October 2020 arXiv papers — page 54

Austin P. Wright, Zijie J. Wang, Haekyu Park, Grace Guo

With the recent release of AI interaction guidelines from Apple, Google, and Microsoft, there is clearly interest in understanding the best practices in human-AI interaction. However, industry standards are not determined by a single company, but rather by the synthesis of knowledge from the whole community. We have surveyed all of the design guidelines from

Jian Ge

In this note, we estimate the upper bound of volume of closed positively or nonnegatively curved Alexandrov space $X$ with strictly convex boundary. We also discuss the equality case. In particular, the Boundary Conjecture holds when the volume upper bound is achieved. Our theorem also can be applied to Riemannian manifolds with non-smooth boundary, which ge

Pat Devlin, Tony Zeng

For natural numbers $x$ and $b$, the classical Kaprekar function is defined as $K_{b} (x) = D-A$, where $D$ is the rearrangement of the base-$b$ digits of $x$ in descending order and $A$ is ascending. The bases $b$ for which $K_b$ has a $4$-digit non-zero fixed point were classified by Hasse and Prichett, and for each base this fixed point is known to be uni

Aniruddha Biswas, Palash Sarkar

We show (almost) separation between certain important classes of Boolean functions. The technique that we use is to show that the total influence of functions in one class is less than the total influence of functions in the other class. In particular, we show (almost) separation of several classes of Boolean functions which have been studied in the coding t

Thiago Lobo, Minos A. Neto, Marcio G. da Silva, Octavio D. R. Salmon

We will study the competitive effect between the transport of a quantum dot adsorbed to a ballistic channel and laterally coupled to a single-walled carbon nanotube (SWNT). We will use the tight-binding approach to analytically write the SWNT Green function and the quantum dot will be solved by the atomic method for U very large. We will present curves of th

Moez Altayeb, Marco Zennaro, Ermanno Pietrosemoli, Pietro Manzoni

Over the last few years we have witnessed an exponential growth in the adoption of LoRaWAN as LPWAN technology for IoT. While LoRaWAN offers many advantages, one of its limitations is the paltry data rate. Most IoT applications don't require a high throughput but there are some that would benefit from a higher data rate. In this paper, we present TurboLo

Ivana Kvapilíková, Tom Kocmi, Ondřej Bojar

This paper presents a description of CUNI systems submitted to the WMT20 task on unsupervised and very low-resource supervised machine translation between German and Upper Sorbian. We experimented with training on synthetic data and pre-training on a related language pair. In the fully unsupervised scenario, we achieved 25.5 and 23.7 BLEU translating from an

Mengshuo Jia, Gabriela Hug, Chen Shen

In chance-constrained OPF models, joint chance constraints (JCCs) offer a stronger guarantee on security compared to single chance constraints (SCCs). Using Boole's inequality or its improved versions to decompose JCCs into SCCs is popular, yet the conservativeness introduced is still significant. In this letter, a non-parametric iterative framework is p

I. B. Elius, A. K. M. Zakaria, J. Maudood, S. Hossain

Manganese ferrite (MnFe2O4) and copper doped manganese ferrite (Mn0.75Cu0.25Fe2O4) soft materials were synthesized through solid-state sintering method. The phase purity and quality were confirmed from x-ray diffraction patterns. Then the samples were subjected to neutron diffraction experiment and the diffraction data were analyzed using FullProf software p

Shen Ren, Qianxiao Li, Liye Zhang, Zheng Qin

The future of mobility-as-a-Service (Maas)should embrace an integrated system of ride-hailing, street-hailing and ride-sharing with optimised intelligent vehicle routing in response to a real-time, stochastic demand pattern. We aim to optimise routing policies for a large fleet of vehicles for street-hailing services, given a stochastic demand pattern in sma

V. Ya. Aleshkin, G. Alymov, A. V. Antonov, A. A. Dubinov

We present detailed theoretical and experimental studies of Auger recombination in narrow-gap mercury cadmium telluride quantum wells (HgCdTe QWs). We calculate the Auger recombination probabilities as functions of non-equilibrium carrier density, temperature and composition of quantum wells taking into account the complex band dispersions and wave functions

Slobodan Djukanović, Jiři Matas, Tuomas Virtanen

The paper presents a method for audio-based vehicle counting (VC) in low-to-moderate traffic using one-channel sound. We formulate VC as a regression problem, i.e., we predict the distance between a vehicle and the microphone. Minima of the proposed distance function correspond to vehicles passing by the microphone. VC is carried out via local minima detecti

