December 2020 arXiv papers — page 93

Raban Iten

Quantum generalizations of R\'enyi's entropies are a useful tool to describe a variety of operational tasks in quantum information processing. Two families of such generalizations turn out to be particularly useful: the Petz quantum R\'enyi divergence $\bar{D}_{\alpha}$ and the minimal quantum R\'enyi divergence $\widetilde{D}_{\alpha}$. Moreover, the maximu

Vasily Repnikov, Boris Faleichik, Andrey Moysa

In this work we present explicit Adams-type multistep methods with extended stability interval, which are analogous to the stabilized Chebyshev Runge--Kutta methods. It is proved that for any $k\geq 1$ there exists an explicit $k$-step Adams-type method of order one with stability interval of length $2k$. The first order methods have remarkably simple expres

Karl Christ, Xiang He, Ilya Tyomkin

We address the following question: Given a polarized toric surface (S,L), and a general integral curve C of geometric genus g in the linear system |L|, do there exist degenerations of C in |L| to general integral curves of smaller geometric genera? We give an affirmative answer to this question for surfaces associated to h-transverse polygons, provided that

Sergio Naval Marimont, Giacomo Tarroni

We propose an out-of-distribution detection method that combines density and restoration-based approaches using Vector-Quantized Variational Auto-Encoders (VQ-VAEs). The VQ-VAE model learns to encode images in a categorical latent space. The prior distribution of latent codes is then modelled using an Auto-Regressive (AR) model. We found that the prior proba

Koji Azuma, Stefan Bäuml, Tim Coopmans, David Elkouss

Quantum networks will enable the implementation of communication tasks with qualitative advantages with respect to the communication networks we know today. While it is expected that the first demonstrations of small scale quantum networks will take place in the near term, many challenges remain to scale them. To compare different solutions, optimize over pa

P. Parfenov, A. Taranenko, D. Idrisov, V. B. Luong

The anisotropic collective flow is one of the key observables to study the properties of dense matter created in heavy-ion collisions. The performance of Multi-Purpose Detector (MPD) at NICA collider for directed and elliptic flow measurements is studied with Monte-Carlo simulations of heavy-ion collisions at energies $\sqrt{s_{NN}}$ = 4 - 11 GeV.

BaoLuo Sun, Ting Ye

Although the exposure can be randomly assigned in studies of mediation effects, any form of direct intervention on the mediator is often infeasible. As a result, unmeasured mediator-outcome confounding can seldom be ruled out. We propose semiparametric identification of natural direct and indirect effects in the presence of unmeasured mediator-outcome confou

Pascal Nasahl, Robert Schilling, Mario Werner, Jan Hoogerbrugge

Memory vulnerabilities are a major threat to many computing systems. To effectively thwart spatial and temporal memory vulnerabilities, full logical memory safety is required. However, current mitigation techniques for memory safety are either too expensive or trade security against efficiency. One promising attempt to detect memory safety vulnerabilities in

G. G. Adamian, N. V. Antonenko, H. Lenske, V. V. Sargsyan

Using the model based on the Regge-like laws, new analytical formulas are obtained for the moment of inertia, the rotation frequency, and the radius of astronomical non-exotic objects (planets, stars, galaxies, and clusters of galaxies). The rotation frequency and moment of inertia of neutron star and the observable Universe are estimated. The estimates of t

Saqib Qamar, Parvez Ahmad, Linlin Shen

The brain tumor segmentation task aims to classify tissue into the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) classes using multimodel MRI images. Quantitative analysis of brain tumors is critical for clinical decision making. While manual segmentation is tedious, time-consuming, and subjective, this task is at the same time very challenging

P. Parfenov

Elliptic ($v_2$) and triangular ($v_3$) anisotropic flow coefficients for inclusive and identified charged hadrons (~$\pi^\pm$, $K^\pm$, $p$, $\bar{p}$~) at midrapidity in Au+Au collisions, measured by the STAR experiment in the Beam Energy Scan (BES) at the Relativistic Heavy Ion Collider at $\sqrt{s_{NN}}$ = $11.5$ - $62.4$ GeV, are presented. We observe t

Gopal Datt

In this paper, we establish various sufficient conditions for a family of holomorphic mappings on a domain $D\subseteq\mathbb{C}$ into $\mathbb{P}^n$ to be normal. Our results are improvements to the Montel-Carath\'eodory Theorem for a family of $\mathbb{P}^n$-valued holomorphic curves.

