bluuebunny/crossref_metadata_2025 huggingface dataset:
Numerical approximation of one model of bacterial self-organization
Authors: Čiegis Raimondas , Bugajev Andrej
Published: August 2019

This paper presents finite difference approximations of one dimensional in space mathematical model of a bacterial self-organization. The dynamics of such nonlinear systems can lead to formation of complicated solution patterns. In this paper we show that this chemotaxisdriven instability can be connected to the ill-posed problem defined by the backward in time diffusion process. The method of lines is used to construct robust numerical approximations. At the first step we approximate spatial derivatives in the PDE by applying approximations targeted for special physical processes described by differential equations. The obtained system of ODE is split into a system describing separately fast and slow physical processes and different implicit and explicit numerical solvers are constructed for each subproblem. Results of numerical experiments are presented and convergence of finite difference schemes is investigated.
Solitary wave fission and fusion in the (2+1)-dimensional generalized Broer–Kaup system
Authors: Dai Chaoqing , Liu Cuiyun
Published: August 2019

Via a special Painlevé–Bäcklund transformation and the linear superposition theorem, we derive the general variable separation solution of the (2 + 1)-dimensional generalized Broer–Kaup system. Based on the general variable separation solution and choosing some suitable variable separated functions, new types of V-shaped and A-shaped solitary wave fusion and Y-shaped solitary wave fission phenomena are reported.
On a third order initial boundary value problem in a plane domain
Authors: Klovienė Neringa
Published: August 2019

Third order initial boundary value problem is studied in a bounded plane domain σ with C4 smooth boundary ∂σ. The existence and uniqueness of the solution is proved using Galerkin approximations and a priory estimates. The problem under consideration appear as an auxiliary problem by studying a second grade fluid motion in an infinite three-dimensional pipe with noncircular cross-section.
Visual decisions in the analysis of customers online shopping behavior
Authors: Pragarauskaitė Julija , Dzemyda Gintautas
Published: August 2019

The analysis of the online customer shopping behavior is an important task nowadays, which allows maximizing the efficiency of advertising campaigns and increasing the return of investment for advertisers. The analysis results of online customer shopping behavior are usually reviewed and understood by a non-technical person; therefore the results must be displayed in the easiest possible way. The online shopping data is multidimensional and consists of both numerical and categorical data. In this paper, an approach has been proposed for the visual analysis of the online shopping data and their relevance. It integrates several multidimensional data visualization methods of different nature. The results of the visual analysis of numerical data are combined with the categorical data values. Based on the visualization results, the decisions on the advertising campaign could be taken in order to increase the return of investment and attract more customers to buy in the online e-shop.
On the analytic solutions for squeezing flow of nanofluid between parallel disks
Authors: Hashmi Meraj Mustafa Mustafa , Hayat Tasawar , Alsaedi Ahmed
Published: August 2019

This article investigates the magnetohydrodynamic squeezing flow of nanofluid between parallel disks. Governing partial differential equations are converted into ordinary differential system via similarity transformations. We employ homotopy analysis method (HAM) to construct analytic expressions of velocity, temperature and nanoparticles volume fraction. Convergence analysis is performed and optimal values of the convergence-control parameters are determined. The computations are validated with the built in routine for solving nonlinear boundary value problems via shooting technique through software Mathematica 8.0. The behaviors of key parameters such as suction/blowing parameter (A), squeeze parameter (S), Hartman number (M), Brownian motion parameter (Nb) and thermophoresis parameter (Nt) are thoroughly examined. It is seen that the parameters have a great impact on the concentration field for the suction flow when compared with the blowing case. An intensification in the Brownian motion and thermophoresis effects results in the appreciable increase in the temperature and nanoparticles concentration.
Goodness-of-fit tests for sparse nominal data based on grouping
Authors: Radavičius Marijus , Samusenko Pavel
Published: August 2019

. For (very) sparse nominal data, common goodness-of-fit tests usually fail. Alternative goodness-of-fit tests based on extended empirical Bayes approach and grouping are proposed and their consistency is proved. The performance of the tests is illustrated and compared with classical criteria by Monte Carlo simulations.
Global attractors for non-linear viscoelastic equation with strong damping
Authors: Ma Zhiyong
Published: August 2019

