsofMath) course even before the first course instance. We were then encouraged to come back to present our experience and the student results. Now, three years later, we have

Per Lindgren, Marcus Lindner, Nils Fitinghoff

Classes on compiler technology are commonly found in Computer Science curricula, covering aspects of parsing, semantic analysis, intermediate transformations and target code generation. This paper reports on introducing certified compilation techniques through a functional language approach in an introductory course on Compiler Construction. Targeting studen

Jinghui Wang, Kejing Ran, Shichao Li, Zhen Ma

Superconductivity mediated by phonons is typically conventional, exhibiting a momentum-independent s-wave pairing function, due to the isotropic interactions between electrons and phonons along different crystalline directions. Here, by performing inelastic neutron scattering measurements on a superconducting single crystal of Sr0.1Bi2Se3, a prime candidate

Marco T. Morazán

Vector programming is an important topic in many Introduction to Computer Science courses. Despite the importance of vectors, learning vector programming is a source of frustration for many students. Much of the frustration is rooted in discovering the source of bugs that are manifested as out-of-bounds indexing. The problem is that such bugs are, sometimes,

Tsukino Furukawa, Youyou Cong, Kenichi Asai

Steppers, which display all the reduction steps of a given program, are a novice-friendly tool for understanding program behavior. Unfortunately, steppers are not as popular as they ought to be; indeed, the tool is only available in the pedagogical languages of the DrRacket programming environment. We present a stepper for a practical fragment of OCaml. Simi

Marco T. Morazán, Josephine A. Des Rosiers

Computer Science students, in general, find Automata Theory difficult and mostly unrelated to their area of study. To mitigate these perceptions, FSM, a library to program state machines and grammars, was developed to bring programming to the Automata Theory classroom. The results of the library's maiden voyage at Seton Hall University had a positive impact

Boris Daszuta, Mikkel F. Andersen

We investigate an atom interferometer in which large momentum differences between the arms are obtained by using quantum resonances in the atom optics $\delta$-kicked rotor. The interferometer can potentially measure the Talbot time (from which $h/m$ can be deduced), the local gravitational field, or can serve as a narrow velocity filter. We present an analy

James Mount, Les Dawes, Michael Milford

Localization is a critical capability for robots, drones and autonomous vehicles operating in a wide range of environments. One of the critical considerations for designing, training or calibrating visual localization systems is the coverage of the visual sensors equipped on the platforms. In an aerial context for example, the altitude of the platform and ca

Masaki Ushitani, Shingo Tagami, Yoshifumi R. Shimizu

Recently we have investigated an effective method of multicranked configuration-mixing for angular-momentum-projection calculation, where several cranked mean-field states are coupled after projection: The basic idea was originally proposed by Peierls and Thouless more than fifty years ago. With this method a good description of the rotational band has been

Ziqi Ren, Jie Li, Xuetong Xue, Xin Li

Reconstructing visual stimulus (image) only from human brain activity measured with functional Magnetic Resonance Imaging (fMRI) is a significant and meaningful task in Human-AI collaboration. However, the inconsistent distribution and representation between fMRI signals and visual images cause the heterogeneity gap. Moreover, the fMRI data is often extremel

Seonho Park, Seung Hyun Jung, Panos M. Pardalos

We focus on minimizing nonconvex finite-sum functions that typically arise in machine learning problems. In an attempt to solve this problem, the adaptive cubic regularized Newton method has shown its strong global convergence guarantees and ability to escape from strict saddle points. This method uses a trust region-like scheme to determine if an iteration

Shizhan Lu

Cluster analysis which focuses on the grouping and categorization of similar elements is widely used in various fields of research. Inspired by the phenomenon of atomic fission, a novel density-based clustering algorithm is proposed in this paper, called fission clustering (FC). It focuses on mining the dense families of a dataset and utilizes the informatio

Kaidi Cao, Jingwei Ji, Zhangjie Cao, Chien-Yi Chang

There is a growing interest in learning a model which could recognize novel classes with only a few labeled examples. In this paper, we propose Temporal Alignment Module (TAM), a novel few-shot learning framework that can learn to classify a previous unseen video. While most previous works neglect long-term temporal ordering information, our proposed model e