Jochem van der Veen, Pablo Borja, Jacquelien M. A. Scherpen

In this work, we propose a passivity-based control approach that addresses the trajectory tracking problem for a class of mechanical systems that comprises a broad range of robotic arms. The resulting controllers can be naturally saturated and do not require velocity measurements. Moreover, the proposed methodology does not require the implementation of obse

Meng-Li Guo, Zhi-Xiang Jin, Bo Li, Bin Hu

Coherence is a fundamental ingredient in quantum physics and a key resource in quantum information processing. The quantification of quantum coherence is of great importance. We present a family of coherence quantifiers based on the Tsallis relative operator entropy. Shannon inequality and its reverse one in Hilbert space operators derived by Furuta [Linear

M. G. Sottile, I. A. Cueto, A. O. Sfriso

The interest of the mining industry on the assessment of tailings static liquefaction has exacerbated after recent failures of upstream-raised tailings storage facilities (TSF). Standard practices to evaluate global stability of TSFs entail the use of limit equilibrium analyses considering peak and residual undrained shear strengths; thus, neglecting the wor

Xiang Dai, Heike Adel

Simple yet effective data augmentation techniques have been proposed for sentence-level and sentence-pair natural language processing tasks. Inspired by these efforts, we design and compare data augmentation for named entity recognition, which is usually modeled as a token-level sequence labeling problem. Through experiments on two data sets from the biomedi

Paulo Henrique Alves, Isabella Z. Frajhof, Fernando A. Correia, Clarisse de Souza

Data privacy is a trending topic in the internet era. Given such importance, many challenges emerged in order to collect, manage, process, and publish data. In this sense, personal data have got attention, and many regulations emerged, such as GDPR in the European Union and LGPD in Brazil. This regulation model aims to protect users' data from misusage a

Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka, Nobukatsu Hojo

Non-parallel voice conversion (VC) is a technique for learning mappings between source and target speeches without using a parallel corpus. Recently, cycle-consistent adversarial network (CycleGAN)-VC and CycleGAN-VC2 have shown promising results regarding this problem and have been widely used as benchmark methods. However, owing to the ambiguity of the eff

Volker Ziegler

Given a finite set of primes $S$ and a $m$-tuple $(a_1,\dots,a_m)$ of positive, distinct integers we call the $m$-tuple $S$-Diophantine, if for each $1\leq i < j\leq m$ the quantity $a_ia_j+1$ has prime divisors coming only from the set $S$. For a given set $S$ we give a practical algorithm to find all $S$-Diophantine quadruples, provided that $|S|=3$.

Ildar Rakhmatulin

Alcoholism is one of the most common diseases in the world. This type of substance abuse leads to mental and physical dependence on ethanol-containing drinks. Alcoholism is accompanied by progressive degradation of the personality and damage to the internal organs. Today still not exists a quick diagnosis method to detect this disease. This article presents

Nadezhda Zueva, Madina Kabirova, Pavel Kalaidin

Toxicity has become a grave problem for many online communities and has been growing across many languages, including Russian. Hate speech creates an environment of intimidation, discrimination, and may even incite some real-world violence. Both researchers and social platforms have been focused on developing models to detect toxicity in online communication

Sasinee Pruekprasert, Clovis Eberhart, Jérémy Dubut

We propose a symbolic self-triggered controller synthesis procedure for non-deterministic continuous-time nonlinear systems without stability assumptions. The goal is to compute a controller that satisfies two objectives. The first objective is represented as a specification in a fragment of LTL, which we call 2-LTL. The second one is an energy objective, in

Floriane Mefo Kue, Thorsten Raasch, Alain B. Zemkoho

We consider a bilevel program involving a linear lower level problem with left-hand-side perturbation. We then consider the Karush-Kuhn-Tucker reformulation of the problem and subsequently build a tractable optimization problem with linear constraints by means of a partial exact penalization. A semismooth system of equations is then generated from the later

Kairan Liu, Leiye Xu, Ruifeng Zhang

In this paper, we consider measure-theoretical restricted sensitivity and topological restricted sensitivities by restricting the first sensitive time. For a given topological dynamical system, we define measure-theoretical restricted asymptotic rate with respect to sensitivity, and obtain that it equal to the reciprocal of the Brin-Katok local entropy for a

Bo Dai, Ofir Nachum, Yinlam Chow, Lihong Li

We study high-confidence behavior-agnostic off-policy evaluation in reinforcement learning, where the goal is to estimate a confidence interval on a target policy&#39;s value, given only access to a static experience dataset collected by unknown behavior policies. Starting from a function space embedding of the linear program formulation of the $Q$-function,

