July 2019 arXiv papers — page 5

Huyen Pham, Xavier Warin, Maximilien Germain

We propose a numerical method for solving high dimensional fully nonlinear partial differential equations (PDEs). Our algorithm estimates simultaneously by backward time induction the solution and its gradient by multi-layer neural networks, while the Hessian is approximated by automatic differentiation of the gradient at previous step. This methodology exte

Saori Takeyama, Shunsuke Ono, Itsuo Kumazawa

We propose a new constrained optimization approach to hyperspectral (HS) image restoration. Most existing methods restore a desirable HS image by solving some optimization problem, which consists of a regularization term(s) and a data-fidelity term(s). The methods have to handle a regularization term(s) and a data-fidelity term(s) simultaneously in one objec

Avishek Garain, Sainik Kumar Mahata, Subhabrata Dutta

This paper presents a method to apply Natural Language Processing for normalizing numeronyms to make them understandable by humans. We approach the problem through a two-step mechanism. We make use of the state of the art Levenshtein distance of words. We then apply Cosine Similarity for selection of the normalized text and reach greater accuracy in solving

Pere Mujal, Artur Polls, Bruno Juliá-Díaz

A system of bosons in a two-dimensional harmonic trap in the presence of Rashba-type spin-orbit coupling is investigated. An analytic treatment of the ground state of a single atom in the weak-coupling regime is presented and used as a basis for a perturbation theory in the interacting two-boson system. The numerical diagonalization of both the single-partic

Vittorio De Falco, Emmanuele Battista

We determine the analytic expression of the Rayleigh potential associated to the general relativistic Poynting-Robertson effect. This constitutes the first example of a physical dissipative system treated analytically in General Relativity. The employed approach and further implications are discussed.

Zhen Gao, Maryam Zand, Jianhua Ruan

Multiple classifier system (MCS) has become a successful alternative for improving classification performance. However, studies have shown inconsistent results for different MCSs, and it is often difficult to predict which MCS algorithm works the best on a particular problem. We believe that the two crucial steps of MCS - base classifier generation and multi

Aitor Villar, Arian Stolk, Alexander Lohrmann, Alexander Ling

We report on the use of elliptical pump spatial modes to increase the observed brightness of spontaneous parametric downconversion in critically phase matched crystals. Simulations qualitatively predict this improvement which depends on the eccentricity and orientation of the pump ellipse. We experimentally confirm a factor of two improvement in brightness w

Xiang Zhang, Xiaocong Chen, Manqing Dong, Huan Liu

Synthesizing geometrical shapes from human brain activities is an interesting and meaningful but very challenging topic. Recently, the advancements of deep generative models like Generative Adversarial Networks (GANs) have supported the object generation from neurological signals. However, the Electroencephalograph (EEG)-based shape generation still suffer f

Hoyeop Lee, Jinbae Im, Seongwon Jang, Hyunsouk Cho

This paper proposes a recommender system to alleviate the cold-start problem that can estimate user preferences based on only a small number of items. To identify a user's preference in the cold state, existing recommender systems, such as Netflix, initially provide items to a user; we call those items evidence candidates. Recommendations are then made based

Delio Mugnolo, Marvin Plümer

A method for estimating the spectral gap along with higher eigenvalues of nonequilateral quantum graphs has been introduced by Amini and Cohen-Steiner recently: it is based on a new transference principle between discrete and continuous models of a graph. We elaborate on it by developing a more general transference principle and by proposing alternative ways

Ke Zhang, Xinsheng Wang, Yurong Guo, Zhenbing Zhao

The development of deep convolutional neural network architecture is critical to the improvement of image classification task performance. A lot of studies of image classification based on deep convolutional neural network focus on the network structure to improve the image classification performance. Contrary to these studies, we focus on the loss function.