Jiarong Xu, Yizhou Sun, Xin Jiang, Yanhao Wang

Adversarial attacks on graphs have attracted considerable research interests. Existing works assume the attacker is either (partly) aware of the victim model, or able to send queries to it. These assumptions are, however, unrealistic. To bridge the gap between theoretical graph attacks and real-world scenarios, in this work, we propose a novel and more reali

Jinraj V Pushpangathan, Harikumar Kandath, Suresh Sundaram, Narasimhan Sundararajan

This paper proposes a novel Gap Reduced Minimum Error Robust Simultaneous (GRMERS) estimator for resource-constrained Nano Aerial Vehicle (NAV) that enables a single estimator to provide simultaneous and robust estimation for a given N unstable and uncertain NAV plant models. The estimated full state feedback enables a stable flight for NAV. The GRMERS estim

Davide Buffelli, Fabio Vandin

Graph Neural Networks (GNNs) are a framework for graph representation learning, where a model learns to generate low dimensional node embeddings that encapsulate structural and feature-related information. GNNs are usually trained in an end-to-end fashion, leading to highly specialized node embeddings. However, generating node embeddings that can be used to

Yichao Luo, Zhengyan Li, Bingning Wang, Xiaoyu Xing

Keyphrase Generation (KG) is the task of generating central topics from a given document or literary work, which captures the crucial information necessary to understand the content. Documents such as scientific literature contain rich meta-sentence information, which represents the logical-semantic structure of the documents. However, previous approaches ig

Jeong-Hyun Cho, Ji-Hoon Jeong, Myoung-Ki Kim, Seong-Whan Lee

Noninvasive brain-computer interface (BCI) is widely used to recognize users' intentions. Especially, BCI related to tactile and sensation decoding could provide various effects on many industrial fields such as manufacturing advanced touch displays, controlling robotic devices, and more immersive virtual reality or augmented reality. In this paper, we intro

Gang Chen

We point out the generic competition between the Hund's coupling and the spin-orbit coupling in correlated materials, and this competition leads to an electronic dilemma between the Hund's metal and the relativistic insulators. Hund's metals refer to the fate of the would-be insulators where the Hund's coupling suppresses the correlation and drives the syste

Guangyan Jia, Jianming Xia, Rongjie Zhao

In this paper, we study general monetary risk measures (without any convexity or weak convexity). A monetary (respectively, positively homogeneous) risk measure can be characterized as the lower envelope of a family of convex (respectively, coherent) risk measures. The proof does not depend on but easily leads to the classical representation theorems for con

Philip Thompson

We study high-dimensional least-squares regression within a subgaussian statistical learning framework with heterogeneous noise. It includes $s$-sparse and $r$-low-rank least-squares regression when a fraction $\epsilon$ of the labels are adversarially contaminated. We also present a novel theory of trace-regression with matrix decomposition based on a new a

Rohit Prabhavalkar, Yanzhang He, David Rybach, Sean Campbell

End-to-end models that condition the output label sequence on all previously predicted labels have emerged as popular alternatives to conventional systems for automatic speech recognition (ASR). Since unique label histories correspond to distinct models states, such models are decoded using an approximate beam-search process which produces a tree of hypothes

Vidhur Kumar, Andrew Szidon

Neural Architecture Search has proven an effective method of automating architecture engineering. Recent work in the field has been to look for architectures subject to multiple objectives such as accuracy and latency to efficiently deploy them on different target hardware. Once-for-All (OFA) is one such method that decouples training and search and is able

Mingde Qin, Qizhang Yan, Haoren Wang, Kenneth S. Vecchio

Six high-entropy rare earth tetraborides of the tetragonal UB4-prototyped structure have been successfully synthesized for the first time. The specimens are prepared from elemental precursors via high-energy ball mill and in-situ reactive spark plasma sintering. The sintered specimens are >98% in relative densities without detectable oxide impurities (albeit

Elliot Anshelevich, Zack Fitzsimmons, Rohit Vaish, Lirong Xia

We study a model of proxy voting where the candidates, voters, and proxies are all located on the real line, and instead of voting directly, each voter delegates its vote to the closest proxy. The goal is to find a set of proxies that is $\theta$-representative, which entails that for any voter located anywhere on the line, its favorite candidate is within a

Cedric Goeury, Yoann Audouin, Fabrice Zaoui

Water resource management is of crucial societal and economic importance, requiring a strong capacity for anticipating environmental change. Progress in physical process knowledge, numerical methods and computational power, allows us to address hydro-environmental problems of growing complexity. Modeling of river and marine flows is no exception. With the in