Gagan Kanojia, Shanmuganathan Raman

Consider a set of n images of a scene with dynamic objects captured with a static or a handheld camera. Let the temporal order in which these images are captured be unknown. There can be n! possibilities for the temporal order in which these images could have been captured. In this work, we tackle the problem of temporally sequencing the unordered set of ima

Faical Ndairou, Delfim F. M. Torres

Distributed-order fractional non-local operators have been introduced and studied by Caputo at the end of the 20th century. They generalize fractional order derivatives/integrals in the sense that such operators are defined by a weighted integral of different orders of differentiation over a certain range. The subject of distributed-order non-local derivativ

Shenhao Wang, Baichuan Mo, Jinhua Zhao

Researchers often treat data-driven and theory-driven models as two disparate or even conflicting methods in travel behavior analysis. However, the two methods are highly complementary because data-driven methods are more predictive but less interpretable and robust, while theory-driven methods are more interpretable and robust but less predictive. Using the

Li-Hsin Chang, Sampo Pyysalo, Jenna Kanerva, Filip Ginter

Language models based on deep neural networks have facilitated great advances in natural language processing and understanding tasks in recent years. While models covering a large number of languages have been introduced, their multilinguality has come at a cost in terms of monolingual performance, and the best-performing models at most tasks not involving c

Étienne Bamas, Andreas Maggiori, Ola Svensson

The extension of classical online algorithms when provided with predictions is a new and active research area. In this paper, we extend the primal-dual method for online algorithms in order to incorporate predictions that advise the online algorithm about the next action to take. We use this framework to obtain novel algorithms for a variety of online coveri

Étienne Bamas, Andreas Maggiori, Lars Rohwedder, Ola Svensson

As power management has become a primary concern in modern data centers, computing resources are being scaled dynamically to minimize energy consumption. We initiate the study of a variant of the classic online speed scaling problem, in which machine learning predictions about the future can be integrated naturally. Inspired by recent work on learning-augmen

Minhao Hong, Fangjun Xu

Given a $(2,d)$-Gaussian field \[ Z=\big\{ Z(t,s)= X^{H_1}_t -\tilde{X}^{H_2}_s, s,t \ge 0\big\}, \] where $X^{H_1}$ and $\tilde{X}^{H_2}$ are independent $d$-dimensional centered Gaussian processes satisfying certain properties, we will give the necessary condition for existence of derivatives of the local time of $Z$.

Viktória Kádár, Gergő Pál, Ferenc Kun

Forecasting the imminent catastrophic failure has a high importance for a large variety of systems from the collapse of engineering constructions, through the emergence of landslides and earthquakes, to volcanic eruptions. Failure forecast methods predict the lifetime of the system based on the time-to-failure power law of observables describing the final ac

Bin Li, Xianzhen Guo, Ruonan Zhang, Xiaojiang Du

Unmanned aerial vehicles (UAVs) have played an important role in air-ground integration network. Especially in Internet of Things (IoT) services, UAV equipped with communication equipments is widely adopted as a mobile base station (BS) for data collection from IoT devices on the ground. In this paper, we consider an air-ground network in which the UAV flies

Nguyen Thi Han, Vo Khuong Dien, Ming-Fa Lin

The Li2SiO3 compound, a ternary electrolyte compound of Lithium-ion based batteries, exhibits unique geometric and band structures, an atom-dominated energy spectrum, charge densities distributions, atom and orbital-projected density of states, and strong optical responses. The state-of-the-art analysis, based on an ab-initio simulation, have successfully co

Florin-Alexandru Vasluianu, Andres Romero, Luc Van Gool, Radu Timofte

Shadow removal is an important computer vision task aiming at the detection and successful removal of the shadow produced by an occluded light source and a photo-realistic restoration of the image contents. Decades of re-search produced a multitude of hand-crafted restoration techniques and, more recently, learned solutions from shad-owed and shadow-free tra

Kelei Wang

For a class of reaction-diffusion equations describing propagation phenomena, we prove that for any entire solution $u$, the level set $\{u=λ\}$ is a Lipschitz graph in the time direction if $λ$ is close to $1$. Under a further assumption that $u$ connects $0$ and $1$, it is shown that all level sets are Lipschitz graphs. By a blowing down analysis, the larg