Rob van Glabbeek

May and must testing were introduced by De Nicola and Hennessy to define semantic equivalences on processes. May-testing equivalence exactly captures safety properties, and must-testing equivalence liveness properties. This paper proposes reward testing and shows that the resulting semantic equivalence also captures conditional liveness properties. It is str

Young-min Song, Kwangjin Yoon, Young-Chul Yoon, Kin-Choong Yow

In this paper, we propose an efficient online multi-object tracking framework based on the GMPHD filter and occlusion group management scheme where the GMPHD filter utilizes hierarchical data association to reduce the false negatives caused by miss detection. The hierarchical data association consists of two steps: detection-to-track and track-to-track assoc

Ken Yamamoto

The Horton-Strahler analysis is a graph-theoretic method to measure the bifurcation complexity of branching patterns, by defining a number called the order to each branch. The main result of this paper is a large deviation theorem for the number of branches of each order in a random binary tree. The rate function associated with a large deviation cannot be d

Ivan Guo, Nicolas Langrené, Grégoire Loeper, Wei Ning

This paper addresses the problem of utility maximization under uncertain parameters. In contrast with the classical approach, where the parameters of the model evolve freely within a given range, we constrain them via a penalty function. We show that this robust optimization process can be interpreted as a two-player zero-sum stochastic differential game. We

Yuriy Yerin, Hiroyuki Tajima, Pierbiagio Pieri, Andrea Perali

We investigate Bardeen-Cooper-Schrieffer (BCS)- Bose-Einstein condensation (BEC) crossover in a two-band superfluid Fermi gas with an energy shift between the bands. When the intraband coupling in the cold (first) band is fixed as weak, we find that in the case of vanishing interband interaction and in the strong-coupling limit of the hot (second) band the s

Dillon Mayhew, Mike Newman, Geoff Whittle

A minor-closed class of matroids is (strongly) fractal if the number of n-element matroids in the class is dominated by the number of n-element excluded minors. We conjecture that when K is an infinite field, the class of K-representable matroids is strongly fractal. We prove that the class of sparse paving matroids with at most k circuit-hyperplanes is a st

MaungMaung AprilPyone, Warit Sirichotedumrong, Hitoshi Kiya

Data for deep learning should be protected for privacy preserving. Researchers have come up with the notion of learnable image encryption to satisfy the requirement. However, existing privacy preserving approaches have never considered the threat of adversarial attacks. In this paper, we ran an adversarial test on learnable image encryption in five different

Jie-Qiong Li, Dong-Xing Zheng, Jing Wang

We attentively investigate the effects of short-range fermion-fermion interactions on the low-energy properties of both two-dimensional type-I and type-II tilted Dirac semimetals by means of the renormalization group framework. Practicing the standard renormalization group procedures via taking into account all one-loop corrections gives rise to the coupled

Sara Kallin, Asim Rashid, Kent Salomonsson, Peter Hansbo

Lower limb amputees often suffer skin and tissue problems from using their prosthesis which is a challenging biomechanical problem. The finite element method (FEM) has previously been applied to analyse internal mechanical conditions of the leg at prosthesis use. However, the representation of soft tissue was simplified to few layers and tissue types. The ef

Ming Li, Philip Broadbridge, Andriy Olenko, Yu Guang Wang

This paper constructs a semi-discrete tight frame of tensor needlets associated with a quadrature rule for tangent vector fields on the unit sphere $\mathbb{S}^2$ of $\mathbb{R}^3$ --- tensor needlets. The proposed tight tensor needlets provide a multiscale representation of any square integrable tangent vector field on $\mathbb{S}^2$, which leads to a multi

T. Miyamoto, T. Kakizaki, T. Terashige, D. Hata

Mott insulators sometimes show dramatic changes in their electronic states after photoirradiation, as indicated by photoinduced Mott-insulator-to-metal transition. In the photoexcited states of Mott insulators, electron wavefunctions are more delocalized than in the ground state, and long-range Coulomb interactions play important roles in charge dynamics. Ho

Jiawei Zhou, Alexander M. Rush

We propose an unsupervised method for sentence summarization using only language modeling. The approach employs two language models, one that is generic (i.e. pretrained), and the other that is specific to the target domain. We show that by using a product-of-experts criteria these are enough for maintaining continuous contextual matching while maintaining o

Stanisław M. Dubiel, Israel Felner

C14 Nb(Fe89.4Al10.6)2 Laves phase intermetallic compound was investigated by DC magnetization (M) measurements performed in the temperature (T) interval of 20 to 175 K,under an applied magnetic field (H) ranging between 50 and 1250 Oe. Magnetization curves were recorded in the field-cooled (FC) and in the zero-field-cooled (ZFC) modes. They clearly showed an

Yongpan Zou

Inspired by the recent works of S. Rao--S. Yang--X.-D. Yang and L. Meng on the blow-up formulae for de Rham and Morse--Novikov cohomology groups, we give a new simple proof of the blow-up formula for Morse--Novikov cohomology by introducing the relative Morse--Novikov cohomology group via sheaf cohomology theory and presenting the explicit isomorphism therei

