March 2020 arXiv papers — page 48

Naoto Ohsaka

Influence maximization is among the most fundamental algorithmic problems in social influence analysis. Over the last decade, a great effort has been devoted to developing efficient algorithms for influence maximization, so that identifying the ``best'' algorithm has become a demanding task. In SIGMOD'17, Arora, Galhotra, and Ranu reported benchmark results

Andong Li, Chengshi Zheng, Linjuan Cheng, Renhua Peng

In this paper, we propose a type of neural network with feedback learning in the time domain called FTNet for monaural speech enhancement, where the proposed network consists of three principal components. The first part is called stage recurrent neural network, which is introduced to effectively aggregate the deep feature dependencies across different stage

S. Zakavati, F. T. Tabesh, S. Salimi

In general, quantum systems most likely undergo open system dynamics due to their smallness and sensitivity. Energy storage devices, so-called quantum batteries, are not excluded from this phenomenon. Here, we study fundamental bounds on the power of open quantum batteries from the geometric point of view. By defining an \emph{activity operator}, a tight upp

Ahmed Allibhoy, Jorge Cortés

We propose a distributed data-based predictive control scheme to stabilize a network system described by linear dynamics. Agents cooperate to predict the future system evolution without knowledge of the dynamics, relying instead on learning a data-based representation from a single sample trajectory. We employ this representation to reformulate the finite-ho

Da Li, Keran Li, Wenyuan Liao

In this paper, we consider the development and analysis of a new explicit compact high-order finite difference scheme for acoustic wave equation formulated in divergence form, which is widely used to describe seismic wave propagation through a heterogeneous media with variable media density and acoustic velocity. The new scheme is compact and of fourth-order

Jin-Long Xu, Ye Xu, Peng Jiang, Ming Zhu

We performed a multi-wavelength observation toward LkHa 101 embedded cluster and its adjacent 85arcmin*60arcmin region. The LkHa 101 embedded cluster is the first and only one significant cluster in California molecular cloud (CMC). These observations have revealed that the LkHa 101 embedded cluster is just located at the projected intersectional region of t

Guo-Shuai Mao

In this paper, we prove a congruence which confirms a conjecture of Adamchuk. For any prime $p\equiv1\pmod3$ and $a\in\mathbb{Z}^{+}$, we have \begin{align*} \sum_{k=1}^{\frac{2}3(p^a-1)}\binom{2k}k\equiv0\pmod{p^2}. \end{align*}

Raghavendra Srikanth Hundi, Itishree Sethi

We have considered a model \cite{ma-weg}, where masses and a mixing pattern for neutrinos are governed by six Higgs triplets and $A_4$ symmetry. In this model we have applied a certain diagonalisation procedure through which we have shown that neutrino masses can have both normal or inverted hierarchy. We have also shown that current neutrino oscillation dat

Rooji Jinan, Parimal Parag, Himanshu Tyagi

Samples from a high-dimensional AR[1] process are observed by a sender which can communicate only finitely many bits per unit time to a receiver. The receiver seeks to form an estimate of the process value at every time instant in real-time. We consider a time-slotted communication model in a slow-sampling regime where multiple communication slots occur betw

Abu Shafin Mohammad Mahdee Jameel, Ahmed P. Mohamed, Xiwen Zhang, Aly El Gamal

We demonstrate a first example for employing deep learning in predicting frame errors for a Collaborative Intelligent Radio Network (CIRN) using a dataset collected during participation in the final scrimmages of the DARPA SC2 challenge. Four scenarios are considered based on randomizing or fixing the strategy for bandwidth and channel allocation, and either

Jinshui Miao, Xiwen Liu, Kiyoung Jo, Kang He

Van der Waals (vdW) semiconductors are attractive for highly scaled devices and heterogeneous integration since they can be isolated into self-passivated, two-dimensional (2D) layers that enable superior electrostatic control. These attributes have led to numerous demonstrations of field-effect devices ranging from transistors to triodes. By exploiting the c

Lorenzo Baldassari, Andrea Scapin

This paper aims at introducing the concept of time-dependent polarization tensors (TDPTs) for the wave equation associated to a diametrically small acoustic inclusion, with constitutive parameters different from those of the background and size smaller than the operating wavelength. Firstly, the solution to the Helmholtz equation is considered, and a rigorou

William McLean

The discontinuous Galerkin dG method provides a robust and flexible technique for the time integration of fractional diffusion problems. However, a practical implementation uses coefficients defined by integrals that are not easily evaluated. We describe specialised quadrature techniques that efficiently maintain the overall accuracy of the dG method. In add