V. V. Denisenko, S. A. Nesterov

We consider the vector functions in a domain homeomorphic to a spherical layer bounded by twice continuously differentiable surfaces. Additional restrictions are imposed on the domain, which allow to conduct proofs using simple methods. On the outer and inner boundaries, the normal and the tangential components of the vector are zero, respectively. For such

Jinliang Yuan, Mengwei Xu, Xiao Ma, Ao Zhou

Federated learning (FL) was designed to enable mobile phones to collaboratively learn a global model without uploading their private data to a cloud server. However, exiting FL protocols has a critical communication bottleneck in a federated network coupled with privacy concerns, usually powered by a wide-area network (WAN). Such a WAN-driven FL design leads

Michele Missikoff

Low Code platforms, according to Gartner Group, represent one of the more disruptive technologies in the development and maintenance of enterprise applications. The key factor is represented by the central involvement of business people and domain expert, with a substantial disintermediation with respect to technical people. In this paper we propose a method

Pramod Kumar Mishra

We consider the lattice model for an ideal-linear polymer chain to mimic the conformations of the semi-flexible homo-polymer chain. The polymer chain is assumed to confine in the fairly small area, such that the flexible chain conformations are easily polymerized in the nano-area. It has been described using analytical calculations that such a situation may

Jens Oliver Gutsfeld, Markus Müller-Olm, Christoph Ohrem

Hyperproperties have received increasing attention in the last decade due to their importance e.g. for security analyses. Past approaches have focussed on synchronous analyses, i.e. techniques in which different paths are compared lockstepwise. In this paper, we systematically study asynchronous analyses for hyperproperties by introducing both a novel automa

Niklas Michel, Natalia S. Oreshkina

We put forward a method for determination of the kaon radius from the spectra of kaonic atoms. We analyze the few lowest transitions and their sensitivity to the size of the kaon for ions in the nuclear charge range Z = 1 - 100, taking into account finite-nuclear-size, finite-kaon-size, recoil and leading-order quantum-electrodynamic effects. Additionally, t

Chong Zhang, Huan Zhang, Cho-Jui Hsieh

We study the problem of efficient adversarial attacks on tree based ensembles such as gradient boosting decision trees (GBDTs) and random forests (RFs). Since these models are non-continuous step functions and gradient does not exist, most existing efficient adversarial attacks are not applicable. Although decision-based black-box attacks can be applied, the

Vitor Cerqueira, Luis Torgo, Carlos Soares

The early detection of anomalous events in time series data is essential in many domains of application. In this paper we deal with critical health events, which represent a significant cause of mortality in intensive care units of hospitals. The timely prediction of these events is crucial for mitigating their consequences and improving healthcare. One of t

Yuanhao Zhai, Le Wang, Wei Tang, Qilin Zhang

Weakly-supervised Temporal Action Localization (W-TAL) aims to classify and localize all action instances in an untrimmed video under only video-level supervision. However, without frame-level annotations, it is challenging for W-TAL methods to identify false positive action proposals and generate action proposals with precise temporal boundaries. In this pa

Hari Krishna Vydana, Lukas Burget, Jan Cernocky

The paper describes the BUT&#39;s speech translation systems. The systems are English$\longrightarrow$German offline speech translation systems. The systems are based on our previous works \cite{Jointly_trained_transformers}. Though End-to-End and cascade~(ASR-MT) spoken language translation~(SLT) systems are reaching comparable performances, a large degrada

Harvey R. Brown, Gal Ben Porath

This paper is concerned with the nature of probability in physics, and in quantum mechanics in particular. It starts with a brief discussion of the evolution of Itamar Pitowsky&#39;s thinking about probability in quantum theory from 1994 to 2008, and the role of Gleason&#39;s 1957 theorem in his derivation of the Born Rule. Pitowsky&#39;s defence of probabil

Mohammad Masjed-Jamei, Zahra Moalemi

The so-called exceptional orthogonal X1-polynomials arise as eigen functions of a Sturm-Liouville problem. In this paper, a generic classification of these polynomials is presented based on Pearson distributions family. Then, six special differential equations of the aforesaid classification are introduced and their polynomial solutions are studied in detail

Xuexing Lu

The notion of an upward plane graph in graph theory and that of a progressive plane graph (or plane string diagram) in category theory are essentially the same thing. In this paper, we combine the ideas in graph theory and category theory to explain why and in what sense upward planarity is a topological property. The main result is that two upward planar dr