Ritvik Ramkumar

We characterize Hilbert polynomials that give rise to Hilbert schemes with two Borel-fixed points and determine when the associated Hilbert schemes or their irreducible components are smooth. In particular, we show that the Hilbert scheme is reduced and has at most two irreducible components. By describing the singularities in a neighbourhood of the Borel-fi

Mikaela Irene D. Fudolig, Kunal Bhattacharya, Daniel Monsivais, Hang-Hyun Jo

We present a link-centric approach to study variation in the mobile phone communication patterns of individuals. Unlike most previous research on call detail records that focused on the variation of phone usage across individual users, we examine how the calling and texting patterns obtained from call detail records vary among pairs of users and how these pa

Dong Han, Jishnu Ray, Feng Wei

For a prime $p>2$, let $G$ be a semi-simple, simply connected, split Chevalley group over $\mathbb{Z}_p$, $G(1)$ be the first congruence kernel of $G$ and $\Omega_{G(1)}$ be the mod-$p$ Iwasawa algebra defined over the finite field $\mathbb{F}_p$. Ardakov, Wei, Zhang have shown that if $p$ is a "nice prime " ($p \geq 5$ and $p \nmid n+1$ if the Lie algebra o

Ilia Tutunnikov, Johannes Floß, Erez Gershnabel, Paul Brumer

Molecular chirality is an omnipresent phenomenon of fundamental significance in physics, chemistry and biology. For this reason, search for novel techniques for enantioselective control, detection and separation of chiral molecules is of particular importance. It has been recently predicted that laser fields with twisted polarization may induce persistent en

Evgenii Safronenko

This paper is devoted to measures of symmetry based on distance between centroid and one of the centers of John and Lowner ellipsoid. The author proves the accuracy of the derived upper bounds for the considered measures of symmetry.

Justin E. Christensen, David Hucul, Wesley C. Campbell, Eric R. Hudson

The recently demonstrated trapping and laser cooling of $^{133}$Ba$^+$ has opened the door to the use of this nearly ideal atom for quantum information processing. However, before high fidelity qubit operations can be performed, a number of unknown state energies are needed. Here, we report measurements of the $^2$P$_{3/2}$ and $^2$D$_{5/2}$ hyperfine splitt

Hanshuang Chen, Feng Huang, Chuansheng Shen, Guofeng Li

By constructing a multicanonical Monte Carlo simulation, we obtain the full probability distribution $\rho_N(r)$ of the degree assortativity coefficient $r$ on configuration networks of size $N$ by using the multiple histogram reweighting method. We suggest that $\rho_N(r)$ obeys a large deviation principle, $\rho_N \left(r-r_N^* \right) \asymp {e^{ - {N^\xi

A. Garai, A. Reza, A. Mazumdar, H. Krishnamoorthy

This paper reports the detailed noise characterization, investigation of various noise sources and its mitigation to improve the performance of a cryogenic bolometer detector. The noise spectrum has been measured for a sapphire bolometer test setup with indigenously developed NTD Ge sensor in the CFDR system at Mumbai. The effect of external noise, arising e

Rob van Glabbeek, Peter Höfner, Michael Markl

We propose a process algebra for link layer protocols, featuring a unique mechanism for modelling frame collisions. We also formalise suitable liveness properties for link layer protocols specified in this framework. To show applicability we model and analyse two versions of the Carrier-Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol. Our a

Eszter Simon, Laszlo Szunyogh

Based on the Korringa-Kohn-Rostoker Green's function technique we present a computational scheme for calculating the electronic structure of layered systems with homogeneous spin-spiral magnetic state. From the self-consistent non-relativistic calculations the total energy of the spin-spiral states is determined as a function of the wave vector, while a rela

Inyoung Paik, Taeyeong Kwak, Injung Kim

In capsule networks, the routing algorithm connects capsules in consecutive layers, enabling the upper-level capsules to learn higher-level concepts by combining the concepts of the lower-level capsules. Capsule networks are known to have a few advantages over conventional neural networks, including robustness to 3D viewpoint changes and generalization capab

Jungseek Hwang

Correlation information in strongly correlated electron systems can be obtained using an extended Drude model. An interesting method related to the extended Drude model analysis of superconducting optical data was proposed recently, and it has attracted attention from researchers. This method aims to extract the optical self-energy of quasiparticles (or resi