Sorb Yesudhas, N. Yedukondalu, Manoj K. Jana, Jianbo Zhang

Analogous to 2D layered transition metal dichalcogenides, the TlSe family of 1D chain materials with Zintl-type structure exhibits exotic phenomena under high-pressure. In the present work, we have systematically investigated the high-pressure behavior of TlInTe 2 using Raman spectroscopy, synchrotron X-ray diffraction, and transport measurements, in combina

Leonardo Ermann, Gabriel G. Carlo, Alexei D. Chepelianskii, Dima L. Shepelyansky

We study analytically and numerically the properties of Jaynes-Cummings model under monochromatic driving. The analytical results allow to understand the regime of two branches of multi-photon excitation in the case of close resonance between resonator and driven frequencies. The rotating wave approximation allows to reduce the description of original driven

Xinyu Liu, Xiren Miao, Hao Jiang, Jing Chen

The widespread popularity of unmanned aerial vehicles enables an immense amount of power lines inspection data to be collected. How to employ massive inspection data especially the visible images to maintain the reliability, safety, and sustainability of power transmission is a pressing issue. To date, substantial works have been conducted on the analysis of

Angxiu Ni

Nonintrusive shadowing algorithms efficiently compute $v$, the difference between shadowing trajectories, then use $v$ to compute derivatives of averaged objectives of chaos with respect to parameters of the dynamical system. However, previous proofs of shadowing methods wrongly assume that shadowing trajectories are representative. In contrast, the linear r

Kedan Li, Min Jin Chong, Jingen Liu, David Forsyth

A virtual try-on method takes a product image and an image of a model and produces an image of the model wearing the product. Most methods essentially compute warps from the product image to the model image and combine using image generation methods. However, obtaining a realistic image is challenging because the kinematics of garments is complex and because

Zixin Hu, Qiyang Ge, Shudi Li, Eric Boerwincle

When the Covid-19 pandemic enters dangerous new phase, whether and when to take aggressive public health interventions to slow down the spread of COVID-19. To develop the artificial intelligence (AI) inspired methods for real-time forecasting and evaluating intervention strategies to curb the spread of Covid-19 in the World. A modified auto-encoder for model

Jiamiao Xu, Fangzhao Wang, Qinmu Peng, Xinge You

Low-rank Multi-view Subspace Learning (LMvSL) has shown great potential in cross-view classification in recent years. Despite their empirical success, existing LMvSL based methods are incapable of well handling view discrepancy and discriminancy simultaneously, which thus leads to the performance degradation when there is a large discrepancy among multi-view

Xu Feng, Mikhail Gorchtein, Lu-Chang Jin, Peng-Xiang Ma

We present the first realistic lattice QCD calculation of the $\gamma W$-box diagrams relevant for beta decays. The nonperturbative low-momentum integral of the $\gamma W$ loop is calculated using a lattice QCD simulation, complemented by the perturbative QCD result at high momenta. Using the pion semileptonic decay as an example, we demonstrate the feasibil

Toshitaka Aoki, Toshiya Yurikusa

The $g$-vectors of two-term presilting complexes are important invariants. We study a fan consisting of all $g$-vector cones for a complete gentle algebra. We show that any complete gentle algebra is $g$-tame, by definition, the closure of a geometric realization of its fan is the entire ambient vector space. Our main ingredients are their surface model and

Jun Chen, Komi Messan, Marisabel Rodriguez Messan, Gloria DeGrandi-Hoffman

Western honeybees (Apis Mellifera) serve extremely important roles in our ecosystem and economics as they are responsible for pollinating $ 215 billion dollars annually over the world. Unfortunately, honeybee population and their colonies have been declined dramatically. The purpose of this article is to explore how we should model honeybee population with a

Yanjun Han, Zhengyuan Zhou, Tsachy Weissman

We study online learning in repeated first-price auctions where a bidder, only observing the winning bid at the end of each auction, learns to adaptively bid in order to maximize her cumulative payoff. To achieve this goal, the bidder faces censored feedback: if she wins the bid, then she is not able to observe the highest bid of the other bidders, which we

John Pasquesi, Flavio Esposito, Gianluca Davoli, Jenna Gorlewicz

The network management community has explored and exploited light, copper, and several wireless spectra (including acoustics) as a medium to transfer control or data traffic. Meanwhile, haptic technologies are being explored in end-user (wearable) devices, and Tactile Internet is being used merely as a metaphor. However, with rare exceptions and for smaller