Yoon Gyo Jung, Jaewoo Park, Cheng Yaw Low, Jacky Chen Long Chai

Periocular biometric, the peripheral area of the ocular, is a collaborative alternative to the face, especially when the face is occluded or masked. However, in practice, sole periocular biometric capture the least salient facial features, thereby lacking discriminative information, particularly in wild environments. To address these problems, we transfer di

Yao Xuan, Robert Balkin, Jiequn Han, Ruimeng Hu

Game theory has been an effective tool in the control of disease spread and in suggesting optimal policies at both individual and area levels. In this paper, we propose a multi-region SEIR model based on stochastic differential game theory, aiming to formulate optimal regional policies for infectious diseases. Specifically, we enhance the standard epidemic S

Jiuyuan Nie, Chongze Hu, Qizhang Yan, Jian Luo

Electric fields and currents, which are used in innovative materials processing and electrochemical energy conversion, can often alter microstructures in unexpected ways. However, little is known about the underlying mechanisms. Using ZnO-Bi2O3 as a model system, this study uncovers how an applied electric current can change the microstructural evolution thr

Chongze Hu, Jian Luo

Grain boundaries (GBs) can critically influence the microstructural evolution and various materials properties. However, a fundamental understanding of GBs in high-entropy alloys (HEAs) is lacking because of the complex couplings of the segregations of multiple elements and interfacial disordering, which can generate new phenomena and challenge the classical

Z. Cai, K. I. Kou, W. Zhang

Most results on quaternion-valued differential equation (QDE) are based on J. Campos and J. Mawhin's fundamental solution of exponential form for the homogeneous linear equation, but their result requires a commutativity property. In this paper we discuss with two problems: What quaternion function satisfies the commutativity property? Without the commutativ

Xiaoying Wang, Changbo Qu, Weiyuan Wu, Jiannan Wang

Cardinality estimation is a fundamental but long unresolved problem in query optimization. Recently, multiple papers from different research groups consistently report that learned models have the potential to replace existing cardinality estimators. In this paper, we ask a forward-thinking question: Are we ready to deploy these learned cardinality models in

Andrew J. Wright, Qingyang Wang, Chongze Hu, Yi-Ting Yeh

Improved thermomechanical properties have been reported for various high-entropy oxides containing typically five metal cations. This study further investigates a series of duodenary (11 metals + oxygen) high-entropy oxides by mixing different fractions of a five-cation fluorite-structured niobate and a seven-cation pyrochlore (both containing Yb) with match

Ruda Zhang, Roger Ghanem

We consider a variant of Cournot competition, where multiple firms allocate the same amount of resource across multiple markets. We prove that the game has a unique pure-strategy Nash equilibrium (NE), which is symmetric and is characterized by the maximal point of a "potential function". The NE is globally asymptotically stable under the gradient adjustment

Alexey Kozharin, Pavel Levashov

We present analytical formulae for the first and second derivatives of the Helmholtz free energy of non-relativistic ideal Fermi-gas. Important thermodynamic quantities such as heat capacity, sound velocity, heat capacity ratio and others can be easily expressed through the derivatives. We demonstrate correct ideal Boltzmann gas and low--temperature Fermi-ga

Emma K. Dahl, Nancy J. Chanover, Glenn S. Orton, Kevin H. Baines

The identity of the coloring agent(s) in Jupiter's atmosphere and the exact structure of Jupiter's uppermost cloud deck are yet to be conclusively understood. The Cr\`{e}me Br\^ul\'ee model of Jupiter's tropospheric clouds, originally proposed by Baines et al. (2014) and expanded upon by Sromovsky et al. (2017) and Baines et al. (2019), presumes that the chr

Sandeep Chinchali, Evgenya Pergament, Manabu Nakanoya, Eyal Cidon

Today's robotic fleets are increasingly measuring high-volume video and LIDAR sensory streams, which can be mined for valuable training data, such as rare scenes of road construction sites, to steadily improve robotic perception models. However, re-training perception models on growing volumes of rich sensory data in central compute servers (or the "cloud")

Albert Tung, Josiah Wong, Ajay Mandlekar, Roberto Martín-Martín

Imitation Learning (IL) is a powerful paradigm to teach robots to perform manipulation tasks by allowing them to learn from human demonstrations collected via teleoperation, but has mostly been limited to single-arm manipulation. However, many real-world tasks require multiple arms, such as lifting a heavy object or assembling a desk. Unfortunately, applying