Yury Grabovsky, Narek Hovsepyan

The need for analytic continuation arises frequently in the context of inverse problems. Notwithstanding the uniqueness theorems, such problems are notoriously ill-posed without additional regularizing constraints. We consider several analytic continuation problems with typical global boundedness constraints that restore well-posedness. We show that all such

Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, Jian Tang

This paper studies learning the representations of whole graphs in both unsupervised and semi-supervised scenarios. Graph-level representations are critical in a variety of real-world applications such as predicting the properties of molecules and community analysis in social networks. Traditional graph kernel based methods are simple, yet effective for obta

David Sarrut, Nils Krah, Jean-Michel Létang

A method is proposed and evaluated to model large and inconvenient phase space files used in Monte Carlo simulations by a compact Generative Adversarial Network (GAN). The GAN is trained based on a phase space dataset to create a neural network, called Generator (G), allowing G to mimic the multidimensional data distribution of the phase space. At the end of

Dehan Kong, Shu Yang, Linbo Wang

Unobserved confounding presents a major threat to causal inference from observational studies. Recently, several authors suggest that this problem may be overcome in a shared confounding setting where multiple treatments are independent given a common latent confounder. It has been shown that under a linear Gaussian model for the treatments, the causal effec

Inyoung Paik, Sangjun Oh, Tae-Yeong Kwak, Injung Kim

To address the issue of catastrophic forgetting in neural networks, we propose a novel, simple, and effective solution called neuron-level plasticity control (NPC). While learning a new task, the proposed method preserves the knowledge for the previous tasks by controlling the plasticity of the network at the neuron level. NPC estimates the importance value

Xiangliang Kong, Fan Guo, Yao Chen, Joe Giacalone

We present numerical modelling of particle acceleration at coronal shocks propagating through a streamer-like magnetic field by solving the Parker transport equation with spatial diffusion both along and across the magnetic field. We show that the location on the shock where the high-energy particle intensity is the largest, depends on the energy of the part

Yalong Yang, Sarah Goodwin

In this paper, we present our analysis of five expert interviews, each from a different application domain. Such analysis is crucial to understanding the real-world scenarios of analysing geographically-embedded flow data. The results of our analysis show that similar high-level tasks were conducted in different domains. To better describe the targets of the

Mosab Khayat, Morteza Karimzadeh, Jieqiong Zhao, David S. Ebert

Social media platforms such as Twitter are filled with social spambots. Detecting these malicious accounts is essential, yet challenging, as they continually evolve and evade traditional detection techniques. In this work, we propose VASSL, a visual analytics system that assists in the process of detecting and labeling spambots. Our tool enhances the perform

N Gopalswamy, P. Makela, S. Yashiro, A. Lara

It has recently been shown that the spatially and temporally extended gamma-ray emission in solar eruptions are caused by greater than 300 MeV protons precipitating on the Sun from shocks driven by coronal mass ejections (CMEs). The gamma-rays result from the decay of neutral pions produced in the proton-proton interaction when the greater than 300 MeV proto

Philip Föhn

This paper presents a simplification of the main argument in "Effective quasimorphisms on right-angled Artin groups" by Fern\'os, Forester and Tao. Their article introduces a family of quasimorphisms on a certain class of groups (called RAAG-like) acting on CAT(0) cube complexes. They show that these have uniform defect of $12$ and take the value of at least

Yuri S. Nagornov, Ryosuke Akashi

Recently a non-empirical stochastic walker algorithm has been developed to search for the minimum-energy escape paths (MEP) from the minima of the potential surface [J. Phys. Soc. Jpn. 87, 063801 (2018); Physica A, 528, 121481 (2019)]. This method is based on the Master equation for the distribution function of the atomic configuration which has a nature to

Xinyu Zhang, Jiewei Cao, Chunhua Shen, Mingyu You

Person re-identification (Re-ID) has achieved great improvement with deep learning and a large amount of labelled training data. However, it remains a challenging task for adapting a model trained in a source domain of labelled data to a target domain of only unlabelled data available. In this work, we develop a self-training method with progressive augmenta

Mosab Khayat, Morteza Karimzadeh, David S. Ebert, Arif Ghafoor

Many evaluation methods have been used to assess the usefulness of Visual Analytics (VA) solutions. These methods stem from a variety of origins with different assumptions and goals, which cause confusion about their proofing capabilities. Moreover, the lack of discussion about the evaluation processes may limit our potential to develop new evaluation method