Yang Li, Zhijin Huang, Zhan Sui, Huajiang Chen

Second harmonic generation (SHG) with a material of large transparency is an attractive way of generating coherent light sources at exotic wavelength range such as VUV, UV and visible light. It is of critical importance to improve nonlinear conversion efficiency in order to find practical applications in quantum light source and high resolution nonlinear mic

Sofia Suvorova, Stephen D. Howard, Bill Moran

Tracking on the rotation group is a key component of many modern systems for estimation of the motion of rigid bodies. To address this problem, here we describe a Bayesian algorithm that relies on directional measurements for tracking on the special orthogonal (rotation) group. Its novelty lies in the use of maximum entropy distributions on these groups as m

Ziyi Zhao, Haowen Fang, Zhao Jin, Qinru Qiu

The trajectory prediction is a critical and challenging problem in the design of an autonomous driving system. Many AI-oriented companies, such as Google Waymo, Uber and DiDi, are investigating more accurate vehicle trajectory prediction algorithms. However, the prediction performance is governed by lots of entangled factors, such as the stochastic behaviors

Tianrong Lin

This short note present a "proof" of $P\neq NP$. The "proof" with double quotation marks is to indicate that we do not know whether the proof is correct or not (We're confused because we do know in which we make the mistakes).

Zhonghua Wu, Qingyi Tao, Guosheng Lin, Jianfei Cai

Fully supervised object detection has achieved great success in recent years. However, abundant bounding boxes annotations are needed for training a detector for novel classes. To reduce the human labeling effort, we propose a novel webly supervised object detection (WebSOD) method for novel classes which only requires the web images without further annotati

Takeru Matsuda, Yuto Miyatake

This study computes the gradient of a function of numerical solutions of ordinary differential equations (ODEs) with respect to the initial condition. The adjoint method computes the gradient approximately by solving the corresponding adjoint system numerically. In this context, Sanz-Serna [SIAM Rev., 58 (2016), pp. 3--33] showed that when the initial value

Hadi Mansourifar, Weidong Shi

Synthetic Minority Over-sampling Technique (SMOTE) is the most popular over-sampling method. However, its random nature makes the synthesized data and even imbalanced classification results unstable. It means that in case of running SMOTE n different times, n different synthesized in-stances are obtained with n different classification results. To address th

Ilan Hirshberg

We establish four results concerning connections between actions on separable C*-algebras with Rokhlin-type properties and absorption of the Jiang-Su algebra Z. For actions of residually finite groups or of the reals which have finite Rokhlin dimension with commuting towers, we show that if the action of any nontrivial group element is approximately inner th

Jehong Yoo, Reza Langari

Merging in the form of a mandatory lane-change is an important issue in transportation research. Even when safely completed, merging may disturb the mainline traffic and reduce the efficiency or capacity of the roadway. In this paper, we consider a Stackelberg game-theoretic driver behavior model where the so-called utilities or payoffs reflect the merging v

Ziyi Zhao, Zhao Jin, Wentian Bai, Wentan Bai

The number of daily sUAS operations in uncontrolled low altitude airspace is expected to reach into the millions in a few years. Therefore, UAS density prediction has become an emerging and challenging problem. In this paper, a deep learning-based UAS instantaneous density prediction model is presented. The model takes two types of data as input: 1) the hist

Yonghui Zhang, Ke Gu

Since Particulate Matters (PMs) are closely related to people's living and health, it has become one of the most important indicator of air quality monitoring around the world. But the existing sensor-based methods for PM monitoring have remarkable disadvantages, such as low-density monitoring stations and high-requirement monitoring conditions. It is highly

Jehong Yoo, Reza Langari

In this paper we consider the application of Stackelberg game theory to model discretionary lane-changing in lightly congested highway setting. The fundamental intent of this model, which is parameterized to capture driver disposition (aggressiveness or inattentiveness), is to help with the development of decision-making strategies for autonomous vehicles in

Shahab Boumi, Adan Vela, Jacquelyn Chini

Simplified categorizations have often led to college students being labeled as full-time or part-time students. However, at many universities student enrollment patterns can be much more complicated, as it is not uncommon for students to alternate between full-time and part-time enrollment each semester based on finances, scheduling, or family needs. While p