Ping-Tao Chen, Gui-Jun Ding, Stephen F. King, Cai-Chang Li

We propose and discuss a new Littlest Seesaw model, realised in the tri-direct CP approach, in which the couplings of the two right-handed neutrinos to the lepton doublets are proportional to $(0,-1,1)$ and $(1,5/2,-1/2)$ respectively with the relative phase $\eta=-\pi/2$. This model can give an excellent description of lepton flavour mixing, including an at

Fuyuan Xiao

In this paper, an evidential distance measure is proposed which can measure the difference or dissimilarity between complex basic belief assignments (CBBAs), in which the CBBAs are composed of complex numbers. When the CBBAs are degenerated from complex numbers to real numbers, i.e., BBAs, the proposed distance will degrade into the Jousselme et al.'s distan

Jie Zhao, Fuqiang Wang

The chiral magnetic effect (CME) in quantum chromodynamics (QCD) refers to a charge separation (an electric current) of chirality imbalanced quarks generated along an external strong magnetic field. The chirality imbalance results from interactions of quarks, under the approximate chiral symmetry restoration, with metastable local domains of gluon fields of

C. Sun, V. Y. Chernyak, A. Piryatinski, N. A. Sinitsyn

We study photon emission by an ensemble of two-level systems, with strong inhomogeneous broadening and coupled to a cavity mode whose frequency has linear time-dependence. The analysis shows that, regardless the distribution of energy level splittings, a sharp phase transition occurs between the weak and strong cooperative emission phases near a critical pho

Hideshi Yamane

We solve Cauchy problems for some $\mu$-Camassa-Holm integro-partial differential equations in the analytic category. The equations to be considered are $\mu$CH of Khesin-Lenells-Misio\l{}ek, $\mu$DP of Lenells-Misio\l{}ek-Ti\u{g}lay, the higher-order $\mu$CH of Wang-Li-Qiao and the non-quasilinear version of Qu-Fu-Liu. We prove the unique local solvability

Linan Huang, Quanyan Zhu

A honeynet is a promising active cyber defense mechanism. It reveals the fundamental Indicators of Compromise (IoCs) by luring attackers to conduct adversarial behaviors in a controlled and monitored environment. The active interaction at the honeynet brings a high reward but also introduces high implementation costs and risks of adversarial honeynet exploit

Jonathan I. Tamir, Frank Ong, Suma Anand, Ekin Karasan

Compressed sensing takes advantage of low-dimensional signal structure to reduce sampling requirements far below the Nyquist rate. In magnetic resonance imaging (MRI), this often takes the form of sparsity through wavelet transform, finite differences, and low rank extensions. Though powerful, these image priors are phenomenological in nature and do not acco

Fuyuan Xiao

Dempster-Shafer evidence theory has been widely used in various fields of applications, because of the flexibility and effectiveness in modeling uncertainties without prior information. However, the existing evidence theory is insufficient to consider the situations where it has no capability to express the fluctuations of data at a given phase of time durin

Sunaina, Mansi Butola, Kedar Khare

Complex object wave recovery from single-shot interference pattern is an important practical problem in interferometry and digital holography. The most popular single-shot interferogram analysis method involves Fourier filtering of cross-term but this method suffers from poor resolution. For obtaining full pixel resolution, it is necessary to model the objec

Bernd Amann, Youry Khmelevsky, Gaetan Hains

The vast amount of processing power and memory bandwidth provided by modern Graphics Processing Units (GPUs) make them a platform for data-intensive applications. The database community identified GPUs as effective co-processors for data processing. In the past years, there were many approaches to make use of GPUs at different levels of a database system. In

Santhosh K. Ramakrishnan, Dinesh Jayaraman, Kristen Grauman

Standard computer vision systems assume access to intelligently captured inputs (e.g., photos from a human photographer), yet autonomously capturing good observations is a major challenge in itself. We address the problem of learning to look around: how can an agent learn to acquire informative visual observations? We propose a reinforcement learning solutio