Jeong San Kim

We show that the mutually exclusive nature of classical and quantum correlations distributed in multi-party quantum systems can be characterized in terms of $q$-expectation. Using Tsallis-$q$ entropy and $q$-expectation, we first provide generalized definitions of classical and quantum correlations, and establish their trade-off relations in three-party quan

Noam Soker

I analyse recent three-dimensional hydrodynamical simulations of exploding core collapse supernovae (CCSNe) and find that a rapid increase in the diagnostic explosion energy occurs parallel to, or very shortly after, the accretion of gas with relatively large amount of angular momentum and/or specific angular momentum. The tendency towards a positive correla

Albert Escrivà, Cristiano Germani, Ravi K. Sheth

In this letter, we argue and show numerically that the threshold to form primordial black holes from an initial spherically symmetric perturbation is, to an excellent approximation, universal, whenever given in terms of the compaction function averaged over a sphere of radius $r_m$, where $r_m$ is the scale on which the compaction function is maximum. This c

Peng Xu, Su Yi, Wenxian Zhang

We generate high-fidelity massively entangled states in an antiferromagnetic spin-1 Bose-Einstein condensate (BEC) by utilizing multilevel oscillations. Combining the multilevel oscillations with additional adiabatic drives, we greatly shorten the necessary evolution time and relax the requirement on the control accuracy of quadratic Zeeman splitting, from m

Avinash Singh, H. K. Jassal, Manabendra Sharma

A non-canonical scalar tachyon field is a viable candidate for dark energy and has been found to be in good agreement with observational data. Background data alone cannot completely rule out degeneracy between this model and others. To further constrain the parameters, apart from the distance measurements, we study perturbations in the tachyon scalar field

Thanh Tung Khuat, Bogdan Gabrys

General fuzzy min-max (GFMM) neural network is a generalization of fuzzy neural networks formed by hyperbox fuzzy sets for classification and clustering problems. Two principle algorithms are deployed to train this type of neural network, i.e., incremental learning and agglomerative learning. This paper presents a comprehensive empirical study of performance

Damek Davis, Dmitriy Drusvyatskiy, Lin Xiao, Junyu Zhang

Standard results in stochastic convex optimization bound the number of samples that an algorithm needs to generate a point with small function value in expectation. More nuanced high probability guarantees are rare, and typically either rely on "light-tail" noise assumptions or exhibit worse sample complexity. In this work, we show that a wide class of stoch

Bin Lin, Yinhao Huang, Jianshan Zhang, Junqin Hu

Currently, deep neural networks (DNNs) have achieved a great success in various applications. Traditional deployment for DNNs in the cloud may incur a prohibitively serious delay in transferring input data from the end devices to the cloud. To address this problem, the hybrid computing environments, consisting of the cloud, edge and end devices, are adopted

José Luis Vilchis Medina, Pierre Siegel, Vincent Risch, Andrei Doncescu

In this article we present an implementation of non-monotonic reasoning in an embedded system. As a part of an autonomous motor-glider, it simulates piloting decisions of an airplane. A real pilot must take care not only about the information arising from the cockpit (airspeed, altitude, variometer, compass...) but also from outside the cabin. Throughout a f

Dirk Veestraeten

This paper uses the convolution theorem of the Laplace transform to derive new inverse Laplace transforms for the product of two parabolic cylinder functions in which the arguments may have opposite sign. These transforms are subsequently specialized for products of the error function and its complement thereby yielding new integral representations for produ

Zekun Li, Zeyu Cui, Shu Wu, Xiaoyu Zhang

Learning the compatibility between fashion items across categories is a key task in fashion analysis, which can decode the secret of clothing matching. The main idea of this task is to map items into a latent style space where compatible items stay close. Previous works try to build such a transformation by minimizing the distances between annotated compatib

Andreas Haungs, Igor Bychkov, Julia Dubenskaya, Oleg Fedorov

A data life cycle (DLC) is a high-level data processing pipeline that involves data acquisition, event reconstruction, data analysis, publication, archiving, and sharing. For astroparticle physics a DLC is particularly important due to the geographical and content diversity of the research field. A dedicated and experiment spanning analysis and data centre w

Robert Tremblay

We determine the conditions for the existence or not of cycles for several families of generalized 3x + 1 mappings and develop a method to find them.