Zijian Kuang, Xinran Tie, Lihang Ying, Shi Jin

Visual defect detection is critical to ensure the quality of most products. However, the majority of small and medium-sized manufacturing enterprises still rely on tedious and error-prone human manual inspection. The main reasons include: 1) the existing automated visual defect detection systems require altering production assembly lines, which is time consu

Tiemo Landes, Markus Allgaier, Sofiane Merkouche, Brian J. Smith

Entangled photon pairs have been promised to deliver a substantial quantum advantage for two-photon absorption spectroscopy. However, recent work has challenged the previously reported magnitude of quantum enhancement in two-photon absorption. Here, we present an experimental comparison of sum-frequency generation and molecular absorption, each driven by iso

Yu Yin, Joseph P. Robinson, Yun Fu

Humans spend vast hours in bed -- about one-third of the lifetime on average. Besides, a human at rest is vital in many healthcare applications. Typically, humans are covered by a blanket when resting, for which we propose a multimodal approach to uncover the subjects so their bodies at rest can be viewed without the occlusion of the blankets above. We propo

Yuliang Guo, Zhong Li, Zekun Li, Xiangyu Du

In this paper, a real-time method called PoP-Net is proposed to predict multi-person 3D poses from a depth image. PoP-Net learns to predict bottom-up part representations and top-down global poses in a single shot. Specifically, a new part-level representation, called Truncated Part Displacement Field (TPDF), is introduced which enables an explicit fusion pr

Ajay Mandlekar, Danfei Xu, Roberto Martín-Martín, Yuke Zhu

Imitation Learning is a promising paradigm for learning complex robot manipulation skills by reproducing behavior from human demonstrations. However, manipulation tasks often contain bottleneck regions that require a sequence of precise actions to make meaningful progress, such as a robot inserting a pod into a coffee machine to make coffee. Trained policies

Tadahiro Oh, Kihoon Seong

We continue the study on the transport properties of the Gaussian measures on Sobolev spaces under the dynamics of the cubic fourth order nonlinear Schr\"odinger equation. By considering the renormalized equation, we extend the quasi-invariance results in [30, 27] to Sobolev spaces of negative regularity. Our proof combines the approach introduced by Plancho

Robin Swezey, Aditya Grover, Bruno Charron, Stefano Ermon

A key challenge with machine learning approaches for ranking is the gap between the performance metrics of interest and the surrogate loss functions that can be optimized with gradient-based methods. This gap arises because ranking metrics typically involve a sorting operation which is not differentiable w.r.t. the model parameters. Prior works have proposed

Yun Meng, Kai Zou, Nan Hu, Liang Xu

The near-unity system detection efficiency (SDE) and excellent timing resolution of superconducting nanowire single-photon detectors (SNSPDs), combined with their other merits, have enabled many classical and quantum photonic applications. However, the prevalent design based on meandering nanowires makes SDE dependent on the polarization states of the incide

Tadahiro Oh, Kihoon Seong, Leonardo Tolomeo

We study Gibbs measures with log-correlated base Gaussian fields on the $d$-dimensional torus. In the defocusing case, the construction of such Gibbs measures follows from Nelson's argument. In this paper, we consider the focusing case with a quartic interaction. Using the variational formulation, we prove non-normalizability of the Gibbs measure. When $d =

Haoya Li, Yuehaw Khoo, Yinuo Ren, Lexing Ying

This paper proposes a new method based on neural networks for computing the high-dimensional committor functions that satisfy Fokker-Planck equations. Instead of working with partial differential equations, the new method works with an integral formulation based on the semigroup of the differential operator. The variational form of the new formulation is the

Srikanth Sastry

Understanding the mechanical response and failure of solids is of obvious importance in their use as structural materials. The nature of plastic deformation leading to yielding of amorphous solids has been vigorously pursued in recent years. Investigations employing both unidirectional and cyclic deformation protocols reveal a strong dependence of yielding b

Nikos Vlassis, Phil Hebda, Stephan McBride, Athanasios Noulas

We generalize the proximal g-formula of Miao, Geng, and Tchetgen Tchetgen (2018) for causal inference under unobserved confounding using proxy variables. Specifically, we show that the formula holds true for all causal models in a certain equivalence class, and this class contains models in which the total number of levels for the set of unobserved confounde