Dai Aoki, Fuminori Honda, Georg Knebel, Daniel Braithwaite

We performed AC calorimetry and magnetoresistance measurements under pressure for H || a-axis (easy-magnetization axis) in the novel heavy-fermion superconductor UTe2. Thanks to the thermodynamic information, multiple superconducting phases have been revealed under pressure and magnetic field. The (H,T) phase diagram of superconductivity under pressure displ

Jasmin Hundall, Benson A. Babu

Abnormal gait, its associated falls and complications have high patient morbidity, mortality. Computer vision detects, predicts patient gait abnormalities, assesses fall risk and serves as clinical decision support tool for physicians. This paper performs a systematic review of how computer vision, machine learning models perform an abnormal patient's gait a

Jin-Lei Yang, Tai-Fu Feng, Hai-Bin Zhang

The theoretical predictions in the standard model (SM) and measurements on the anomalous magnetic dipole moments (MDM) of muon and electron have great precision, hence the MDMs of muon and electron have close relation with the new physics (NP) beyond the SM. Recently, a negative $\sim2.4\sigma$ discrepancy between the measured electron MDM and the SM predict

M. Kerr, D. J. Reardon, G. Hobbs, R. M. Shannon

We describe 14 years of public data from the Parkes Pulsar Timing Array (PPTA), an ongoing project that is producing precise measurements of pulse times of arrival from 26 millisecond pulsars using the 64-m Parkes radio telescope with a cadence of approximately three weeks in three observing bands. A comprehensive description of the pulsar observing systems

Amirreza Farnoosh, Behnaz Rezaei, Eli Zachary Sennesh, Zulqarnain Khan

We introduce deep Markov spatio-temporal factorization (DMSTF), a generative model for dynamical analysis of spatio-temporal data. Like other factor analysis methods, DMSTF approximates high dimensional data by a product between time dependent weights and spatially dependent factors. These weights and factors are in turn represented in terms of lower dimensi

Adrian Ka-Wai Chung, Mairi Sakellariadou

We present a method to constrain the temperature of astrophysical black holes through detecting the inspiral phase of binary black hole coalescences. At sufficient separation, inspiraling black holes can be regarded as isolated objects, hence their temperature can still be defined. Due to their intrinsic radiation, inspiraling black holes lose part of their

Andrés Angel, Edward Becerra, Mario Velásquez

In this paper we study a natural decomposition of $G$-equivariant $K$-theory of a proper $G$-space, when $G$ is a Lie group with a compact normal subgroup $A$ acting trivially. Our decomposition could be understood as a generalization of the theory known as Mackey machine under suitable hypotheses, since it decomposes $G$-equivariant K-theory in terms of twi

Pengfei Li, Huiyuan Wang, H. J. Mo, Enci Wang

A clear transition feature of galaxy quenching is identified in the multi-parameter space of stellar mass ($M_*$), bulge to total mass ratio ($B/T_{\rm m}$), halo mass ($M_{\rm h}$) and halo-centric distance ($r/r_{180}$). For given halo mass, the characteristic stellar mass ($M_{*, \rm ch}$) for the transition is about one-fifth of that of the corresponding

Jan Brink Valentin

Objective: To conduct an early estimation of the Basic Reproduction Number (BRN) induced by government interference, and to project resulting day to day number of in-patients, ICU-patients and cumulative number of deaths in a Danish setting. Method: We used the Kermack and McKendrick model with varying basic reproduction number to estimate number infected an

Hamada M. Zahera, Mohamed Ahmed Sherif, Axel Ngonga

Disaster prediction is one of the most critical tasks towards disaster surveillance and preparedness. Existing technologies employ different machine learning approaches to predict incoming disasters from historical environmental data. However, for short-term disasters (e.g., earthquakes), historical data alone has a limited prediction capability. Therefore,

Hong-Mei Zou, Rongfang Liu, Dan Long, Jianhe Yang

We study the non-Markovianity and quantum speedup of a two-level atom (quantum system of interest) in a dissipative Jaynes-Cumming model, where the atom is embedded in a single-mode cavity, which is leaky being coupled to an external reservoir with Ohmic spectral density. We obtain the non-Markovianity characterized by using the probability of the atomic exc

Chiranjibi Sitaula, Yong Xiang, Anish Basnet, Sunil Aryal

Nowadays it is prevalent to take features extracted from pre-trained deep learning models as image representations which have achieved promising classification performance. Existing methods usually consider either object-based features or scene-based features only. However, both types of features are important for complex images like scene images, as they ca

Chuanqiang Chen, Dekai Zhang

In this paper, we obtain some important inequalities of Hessian quotient operators, and global $C^2$ estimates of the Neumann problem of Hessian quotient equations. By the method of continuity, we establish the existence theorem of $k$-admissible solutions of the Neumann problem of Hessian quotient equations.

