May 2020 arXiv papers — page 50

Xi Li, Huimin Ma, Hongbing Ma, Yidong Wang

Foreground segmentation is an essential task in the field of image understanding. Under unsupervised conditions, different images and instances always have variable expressions, which make it difficult to achieve stable segmentation performance based on fixed rules or single type of feature. In order to solve this problem, the research proposes an unsupervis

Miguel Acosta

We consider actions of complex algebraic groups $\mathbf{G}$ on complex algebraic varieties $\mathbf{X}$, coming from actions of real forms $G$ of $\mathbf{G}$ and $X$ of $\mathbf{X}$. We explore the links between the real points of the complex GIT quotient $\mathbf{X /\!\!/ G}$ and the real GIT quotient $X /\!\!/ G$ defined by Richardson and Slodowy. We pro

Ronghao Guo, Chen Lin, Chuming Li, Keyu Tian

One-shot NAS method has attracted much interest from the research community due to its remarkable training efficiency and capacity to discover high performance models. However, the search spaces of previous one-shot based works usually relied on hand-craft design and were short for flexibility on the network topology. In this work, we try to enhance the one-

Kanyuan Han, Zhihua Guo, Huaixin Cao, Yuxing Du

Since masking of quantum information was introduced by Modi et al. in [PRL 120, 230501 (2018)], many discussions on this topic have been published. In this paper, we consider relationship between quantum multipartite maskers (QMMs) and quantum error-correcting codes (QECCs). We say that a subset $Q$ of pure states of a system $K$ can be masked by an operator

Junbum Cha, Sanghyuk Chun, Gayoung Lee, Bado Lee

Generating a new font library is a very labor-intensive and time-consuming job for glyph-rich scripts. Despite the remarkable success of existing font generation methods, they have significant drawbacks; they require a large number of reference images to generate a new font set, or they fail to capture detailed styles with only a few samples. In this paper,

Akiko Yazawa

Let us consider a truncated matroid $M_{\Gamma}^{r}$ of rank $r$ of a graphic matroid of a graph $\Gamma$. The basis for $M_{\Gamma}^{r}$ is the set of the forests with $r$ edges in $\Gamma$. We consider this basis generating function and compute its Hessian. In this paper, we show that the Hessian of the basis generating function of the truncated matroid of

A. B. Németh, S. Z. Németh

Asymmetric vector norms are generalizations of asymmetric norms, where the subadditivity inequality is understood in ordered vector space sense. This relation imposes strong conditions on the ordering itself. This note studies on these conditions in the general case, and in the case when the asymmetric vector norm is the metric projection onto the cone engen

Yong Huang, Wei Wang, Hao Wang, Tao Jiang

By adding users as a new dimension to connectivity, on-body Internet-of-Things (IoT) devices have gained considerable momentum in recent years, while raising serious privacy and safety issues. Existing approaches to authenticate these devices limit themselves to dedicated sensors or specified user motions, undermining their widespread acceptance. This paper

Sergei V. Kalinin, Andrew R. Lupini, Rama K. Vasudevan, Maxim Ziatdinov

Advances in hyperspectral imaging modes including electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM) bring forth the challenges of exploratory and subsequently physics-based analysis of multidimensional data sets. The (by now common) multivariate unsupervised linear unmixing methods and their nonlinear analogs genera

Diogo M. Costa, Alexandre P. Francisco, Luís M. S. Russo

We consider the complexity properties of modern puzzle games, Hexiom, Cut the Rope and Back to Bed. The complexity of games plays an important role in the type of experience they provide to players. Back to Bed is shown to be PSPACE-Hard and the first two are shown to be NP-Hard. These results give further insight into the structure of these games and the re

Wei-Chen Guo, Bao-Quan Ai, Liang He

We develop the neural network based "learning from regression uncertainty" approach for automated detection of phases of matter in nonequilibrium active systems. Taking the flocking phase transition of self-propelled active particles described by the Vicsek model for example, we find that after training a neural network for solving the inverse statistical pr

T. van der Zwaard, L. A. Grzelak, C. W. Oosterlee

This study contributes to understanding Valuation Adjustments (xVA) by focussing on the dynamic hedging of Credit Valuation Adjustment (CVA), corresponding Profit & Loss (P&L) and the P&L explain. This is done in a Monte Carlo simulation setting, based on a theoretical hedging framework discussed in existing literature. We look at hedging CVA market risk for