Sergey Khoperskov, Igor Zinchenko, Branislav Avramov, Sergey Khrapov

Modern galaxy formation theory suggests that the misalignment between stellar and gaseous components usually results from an external gas accretion and/or interaction with other galaxies. The extreme case of the kinematic misalignment is demonstrated by so-called galaxies with counterrotation that possess two distinct components rotating in opposite directio

Stav Ashur, Matthew J. Katz

Bounded-angle (minimum) spanning trees were first introduced in the context of wireless networks with directional antennas. They are reminiscent of bounded-degree spanning trees, which have received significant attention. Let $P = \{p_1,\ldots,p_n\}$ be a set of $n$ points in the plane, let $Π$ be the polygonal path $(p_1,\ldots,p_n)$, and let $0 < α< 2π$ be

Masahiro Koike, Mitsuharu Ôtani, Shun Uchida

We are concerned with the time-periodic problem of some doubly nonlinear equations governed by differentials of two convex functionals over uniformly convex Banach spaces. Akagi--Stefanelli (2011) considered Cauchy problem of the same equation via the so-called WED functional approach. Main purpose of this paper is to show the existence of the time-periodic

Ali Aroudi, Sebastian Braun

Many deep learning techniques are available to perform source separation and reduce background noise. However, designing an end-to-end multi-channel source separation method using deep learning and conventional acoustic signal processing techniques still remains challenging. In this paper we propose a direction-of-arrival-driven beamforming network (DBnet) c

O. V. Borovkova, D. O. Ignatyeva, V. I. Belotelov

Here we propose a magnetophotonic structure for the layer-selective magnetization switching with ultrashort laser pulses of different wavelengths. It is based on a chirped magnetophotonic crystal (MPC) containing magnetic GdFeCo and nonmagnetic dielectric layers. At each operating wavelength the laser pulses heat up to necessary level only one GdFeCo layer t

Amit Gajbhiye, Thomas Winterbottom, Noura Al Moubayed, Steven Bradley

We consider the task of incorporating real-world commonsense knowledge into deep Natural Language Inference (NLI) models. Existing external knowledge incorporation methods are limited to lexical level knowledge and lack generalization across NLI models, datasets, and commonsense knowledge sources. To address these issues, we propose a novel NLI model-indepen

Nakia Carlevaro, Matteo Del Prete, Giovanni Montani, Fabio Squillaci

We focus our attention on some relevant aspects of the beam-plasma instability in order to refine some features of the linear and non-linear dynamics. After a re-analysis of the Poisson equation and of the assumption dealing with the background plasma in the form of a linear dielectric, we study the non-perturbative properties of the linear dispersion relati

Hrituraj Singh, Gaurav Verma, Balaji Vasan Srinivasan

While recent advances in language modeling have resulted in powerful generation models, their generation style remains implicitly dependent on the training data and can not emulate a specific target style. Leveraging the generative capabilities of a transformer-based language models, we present an approach to induce certain target-author attributes by incorp

Keyu Wen, Xiaodong Gu, Qingrong Cheng

Image-Text Matching is one major task in cross-modal information processing. The main challenge is to learn the unified visual and textual representations. Previous methods that perform well on this task primarily focus on not only the alignment between region features in images and the corresponding words in sentences, but also the alignment between relatio

Shuhei Kato, Yusuke Yasuda, Xin Wang, Erica Cooper

We have been working on speech synthesis for rakugo (a traditional Japanese form of verbal entertainment similar to one-person stand-up comedy) toward speech synthesis that authentically entertains audiences. In this paper, we propose a novel evaluation methodology using synthesized rakugo speech and real rakugo speech uttered by professional performers of t

S. D. Pogorilyy, A. A. Kramov

Introduction. The area of natural language processing considers AI-complete tasks that cannot be solved using traditional algorithmic actions. Such tasks are commonly implemented with the usage of machine learning methodology and means of computer linguistics. One of the preprocessing tasks of a text is the search of noun phrases. The accuracy of this task h

Dongyoung Kim, Myungsung Kwak, Eunji Won, Sejung Shin

Text localization from the digital image is the first step for the optical character recognition task. Conventional image processing based text localization performs adequately for specific examples. Yet, a general text localization are only archived by recent deep-learning based modalities. Here we present document Text Localization Generative Adversarial N

Huaxiu Yao, Yingbo Zhou, Mehrdad Mahdavi, Zhenhui Li

Learning quickly is of great importance for machine intelligence deployed in online platforms. With the capability of transferring knowledge from learned tasks, meta-learning has shown its effectiveness in online scenarios by continuously updating the model with the learned prior. However, current online meta-learning algorithms are limited to learn a global