Yan Wu, Jingming Zhu

We construct a metric space whose transfinite asymptotic dimension and complementary-finite asymptotic dimension $2\omega+1$.

Shiyu Chang, Yang Zhang, Mo Yu, Tommi S. Jaakkola

Selective rationalization improves neural network interpretability by identifying a small subset of input features -- the rationale -- that best explains or supports the prediction. A typical rationalization criterion, i.e. maximum mutual information (MMI), finds the rationale that maximizes the prediction performance based only on the rationale. However, MM

Lei Shi, Zhi-Gang Wang, Ren-Li Su, Muhammad Arif

Let $\mathcal{S}$ denote the family of all functions that are analytic and univalent in the unit disk $\mathbb{D}:=\{z: |z|<1\}$ and satisfy $f(0)=f^{\prime}(0)-1=0$. In the present paper, we consider certain subclasses of univalent functions associated with the exponential function, and obtain the sharp upper bounds on the initial coefficients and the diffe

Richard Wiltshaw, Richard V. Craster, Mehul P. Makwana

We study the canonical problem of wave scattering by periodic arrays, either of infinite or finite extent, of Neumann scatterers in the plane; the characteristic lengthscale of the scatterers is considered small relative to the lattice period. We utilise the method of matched asymptotic expansions, together with Fourier series representations, to create an e

Cesar Aguado-Cortés, Victor M. Castaño

A translational knowledge map of COVID-19, based on the analysis of scientific papers and networks citation concurrence of terms and keywords of the terms: covid- 19, 2019-ncov and sars-cov-2 in leading databases (MEDLINE, web of Science and Scopus), was constructed. Some fields of the research on covid-19 are connected together, differing in structure, cont

Zhong Xu, Hongyan Wu, Zaifeng Xu, Xiaoping Liu

In order to break the limitation of plasma nitriding technology,which can be applied to a few nonmetallic gaseous elements, the "Double Glow Discharge Phenomenon" was found and then invented the "Double Glow Plasma Surface Metallurgy Technology". This double glow plasma surface metallurgy technology can use any element in the periodic table of chemical eleme

J. C. Phillips

Cytoskeletons are self organized networks based on polymerized proteins, actin, tubulin, and driven by motor proteins, such as myosin, kinesin and dynein. Their positive Darwinian evolution enables them to approach optimized functionality self organized criticality. Dynein has three distinct titled subunits, but how these units connect to function as a molec

Leonidas Fegaras, Md Hasanuzzaman Noor

Large volumes of data generated by scientific experiments and simulations come in the form of arrays, while programs that analyze these data are frequently expressed in terms of array operations in an imperative, loop-based language. But, as datasets grow larger, new frameworks in distributed Big Data analytics have become essential tools to large-scale scie

Steven P. Harris, Jean-Francois Fortin, Kuver Sinha, Mark G. Alford

Supernovae and cooling neutron stars have long been used to constrain the properties of axions, such as their mass and interactions with nucleons and other Standard Model particles. We investigate the prospects of using neutron star mergers as a similar location where axions can be probed in the future. We examine the impact axions would have on mergers, con

Jesús M. F. Castillo, Valentin Ferenczi

We study bounded actions of groups and semigroups $G$ on exact sequences of Banach spaces from the point of view of quasilinear maps, characterize the actions on the twisted sum space by commutator estimates and introduce the associated notions of $G$-centralizer and $G$-equivariant map. We will show that when (A) $G$ is an amenable group and (U) the target

Pengfei Zhang, Chunxiao Liu, Xiao Chen

The Sachdev-Ye-Kitaev model is an $N$-modes fermionic model with infinite range random interactions. In this work, we study the thermal R\'enyi entropy for a subsystem of the SYK model using the path-integral formalism in the large-$N$ limit. The results are consistent with exact diagonalization [1] and can be well approximated by thermal entropy with an eff

S. Pilati, P. Pieri

In recent years, generative artificial neural networks based on restricted Boltzmann machines (RBMs) have been successfully employed as accurate and flexible variational wave functions for clean quantum many-body systems. In this article we explore their use in simulations of disordered quantum spin models. The standard dense RBM with all-to-all inter-layer