Rajashekhar V S, Rokesh Laishram, Kaushik Das, Debasish Ghose

In this paper, a 1-DoF gripper mechanism has been synthesized for the type of mechanism, number of links and joints, and the dimensions of length, width and thickness of links. The type synthesis is done by selecting the proper class of mechanism from Reuleaux's six classes of mechanisms. The number synthesis is done by using an algebraic method. The dimensi

Sahil Sidheekh, Aroof Aimen, Vineet Madan, Narayanan C. Krishnan

Generative adversarial network (GAN) is among the most popular deep learning models for learning complex data distributions. However, training a GAN is known to be a challenging task. This is often attributed to the lack of correlation between the training progress and the trajectory of the generator and discriminator losses and the need for the GAN's subjec

Qihang Yu, Jianming Zhang, He Zhang, Yilin Wang

We propose Mask Guided (MG) Matting, a robust matting framework that takes a general coarse mask as guidance. MG Matting leverages a network (PRN) design which encourages the matting model to provide self-guidance to progressively refine the uncertain regions through the decoding process. A series of guidance mask perturbation operations are also introduced

Heba Sami, Amare Abebe

In this contribution, we consider the equivalence between $f(R)$ gravity and scalar-tensor theories to study the evolution of scalar cosmological perturbations in the $1 + 3$ covariant formalism for the classes of shear-free cosmological dust models with irrotational fluid flows. The $f(R)$ gravity is considered to be a subclass of Brans-Dicke models, we gav

Huachuan Wang, James Ting-Ho Lo

A biologically plausible low-order model (LOM) of biological neural networks is a recurrent hierarchical network of dendritic nodes/trees, spiking/nonspiking neurons, unsupervised/ supervised covariance/accumulative learning mechanisms, feedback connections, and a scheme for maximal generalization. These component models are motivated and necessitated by mak

Bishal Chhetri, D. K. K Vamsi, Carani B Sanjeevi

In this study initially, we propose an age structured model and calculate the equilibrium points and basic reproduction number. Later we propose an optimal control problem to understand the roles of treatment in controlling the epidemic. From the Stability analysis we see that the infection free equilibrium remains asymptotically stable whenever $R_0 < 1$ an

Gabriel Hope, Madina Abdrakhmanova, Xiaoyin Chen, Michael C. Hughes

We develop a new framework for learning variational autoencoders and other deep generative models that balances generative and discriminative goals. Our framework optimizes model parameters to maximize a variational lower bound on the likelihood of observed data, subject to a task-specific prediction constraint that prevents model misspecification from leadi

Hsiang-Yun Sherry Chien, Jinhan Zhang, Christopher. J. Honey

In the human brain, sequences of language input are processed within a distributed and hierarchical architecture, in which higher stages of processing encode contextual information over longer timescales. In contrast, in recurrent neural networks which perform natural language processing, we know little about how the multiple timescales of contextual informa

Gyu Hyun Kim

This study investigates the relationship of the equity home bias with 1) the country-level behavioral unfamiliarity, and 2) the home-foreign return correlation. We set the hypotheses that 1) unfamiliarity about foreign equities plays a role in the portfolio set up and 2) the correlation of return on home and foreign equities affects the equity home bias when

Guanyu Hu, Hou-Cheng Yang, Yishu Xue, Dipak K. Dey

Although basketball is a dynamic process sport, with 5 plus 5 players competing on both offense and defense simultaneously, learning some static information is predominant for professional players, coaches and team mangers. In order to have a deep understanding of field goal attempts among different players, we propose a zero inflated Poisson model with clus

Samragni Banerjee, Alexander Yu. Sokolov

We present an efficient implementation of the second- and third-order single-reference algebraic diagrammatic construction theory for electron attachment (EA) and ionization (IP) energies and spectra (EA/IP-ADC(n), n = 2, 3). Our new EA/IP-ADC program features spin adaptation for closed-shell systems, density fitting for efficient handling of the two-electro

Sami Davies, Miklos Z. Racz, Cyrus Rashtchian, Benjamin G. Schiffer

In the usual trace reconstruction problem, the goal is to exactly reconstruct an unknown string of length $n$ after it passes through a deletion channel many times independently, producing a set of traces (i.e., random subsequences of the string). We consider the relaxed problem of approximate reconstruction. Here, the goal is to output a string that is clos

Mohammad Jahanbakht, Wei Xiang, Lajos Hanzo, Mostafa Rahimi Azghadi

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster

Rongye Meng, Sanping Zhou, Xingyu Wan, Mengliu Li

Due to the high annotation cost of large-scale facial landmark detection tasks in videos, a semi-supervised paradigm that uses self-training for mining high-quality pseudo-labels to participate in training has been proposed by researchers. However, self-training based methods often train with a gradually increasing number of samples, whose performances vary