In this paper, we consider the long-time dynamical behavior of the viscoelastic equations with strong damping and further prove the existence of global attractors for this system.
Adaptive hybrid function projective synchronization of chaotic systems with fully unknown periodical time-varying parameters
Authors: Xing Jinsheng
Published: August 2019

In this paper, an adaptive learning control approach is presented for the hybrid functional projective synchronization (HFPS) of different chaotic systems with fully unknown periodical time-varying parameters. Differential-difference hybrid parametric learning laws and an adaptive learning control law are constructed via the Lyapunov–Krasovskii functional stability theory, which make the states of two different chaotic systems asymptotically synchronized in the sense of mean square norm. Moreover, the boundedness of the parameter estimates are also obtained. The Lorenz system and Chen system are illustrated to show the effectiveness of the hybrid functional projective synchronization scheme.
Large deviations for weighted random sums
Authors: Kasparavičiūtė Aurelija , Saulis Leonas
Published: August 2019

In the present paper we consider weighted random sums ZN = ∑j=1NajXj, where 0 ≤ aj < ∞, N denotes a non-negative integer-valued random variable, and {X, Xj , j = 1, 2,...} is a family of independent identically distributed random variables with mean EX = µ and variance DX = σ2 > 0. Throughout this paper N is independent of {X, Xj , j = 1, 2,...} and, for definiteness, it is assumed Z0 = 0. The main idea of the paper is to present results on theorems of large deviations both in the Cramér and power Linnik zones for a sum ~ZN = (ZN − EZN )(DZN )−1/2 , exponential inequalities for a tail probability P(~ZN > x) in two cases: µ = 0 and µ ≠ 0 pointing out the difference between them. Only normal approximation is considered. It should be noted that large deviations when µ ≠ 0 have been already considered in our papers [1,2].
Nonlinear generalized cyclic contractions in complete G-metric spaces and applications to integral equations
Authors: Nashine Hemant Kumar Kumar , Kadelburg Zoran
Published: August 2019

In this paper we introduce generalized cyclic contractions in G-metric spaces and establish some fixed point theorems. The presented theorems extend and unify various known fixed point results. Examples are given in the support of these results. Finally, an application to the study of existence and uniqueness of solutions for a class of nonlinear integral equations is given.
Detection of multiple changes in mean by sparse parameter estimation
Authors: Neubauer Jirí , Veselý Vítezslav
Published: August 2019

The contribution is focused on detection of multiple changes in the mean in a onedimensional stochastic process by sparse parameter estimation from an overparametrized model. The authors’ approach to change point detection differs entirely from standard statistical techniques. A stochastic process residing in a bounded interval with changes in the mean is estimated using dictionary (a family of functions, the so-called atoms, which are overcomplete in the sense of being nearly linearly dependent) and consisting of Heaviside functions. Among all possible representations of the process we want to find a sparse one utilizing a significantly reduced number of atoms. This problem can be solved by ℓ1-minimization. The basis pursuit algorithm is used to get sparse parameter estimates. In this contribution the authors calculate empirical probability of successful change point detection as a function depending on the number of change points and the level of standard deviation of additive white noise of the stochastic process. The empirical probability was computed by simulations where locations of change points were chosen randomly from uniform distribution. The authors’ approach is compared with LASSO algorithm, ℓ1 trend filtering and selected statistical methods. Such probability decreases with increasing number of change points and/or standard deviation of white noise. The proposed method was applied on the time series of nuclear magnetic response during the drilling of a well.
Stability and absorbing set of parabolic chemotaxis model of Escherichia coli
Authors: Rionero Salvatore , Vitiello Maria
Published: August 2019