Roy Or-El, Soumyadip Sengupta, Ohad Fried, Eli Shechtman

We address the problem of single photo age progression and regression-the prediction of how a person might look in the future, or how they looked in the past. Most existing aging methods are limited to changing the texture, overlooking transformations in head shape that occur during the human aging and growth process. This limits the applicability of previou

Minghan Zhu, Maani Ghaffari, Yuanxin Zhong, Pingping Lu

This paper reports a new continuous 3D loss function for learning depth from monocular images. The dense depth prediction from a monocular image is supervised using sparse LIDAR points, which enables us to leverage available open source datasets with camera-LIDAR sensor suites during training. Currently, accurate and affordable range sensor is not readily av

Fedor Ratnikov

LHCb is one of the major experiments operating at the Large Hadron Collider at CERN. The richness of the physics program and the increasing precision of the measurements in LHCb lead to the need of ever larger simulated samples. This need will increase further when the upgraded LHCb detector will start collecting data in the LHC Run 3. Given the computing re

Devon Graham, Satish Kumar Sarraf, Taylor Lundy, Ali MohammadMehr

On-street parking is convenient, but has many disadvantages: on-street spots come at the expense of other road uses such as traffic lanes, transit lanes, bike lanes, or parklets; drivers looking for parking contribute substantially to traffic congestion and hence to greenhouse gas emissions; safety is reduced both due to the fact that drivers looking for spo

D. A. St-Onge, M. W. Kunz, J. Squire, A. A. Schekochihin

The turbulent amplification of cosmic magnetic fields depends upon the material properties of the host plasma. In many hot, dilute astrophysical systems, such as the intracluster medium (ICM) of galaxy clusters, the rarity of particle--particle collisions allows departures from local thermodynamic equilibrium. These departures exert anisotropic viscous stres

Matthew Heiner, Athanasios Kottas

We develop a Bayesian nonparametric autoregressive model applied to flexibly estimate general transition densities exhibiting nonlinear lag dependence. Our approach is related to Bayesian density regression using Dirichlet process mixtures, with the Markovian likelihood defined through the conditional distribution obtained from the mixture. This results in a

Nadiia Chepurko, Ryan Marcus, Emanuel Zgraggen, Raul Castro Fernandez

Automatic machine learning (\AML) is a family of techniques to automate the process of training predictive models, aiming to both improve performance and make machine learning more accessible. While many recent works have focused on aspects of the machine learning pipeline like model selection, hyperparameter tuning, and feature selection, relatively few wor

Hossein Azizi, Alireza Ebrahimi, Nana Ofori-Opoku, Michael Greenwood

In this work, the effect of building direction on the microstructure evolution of laser-powder bed fusion (LPBF) processed AlSi10Mg alloy was investigated. The building direction, as shown in experimentally fabricated parts, can influence the solidification behavior and promote morphological transitions in cellular dendritic microstructures. We develop a the

Michael Kapralov, Navid Nouri, Ilya Razenshteyn, Ameya Velingker

Random binning features, introduced in the seminal paper of Rahimi and Recht (2007), are an efficient method for approximating a kernel matrix using locality sensitive hashing. Random binning features provide a very simple and efficient way of approximating the Laplace kernel but unfortunately do not apply to many important classes of kernels, notably ones t

J L Kaplan, A Bonfanti, A Kabla

Rheology is the science of deformation and flow, with a focus on materials that do not exhibit simple linear elastic or viscous Newtonian behaviours. Rheology plays an important role in the empirical characterisation of soft viscoelastic materials commonly found in the food and cosmetics industry, as well as in biology and bioengineering. A broad range of th

Morteza Nikaeen, Mehdi Ramezani, Alireza Bahrampour

Transmission efficiency (TE) of remote state preparation (RSP) with a shared quantum state and one bit of classical communication is considered. Following [B. Daki et al., Nat. Phys. 8, 666 (2012)], the encoding and decoding strategies of the protocol are restricted to the physically relevant classes of projective measurements and unitary operators, respecti

Yichen Li, Kaichun Mo, Lin Shao, Minhyuk Sung

Autonomous assembly is a crucial capability for robots in many applications. For this task, several problems such as obstacle avoidance, motion planning, and actuator control have been extensively studied in robotics. However, when it comes to task specification, the space of possibilities remains underexplored. Towards this end, we introduce a novel problem

Craig Gross, Mark A. Iwen, Lutz Kämmerer, Toni Volkmer

In this paper we present the first known deterministic algorithm for the construction of multiple rank-1 lattices for the approximation of periodic functions of many variables. The algorithm works by converting a potentially large reconstructing single rank-1 lattice for some $ d $-dimensional frequency set $ I \subset [N]^d $ into a collection of much small