Scott Alsid, John Barry, Linh Pham, Jennifer Schloss

Treatment of lab-grown diamond by electron irradiation and annealing has enabled quantum sensors based on negatively-charged nitrogen-vacancy (NV$^\text{-}$) centers to demonstrate record sensitivities. \cite{Clevenson2015,Wolf2015,Barry2016,Chatzidrosos2017}. Here we investigate the irradiation and annealing process applied to 28 diamond samples using a new

Heer Ambavi, Ayush Garg, Ayush Garg, Nitiksha

A biography of a person is the detailed description of several life events including his education, work, relationships, and death. Wikipedia, the free web-based encyclopedia, consists of millions of manually curated biographies of eminent politicians, film and sports personalities, etc. However, manual curation efforts, even though efficient, suffers from s

Basant Lal Sharma, Gaurav Maurya

Scattering of a time harmonic anti-plane shear wave due to either a pair of crack tips or a pair of rigid constraint tips on square lattice is considered. The two problems correspond to the so called zero-offset case of scattering due to a pair of identical Sommerfeld screens. The peculiar structural symmetry allows the reduction of coupled equations to two

M. Axelsson, E. Bissaldi, N. Omodei, G. Vianello

The Large Area Telescope (LAT) aboard the $Fermi$ spacecraft routinely observes high-energy emission from gamma-ray bursts (GRBs). Here we present the second catalog of LAT-detected GRBs, covering the first 10 years of operations, from 2008 August 4 to 2018 August 4. A total of 186 GRBs are found; of these, 91 show emission in the range $30-100\,$MeV (17 of

Jie Feng, Yongpeng Wu, Guangtao Zhai, Ning Liu

Virtual reality (VR) is making waves around the world recently. However, traditional video streaming is not suitable for VR video because of the huge size and view switch requirements of VR videos. Since the view of each user is limited, it is unnecessary to send the whole 360-degree scene at high quality which can be a heavy burden for the transmission syst

Hsuan-Hao Lu, Zixuan Hu, Mohammed S. Alshaykh, Alexandria J. Moore

The Phase Estimation Algorithm (PEA) is an important quantum algorithm used independently or as a key subroutine in other quantum algorithms. Currently most implementations of the PEA are based on qubits, where the computational units in the quantum circuits are two-dimensional states. Performing quantum computing tasks with higher dimensional states -- qudi

Mario Sucerquia, Jaime A. Alvarado-Montes, Jorge I. Zuluaga, Nicolás Cuello

Close-in giant planets represent the most significant evidence of planetary migration. If large exomoons form around migrating giant planets which are more stable (e.g. those in the Solar System), what happens to these moons after migration is still under intense research. This paper explores the scenario where large regular exomoons escape after tidal-inter

Hyungjin Kim, Hussein Nili, Mahmood Mahmoodi, Dmitri Strukov

The superior density of passive analog-grade memristive crossbars may enable storing large synaptic weight matrices directly on specialized neuromorphic chips, thus avoiding costly off-chip communication. To ensure efficient use of such crossbars in neuromorphic computing circuits, variations of current-voltage characteristics of crosspoint devices must be s

Rankeya Datta, Takumi Murayama

We prove that $F$-injectivity localizes, descends under faithfully flat homomorphisms, and ascends under flat homomorphisms with Cohen-Macaulay and geometrically $F$-injective fibers, all for arbitrary Noetherian rings of prime characteristic. As a consequence, we show that the $F$-injective locus is open on most rings arising in arithmetic and geometry. As

Cameron Chalk, Niels Kornerup, Wyatt Reeves, David Soloveichik

Biological regulatory networks depend upon chemical interactions to process information. Engineering such molecular computing systems is a major challenge for synthetic biology and related fields. The chemical reaction network (CRN) model idealizes chemical interactions, allowing rigorous reasoning about the computational power of chemical kinetics. Here we