This paper is devoted to model (1) for escherichia coli, introduced in [1]. Based on the experimental observations of Budrene and Berg [2, 3], Tyson and coworkers derived (1) with n cell density, c chemotrattactant concentration and s stimulant concentration. Our aim is to study the stability of constant meaning full solution and ultimately boundedness of the solutions. Precisely:    (i) linear and nonlinear stability is proved by using a peculiar Lyapunov function,  (ii) the ultimately boundedness of the solutions in the L2-norm is obtained, (iii) conditions guaranteeing the global stability are also obtained.
Multiple cycles and the Bautin bifurcation in the Goodwin model of a class struggle
Authors: Bella Giovanni
Published: August 2019

The aim of this paper is to present the necessary and sufficient conditions for the emergence of a generalized Hopf (i.e., Bautin) bifurcation in the Goodwin’s model of a class struggle, and determine the parameter regions where multiple attracting and repelling limit cycles around the steady state may coexist.
Multi-objective single agent stochastic search in non-dominated sorting genetic algorithm
Authors: Lančinskas Algirdas , Ortigosa Pilar Martinez Martinez , Žilinskas Julius
Published: August 2019

A hybrid multi-objective optimization algorithm based on genetic algorithm and stochastic local search is developed and evaluated. The single agent stochastic search local optimization algorithm has been modified in order to be suitable for multi-objective optimization where the local optimization is performed towards non-dominated points. The presented algorithm has been experimentally investigated by solving a set of well known test problems, and evaluated according to several metrics for measuring the performance of algorithms for multi-objective optimization. Results of the experimental investigation are presented and discussed.
Joint universality of the Riemann zeta-function and Lerch zeta-functions
Authors: Laurinčikas Antanas , Macaitienė˙ Renata
Published: August 2019

In the paper, we prove a joint universality theorem for the Riemann zeta-function and a collection of Lerch zeta-functions with parameters algebraically independent over the field of rational numbers.
Estimation of parameters of finite population L-statistics
Authors: Pumputis Dalius , Čiginas Andrius
Published: August 2019

We consider the estimation of important parameters of a linear combination of order statistics (L-statistic) in a finite population, emphasizing the influence of auxiliary information on the estimation accuracy. Assuming that values of an auxiliary variable are available for all population units, we construct calibrated estimators for the variance of L-statistics and for the parameters, which define one-term Edgeworth expansions of distributions of L-statistics. The gain of the new estimators is demonstrated by the simulation study.
Triple-zero singularity of a Kaldor–Kalecki model of business cycles with delay
Authors: Wu Xiaoqin P. P.
Published: August 2019

In this manuscript, we study triple zero singularity of a Kaldor–Kalecki model of business cycles with delay in both the gross product and the capital stock. By using the frameworks of Campbell–Yuan [1] and Faria–Magalhães [2, 3], the normal form on the center manifold is derived for this singularity and hence the corresponding bifurcation diagrams such as Hopf, BT, zero-Hopf, and homoclinic bifurcations are obtained. An example is given to verify some theoretical results.
Dynamic properties of the coupled Oregonator model with delay
Authors: Wu Xiang , Zhang Chunrui
Published: August 2019

This work explores a coupled Oregonator model. By analyzing the associated characteristic equation, linear stability is investigated and Hopf bifurcations are demonstrated, as well as the stability and direction of the Hopf bifurcation are determined by employing the normal form method and the center manifold reduction. We also discussed the Z2 equivariant property and the existence of multiple periodic solutions. Numerical simulations are presented to illustrate the results in Section 5.
Pulsating flow of an incompressible micropolar fluid between permeable beds
Authors: Bitla Punnamchandar , Iyengar Telikicherla Kandala Venkatacharyulu Kandala Venkatacharyulu
Published: August 2019

The paper deals with the pulsating flow of an incompressible micropolar fluid through a channel bounded by permeable beds. The fluid is injected into the channel from the lower permeable bed with a certain velocity and is sucked into the upper permeable bed with the same velocity. The flow between the permeable beds is assumed to be governed by micropolar fluid flow equations and that in the permeable regions by Darcy law. The Beavers–Joseph (BJ) slip boundary conditions are used at the interfaces of the permeable beds. The governing equations are solved analytically and the expressions for velocity, microrotation, mass flux and shear stress are obtained. The effects of diverse parameters on the velocity and microrotation are studied numerically and the results are presented through graphs.
Numerical solution of nonlinear elliptic equation with nonlocal condition
Authors: Čiupaila Regimantas , Sapagovas Mifodijus , Štikonienė Olga
Published: August 2019