D. S. Slaughter, Th. Weber, A. Belkacem, C. S. Trevisan

We report the results of a joint experimental {and} theoretical study of dissociative electron attachment to formic acid (HCOOH) in the 6-9 eV region, where H$^-$ fragment ions are a dominant product. Breaking of the CH and OH bonds is distinguished experimentally by deuteration of either site. We show that in this region H$^-$ ions can be produced by format

Tiberiu Harko, Francisco S. N. Lobo, Hilberto M. R. da Silva

We consider static and cylindrically symmetric interior string type solutions in the scalar-tensor representation of the hybrid metric-Palatini modified theory of gravity. As a first step in our study, we obtain the gravitational field equations and further simplify the analysis by imposing Lorentz invariance along the $t$ and $z$ axes, which reduces the num

Abdullah Khanfor, Hakim Ghazzai, Ye Yang, Mohammad Rafiqul Haider

In this paper, we propose to design an automated service discovery process to allow mobile crowdsourcing task requesters select a small set of devices out of a large-scale Internet-of-things (IoT) network to execute their tasks. To this end, we proceed by dividing the large-scale IoT network into several virtual communities whose members share strong social

Michael Wigal, Xingxing Yu

Tutte proved that every 4-connected planar graph contains a Hamilton cycle, but there are 3-connected $n$-vertex planar graphs whose longest cycles have length $\Theta(n^{\log_32})$. On the other hand, Jackson and Wormald in 1992 proved that an essentially 4-connected $n$-vertex planar graph contains a cycle of length at least $(2n+4)/5$, which was recently

Ajay K. Gogineni, S. Swayamjyoti, Devadatta Sahoo, Kisor K. Sahu

Vulnerability detection and safety of smart contracts are of paramount importance because of their immutable nature. Symbolic tools like OYENTE and MAIAN are typically used for vulnerability prediction in smart contracts. As these tools are computationally expensive, they are typically used to detect vulnerabilities until some predefined invocation depth. Th

Luan Hoang

Consider any Leray-Hopf weak solution of the three-dimensional Navier-Stokes equations for incompressible, viscous fluid flows. We prove that any Lagrangian trajectory associated with such a velocity field has an asymptotic expansion, as time tends to infinity, which describes its long-time behavior very precisely.

Simran Tinani, Joachim Rosenthal

Normal bases in finite fields constitute a vast topic of large theoretical and practical interest. Recently, $k$-normal elements were introduced as a natural extension of normal elements. The existence and the number of $k$-normal elements in a fixed extension of a finite field are both open problems in full generality, and comprise a promising research aven

Abderramán Amr, Ignacio Villanueva

In this work we give an example of exponential separation between quantum and classical resources in the setting of XOR games assisted with communication. Specifically, we show an example of a XOR game for which $O(n)$ bits of two way classical communication are needed in order to achieve the same value as can be attained with $\log n$ qubits of one way comm

Shushman Choudhury, Nate Gruver, Mykel J. Kochenderfer

Adaptive Informative Path Planning (AIPP) problems model an agent tasked with obtaining information subject to resource constraints in unknown, partially observable environments. Existing work on AIPP has focused on representing observations about the world as a result of agent movement. We formulate the more general setting where the agent may choose betwee

Jamshed Anwar, Christian Leitold, Baron Peters

Solid solutions, structurally ordered but compositionally disordered mixtures, can form for salts, metals, and even organic compounds. The NaCl-KCl system forms a solid solution at all compositions between 657{\deg}C and 505{\deg}C. Below a critical temperature of 505{\deg}C, the system exhibits a miscibility gap with coexisting Na-rich and K-rich rocksalt p

Philipp Brune

Blockchain technology and artificial intelligence (AI) are current hot topics in research and practice. However, the potentials of their combination have been studied just recently to a larger extend. While different use cases for combining AI and blockchain have been discussed, the idea of enabling blockchain-based smart contracts to perform "smarter" decis

Sairamvinay Vijayaraghavan, Debraj Basu

Sentiment Analysis is an important algorithm in Natural Language Processing which is used to detect sentiment within some text. In our project, we had chosen to work on analyzing reviews of various drugs which have been reviewed in form of texts and have also been given a rating on a scale from 1-10. We had obtained this data set from the UCI machine learnin