Christian Maes

We collect recent results on deriving useful response relations also for nonequilibrium systems. The approach is based on dynamical ensembles, determined by an action on trajectory space. (Anti)Symmetry under time-reversal separates two complementary contributions in the response, one entropic the other frenetic. Under time-reversal invariance of the unpertu

Florian Besau, Daniel Rosen, Christoph Thäle

We establish central limit theorems for natural volumes of random inscribed polytopes in projective Riemannian or Finsler geometries. In addition, normal approximation of dual volumes and the mean width of random polyhedral sets are obtained. We deduce these results by proving a general central limit theorem for the weighted volume of the convex hull of rand

Chenzhou Cui, Yihan Tao, Changhua Li, Dongwei Fan

In the era of big data astronomy, next generation telescopes and large sky surveys produce data sets at the TB or even PB level. Due to their large data volumes, these astronomical data sets are extremely difficult to transfer and analyze using personal computers or small clusters. In order to offer better access to data, data centers now generally provide o

Alexander E. Beasley, Mohammed-Salah Abdelouahab, René Lozi, Anna L. Powell

The key feature of a memristor is that the resistance is a function of its previous resistance, thereby the behaviour of the device is influenced by changing the way in which potential is applied across it. Ultimately, information can be encoded on memristors, which can then be used to implement a number of circuit topologies. Biological substrates have alre

Johannes Brünger, Maria Gentz, Imke Traulsen, Reinhard Koch

The behavioural research of pigs can be greatly simplified if automatic recognition systems are used. Especially systems based on computer vision have the advantage that they allow an evaluation without affecting the normal behaviour of the animals. In recent years, methods based on deep learning have been introduced and have shown pleasingly good results. E

Yutao Tang, Ding Wang

In this paper, we investigate a constrained optimal coordination problem for a class of heterogeneous nonlinear multi-agent systems described by high-order dynamics subject to both unknown nonlinearities and external disturbances. Each agent has a private objective function and a steady-state constraint about its output. We develop a composite distributed co

Yonghyun Kim, Wonpyo Park, Myung-Cheol Roh, Jongju Shin

In the field of face recognition, a model learns to distinguish millions of face images with fewer dimensional embedding features, and such vast information may not be properly encoded in the conventional model with a single branch. We propose a novel face-recognition-specialized architecture called GroupFace that utilizes multiple group-aware representation

Andrew W. Macpherson

Everyone knows that if you have a bivariant homology theory satisfying a base change formula, you get an representation of a category of correspondences. For theories in which the covariant and contravariant transfer maps are in mutual adjunction, these data are actually equivalent. In other words, a 2-category of correspondences is the universal way to atta

Yutao Tang, Peng Yi, Yanqiong Zhang, Dawei Liu

In this paper, we aim to develop distributed continuous-time algorithms over directed graphs to seek the Nash equilibrium in a noncooperative game. Motivated by the recent consensus-based designs, we present a distributed algorithm with a proportional gain for weight-balanced directed graphs. By further embedding a distributed estimator of the left eigenvect

Yitao Lu, Julie Zhou, Li Xing, Xuekui Zhang

As a future trend of healthcare, personalized medicine tailors medical treatments to individual patients. It requires to identify a subset of patients with the best response to treatment. The subset can be defined by a biomarker (e.g. expression of a gene) and its cutoff value. Topics on subset identification have received massive attention. There are over 2

Atreyee Kundu

This paper deals with stability of discrete-time switched linear systems whose all subsystems are unstable and the set of admissible switching signals obeys pre-specified restrictions on switches between the subsystems and dwell times on the subsystems. We derive sufficient conditions on the subsystems matrices such that a switched system is globally exponen

Yutao Tang

This paper studies an optimal consensus problem for a group of heterogeneous high-order agents with unknown control directions. Compared with existing consensus results, the consensus point is further required to an optimal solution to some distributed optimization problem. To solve this problem, we first augment each agent with an optimal signal generator t

Boštjan Brešar, Jasmina Ferme, Karolína Kamenická

Given a graph $G$ and a non-decreasing sequence $S=(a_1,a_2,\ldots)$ of positive integers, the mapping $f:V(G) \rightarrow \{1,\ldots,k\}$ is an $S$-packing $k$-coloring of $G$ if for any distinct vertices $u,v\in V(G)$ with $f(u)=f(v)=i$ the distance between $u$ and $v$ in $G$ is greater than $a_i$. The smallest $k$ such that $G$ has an $S$-packing $k$-colo