Two iterative methods are considered for the system of difference equations approximating two-dimensional nonlinear elliptic equation with the nonlocal integral condition. Motivation and possible applications of the problem present in the paper coincide with the small volume problems in hydrodynamics. The differential problem considered in the article is some generalization of the boundary value problem for minimal surface equation.
Weaker cyclic (phi,phi)-contractive mappings with an application to integro-differential equations
Authors: Nashine Hemant Kumar Kumar , Kadelburg Zoran
Published: August 2019

We introduce a new variant of cyclic contractive mapping in a metric space and originate existence and uniqueness results of fixed points for such mappings. Examples are given to support the usability of our results. After these results, an application to integro-differential equations is given.
A survey of models for inference of gene regulatory networks
Authors: Ristevski Blagoj
Published: August 2019

In this article, I present the biological backgrounds of microarray, ChIP-chip and ChIPSeq technologies and the application of computational methods in reverse engineering of gene regulatory networks (GRNs). The most commonly used GRNs models based on Boolean networks, Bayesian networks, relevance networks, differential and difference equations are described. A novel model for integration of prior biological knowledge in the GRNs inference is presented, too. The advantages and disadvantages of the described models are compared. The GRNs validation criteria are depicted. Current trends and further directions for GRNs inference using prior knowledge are given at the end of the paper.
Common fixed point theorems on non-complete partial metric spaces
Authors: Sedghi Shaban , Shobkolaei Nabi , Altun Ishak
Published: August 2019

In the peresent paper, we give a common fixed point theorem for four weakly compatible mappings on non-complete partial metric spaces. Some supporting examples are provided.
Spatiotemporal chaos in Arnold coupled logistic map lattice
Authors: Zhang Ying-Qian , Wang Xing-Yuan
Published: August 2019

In this paper, we propose a new spatiotemporal dynamics of Arnold coupled logistic map lattice (ACLML). Here, the coupling method between lattices is not a neighborhood coupling but the non-neighborhood of Arnold cat maps. In the proposed system, the criteria such as Kolmogorov–Sinai entropy density and universality, bifurcation diagram, mutual information, space amplitude and space-time diagrams are investigated in this paper. The new features of the proposed system include the lower mutual information between lattices, larger range of parameters for chaotic behaviors, the higher percentage of lattices in chaotic behaviors for most of parameters and less periodic window in bifurcation diagram. These features are more suitable for cryptography. For numerical simulations, we have employed the coupled map lattices system (CML) for comparison. The results indicate that the proposed system has those superior features to the coupled map lattice system (CML). It should be highlighted that the proposed ACLML is a suitable chaotic system for cryptography.
A bűnözői életnarratíva konstrukciói és kapcsolatuk az érzelmekkel
Authors: Kovács Zsuzsanna , Boros János , Patyi Dániel , Szucsáki Melinda
Published: August 2019

Háttér és célkitűzések

Canter és Youngs (2009; Youngs és Canter, 2012) munkásságára támaszkodva a bűnözők kriminális identitásának és emocionális élményének kapcsolatát kívántuk feltárni vizsgálatunkban.

Módszer

Youngs és Canter (2012) interjúmódszerét alkalmaztuk kutatásunk során, melynek eredményeként kimutattuk a magyar bűnelkövetők mintájában is azt a négy narratívatípust (Bosszúálló, Profi, Tragikus hős, Áldozat), amelyet a szerzők leírtak (vö. Boros, Kovács, Zs., Patyi, Szucsáki és Kovács, K., 2017). Kiegészítettük a típusok jellemzését a bűncselekmény-narratíva, a kriminális előtörténet és demográfiai adatok alapján. Megvizsgáltuk a négy narratívatípus érzelmi szerveződését. Az érzelmi kérdőív kvantitatív elemzését és a narratívumok tartalomelemzését végeztük el.