E. Abdikamalov, T. Foglizzo, O. Mukazhanov

We study the impact of rotation on the hydrodynamic evolution of convective vortices during stellar collapse. Using linear hydrodynamics equations, we study the evolution of the vortices from their initial radii in convective shells down to smaller radii where they are expected to encounter the supernova shock. We find that the evolution of vortices is mainl

Yi Feng, George Hobbs, Di Li, Shi Dai

Using the Five-hundred-meter Aperture Spherical radio Telescope (FAST), we have recorded 10^5 single pulses from PSR J1022+1001. We studied the polarization properties, their energy distribution and their times of arrival. This is only possible with the high sensitivity available using FAST. There is no indication that PSR~J1022+1001 exhibits giant pulse, nu

Anna-Maria Gueorguieva, Gulnar Rakhmetulla, Ahmed Sabbir Arif

This paper presents results of a pilot study that explored the potential of Morse code as a method for text entry on mobile devices. In the study, participants without prior experience with Morse code reached 6.7 wpm with a Morse code keyboard in three short sessions. Learning was observed both in terms of text entry speed and accuracy, which suggests that t

Zhuangzhuang Cui, Ke Guan, César Briso-Rodríguez, Bo Ai

As essential aerial platforms, unmanned aerial vehicles (UAVs) play an increasingly important role in broad wireless connectivity and high-data-rate transmission for future communication systems. Notably, various communication scenarios are involved in UAV communications, such as intercommunications between UAVs and communications with the ground user equipm

Yuhao Zhou, Ye Qing, Jiancheng Lv

Petabytes of data are generated each day by emerging Internet of Things (IoT), but only few of them can be finally collected and used for Machine Learning (ML) purposes due to the apprehension of data & privacy leakage, which seriously retarding ML's growth. To alleviate this problem, Federated learning is proposed to perform model training by multiple clien

Nehali Mhatre, Daniel Cooley

In order to capture the dependence in the upper tail of a time series, we develop non-negative regularly-varying time series models that are constructed similarly to classical non-extreme ARMA models. Rather than fully characterizing tail dependence of the time series, we define the concept of weak tail stationarity which allows us to describe a regularly-va

Chaohao Chen, Baolei Liu, Yongtao Liu, Jiayan Liao

Point spread function (PSF) engineering of the emitter can code higher spatial frequency information of an image to break diffraction limit but suffer from the complexed optical systems. Here we present a robust strategy to simultaneously achieve diverse PSFs from upconversion nanoparticles under a single doughnut-shape scanning beam. By saturating the four-

Zhuo Jin, Zuo Quan Xu, Bin Zou

We study an optimal dividend problem for an insurer who simultaneously controls investment weights in a financial market, liability ratio in the insurance business, and dividend payout rate. The insurer seeks an optimal strategy to maximize her expected utility of dividend payments over an infinite horizon. By applying a perturbation approach, we obtain the

Henry Adams, Leah Gibson, Jack Pfaffinger

Suppose each vertex of a graph is originally occupied by contamination, except for those vertices occupied by lions. As the lions wander on the graph, they clear the contamination from each vertex they visit. However, the contamination simultaneously spreads to any adjacent vertex not occupied by a lion. How many lions are required in order to clear the grap

Hui Liu, Li Yang

The quantum security of lightweight block ciphers is receiving more and more attention. However, the existing quantum attacks on lightweight block ciphers mainly focused on the quantum exhaustive search, while the quantum dedicated attacks combined with classical cryptanalysis methods haven't been well studied. In this paper, we study quantum key recovery at

Jiahao Yao, Paul Köttering, Hans Gundlach, Lin Lin

Variational quantum eigensolvers have recently received increased attention, as they enable the use of quantum computing devices to find solutions to complex problems, such as the ground energy and ground state of strongly-correlated quantum many-body systems. In many applications, it is the optimization of both continuous and discrete parameters that poses

Ryuki Hyodo, Tristan Guillot, Shigeru Ida, Satoshi Okuzumi

Around the snow line, icy pebbles and silicate dust may locally pile-up and form icy and rocky planetesimals via streaming instability and/or gravitational instability. We perform 1D diffusion-advection simulations that include the back-reaction to radial drift and diffusion of icy pebbles and silicate dust, ice sublimation, release of silicate dust, and the