Simon Eberle, Georg S. Weiss

Motivated by the almost completely open problem of characterizing unbounded coincidence sets of global solutions of the classical obstacle problem in higher dimensions, we give in this note a concise and easy-to-extend proof of the known fact that if the coincidence set $\{u=0 \}$ of a global solution $u$ is bounded with nonempty interior then it is an ellip

Dibyendu Roy, Tomaž Prosen

We study quantum chaos and spectral correlations in periodically driven (Floquet) fermionic chains with long-range two-particle interactions, in the presence and absence of particle number conservation ($U(1)$) symmetry. We analytically show that the spectral form factor precisely follows the prediction of random matrix theory in the regime of long chains, a

Takanobu Mizuta

Who should be charged with responsibility for an artificial intelligence performing market manipulation have been discussed. In this study, I constructed an artificial intelligence using a genetic algorithm that learns in an artificial market simulation, and investigated whether the artificial intelligence discovers market manipulation through learning with

Heinz-Jürgen Schmidt, Wojciech Florek

We formulate part VI of a rigorous theory of ground states for classical, finite, Heisenberg spin systems. After recapitulating the central results of the parts I - V previously published we consider a magnetic field and analytically calculate the susceptibility at the saturation point. To this end we have to distinguish between parabolic and non-parabolic s

M. A. P. Chamikara, P. Bertok, I. Khalil, D. Liu

Facial recognition technologies are implemented in many areas, including but not limited to, citizen surveillance, crime control, activity monitoring, and facial expression evaluation. However, processing biometric information is a resource-intensive task that often involves third-party servers, which can be accessed by adversaries with malicious intent. Bio

Jian-hua He

We present the first numerical simulations of gravitational waves (GWs) passing through a potential well generated by a compact object in 3-D space, with a realistic source waveform derived from numerical relativity for the merger of two black holes. Unlike the previous work, our analyses focus on the time-domain, in which the propagation of GWs is a well-po

Amir Dembo, Sreeram Kannan, Ertem Nusret Tas, David Tse

Nakamoto invented the longest chain protocol, and claimed its security by analyzing the private double-spend attack, a race between the adversary and the honest nodes to grow a longer chain. But is it the worst attack? We answer the question in the affirmative for three classes of longest chain protocols, designed for different consensus models: 1) Nakamoto'

Gherardo Varando, Niels Richard Hansen

The linear Lyapunov equation of a covariance matrix parametrizes the equilibrium covariance matrix of a stochastic process. This parametrization can be interpreted as a new graphical model class, and we show how the model class behaves under marginalization and introduce a method for structure learning via $\ell_1$-penalized loss minimization. Our proposed m

Mohsen Khorasany, Ali Dorri, Reza Razzaghi, Raja Jurdak

Peer-to-Peer (P2P) energy trading can facilitate integration of a large number of small-scale producers and consumers into energy markets. Decentralized management of these new market participants is challenging in terms of market settlement, participant reputation and consideration of grid constraints. This paper proposes a blockchain-enabled framework for

Yi-Zhong Fan, Jin-Liang Jiang, Shao-Peng Tang, Zhi-Ping Jin

The post-merger gravitational wave (GW) radiation of the remnant formed in the binary neutron star (BNS) coalescence has not been directly measured, yet. We show in this work that the properties of the BNS involved in GW170817, additionally constrained by PSR J0030+0451, the lower limit on the maximum gravitational mass of non-rotating neutron star (NS) and

Maxim Berman, Leonid Pishchulin, Ning Xu, Matthew B. Blaschko

Neural architecture search (NAS) approaches aim at automatically finding novel CNN architectures that fit computational constraints while maintaining a good performance on the target platform. We introduce a novel efficient one-shot NAS approach to optimally search for channel numbers, given latency constraints on a specific hardware. We first show that we c

Theekshana Dissanayake, Tharindu Fernando, Simon Denman, Sridha Sridharan

Traditionally, abnormal heart sound classification is framed as a three-stage process. The first stage involves segmenting the phonocardiogram to detect fundamental heart sounds; after which features are extracted and classification is performed. Some researchers in the field argue the segmentation step is an unwanted computational burden, whereas others emb

Wangyou Zhang, Aswin Shanmugam Subramanian, Xuankai Chang, Shinji Watanabe

Despite successful applications of end-to-end approaches in multi-channel speech recognition, the performance still degrades severely when the speech is corrupted by reverberation. In this paper, we integrate the dereverberation module into the end-to-end multi-channel speech recognition system and explore two different frontend architectures. First, a multi