Eredmények

A bűncselekmény közben átélt érzelmi élményről való utólagos beszámolóban az érzelmek a hatalom dimenziója mentén szerveződnek. A Profik esetében kevesebb negatív érzelem jelenik meg, mint a többi három típusnál. Az életnarratívum megkonstruálása az elvárt jellegzetességek szerint alakult, a Profik a redemptív, pozitívba hajló, a Bosszúállók a kontaminációs, negatívba hajló narratívaszerveződést mutatták, míg az Áldozatok elhárító jelleggel a negatív eseményeket ignoráló, kizárólag a pozitív eseményekre fókuszáló történetszövést preferálták.

Következtetések

A bűnelkövetők narratívaszerveződésében jól azonosíthatók az identitás-konstruálás érzelmi vetületei, amelyek segítségünkre lehetnek a társadalmi visszailleszkedési programok megtervezésében.
Az észlelt autonómiatámogatás hatása a teljesítéscélokra karate utánpótláskorosztályoknál
Authors: Kovács Krisztina , Gyömbér Noémi , Kelemen Ákos , Fodorné Földi Rita
Published: August 2019

Háttér és célkitűzések

Az utánpótlássport specifikumai közé tartozik a korai lemorzsolódás elleni küzdelem, mely területen a szülői és edzői támogatás minősége kiemelt jelentőségű a sportolók motivációjának fenntartásában. A vizsgálat célja, hogy felmérje a kapcsolatot az utánpótláskorú sportolók célorientációja és az észlelt szülői és edzői autonómia támogatás mértéke között.

Módszer

A felmérésben 127 (fiú n = 65, lány n = 62), 12–21 év közötti (átlagéletkor:16,16 év; szórás 3,00) karatéka vett részt és töltötte ki a 3×2 Teljesítés–cél kérdőív a sportban (3×2 AGQ-S; Mascret, Elliot és Curry, 2015) skálát, az Észlelt edzői autonómia támogatás kérdőívet (SCQ; Deci, 2001; Hagger, Chatzisarantis, Barkoukis, Wang és Baranowski, 2005) és az Észlelt szülői autonómia támogatás kérdőívet (PASSES; Hagger és mtsai, 2007).

Eredmények

Az eredmények alapján kapcsolat van a sportoló feladatcéljai és az észlelt autonómia támogatás között (edzői rs = 0,314 p < 0,001; szülői rs = 0,292 p = 0,002). Ez a kapcsolat különösen az alacsonyabb edzésszámmal és kisebb eredményekkel jellemezhető sportolóknál lelhető fel, de eltűnik, ha az edzések száma emelkedik, vagy ha országos szinten értek el eredményeket. A kiemelt eredményekkel rendelkező sportolók esetében kapcsolat jelenik meg a célorientációk és az észlelt szülői autonómia támogatás között (rs = 0,707 p = 0,005). A feladatcélokat a lineáris regressziós modellek szerint az észlelt szülői autonómia támogatás növekvő mértéke jelezte előre.

Következtetések

Eredményeink alapján kitűnik, hogy a sportolói karrier kezdeti szakaszában a szülők és az edzők szerepe is jelentős, valamint kirajzolódik az, hogy a szülői szerepkör befolyását érdemes lehet újraértelmezni. Gyakorlati szempontból – a korai lemorzsolódás mértékének csökkentése érdekében – az utánpótlásképzés rendszerébe hasznos lenne beépíteni a sportháromszög dinamikai sajátosságainak megismertetését pszichoedukációs keretek között.
Vak gyermekek nyelv- és beszédfejlődésének sajátos vonásai és háttere
Authors: Pajor Emese , Beke Anna Mária , Csépe Valéria
Published: August 2019

Célkituzés

Áttekinto tanulmányunk célja egy kevéssé kutatott terület, a súlyos fokban látássérült (vak) gyermekek nyelv- és beszédfejlodésének bemutatása.