Alejandro Parada-Mayorga, Alejandro Ribeiro

Algebraic neural networks (AlgNNs) are composed of a cascade of layers each one associated to and algebraic signal model, and information is mapped between layers by means of a nonlinearity function. AlgNNs provide a generalization of neural network architectures where formal convolution operators are used, like for instance traditional neural networks (CNNs

Qingshan Zhou, Antti Rasila

Let $\mathcal{T}_K(D)$ be the class of $K$-quasiconformal automorphisms of a domain $D\subsetneq \mathbb{R}^n$ with identity boundary values. Teichmüller&#39;s problem is to determine how far a given point $x\in D$ can be mapped under a mapping $f\in \mathcal{T}_K(D)$. We estimate this distance between $x$ and $f(x)$ from the above by using two different met

Changzhen Ji, Xin Zhou, Yating Zhang, Xiaozhong Liu

In the past few years, audiences from different fields witness the achievements of sequence-to-sequence models (e.g., LSTM+attention, Pointer Generator Networks, and Transformer) to enhance dialogue content generation. While content fluency and accuracy often serve as the major indicators for model training, dialogue logics, carrying critical information for

Lei Zheng, Ziming Shen, Hongzhi Wang

Knowledge graph is an important cornerstone of artificial intelligence. The construction and release of large-scale knowledge graphs in various fields pose new challenges to knowledge graph data management. Due to the maturity and stability, relational database is also suitable for RDF data storage. However, the complex structure of RDF graph brings challeng

Luc Devroye, Silvio Lattanzi, Gabor Lugosi, Nikita Zhivotovskiy

We study the problem of estimating the common mean $μ$ of $n$ independent symmetric random variables with different and unknown standard deviations $σ_1 \le σ_2 \le \cdots \leσ_n$. We show that, under some mild regularity assumptions on the distribution, there is a fully adaptive estimator $\widehatμ$ such that it is invariant to permutations of the elements

Yu-Hao Deng

Graphene, a monolayer of carbon atoms packed into a two-dimensional crystal structure, attracted intense attention owing to its unique structure and optical, electronic properties. Recent advances in chemical vapor deposition (CVD) have led to the batch production of high quality graphene on metal foils. However, further applications are required in the way

Ferdinand Fischer, Joachim Deutscher

This paper is concerned with the fault diagnosis problem for general linear heterodirectional hyperbolic ODE-PDE systems. A systematic solution is presented for additive time-varying actuator, process and sensor faults in the presence of disturbances. The faults and disturbances are represented by the solutions of finite-dimensional signal models, which allo

Alejandro Parada-Mayorga, Hans Riess, Alejandro Ribeiro, Robert Ghrist

In this paper we state the basics for a signal processing framework on quiver representations. A quiver is a directed graph and a quiver representation is an assignment of vector spaces to the nodes of the graph and of linear maps between the vector spaces associated to the nodes. Leveraging the tools from representation theory, we propose a signal processin

Subrata Sarkar, Rati Sharma, Kushal Shah

The advent of Deep Learning models like VGG-16 and Resnet-50 has considerably revolutionized the field of image classification, and by using these Convolutional Neural Networks (CNN) architectures, one can get a high classification accuracy on a wide variety of image datasets. However, these Deep Learning models have a very high computational complexity and

Rupamanjari Majumder, Sayedeh Hussaini, Vladimir S. Zykov, Stefan Luther

Interruptions in nonlinear wave propagation, commonly referred to as wave breaks, are typical of many complex excitable systems. In the heart they lead to fatal rhythm disorders, the so-called arrhythmias, which are one of the main causes of sudden death in the industrialized world. Progress in the treatment and therapy of cardiac arrhythmias requires a deta

Clément Chadebec, Clément Mantoux, Stéphanie Allassonnière

Variational auto-encoders (VAEs) have proven to be a well suited tool for performing dimensionality reduction by extracting latent variables lying in a potentially much smaller dimensional space than the data. Their ability to capture meaningful information from the data can be easily apprehended when considering their capability to generate new realistic sa

Jürgen Herzog, Takayuki Hibi, Somayeh Moradi

Let $S=K[x_1,\ldots,x_n]$ be the polynomial ring over a field and $A$ a standard graded $S$-algebra. In terms of the Gröbner basis of the defining ideal $J$ of $A$ we give a condition, called the x-condition, which implies that all graded components $A_k$ of $A$ have linear quotients and with additional assumptions are componentwise linear. A typical example