Sachin Verma, Ajay

We study the influence of superconductivity on the formation of the localized magnetic moment for a single-level quantum impurity embedded in an s-wave Bardeen-Cooper-Schrieffer (BCS) superconducting medium, modeled by single-impurity Anderson Hamiltonian. We have combined Bogoliubov transformation with Green's function method within self-consistent Hartree-

Bilguun Bayarsaikhan, Seoktae Koh, Enkhbat Tsedenbaljir, Gansukh Tumurtushaa

In light of the cosmological observations, we investigate dark energy models from the Horndeski theory of gravity. In particular, we consider cosmological models with the derivative self-interaction of the scalar field and the derivative coupling between the scalar field and gravity. We choose the self-interaction term to have an exponential function of the

Siamak Zamani Dadaneh, Shahin Boluki, Mingzhang Yin, Mingyuan Zhou

Semantic hashing has become a crucial component of fast similarity search in many large-scale information retrieval systems, in particular, for text data. Variational auto-encoders (VAEs) with binary latent variables as hashing codes provide state-of-the-art performance in terms of precision for document retrieval. We propose a pairwise loss function with di

M. Kimura, Y. Taniguchi

The properties of the alpha+28Si and 16O+16O molecular states which are embedded in the excited states of 32S and can have an impact on the stellar reactions are investigated using the antisymmetrized molecular dynamics. From the analysis of the cluster spectroscopic factors, the candidates of alpha+28Si and 16O+16O molecular states are identified close to a

Thejan Rajapakshe, Siddique Latif, Rajib Rana, Sara Khalifa

Deep reinforcement learning (deep RL) is a combination of deep learning with reinforcement learning principles to create efficient methods that can learn by interacting with its environment. This has led to breakthroughs in many complex tasks, such as playing the game "Go", that were previously difficult to solve. However, deep RL requires significant traini

Søren Eilers

The class of AD algebras of real rank zero is classified by an exact sequence of K-groups with coefficients, equipped with certain order structures. Such a sequence is always split, and one may ask why, then, the middle group is relevant for classification. The answer is that the splitting map can not always be chosen to respect the order structures involved

Qi Zhang

The non-Hermitian Schr\"odinger equation is re-expressed generally in the form of Hamilton's canonical equation without any approximation. Its quantization called non-Hermitian quantum field theory is discussed. By virtue of the canonical equation, the theory of non-Hermitian quantum mechanics is totally reformulated, including the probability amplitudes of

Adit Krishnan, Mahashweta Das, Mangesh Bendre, Hao Yang

The rapid proliferation of new users and items on the social web has aggravated the gray-sheep user/long-tail item challenge in recommender systems. Historically, cross-domain co-clustering methods have successfully leveraged shared users and items across dense and sparse domains to improve inference quality. However, they rely on shared rating data and cann

Shigenori Nakatsuka

We prove the injectivity of the Miura maps for W-superalgberas and the isomorphisms between the Poisson vertex superalgebras obtained as the associated graded of the W-superalgebras in terms of the Li's filtration and the level 0 Poisson vertex superalgebras associated with the arc spaces of the corresponding Slodowy slices in full generality.

Farid Labib, Daisuke Okuyama, Nobuhisa Fujita, Tsunetomo Yamada

A systematic investigation has been performed to elucidate effects of Rare-Earth (RE) type and local atomic configuration on magnetic properties of icosahedral quasicrystal (iQC) and their cubic approximants (2/1 and 1/1 ACs) in the ternary Cd-Mg-RE (RE = Gd, Tb, Dy, Ho, Er, and Tm) systems. At low temperatures, iQC and 2/1 ACs exhibit spin-glass-like freezi

Eric Ngondiep

This paper considers an explicit numerical scheme for solving the mathematical model of the propagation of Covid-19 epidemic with undetected infectious cases. We analyze the stability and convergence rate of the new approach in $L^{\infty}$-norm. The proposed method is less time consuming. Furthermore, the method is stable, at least second-order convergent a

Kyu J. Han, Jing Pan, Venkata Krishna Naveen Tadala, Tao Ma

This paper proposes multistream CNN, a novel neural network architecture for robust acoustic modeling in speech recognition tasks. The proposed architecture processes input speech with diverse temporal resolutions by applying different dilation rates to convolutional neural networks across multiple streams to achieve the robustness. The dilation rates are se