Módszertan

A témában kevés publikáció jelent meg, leginkább esettanulmányok, aminek oka a populáció (nagyon) kis elemszáma és a vak gyermekek egymástól jelentosen eltéro fejlodési üteme. A vizsgálatok és publikációk hiányát tovább fokozza, hogy a magyar nyelvi sajátosságok nehezen hasonlíthatók a kutatások zömét nyújtó angol nyelvu irodalom tényeihez. Tanulmányunkban a témában keletkezett kutatások eredményeit mutatjuk be.

Következtetések

A vak gyermekek nyelv- és beszédfejlodésében megfigyelheto eltérések különösen a fogalmak és reprezentációk kialakulásában, illetve a kommunikáció területén jelentosek, melyek iskoláskorra csökkennek. Látó gyermekekkel összehasonlítva, a vak csoport esetén az auditív információk jobb kódolása, illetve a fonémaalapú szóelohívási stratégia lehet a nagyobb verbális munkamemória-kapacitás és a fonémafluencia-feladatokban mérheto jobb teljesítmény hátterében. A verbális munkamemória és a jobb auditív percepciós elony felnottkorra is jellemzo. Ennek neurális háttere biztosított, mivel vizuális depriváció hatására sem inaktív egy agykérgi terület sem. A vak gyermekek nyelv- és beszédfejlodésében ezért megno a környezet szerepe.
Research on fabricartion of non-calcined geopolymer ceramics from kaolin Tung Ba, Vi Xuyen, Ha Giang
Authors: Lan Tran Thi , Duong Nguyen Anh , Anh Phan Luu , Man Tran Thi
Published: August 2019

The mineral component of kaolin clay in Tung Ba commune analyzed by X-ray diffraction is composed mainly of kaolinite (24–27%), illite (26–30%) and quartz (40–44%). Chemical composition (wt%) of Tung Ba kaolin determined by X-ray fluorescence (XRF) shows SiO2 at 59.92–64.98, Al2O3: 18.95–20.82, Fe2O3: 2.28–3.95, SO3: 2.47–4.66, CaO: 0.12–0.36, MgO: 1.72–2.13 and TiO2 at 0.75–1,08. To fabricate geopolymer ceramic, kaolin clay was transformed into metakaolin with the optimal parameters of calcination: the temperature at 750°C and heating time of 120 min. Obtained metakaolin is mixed with mixtures of NaOH/Na2SiO3 (40%wt) with ratios of 0.2, 0.25, 0.33, 0.5 and NaOH molarities are 10M, 12M, 14M. Testing geopolymer ceramics after 28 days had a compressive strength of 40–196 KG/cm2, water absorption of 15.25–17.98% and density of 1.54–1.69 g/cm3, totally satisfied the Vietnamese standard for construction bricks and ceramics.
A Civilization of Love according to John Paul II
Authors: Osewska Elżbieta , Simonič Barbara
Published: August 2019

During his pontificate, Pope John Paul II strongly supported the idea of “civilization of love”. To the Polish Pope, the existence of ‘the civilization of love’ was the foundation of a humane world in the context of a civilization depreciating the value of the human being. This article addresses the concept of “the civilization of love”, with a special focus drawn to the aims, principles and fundamental assumptions of “the civilization of love.”
The Lay Person and his Christian Vocation in the Teaching of Pope John Paul II
Authors: Stala Józef , Vodičar Jenez
Published: August 2019

Pope John Paul II in his oral and written teaching emphasised that Jesus Christ is the Redeemer of every human being and the same time underlined the value and dignity of every person, as well as his/her calling. In the 21st century, it is still worth recollecting the depth of teaching of John Paul II, especially the truth about the human being and his/her vocation through the mystery of the Redeemer. Therefore, this article will present some points from the documents written by Pope John Paul II, which refer to the Christian vocation of a lay man.
21 Gdańsk Demands – a Political Monument
Authors: Bobko Aleksander
Published: August 2019