Gal Sadeh, Edith Cohen, Haim Kaplan

Influence maximization (IM) is the problem of finding for a given $s\geq 1$ a set $S$ of $|S|=s$ nodes in a network with maximum influence. With stochastic diffusion models, the influence of a set $S$ of seed nodes is defined as the expectation of its reachability over simulations, where each simulation specifies a deterministic reachability function. Two we

Chunyu Jia, Rukuan Wu, Ying Hu, Wuming Liu

Dissipative solitons are non-decaying out-of-equilibrium entities that result from double balances between gain and loss, as well as nonlinearity and dispersion. Here we describe a scenario where double balances rely on the presence of multiple collective excitation channels in open-dissipative quantum systems. It differs from conventional single-channel sce

Serang Park, Yanzeng Li, Daniel B. Fullager, Stefan Schöche

The fabrication of terahertz (THz) optics with arbitrary shapes via poly-methacrylate-based stereolithography is very attractive as it may offer a rapid, low-cost avenue towards optimized THz imaging applications. In order to design such THz optical components appropriately, accurate knowledge of the complex dielectric function of the materials used for ster

Youhua Xu, Hu Zhan, Yeuk-Kwan Edna Cheung

Non-parametric reconstruction of the dark energy equation of state (EoS) aims to determine the EoS as a function of redshift without invoking any particular dark energy model, so that the resulting EoS can be free of model-induced biases or artifacts. Without proper regularization, however, such reconstruction is often overwhelmed by the noise of poorly cons

Wanchun Liu, Petar Popovski, Yonghui Li, Branka Vucetic

Wireless networked control systems for the Industrial Internet of Things (IIoT) require low latency communication techniques that are very reliable and resilient. In this paper, we investigate a coding-free control method to achieve ultra-low latency communications in single-controller-multi-plant networked control systems for both slow and fast fading chann

Yuan Cheng, Yanbo Xue, Meng Chang

We propose an extended spatial evolutionary public goods game (SEPGG) model to study the dynamics of individual career choice and the corresponding social output. Based on the social value orientation theory, we categorized two classes of work, namely the public work if it serves public interests, and the private work if it serves personal interests. Under t

Sahisnu Mazumder, Bing Liu, Shuai Wang, Nianzu Ma

Dialogue systems are increasingly using knowledge bases (KBs) storing real-world facts to help generate quality responses. However, as the KBs are inherently incomplete and remain fixed during conversation, it limits dialogue systems' ability to answer questions and to handle questions involving entities or relations that are not in the KB. In this paper, we

R. K. Jagpal, R. Siddiqui, S. M. Abrarov, B. M. Quine

The micro-spectrometer Argus 1000 being in space continuously monitors the sources and sinks of the trace gases. It is commonly believed that among other gases $\text{CO}_\text{2}$ is the major contributor causing the greenhouse effect. Argus 1000 along its orbit gathers the valuable spectral data that can be analyzed and retrieved. In this paper we present

Rawan Naous, Lazar Supic, Yoonhwan Kang, Ranko Sredojevic

A surge in artificial intelligence and autonomous technologies have increased the demand toward enhanced edge-processing capabilities. Computational complexity and size of state-of-the-art Deep Neural Networks (DNNs) are rising exponentially with diverse network models and larger datasets. This growth limits the performance scaling and energy-efficiency of b

Ramin Kaviani, Kory W. Hedman

This paper presents a real-time non-probabilistic detection mechanism to detect load-redistribution (LR) attacks against energy management systems (EMSs). Prior studies have shown that certain LR attacks can bypass conventional bad data detectors (BDDs) and remain undetectable, which implies that presence of a reliable and intelligent detection mechanism to

Qinghua Lu, Xiwei Xu, Yue Liu, Ingo Weber

Blockchain is an innovative distributed ledger technology which has attracted a wide range of interests for building the next generation of applications to address lack-of-trust issues in business. Blockchain as a service (BaaS) is a promising solution to improve the productivity of blockchain application development. However, existing BaaS deployment soluti

Charli M. Sakari

Integrated light (IL) spectroscopy enables studies of stellar populations beyond the Milky Way and its nearest satellites. In this paper, I will review how IL spectroscopy reveals essential information about globular clusters and the assembly histories of their host galaxies, concentrating particularly on the metallicities and detailed chemical abundances of

Jeong-Myeong Bae, Subeom Kang, Dong-han Yeom, Heeseung Zoe

We construct the Hayden-Preskill protocol by using a system of spin-1/2 particles and demonstrate information flows of this system which can mimic black holes. We first define an analogous black hole $A$ as a collection of such particles. Second, we take the particles from inside to outside the black hole to define an analogous system of Hawking radiation $B