James Hope, Zaid Aqrawe, Marshall Lim, Frederique Vanholsbeeck

Objective: To increase the impedance signal amplitude produced during neural activity using a novel approach of implementing a parallel resistor inductor capacitor (RLC) circuit across the current source used in electrical impedance tomography (EIT) of peripheral nerve. Approach: Experiments were performed in vitro on sciatic nerve of Sprague-Dawley rats. De

Richard Wilson, Sinisa Coh

Understanding how photoexcited electron dynamics depend on electron-electron (e-e) and electron-phonon (e-p) interaction strengths is important for many fields, e.g. ultrafast magnetism, photocatalysis, plasmonics, and others. Here, we report simple expressions that capture the interplay of e-e and e-p interactions on electron distribution relaxation times.

Julien Radoux, François Waldner, Patrick Bogaert

Reference data collected to validate land cover maps are generally considered free of errors. In practice, however, they contain errors despite all efforts to minimise them. These errors then propagate up to the accuracy assessment stage and impact the validation results. For photo-interpreted reference data, the three most widely studied sources of error ar

Olufemi O. Oyadare

The Harish-Chandra Fourier transform, $f\mapsto\mathcal{H}f,$ is a linear topological algebra isomorphism of the spherical (Schwartz) convolution algebra $\mathcal{C}^{p}(G//K)$ (where $K$ is a maximal compact subgroup of any arbitrarily chosen group $G$ in the Harish-Chandra class and $0<p\leq2$) onto the (Schwartz) multiplication algebra $\bar{\mathcal{Z}}

Nikolai Matni, Stephen Tu

We provide a brief tutorial on the use of concentration inequalities as they apply to system identification of state-space parameters of linear time invariant systems, with a focus on the fully observed setting. We draw upon tools from the theories of large-deviations and self-normalized martingales, and provide both data-dependent and independent bounds on

Christophe Vuillot, Nikolas P. Breuckmann

We introduce quantum pin codes: a class of quantum CSS codes. Quantum pin codes are a generalization of quantum color codes and Reed-Muller codes and share a lot of their structure and properties. Pin codes have gauge operators, an unfolding procedure and their stabilizers form so-called $\ell$-orthogonal spaces meaning that the joint overlap between any $\e

Shohei Watabe, Yuya Seki, Shiro Kawabata

Quantum annealing is an innovative idea and method for avoiding the increase of the calculation cost of the combinatorial optimization problem. Since the combinatorial optimization problems are ubiquitous, quantum annealing machine with high efficiency and scalability will give an immeasurable impact on many fields. However, the conventional quantum annealin

Anna Caroline Felix Santos de Jesus

In this work we propose a non-dimensionalization approach for the Stokes-Brinkman model for flow in porous media. We study the effect of the dimensionless number found, which will be denoted by A and named as Anna's number, has on the outflow and transition between the Darcy and Stokes regime.

Nikolai Matni, Alexandre Proutiere, Anders Rantzer, Stephen Tu

Machine and reinforcement learning (RL) are increasingly being applied to plan and control the behavior of autonomous systems interacting with the physical world. Examples include self-driving vehicles, distributed sensor networks, and agile robots. However, when machine learning is to be applied in these new settings, the algorithms had better come with the

Laura Doval

I introduce a stability notion, dynamic stability, for two-sided dynamic matching markets where (i) matching opportunities arrive over time, (ii) matching is one-to-one, and (iii) matching is irreversible. The definition addresses two conceptual issues. First, since not all agents are available to match at the same time, one must establish which agents are a

Alexander A. Kunitsa, Ksenia B. Bravaya

Autoionizing electronic states are common intermediates in processes initiated by electron impact or high-energy radiation. These states belong to the continuous spectrum of the Hamiltonian, and as such cannot be treated with methods developed for bound electronic states. Here we propose a new model for describing metastable electronic states, which combines

Max Vladymyrov

Nonlinear embedding manifold learning methods provide invaluable visual insights into the structure of high-dimensional data. However, due to a complicated nonconvex objective function, these methods can easily get stuck in local minima and their embedding quality can be poor. We propose a natural extension to several manifold learning methods aimed at ident