Mark Andrews

Moments are expectation values of products of powers of position and momentum, taken over quantum states (or averages over a set of classical particles). For free particles, the evolution in the quantum case is closely related to that of a set of classical particles. Here we consider the evolution of symmetrized moments for free particles in one dimension, f

Yuefang Sun

For a digraph $D=(V(D), A(D))$, and a set $S\subseteq V(D)$ with $r\in S$ and $|S|\geq 2$, an $(S, r)$-tree is an out-tree $T$ rooted at $r$ with $S\subseteq V(T)$. Two $(S, r)$-trees $T_1$ and $T_2$ are said to be arc-disjoint if $A(T_1)\cap A(T_2)=\emptyset$. Two arc-disjoint $(S, r)$-trees $T_1$ and $T_2$ are said to be internally disjoint if $V(T_1)\cap

Xijiang Miao, Michael G. Bryson, Homayoun Valafar

This article introduces a novel protein structure alignment method (named TALI) based on the protein backbone torsion angle instead of the more traditional distance matrix. Because the structural alignment of the two proteins is based on the comparison of two sequences of numbers (backbone torsion angles), we can take advantage of a large number of well-deve

Zubia Hasan, Eli Zoghlin, Michal J. Winiarski, Kathryn E. Arpino

CuTeO4 has been proposed as a crystallographically distinct, yet electronic structure analog, of the superconducting cuprates. Here, we present detailed characterization of the of the physical properties of CuTeO4 to address this proposal. Fitting of magnetic susceptibility data indicates unexpected quasi-one-dimensional, antiferromagnetic correlations at hi

Udaya Ghai, David Snyder, Anirudha Majumdar, Elad Hazan

We consider the problem of generating maximally adversarial disturbances for a given controller assuming only blackbox access to it. We propose an online learning approach to this problem that \emph{adaptively} generates disturbances based on control inputs chosen by the controller. The goal of the disturbance generator is to minimize \emph{regret} versus a

Shima Rahimi Moghaddam, Fanjun Bu, Christopher J. Honey

In the human brain, internal states are often correlated over time (due to local recurrence and other intrinsic circuit properties), punctuated by abrupt transitions. At first glance, temporal smoothness of internal states presents a problem for learning input-output mappings (e.g. category labels for images), because the internal representation of the input

Xiaodong Zhou, Wanxiang Feng, Xiuxian Yang, Guang-Yu Guo

The spin chirality, created by magnetic atoms, has been comprehensively understood to generate and control the magneto-optical effects. In comparison, the role of the crystal chirality that relates to nonmagnetic atoms has received much less attention. Here, we theoretically discover the crystal chirality magneto-optical (CCMO) effects, which depend on the c

Hengameh R. Dehkordi

The devastating effects of wildfires on the wildlife and their impact on human lives and properties are undeniable. This shows the importance of studying the spread of wildfire, predicting its behavior and presenting more reliable models for its propagation. Here, by using the validity of the Huygens` envelope principle for wavefronts in Randers spaces, we p

Johann Rudi, Julie Bessac, Amanda Lenzi

Machine learning algorithms have been successfully used to approximate nonlinear maps under weak assumptions on the structure and properties of the maps. We present deep neural networks using dense and convolutional layers to solve an inverse problem, where we seek to estimate parameters of a FitzHugh-Nagumo model, which consists of a nonlinear system of ord

Zefang Liu

We predict restaurant ratings from Yelp reviews based on Yelp Open Dataset. Data distribution is presented, and one balanced training dataset is built. Two vectorizers are experimented for feature engineering. Four machine learning models including Naive Bayes, Logistic Regression, Random Forest, and Linear Support Vector Machine are implemented. Four transf

Zhiwan Xu, Xiatong Wu, Caleb Sword, Gang Wang

The azimuthal correlations between local flow planes at different (pseudo)rapidities ($\eta$) may reveal important details of the initial nuclear matter density distributions in heavy-ion collisions. Extensive experimental measurements of a factorization ratio ($r_2$) and its derivative ($F_2$) have shown evidence of the longitudinal flow-plane decorrelation

Aden Zhenhao Lam, Claire Warner, Niccolò Bigagli, Stephan Roschinski

We report on the design, implementation, and performance of a compact two-dimensional magneto-optical trap (2D MOT) for cesium. In a small-volume vacuum chamber, the setup uses cesium dispensers in close proximity to the trapping region of the 2D MOT and operates at low vapor pressures in the $10^{-9}$ torr range. We achieve a cold atom flux of $4 \times 10^