Jing Pan, Joshua Shapiro, Jeremy Wohlwend, Kyu J. Han

In this paper we present state-of-the-art (SOTA) performance on the LibriSpeech corpus with two novel neural network architectures, a multistream CNN for acoustic modeling and a self-attentive simple recurrent unit (SRU) for language modeling. In the hybrid ASR framework, the multistream CNN acoustic model processes an input of speech frames in multiple para

Sebastian Kish, Eduardo Villaseñor, Robert Malaney, Kerry Mudge

Currently, quantum key distribution (QKD) using continuous variable (CV) technology has only been demonstrated over short-range terrestrial links. Here we attempt to answer whether CV-QKD over the much longer satellite-to-Earth channel is feasible. To this end, we first review the concepts and technologies that will enable CV-QKD over the satellite-to-Earth

Moumita Das, Kishore Thapliyal, Biswajit Sen, Jan Perina

Quantum Zeno and anti-Zeno effects are studied in an asymmetric nonlinear optical coupler composed of a probe waveguide and a system waveguide. The system is a nonlinear waveguide operating under non-degenerate hyper-Raman process, while both the pump modes in the system are constantly interacting with the probe waveguide. The effect of the presence of probe

R. Dragomir, V. Moldoveanu, S. Stanciu, B. Tanatar

The effects of a turnstile operation on the current-induced vibron dynamics in nanoelectromechanical systems (NEMS) are analyzed in the framework of the generalized master equation. In our simulations each turnstile cycle allows the pumping of up to two interacting electrons across a biased mesoscopic subsystem which is electrostatically coupled to the vibra

Eduardo Villaseñor, Robert Malaney, Kerry A. Mudge, Kenneth J. Grant

Satellite-based quantum cryptography has already been demonstrated using discrete variable technology. Nonetheless, there is great interest in using weak coherent pulses to perform quantum key distribution (QKD) in the continuous variable (CV) paradigm. In this work, we study the feasibility of performing coherent-state CV-QKD via the satellite-to-ground cha

Ashutosh Baheti, Alan Ritter, Kevin Small

Question answering (QA) is an important aspect of open-domain conversational agents, garnering specific research focus in the conversational QA (ConvQA) subtask. One notable limitation of recent ConvQA efforts is the response being answer span extraction from the target corpus, thus ignoring the natural language generation (NLG) aspect of high-quality conver

Haoneng Luo, Shiliang Zhang, Ming Lei, Lei Xie

Transformer models have been introduced into end-to-end speech recognition with state-of-the-art performance on various tasks owing to their superiority in modeling long-term dependencies. However, such improvements are usually obtained through the use of very large neural networks. Transformer models mainly include two submodules - position-wise feedforward

Muhammad Muddassir, Domingo Gomez, Shujian Chen, Luyin Hu

In this paper, we present a novel robotic system for skin photo-rejuvenation procedures, which can uniformly deliver the laser's energy over the skin of the face. The robotised procedure is performed by a manipulator whose end-effector is instrumented with a depth sensor, a thermal camera, and a cosmetic laser generator. To plan the heat stimulating trajecto

J. A. W. Straquadine, M. S. Ikeda, I. R. Fisher

We report the evolution of a charge density wave (CDW) state in the quasi-2D rare-earth tritellurides ($R$Te$_3$ for $R$=Er,Tm) as a function of in-plane uniaxial stress. Measurements of the elastocaloric effect, resistivity, and elastoresistivity allow us to demonstrate the importance of in-plane antisymmetric strain on the CDW and to establish a phase diag

Redowan Mahmud, Kotagiri Ramamohanarao, Rajkumar Buyya

The Internet of Things (IoT) paradigm is being rapidly adopted for the creation of smart environments in various domains. The IoT-enabled Cyber-Physical Systems (CPSs) associated with smart city, healthcare, Industry 4.0 and Agtech handle a huge volume of data and require data processing services from different types of applications in real-time. The Cloud-c

Ankang Liu, Alexander M. Finkel'stein

We discuss valley current, which is carried by quasiparticles in graphene. We show that the valley current arises owing to a peculiar term in the electron-phonon collision integral that mixes the scalar and vector gauge-field-like vertices in the electron-phonon interaction. This mixing makes collisions of phonons with electrons sensitive to their chirality,

Hong Li, Xin Zhang

The absolute distance scale measurements in cosmology have always been an important mission. In particular, in recent years, the Hubble constant tension between the measurements from early and late universe has become a new crisis for cosmology, which calls for new, independent absolute cosmological distance measurements. Recently, a result of measuring the