N. Gupta, M. Pannella, J. J. Mohr, M. Klein

We study the properties of the Sydney University Molonglo Sky Survey (SUMSS) 843~MHz radio AGN population in galaxy clusters from two large catalogs created using the Dark Energy Survey (DES): $\sim$11,800 optically selected RM-Y3 and $\sim$1,000 X-ray selected MARD-Y3 clusters. We show that cluster radio loud AGN are highly concentrated around cluster cente

Uthpala Herath, Pedram Tavadze, Xu He, Eric Bousquet

The PyProcar Python package plots the band structure and the Fermi surface as a function of site and/or s,p,d,f - projected wavefunctions obtained for each $k$-point in the Brillouin zone and band in an electronic structure calculation. This can be performed on top of any electronic structure code, as long as the band and projection information is written in

Thiem Hoang, Ngo-Duy Tung

It is widely believed that water and complex organic molecules (COMs) first form in the ice mantle of dust grains and are subsequently returned into the gas due to grain heating by intense radiation of protostars. Previous research on the desorption of molecules from the ice mantle assumed that grains are at rest which is contrary to the fact that grains are

Ray Li, Percy Liang, Stephen Mussmann

Decision Tree is a classic formulation of active learning: given $n$ hypotheses with nonnegative weights summing to 1 and a set of tests that each partition the hypotheses, output a decision tree using the provided tests that uniquely identifies each hypothesis and has minimum (weighted) average depth. Previous works showed that the greedy algorithm achieves

Junyi Du, He Jiang, Jiaming Shen, Xiang Ren

Cognitive task analysis (CTA) is a type of analysis in applied psychology aimed at eliciting and representing the knowledge and thought processes of domain experts. In CTA, often heavy human labor is involved to parse the interview transcript into structured knowledge (e.g., flowchart for different actions). To reduce human efforts and scale the process, aut

Changyang She, Yifan Duan, Guodong Zhao, Tony Q. S. Quek

In this work, we propose a cross-layer framework for optimizing user association, packet offloading rates, and bandwidth allocation for Mission-Critical Internet-of-Things (MC-IoT) services with short packets in Mobile Edge Computing (MEC) systems, where enhanced Mobile BroadBand (eMBB) services with long packets are considered as background services. To red

W. Xie, Y. Wu, F. Du, A. Wang

A number of rare-earth monopnictides have topologically non-trivial band structures together with magnetism and strong electronic correlations. In order to examine whether the antiferromagnetic (AFM) semimetal YbAs ($T\rm_N$ = 0.5 K) exhibits such a scenario, we have grown high-quality single crystals using a flux method, and characterized the magnetic prope

Yuta Umezu, Ichiro Takeuchi

Post-selection inference is a statistical technique for determining salient variables after model or variable selection. Recently, selective inference, a kind of post-selection inference framework, has garnered the attention in the statistics and machine learning communities. By conditioning on a specific variable selection procedure, selective inference can

L. Y. Jia

Recently we proposed a scheme that applies the variational principle to a coherent-pair condensate in the BCS case [Phys. Rev. C 99, 014302 (2019)]. This work extends the scheme to the HFB case by allowing variation of the canonical single-particle basis. The result is equivalent to that of the so-called variation after particle-number projection in the HFB

M. Smidman, C. Ritter, D. T. Adroja, S. Rayaprol

The rare-earth based ternary intermetallic compounds $R_2TX_3$ ($R$ = rare-earth, $T$ = transition-metal, $X$ = Si, Ge, Ga, In) have attracted considerable interest due to a wide range of interesting low temperature properties. Here we investigate the magnetic state of Nd$_{2}$PdSi$_{3}$ using neutron diffraction, muon spin relaxation ($\mu$SR) and inelastic

Doeke Buursma, Alexander Kleshchev, David J. Steinberg

Khovanov-Lauda-Rouquier algebras $R_\theta$ of finite Lie type are affine quasihereditary with standard modules $\Delta(\pi)$ labeled by Kostant partitions of $\theta$. Let $\Delta$ be the direct sum of all standard modules. It is known that the Yoneda algebra $\mathcal{E}_\theta:=\operatorname{Ext}_{R_\theta}^*(\Delta, \Delta)$ carries a structure of an $A_