A. K. Boltaev, A. R. Hayotov, Kh. M. Shadimetov

The paper studies Sard's problem on construction of optimal quadrature formulas in the space $W_2^{(m,0)}$ by Sobolev's method. This problem consists of two parts: first calculating the norm of the error functional and then finding the minimum of this norm by coefficients of quadrature formulas. Here the norm of the error functional is calculated with the he

Vasudevan Nagendra, Arani Bhattacharya, Vinod Yegneswaran, Amir Rahmati

Consumer IoT is characterized by heterogeneous devices with diverse functionality and programming interfaces. This lack of homogeneity makes the integration and security management of IoT infrastructures a daunting task for users and administrators. In this paper, we introduce VISCR, a Vendor-Independent policy Specification and Conflict Resolution engine th

Pritam Sarkar, Kyle Ross, Aaron J. Ruberto, Dirk Rodenburg

Simulations are a pedagogical means of enabling a risk-free way for healthcare practitioners to learn, maintain, or enhance their knowledge and skills. Such simulations should provide an optimum amount of cognitive load to the learner and be tailored to their levels of expertise. However, most current simulations are a one-type-fits-all tool used to train di

Bing-Xiang Li, Rui-Lin Xiao, Sathyanarayana Paladugu, Sergij V. Shiyanovskii

Production of stable multidimensional solitary waves is a grand challenge in modern science. Steering their propagation is an even harder problem. In this work we demonstrate three-dimensional solitary waves in a nematic, trajectories of which can be steered by the electric field in a plane perpendicular to the field. The steering does not modify the propert

Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher

Recommender systems are known to suffer from the popularity bias problem: popular (i.e. frequently rated) items get a lot of exposure while less popular ones are under-represented in the recommendations. Research in this area has been mainly focusing on finding ways to tackle this issue by increasing the number of recommended long-tail items or otherwise the

Ue-Hwan Kim, Sahng-Min Yoo, Jong-Hwan Kim

Text-entry aims to provide an effective and efficient pathway for humans to deliver their messages to computers. With the advent of mobile computing, the recent focus of text-entry research has moved from physical keyboards to soft keyboards. Current soft keyboards, however, increase the typo rate due to lack of tactile feedback and degrade the usability of

Aron J. Beekman

The DC Josephson effect is the flow of supercurrent across a weak link between two superconductors with a different value of their order parameter, the phase. We generalize this notion to any kind of spontaneous continuous symmetry breaking. The quantity that flows between the two systems is identified as the Noether current associated with the broken symmet

Carl Dunlea, Ivan Khalzov

The DELiTE (Differential Equations on Linear Triangular Elements) framework was developed for spatial discretisation of partial differential equations on an unstructured triangular grid in axisymmetric geometry. The framework is based on discrete differential operators in matrix form, which are derived using linear finite elements and mimic some of the prope

Xiaodong Zhang, Junyi Zhu

One of the keys to the realization of Quantum Anomalous Hall effect (QAHE) is long range ferromagnetism, which is only experimentally realized in Cr or V doped (Bi,Sb)2Te3 system. Both elements are 3d transition metals and 4d transition metals are found to be ineffective to produce long range ferromagnetism in Bi2Se3. Still, whether long range ferromagnetism

Sheng Rao, Song Yang, Xiangdong Yang, Xun Yu

We establish a blow-up formula for Hodge cohomology of locally free sheaves on smooth proper varieties over an algebraically closed field of positive characteristic. For this, we introduce a notion of relative Hodge sheaves and study their behavior under blow-ups along smooth centers. In particular, as an application, we study the blow-up invariance of the $

Guan-Lin Chao, Abhinav Rastogi, Semih Yavuz, Dilek Hakkani-Tür

Understanding and conversing about dynamic scenes is one of the key capabilities of AI agents that navigate the environment and convey useful information to humans. Video question answering is a specific scenario of such AI-human interaction where an agent generates a natural language response to a question regarding the video of a dynamic scene. Incorporati

Austin K. Daniel, Rafael N. Alexander, Akimasa Miyake

What kinds of symmetry-protected topologically ordered (SPTO) ground states can be used for universal measurement-based quantum computation in a similar fashion to the 2D cluster state? 2D SPTO states are classified not only by global on-site symmetries but also by subsystem symmetries, which are fine-grained symmetries dependent on the lattice geometry. Rec