Nasim Sonboli, Farzad Eskandanian, Robin Burke, Weiwen Liu

As recommender systems have become more widespread and moved into areas with greater social impact, such as employment and housing, researchers have begun to seek ways to ensure fairness in the results that such systems produce. This work has primarily focused on developing recommendation approaches in which fairness metrics are jointly optimized along with

Xingfu Zhong, Zhijing Chen, Yu Huang

In the paper we introduce the notions of bounded invariance complexity, bounded invariance complexity in the mean and mean L-stability for control systems. Then we characterize these notions by introducing six types of equi-invariability. As by product, two new dichotomy theorems for control system on control sets are established.

Abhisha Mano

The segmentation of Lamina Cribrosa(LC) is a challenging task to detect the glaucomatous damage. In this paper a new method of segmenting the LC using bat optimized Watershed segmentation is done. By using wavelet transform LC structures are decomposed. Then, the decomposed image is optimized using Bat algorithm and by applying histogram equalization the opt

Sunghee Jung, Hoirin Kim

In this paper, we explore prosody transfer for audiobook generation under rather realistic condition where training DB is plain audio mostly from multiple ordinary people and reference audio given during inference is from professional and richer in prosody than training DB. To be specific, we explore transferring Korean dialects and emotive speech even thoug

Wenjie Ai, Xiaoguang Tu, Shilei Cheng, Mei Xie

Deep Convolutional Neural Networks (DCNNs) have achieved impressive performance in Single Image Super-Resolution (SISR). To further improve the performance, existing CNN-based methods generally focus on designing deeper architecture of the network. However, we argue blindly increasing network's depth is not the most sensible way. In this paper, we propose a

Curtis Murray, Lewis Mitchell, Jonathan Tuke, Mark Mackay

Social media discussion of COVID-19 provides a rich source of information into how the virus affects people's lives that is qualitatively different from traditional public health datasets. In particular, when individuals self-report their experiences over the course of the virus on social media, it can allow for identification of the emotions each stage of s

Ryan Bignell, Waseem Kamleh, Derek Leinweber

The magnetic polarisability is a fundamental property of hadrons, which provides insight into their structure in the low-energy regime. The pion magnetic polarisability is calculated using lattice QCD in the presence of background magnetic fields. The results presented are facilitated by the introduction of a new magnetic-field dependent quark-propagator eig

Hanchen Wang, Jilei Chen, Tao Yu, Chuanpu Liu

Non-Hermitian physics has recently attracted much attention in optics and photonics. Less explored is non-Hermitian magnonics that provides opportunities to take advantage of the inevitable dissipation of magnons or spin waves in magnetic systems. Here we demonstrate non-Hermitian coherent coupling of two distant nanomagnets by fast spin waves with sub-50 nm

Yucong Shen, Li Shen, Hao-Zhi Huang, Xuan Wang

Recent advances in deep neural networks (DNNs) lead to tremendously growing network parameters, making the deployments of DNNs on platforms with limited resources extremely difficult. Therefore, various pruning methods have been developed to compress the deep network architectures and accelerate the inference process. Most of the existing channel pruning met

Shuke Peng, Feng Ji, Zehao Lin, Shaobo Cui

How to build a high-quality multi-domain dialogue system is a challenging work due to its complicated and entangled dialogue state space among each domain, which seriously limits the quality of dialogue policy, and further affects the generated response. In this paper, we propose a novel method to acquire a satisfying policy and subtly circumvent the knotty

William Rea

We examined the properties of the coefficients of the \cite{lanczos1964} approximation of the $\Gamma$-function with complex values of the free parameter together with the convergence properties of the approximation when using these coefficients. We report that for fixed real parts of the free parameter that using complex coefficients both increases the comp

Shiliang Zhang, Zhifu Gao, Haoneng Luo, Ming Lei

Recently, streaming end-to-end automatic speech recognition (E2E-ASR) has gained more and more attention. Many efforts have been paid to turn the non-streaming attention-based E2E-ASR system into streaming architecture. In this work, we propose a novel online E2E-ASR system by using Streaming Chunk-Aware Multihead Attention(SCAMA) and a latency control memor

Matin Macktoobian, Denis Gillet, Jean-Paul Kneib

Robotic fiber positioners play a vital role in the generation of massive spectroscopic surveys. The more complete a positioners set is coordinated, the more information its corresponding spectrograph receives during an observation. The complete coordination problem of positioners sets is studied in this paper. We first define the local and the global complet