Filip Korzeniowski, Oriol Nieto, Matthew McCallum, Minz Won

The mood of a song is a highly relevant feature for exploration and recommendation in large collections of music. These collections tend to require automatic methods for predicting such moods. In this work, we show that listening-based features outperform content-based ones when classifying moods: embeddings obtained through matrix factorization of listening

Adrià Gràcia-Condal, Tino Gottschall, Lukas Pfeuffer, Oliver Gutfleisch

The world&#39;s growing hunger for artificial cold on the one hand, and the ever more stringent climate targets on the other, pose an enormous challenge to mankind. Novel, efficient and environmentally friendly refrigeration technologies based on solid-state refrigerants can offer a way out of the problems arising from climate-damaging substances used in con

Hao Zou, Jinhao Cui, Xin Kong, Chujuan Zhang

This paper presents F-Siamese Tracker, a novel approach for single object tracking prominently characterized by more robustly integrating 2D and 3D information to reduce redundant search space. A main challenge in 3D single object tracking is how to reduce search space for generating appropriate 3D candidates. Instead of solely relying on 3D proposals, first

Huaqiao Wang, Juan Wang, Guochun Wu, Yinghui Zhang

We are concerned with the time decay rates of strong solutions to a non-conservative compressible viscous two-phase fluid model in the whole space R3. Compared to the previous related works, the main novelty of this paper lies in the fact that it provides a general framework that can be used to extract the optimal decay rates of the solution as well as its a

Kejie Bao, Haolin Liu, Kinfai Tse, Chunlei Yang

Cu2ZnSnS4 (CZTS) is a promising thin-film solar-cell material consisted of earth abundant and nontoxic elements. Yet, there exists a fundamental bottle neck that hinders the performance of the device due to complexed intrinsic defects properties and detrimental secondary phases. Recently, it was proven experimentally that Na and K in co-evaporation growth of

B. Miquel, S. Lepot, V. Bouillaut, B. Gallet

Thermal convection driven by internal heat sources and sinks was recently shown experimentally to exhibit the mixing-length, or &#34;ultimate&#34;, scaling-regime: the Nusselt number $Nu$ (dimensionless heat flux) increases as the square-root of the Rayleigh-number $Ra$ (dimensionless internal temperature difference). While for standard Rayleigh-Bénard conve

Lingkai Kong, Haoming Jiang, Yuchen Zhuang, Jie Lyu

Fine-tuned pre-trained language models can suffer from severe miscalibration for both in-distribution and out-of-distribution (OOD) data due to over-parameterization. To mitigate this issue, we propose a regularized fine-tuning method. Our method introduces two types of regularization for better calibration: (1) On-manifold regularization, which generates ps

Lingjing Wang, Yu Hao, Xiang Li, Yi Fang

Deep learning-based point cloud registration models are often generalized from extensive training over a large volume of data to learn the ability to predict the desired geometric transformation to register 3D point clouds. In this paper, we propose a meta-learning based 3D registration model, named 3D Meta-Registration, that is capable of rapidly adapting a

Thomas Gogacz, Víctor Gutiérrez-Basulto, Yazmín Ibáñez-García, Jean Christoph Jung

We study the description logic SQ with number restrictions applicable to transitive roles, extended with either nominals or inverse roles. We show tight 2EXPTIME upper bounds for unrestricted entailment of regular path queries for both extensions and finite entailment of positive existential queries for nominals. For inverses, we establish 2EXPTIME-completen

Luca De Gennaro Aquino, Stephan Eckstein

We study MinMax solution methods for a general class of optimization problems related to (and including) optimal transport. Theoretically, the focus is on fitting a large class of problems into a single MinMax framework and generalizing regularization techniques known from classical optimal transport. We show that regularization techniques justify the utiliz

George Giakkoupis, Anne-Marie Kermarrec, Olivier Ruas, François Taïani

K-Nearest-Neighbors (KNN) graphs are central to many emblematic data mining and machine-learning applications. Some of the most efficient KNN graph algorithms are incremental and local: they start from a random graph, which they incrementally improve by traversing neighbors-of-neighbors links. Paradoxically, this random start is also one of the key weaknesse

Joseph E. Lawrence, David E. Manolopoulos

We revisit the well-known aqueous ferrous-ferric electron transfer reaction in order to address recent suggestions that nuclear tunnelling can lead to significant deviation from the linear response assumption inherent in the Marcus picture of electron transfer. A recent study of this reaction by Richardson and coworkers has found a large difference between t