Pierluigi Colli, Takeshi Fukao, Hao Wu

This paper is concerned with well-posedness of the Cahn-Hilliard equation subject to a class of new dynamic boundary conditions. The system was recently derived in Liu-Wu (Arch. Ration. Mech. Anal. 233 (2019), 167-247) via an energetic variational approach and it naturally fulfills three physical constraints such as mass conservation, energy dissipation and

Xiao-Ping Luo, Yang-Yu Guo, Mo-Ran Wang, Hong-Liang Yi

The direct simulation of the dynamics of second sound in graphitic materials remains a challenging task due to lack of methodology for solving the phonon Boltzmann equation in such a stiff hydrodynamic regime. In this work, we aim to tackle this challenge by developing a multiscale numerical scheme for the transient phonon Boltzmann equation under Callaway's

Laure Berti-Equille, Ji Meng Loh, Saravanan Thirumuruganathan

Outlier detection is a fundamental task in data mining and has many applications including detecting errors in databases. While there has been extensive prior work on methods for outlier detection, modern datasets often have sizes that are beyond the ability of commonly used methods to process the data within a reasonable time. To overcome this issue, outlie

Rocio Gomez, Mohan Sridharan, Heather Riley

The architecture described in this paper encodes a theory of intentions based on the the key principles of non-procrastination, persistence, and automatically limiting reasoning to relevant knowledge and observations. The architecture reasons with transition diagrams of any given domain at two different resolutions, with the fine-resolution description defin

Ue-Hwan Kim, Jong-Hwan Kim

The automated home referred to as Smart Home is expected to offer fully customized services to its residents, reducing the amount of home labor, thus improving human beings' welfare. Service robots and Internet of Things (IoT) play the key roles in the development of Smart Home. The service provision with these two main components in a Smart Home environment

Dae Won Yoon, Zuhal Kucukarslan Yuzbasi

In this paper, our aim is to give surfaces in the Galilean 3-space G3 with the property that there exist four geodesics through each point such that every surface built with the normal lines and the binormal lines along these geodesics is a surface with a minimal surface and a constant negative Gaussian curvature. We show that $\psi $ should be an isoparamet

Maximiliano Klemen, Pedro Lopez-Garcia, John P. Gallagher, Jose F. Morales

The estimation and control of resource usage is now an important challenge in an increasing number of computing systems. In particular, requirements on timing and energy arise in a wide variety of applications such as internet of things, cloud computing, health, transportation, and robots. At the same time, parallel computing, with (heterogeneous) multi-core

Shin'ichirou Yoshida

An analytic model is presented for the fastest-declining Type I supernova SN2019bkc. The model of the central engine consists of the magnetic dipole radiation from a non-explosive remnant of a double white dwarf merger. We consider the viscous evolution of the rotating remnant, which may lead to the diminishing energy supply in an appropriate time scale for

Yusuke Suda, Koji Okuda

Chimera states in one-dimensional nonlocally coupled phase oscillators are mostly assumed to be stationary, but breathing chimeras can occasionally appear, branching from the stationary chimeras via Hopf bifurcation. In this paper, we demonstrate two types of breathing chimeras: The type I breathing chimera looks the same as the stationary chimera at a glanc

Jiajia Guo, Jinghe Wang, Chao-Kai Wen, Shi Jin

Deep learning (DL) has achieved great success in signal processing and communications and has become a promising technology for future wireless communications. Existing works mainly focus on exploiting DL to improve the performance of communication systems. However, the high memory requirement and computational complexity constitute a major hurdle for the pr

Gil Avraham, Yan Zuo, Thanuja Dharmasiri, Tom Drummond

Continuously estimating an agent's state space and a representation of its surroundings has proven vital towards full autonomy. A shared common ground among systems which successfully achieve this feat is the integration of previously encountered observations into the current state being estimated. This necessitates the use of a memory module for incorporati

E. V. Gotthelf, J. P. Halpern, K. Mori, A. M. Beloborodov

We present the results of our 8 year X-ray monitoring campaign on CXOU J171405.7-381031, the magnetar associated with the faint supernova remnant (SNR) CTB 37B. It is among the youngest by inferred spin-down age, and most energetic in spin-down power of magnetars, and may contribute, at least partially, to the GeV and TeV emission coincident with the SNR. We