Jia-Wei Ji, Yu-Feng Wu, Stephen C. Wein, Faezeh Kimiaee Asadi

We propose a quantum repeater architecture that can operate under ambient conditions. Our proposal builds on recent progress towards non-cryogenic spin-photon interfaces based on nitrogen-vacancy centers, which have excellent spin coherence times even at room temperature, and optomechanics, which allows to avoid phonon-related decoherence and also allows the

Ankit Singhal, Thanh Long Vu, Wei Du

In a purely inverter-based microgrid, both grid-forming (GFM) and grid-following (GFL) inverters will have a crucial role to play in frequency/voltage regulation and maintaining power sharing through their grid support capabilities. Consequently, the coordination between these two technologies becomes increasingly important for optimal system performance. Ho

Samuel Ainsworth, Kendall Lowrey, John Thickstun, Zaid Harchaoui

We study the estimation of policy gradients for continuous-time systems with known dynamics. By reframing policy learning in continuous-time, we show that it is possible construct a more efficient and accurate gradient estimator. The standard back-propagation through time estimator (BPTT) computes exact gradients for a crude discretization of the continuous-

Vikram Nathan, Jialin Ding, Tim Kraska, Mohammad Alizadeh

Databases employ indexes to filter out irrelevant records, which reduces scan overhead and speeds up query execution. However, this optimization is only available to queries that filter on the indexed attribute. To extend these speedups to queries on other attributes, database systems have turned to secondary and multi-dimensional indexes. Unfortunately, the

Edith Elkind, Erel Segal-Halevi, Warut Suksompong

We study the problem of fairly allocating a divisible resource, also known as cake cutting, with an additional requirement that the shares that different agents receive should be sufficiently separated from one another. This captures, for example, constraints arising from social distancing guidelines. While it is sometimes impossible to allocate a proportion

Matthew Mewes

The effects of Lorentz and CPT violations on macroscopic objects are explored. Effective composite coefficients for Lorentz violation are derived in terms of coefficients for electrons, protons, and neutrons in the Standard-Model Extension, including all minimal and nonminimal violations. The hamiltonian and modified Newton's second law for a test body are d

Alexander Engel, Graeme Smith, Scott E. Parker

The simulation of large nonlinear dynamical systems, including systems generated by discretization of hyperbolic partial differential equations, can be computationally demanding. Such systems are important in both fluid and kinetic computational plasma physics. This motivates exploring whether a future error-corrected quantum computer could perform these sim

Bai Xue, Qiuye Wang, Naijun Zhan, Shijie Wang

In this paper we propose a novel semi-definite programming based method to compute robust domains of attraction for state-constrained perturbed polynomial systems. A robust domain of attraction is a set of states such that every trajectory starting from it will approach an equilibrium while never violating a specified state constraint, regardless of the actu

Adriana Gazol, Marco Villagran

The objective of this paper is to help shedding some light on the nature and the properties of the cold structures formed via thermal instability in the magnetized atomic interstellar medium. To this end, we searched for clumps formed in forced (magneto)hydrodynamic simulations with an initial magnetic field ranging from 0 to 8.3$\mu$G. We statistically anal

John Burge, Matthew Bonanni, Matthias Ihme, Lily Hu

As the climate changes, the severity of wildland fires is expected to worsen. Models that accurately capture fire propagation dynamics greatly help efforts for understanding, responding to and mitigating the damages caused by these fires. Machine learning techniques provide a potential approach for developing such models. The objective of this study is to ev

Xin Huang, Ashish Khetan, Milan Cvitkovic, Zohar Karnin

We propose TabTransformer, a novel deep tabular data modeling architecture for supervised and semi-supervised learning. The TabTransformer is built upon self-attention based Transformers. The Transformer layers transform the embeddings of categorical features into robust contextual embeddings to achieve higher prediction accuracy. Through extensive experimen

Keith Dillon

We consider the numerical optimization of performance for a computational extension of a confocal microscope. Using a system where the pinhole detector is replaced with a detector array, we seek to exploit this additional information for each point in the scan. We derive an optimal estimate of the light at focus which minimizes the contribution of out-of-foc

Frank Garvan

In 1944 Dyson defined the rank of a partition as the largest part minus the number of parts, and conjectured that the residue of the rank mod 5 divides the partitions of 5n+4 into five equal classes. This gave a combinatorial explanation of Ramanujan's famous partition congruence mod 5. He made an analogous conjecture for the rank mod 7 and the partitions of