Larry Jin, Hannah Lu, Gege Wen

Quantification of uncertainty in production/injection forecasting is an important aspect of reservoir simulation studies. Conventional approaches include intrusive Galerkin-based methods (e.g., generalized polynomial chaos (gPC) and stochastic collocation (SC) methods) and non-intrusive Monte Carlo (MC) based methods. Nevertheless, the quantification is cond

Miguel Sofo Haro, Guillermo Fernandez Moroni, Javier Tiffenberg

In this work, we will present a physical model and measurements of the transport of small charge packets in the bulk of thick high resistivity CCD before being collected by the pixel potential wells. A new technique to measure the lateral spread of the charge as a function of the ionization depth in the bulk is presented. Results from measurements on CCD cur

Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman

Data selection methods, such as active learning and core-set selection, are useful tools for machine learning on large datasets. However, they can be prohibitively expensive to apply in deep learning because they depend on feature representations that need to be learned. In this work, we show that we can greatly improve the computational efficiency by using

Yingying Li, Xin Chen, Na Li

This paper studies the online optimal control problem with time-varying convex stage costs for a time-invariant linear dynamical system, where a finite lookahead window of accurate predictions of the stage costs are available at each time. We design online algorithms, Receding Horizon Gradient-based Control (RHGC), that utilize the predictions through finite

Maite Fernández-Unzueta, Luisa F. Higueras-Montaño

We introduce both the notions of tensor product of convex bodies that contain zero in the interior, and of tensor product of $0$-symmetric convex bodies in Euclidean spaces. We prove that there is a bijection between tensor products of $0$-symmetric convex bodies and tensor norms on finite dimensional spaces. This bijection preserves duality, injectivity and

Doeke Buursma, Alexander Kleshchev, David J. Steinberg

Khovanov-Lauda-Rouquier algebras $R_\theta$ of finite Lie type are affine quasihereditary with standard modules $\Delta(\pi)$ labeled by Kostant partitions $\pi$ of $\theta$. In type $A$, we construct explicit projective resolutions of standard modules $\Delta(\pi)$.

João Paulo Almeida de Mendonça, Isaac Andrade Santece, Maxwel Gama Monteiro, Sidiney Andrade Leonel

In this work we present a new bio-inspired method (using an adapted, frog based Particle Swarm Optimization) that can track a skyrmion's position. The structure and position of skyrmions in a given system is usually determined either by visual impression through a vector plot, or by averaging over magnetization and topological charge. Due to the inherent dif

Jennifer M Rieser, Baxi Chong, Chaohui Gong, Henry C Astley

Self-propelling organisms locomote via generation of patterns of self-deformation. Despite the diversity of body plans, internal actuation schemes and environments in limbless vertebrates and invertebrates, such organisms often use similar travelling waves of axial body bending for movement. Delineating how parameters (wave amplitudes, frequencies) lead to l

Rishav Dutta, Ronald Yurko, Samuel Ventura

Analysis of player tracking data for American football is in its infancy, since the National Football League (NFL) released its Next Gen Stats tracking data publicly for the first time in December 2018. While tracking datasets in other sports often contain detailed annotations of on-field events, annotations in the NFL's tracking data are limited. Methods fo

Carlos Barreto, Eduardo Mojica-Nava, Nicanor Quijano

The modernization of the power system introduces technologies that may improve the system's efficiency by enhancing the capabilities of users. Despite their potential benefits, such technologies can have a negative impact. This subject has widely analyzed, mostly considering for-profit electric utilities. However, the literature has a gap regarding the impac

Noel M. Naughton, Caroline G. Tennyson, John G. Georgiadis

We report an implementation of the lattice Boltzmann method (LBM) to integrate the Bloch-Torrey equation, which describes the evolution of the transverse magnetization vector and the fate of the signal of diffusion magnetic resonance imaging (dMRI). Motivated by the need to interpret dMRI experiments in biological tissues, and to offset the small time-step l