Zhifu Gao, Shiliang Zhang, Ming Lei, Ian McLoughlin

End-to-end speech recognition has become popular in recent years, since it can integrate the acoustic, pronunciation and language models into a single neural network. Among end-to-end approaches, attention-based methods have emerged as being superior. For example, Transformer, which adopts an encoder-decoder architecture. The key improvement introduced by Tr

Peter Hintz, Gunther Uhlmann, Jian Zhai

We consider an inverse boundary value problem for a semilinear wave equation on a time-dependent Lorentzian manifold with time-like boundary. The time-dependent coefficients of the nonlinear terms can be recovered in the interior from the knowledge of the Neumann-to-Dirichlet map. Either distorted plane waves or Gaussian beams can be used to derive uniquenes

Thiem Hoang, Ngo-Duy Tung

Comets provide unique information about the physical and chemical properties of the environment in which the Solar system was formed. Understanding how cometary dust and ice evolve under the effect of sunlight is essential for constraining nuclear structure and triggering mechanism of comet activity. In this paper, we first study rotational disruption of dus

Yaniv Rubinpur, Sivan Toledo

We present robust high-performance implementations of signal-processing tasks performed by a high-throughput wildlife tracking system called ATLAS. The system tracks radio transmitters attached to wild animals by estimating the time of arrival of radio packets to multiple receivers (base stations). Time-of-arrival estimation of wideband radio signals is comp

Jingjing Fan, Bing Tan, Songxiao Li

In this paper, we investigate a new extragradient algorithm for solving pseudomonotone equilibrium problems on Hadamard manifolds. The algorithm uses a variable stepsize which is updated at each iteration and based on some previous iterates. The convergence analysis of the proposed algorithm is discussed under mild assumptions. In the case where the equilibr

Javier Alejandro Chávez-Domínguez, Andrew T. Swift

We generalize the notions of asymptotic dimension and coarse embeddings from metric spaces to quantum metric spaces in the sense of Kuperberg and Weaver. We show that quantum asymptotic dimension behaves well with respect to metric quotients and direct sums, and is preserved under quantum coarse embeddings. Moreover, we prove that a quantum metric space that

Naoto Sato, Hironobu Kuruma, Hideto Ogawa

As for software development by machine learning, a trained model is evaluated by using part of an existing dataset as test data. However, if data with characteristics that differ from the existing data is input, the model does not always behave as expected. Accordingly, to confirm the behavior of the model more strictly, it is necessary to create data that d

Zexin Cai, Yaogen Yang, Ming Li

Modeling voices for multiple speakers and multiple languages in one text-to-speech system has been a challenge for a long time. This paper presents an extension on Tacotron2 to achieve bilingual multispeaker speech synthesis when there are limited data for each language. We achieve cross-lingual synthesis, including code-switching cases, between English and

Zhenyu Wang, Yunshan Cao, Ruifang Wang, Bo Liu

We theoretically investigate the radial-spin-wave induced magnetic vortex switching in the presence of Dzyaloshinskii-Moriya interaction (DMI). From micromagnetic simulations, we observe a circular-to-radial vortex phase transition by increasing the DMI strength. The radial spin-wave excitation spectrum for each magnetization configuration is analyzed, showi

Kelei He, Chunfeng Lian, Bing Zhang, Xin Zhang

Accurate segmentation of the prostate is a key step in external beam radiation therapy treatments. In this paper, we tackle the challenging task of prostate segmentation in CT images by a two-stage network with 1) the first stage to fast localize, and 2) the second stage to accurately segment the prostate. To precisely segment the prostate in the second stag

Haohan Guo, Shaofei Zhang, Frank K. Soong, Lei He

End-to-end neural TTS has achieved superior performance on reading style speech synthesis. However, it's still a challenge to build a high-quality conversational TTS due to the limitations of the corpus and modeling capability. This study aims at building a conversational TTS for a voice agent under sequence to sequence modeling framework. We firstly constru

Mengfei He, Sidney R. Nagel

We describe a high-speed interferometric method, using multiple angles of incidence and multiple wavelengths, to measure the absolute thickness, tilt, the local angle between the surfaces, and the refractive index of a fluctuating transparent wedge. The method is well suited for biological, fluid and industrial applications.