E. V. Repin, Y. V. Nazarov

The technology of superconductor-semiconductor nanowire devices has matured in the last years in the quest for topological quantum computing. This makes it feasible to make more complex and sophisticated devices. We investigate multi-terminal superconductor-semiconductor wires to access feasibility of another topological phenomenon: Weyl singularities in the

Gyanu Prasad Kafle, Christoph Heil, Hari Paudyal, Elena R. Margine

The tin-selenide and tin-sulfide classes of materials undergo multiple structural transitions under high pressure leading to periodic lattice distortions, superconductivity, and topologically non-trivial phases, yet a number of controversies exist regarding the structural transformations in these systems. We perform first-principles calculations within the f

Joseph E. Lawrence, David E. Manolopoulos

We present a simple method for the calculation of reaction rates in the Fermi golden-rule limit, which accurately captures the effects of tunnelling and zero-point energy. The method is based on a modification of the recently proposed golden-rule quantum transition state theory (GR-QTST) of Thapa, Fang and Richardson. While GR-QTST is not size consistent, le

Yuting Zhu, Liyong Lin, Ruochen Tai, Rong Su

This paper presents an overview of the networked supervisory control framework for discrete event systems with imperfect communication networks, which can be divided into the centralized supervisory control setup and the decentralized supervisory control setup. We review the state-of-art networked control frameworks with observation channel delays and contro

Christophe Cerisara, Pavel Kral, Ladislav Lenc

Dialogue act recognition is an important component of a large number of natural language processing pipelines. Many research works have been carried out in this area, but relatively few investigate deep neural networks and word embeddings. This is surprising, given that both of these techniques have proven exceptionally good in most other language-related do

Haobo Zhang, Tingzhi Mao, Haihua Xu, Hao Huang

We report our NTU-AISG Text-to-speech (TTS) entry systems for the Blizzard Challenge 2020 in this paper. There are two TTS tasks in this year&#39;s challenge, one is a Mandarin TTS task, the other is a Shanghai dialect TTS task. We have participated both. One of the main challenges is to build TTS systems with low-resource constraints, particularly for the c

Cheng Lin, Lingjie Liu, Changjian Li, Leif Kobbelt

Segmenting arbitrary 3D objects into constituent parts that are structurally meaningful is a fundamental problem encountered in a wide range of computer graphics applications. Existing methods for 3D shape segmentation suffer from complex geometry processing and heavy computation caused by using low-level features and fragmented segmentation results due to t

Aneta Neumann, Jakob Bossek, Frank Neumann

Submodular functions allow to model many real-world optimisation problems. This paper introduces approaches for computing diverse sets of high quality solutions for submodular optimisation problems. We first present diversifying greedy sampling approaches and analyse them with respect to the diversity measured by entropy and the approximation quality of the

Ben Deaner

Estimation and inference in dynamic discrete choice models often relies on approximation to lower the computational burden of dynamic programming. Unfortunately, the use of approximation can impart substantial bias in estimation and results in invalid confidence sets. We present a method for set estimation and inference that explicitly accounts for the use o

O. I. Hryhorchak

A method for a calculation of quantum capacitance for a two-dimesional electron gas (2DEG) in potential wells of complicated geometry on the base of a quantum wave impedance technique was proposed. The application of this method was illustated on four different forms of potential wells: infinite and finit rectangular well, a finit rectangular double well and

Dimuthu D. Arachchige, Yue Chen, Isuru S. Godage

Snakes are a remarkable evolutionary success story. Many snake-inspired robots have been proposed over the years. Soft robotic snakes (SRS) with their continuous and smooth bending capability better mimic their biological counterparts&#39; unique characteristics. Prior SRSs are limited to planar operation with a limited number of planar gaits. We propose a n

Konpat Preechakul, Sira Sriswasdi, Boonserm Kijsirikul, Ekapol Chuangsuwanich

In medical imaging, Class-Activation Map (CAM) serves as the main explainability tool by pointing to the region of interest. Since the localization accuracy from CAM is constrained by the resolution of the model&#39;s feature map, one may expect that segmentation models, which generally have large feature maps, would produce more accurate CAMs. However, we h

Graeme H. Smith, Matthew Shetrone

The productivity of Lick Observatory (LO) is reviewed over the years from 1965 to 2019, a 55 yr period which commences with the Shane 3 m telescope being the second-largest astronomical reflector in the world, but transitions into the era of 10 m ground-based optical telescopes. The metric of productivity used here is the annual number of refereed papers wit