Onur Varol, Ismail Uluturk

Communication plays a major role in social systems. Effective communications, which requires transmission of the messages between individuals without disruptions or noise, can be a powerful tool to deliver intended impact. Language and style of the content can be leveraged to deceive and manipulate recipients. These deception and persuasion strategies can be

Gerardo Arizmendi, Marco Antonio Pérez-de la Rosa

It is known that the groups of Euclidean rotations in dimension 3 (isometries of $S^2$), general Lorentz transformations in dimension 4 (Hyperbolic isometries in dimension 3), and screw motions in dimension 3 can be represented by the groups of unit--norm elements in the algebras of real quaternions, biquaternions (complex quaternions) and dual quaternions,

Ankush Chakrabarty, Rien Quirynen, Claus Danielson, Weinan Gao

Enforcing state and input constraints during reinforcement learning (RL) in continuous state spaces is an open but crucial problem which remains a roadblock to using RL in safety-critical applications. This paper leverages invariant sets to update control policies within an approximate dynamic programming (ADP) framework that guarantees constraint satisfacti

Marko Gacesa, Robert J. Lillis, Kevin J. Zahnle

We report new elastic and inelastic cross sections for O($^3P$)+CO$_2$ scattering at collision energies from 0.03 to 5 eV, of major importance to O escape from Mars, Venus, and CO$_2$-rich atmospheres. The cross sections were calculated from first principles using three newly constructed ab-initio potential energy surfaces correlating to the lowest energy as

Linguang Zhang, Maciej Halber, Szymon Rusinkiewicz

Expanding the receptive field to capture large-scale context is key to obtaining good performance in dense prediction tasks, such as human pose estimation. While many state-of-the-art fully-convolutional architectures enlarge the receptive field by reducing resolution using strided convolution or pooling layers, the most straightforward strategy is adopting

Yihe Dong, Samuel B. Hopkins, Jerry Li

We study two problems in high-dimensional robust statistics: \emph{robust mean estimation} and \emph{outlier detection}. In robust mean estimation the goal is to estimate the mean $\mu$ of a distribution on $\mathbb{R}^d$ given $n$ independent samples, an $\varepsilon$-fraction of which have been corrupted by a malicious adversary. In outlier detection the g

Eshan D. Mitra, William S. Hlavacek

Mathematical models can provide quantitative insight into immunoreceptor signaling, but require parameterization and uncertainty quantification before making reliable predictions. We review currently available methods and software tools to address these problems. We consider gradient-based and gradient-free methods for point estimation of parameter values, a

Daren Wang, Zifeng Zhao, Kevin Lin, Rebecca Willett

Detecting when the underlying distribution changes for the observed time series is a fundamental problem arising in a broad spectrum of applications. In this paper, we study multiple change-point localization in the high-dimensional regression setting, which is particularly challenging as no direct observations of the parameter of interest is available. Spec

Dominic Liao-McPherson, Marco M. Nicotra, Asen L. Dontchev, Ilya V. Kolmanovsky

Model predictive control (MPC) is of increasing interest in applications for constrained control of multivariable systems. However, one of the major obstacles to its broader use is the computation time and effort required to solve a possibly non-convex optimal control problem (OCP) online. This paper introduces a sensitivity-based warmstarting strategy for s

Hossein Rastgoftar

This paper proposes a new physics-based approach to effectively control congestion in a network of interconnected roads (NOIR). The paper integrates mass flow conservation and diffusion-based dynamics to model traffic coordination in a NOIR. The mass conservation law is used to model the traffic density dynamics across the NOIR while the diffusion law is app

Thomas M. Henderson, Gustavo E. Scuseria

The antisymmetrized geminal power (AGP) wave function has a long history and considerable conceptual appeal, but in many situations its accuracy is wanting. Here, we consider a form of configuration interaction (CI) based upon the AGP wave function and taking advantage of its killing operators to construct an excitation manifold. Our geminal CI reduces to st