Muhammad A. Latif, Sadegh Khochfar

Earliest quasars at the cosmic dawn are powered by mass accretion onto supermassive black holes of a billion solar masses. Massive black hole seeds forming through the direct collapse mechanism are considered the most promising candidates but how do they grow and co-evolve with their host galaxies at early cosmic times remains unknown. We here present result

Jun Yu, HaiYing Wang, Mingyao Ai, Huiming Zhang

Nonuniform subsampling methods are effective to reduce computational burden and maintain estimation efficiency for massive data. Existing methods mostly focus on subsampling with replacement due to its high computational efficiency. If the data volume is so large that nonuniform subsampling probabilities cannot be calculated all at once, then subsampling wit

Yu Song, Zilong Huang, Chuanyue Shen, Humphrey Shi

The standard petrography test method for measuring air voids in concrete (ASTM C457) requires a meticulous and long examination of sample phase composition under a stereomicroscope. The high expertise and specialized equipment discourage this test for routine concrete quality control. Though the task can be alleviated with the aid of color-based image segmen

Mihir Kale, Abhinav Rastogi

We study the pre-train + fine-tune strategy for data-to-text tasks. Our experiments indicate that text-to-text pre-training in the form of T5, enables simple, end-to-end transformer based models to outperform pipelined neural architectures tailored for data-to-text generation, as well as alternative language model based pre-training techniques such as BERT a

Jian-Hua Gao, Guo-Liang Ma, Shi Pu, Qun Wang

We give a brief overview of recent theoretical and experimental results on the chiral magnetic effect and spin polarization effect in heavy-ion collisions. We present updated experimental results for the chiral magnetic effect and related phenomena. The time evolution of the magnetic fields in different models is discussed. The newly developed quantum kineti

Qimen Xu, Abhiraj Sharma, Benjamin Comer, Hua Huang

We present SPARC: Simulation Package for Ab-initio Real-space Calculations. SPARC can perform Kohn-Sham density functional theory calculations for isolated systems such as molecules as well as extended systems such as crystals and surfaces, in both static and dynamic settings. It is straightforward to install/use and highly competitive with state-of-the-art

Jungseock Joo, Kimmo Kärkkäinen

Automated computer vision systems have been applied in many domains including security, law enforcement, and personal devices, but recent reports suggest that these systems may produce biased results, discriminating against people in certain demographic groups. Diagnosing and understanding the underlying true causes of model biases, however, are challenging

Christiana Chamon, Shahriar Ferdous, Laszlo B. Kish

This paper introduces and demonstrates two new attacks against the Kirchhoff-Law-Johnson-Noise (KLJN) secure key exchange scheme. The attacks are based on random number generators with compromised security. First we explore the situation in which Eve knows the seed of both Alice's and Bob's random number generators. We show that in this situation Eve can cra

Gyubaek Shin, Jin Wang

The critical role of energy consumption in biological systems including T cell discrimination process has been investigated in various ways. The kinetic proofreading(KPR) in T cell recognition involving different levels of energy dissipation influences functional outcomes such as error rates and specificity. In this work, we study quantitatively how the ener

Suzanne Mueller, Willow Ahrens, Stephen Chou, Fredrik Kjolstad

We present a new algorithm for transposing sparse tensors called Quesadilla. The algorithm converts the sparse tensor data structure to a list of coordinates and sorts it with a fast multi-pass radix algorithm that exploits knowledge of the requested transposition and the tensors input partial coordinate ordering to provably minimize the number of parallel p

Chang-Yin Ji, Yongyou Zhang, Bingsuo Zou, Yugui Yao

Rapidly developing photonics brings many interesting resonant optical phenomena, in which the Fano resonance (FR) always intrigues researchers because of its applications in optical switching and sensing. However, its sensitive dependence on environmental conditions makes it hard to implement in experiments. We in this work suggest a topologically-protected

Walter Dempsey

Coronavirus case-count data has influenced government policies and drives most epidemiological forecasts. Limited testing is cited as the key driver behind minimal information on the COVID-19 pandemic. While expanded testing is laudable, measurement error and selection bias are the two greatest problems limiting our understanding of the COVID-19 pandemic; ne

Yanze Wu, Gaohan Miao, Joseph Subotnik

Within the context of very simple avoided crossing, we investigate the investigate the effect of a complex diabatic coupling in determining spin-dependent rate constants and scattering states. We find that, if the molecular geometry is not linear and the Berry force is not zero, one can find significant spin polarization of the products. This study emphasize