P. N. Bibikov

Dyson equation for the real two-time commutator retarded one-magnon Green function of the ferromagnetically polarized XX chain is suggested following the Plakida-Tserkovnikov algorithm. Starting from this result a low-temperature integral representation for the corresponding magnon self energy is obtained by the truncated form factor expansion however withou

Charalampos Charitos, Ioannis Papadoperakis, Georgios Tsapogas

If $\Omega$ is the interior of a convex polygon in $\mathbb{R}^{2}$ and $f,g$ two asymptotic geodesics, we show that the distance function $d\left(f\left(t\right),g\left(t\right)\right)$ is convex for $t$ sufficiently large. The same result is obtained in the case $\partial \Omega$ is of class $C^{2}$ and the curvature of $\partial \Omega$ at the point $f\le

Michael C. Lucic, Hakim Ghazzai, Ahmad Alsharoa, Yehia Massoud

Recently, elevated LiDAR (ELiD) has been proposed as an alternative to local LiDAR sensors in autonomous vehicles (AV) because of the ability to reduce costs and computational requirements of AVs, reduce the number of overlapping sensors mapping an area, and to allow for a multiplicity of LiDAR sensing applications with the same shared LiDAR map data. Since

Ginevra Buratti, Jose Calderon, Alessandro Mininno, Angel M. Uranga

We argue that in theories of quantum gravity with discrete gauge symmetries, e.g. $\textbf{Z}_k$, the gauge couplings of U$(1)$ gauge symmetries become weak in the limit of large $k$, as $g\to k^{-\alpha}$ with $\alpha$ a positive order 1 coefficient. The conjecture is based on black hole arguments combined with the Weak Gravity Conjecture (or the BPS bound

Shanshi Huang, Xiaochen Peng, Hongwu Jiang, Yandong Luo

Machine learning inference engine is of great interest to smart edge computing. Compute-in-memory (CIM) architecture has shown significant improvements in throughput and energy efficiency for hardware acceleration. Emerging non-volatile memory technologies offer great potential for instant on and off by dynamic power gating. Inference engine is typically pre

Domingo García-Senz, Rubén M. Cabezón, José M. Blanco Iglesias, Pablo Lorén-Aguilar

We present a novel relaxation method to build three-dimensional rotating structures of barotropic bodies using the SPH technique. The method is able to relax gaseous structures in rigid as well as differential rotation. The relaxation procedure strongly relies on the excellent conservation of angular momentum that characterizes the SPH technique. The method

Changming Zhao, Dongrui Wu, Jian Huang, Ye Yuan

Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and more reliable performance. They have been widely used in biology, engineering, healthcare, etc. This paper proposes BoostForest, which is an ensemble learning approach using BoostT

Jeremy N. Wong, David J. Yoon, Angela P. Schoellig, Timothy D. Barfoot

We present parameter learning in a Gaussian variational inference setting using only noisy measurements (i.e., no groundtruth). This is demonstrated in the context of vehicle trajectory estimation, although the method we propose is general. The paper extends the Exactly Sparse Gaussian Variational Inference (ESGVI) framework, which has previously been used f

Paweł Buczek, Nadine Buczek, Giovanni Vignale, Arthur Ernst

The fluctuations of the magnetic order parameter, or longitudinal spin excitations, are investigated theoretically in the ferromagnetic Fe and Ni as well as in the antiferromagnetic phase of the pnictide superconductor FeSe. The charge and spin dynamics of these systems is described by evaluating the generalized charge and spin density response function calc

Maxime Lucas, Giulia Cencetti, Federico Battiston

Traditionally, interaction systems have been described as networks, where links encode information on the pairwise influences among the nodes. Yet, in many systems, interactions take place in larger groups. Recent work has shown that higher-order interactions between oscillators can significantly affect synchronization. However, these early studies have most

Gil Farkash, Gilad Liberman, Ricardo P. Martinho, Lucio Frydman

Purpose: To develop schemes that deliver faithful 2D slices near field heterogeneities of the kind arising from non-ferromagnetic metal implants, with reduced artifacts and shorter scan times. Methods: An excitation scheme relying on cross-term spatio-temporal encoding (xSPEN) was used as basis for developing the new inhomogeneity-insensitive, slice-selectiv

Omar Bouhamed, Hakim Ghazzai, Hichem Besbes, Yehia Massoud

In this paper, we propose an autonomous UAV path planning framework using deep reinforcement learning approach. The objective is to employ a self-trained UAV as a flying mobile unit to reach spatially distributed moving or static targets in a given three dimensional urban area. In this approach, a Deep Deterministic Policy Gradient (DDPG) with continuous act