In the history of European civilisation there have been several documents of particular cultural weight and symbolic meaning. From Magna Carta Libertatum of 1215 to the Universal Declaration of Human Rights from 1948 we can trace the development of political ideas which, while being rooted in religious and philosophical thought, have had a strong impact on the lives of whole societies. In this article I attempt to look at the famous 21 Gdańsk demands from that perspective. The document, signed in August 1980 kindled a peace process which culminated in the collapse of communism in Europe. I would like to infer a relation between the possibility of signing such a document and the election of Karol Wojtyła as Pope. It also seemed interesting to compare the content of the 21 Gdańsk Agreement with that of the less well-known Rzeszów-Ustrzyki Dolne Protocol signed several months later.
Imagination and Belief- Newman’s Contribution to the New Evangelization
Authors: Szydlik Thomas
Published: August 2019

As the Church follows the call of John Paul II for a New Evangelization, she must consider how the individual person actually comes to belief. Bl. John Henry Newman, soon to be canonized, addressed this question in An Essay in Aid of A Grammar of Assent. According to Newman, religious belief is not just a logical acceptance of intellectual propositions. It is a commitment of the whole person to God, according to the image of God formed in the person’s imagination. (The imagination is that faculty possessed by every human person, which coordinates real life experiences and allows the person to comprehend the real world around him).It is by intentionally employing the imagination, rather than demonstrative logic, that the human person arrives at a belief that is well-grounded, neither cold nor fanatical. Accordingly, our evangelical activity, while never neglecting the truth of the Gospel, should seek to engage the senses and feelings of the human ­person and not merely the intellect.
Learning to Teach in Cooperative Multiagent Reinforcement Learning
Authors: Omidshafiei Shayegan , Kim Dong-Ki , Liu Miao , Tesauro Gerald , Riemer Matthew , Amato Christopher , Campbell Murray , How Jonathan P.
Published: August 2019

Collective human knowledge has clearly benefited from the fact that innovations by individuals are taught to others through communication. Similar to human social groups, agents in distributed learning systems would likely benefit from communication to share knowledge and teach skills. The problem of teaching to improve agent learning has been investigated by prior works, but these approaches make assumptions that prevent application of teaching to general multiagent problems, or require domain expertise for problems they can apply to. This learning to teach problem has inherent complexities related to measuring long-term impacts of teaching that compound the standard multiagent coordination challenges. In contrast to existing works, this paper presents the first general framework and algorithm for intelligent agents to learn to teach in a multiagent environment. Our algorithm, Learning to Coordinate and Teach Reinforcement (LeCTR), addresses peer-to-peer teaching in cooperative multiagent reinforcement learning. Each agent in our approach learns both when and what to advise, then uses the received advice to improve local learning. Importantly, these roles are not fixed; these agents learn to assume the role of student and/or teacher at the appropriate moments, requesting and providing advice in order to improve teamwide performance and learning. Empirical comparisons against state-of-the-art teaching methods show that our teaching agents not only learn significantly faster, but also learn to coordinate in tasks where existing methods fail.
Enriching Word Embeddings with a Regressor Instead of Labeled Corpora
Authors: Abdalla Mohamed , Sahlgren Magnus , Hirst Graeme
Published: August 2019

We propose a novel method for enriching word-embeddings without the need of a labeled corpus. Instead, we show that relying on a regressor – trained with a small lexicon to predict pseudo-labels – significantly improves performance over current techniques that rely on human-derived sentence-level labels for an entire corpora. Our approach enables enrichment for corpora that have no labels (such as Wikipedia). Exploring the utility of this general approach in both sentiment and non-sentiment-focused tasks, we show how enriching embeddings, for both Twitter and Wikipedia-based embeddings, provide notable improvements in performance for: binary sentiment classification, SemEval Tasks, embedding analogy task, and, document classification. Importantly, our approach is notably better and more generalizable than other state-of-the-art approaches for enriching both labeled and unlabeled corpora.
Online Embedding Compression for Text Classification Using Low Rank Matrix Factorization
Authors: Acharya Anish , Goel Rahul , Metallinou Angeliki , Dhillon Inderjit
Published: August 2019

Deep learning models have become state of the art for natural language processing (NLP) tasks, however deploying these models in production system poses significant memory constraints. Existing compression methods are either lossy or introduce significant latency. We propose a compression method that leverages low rank matrix factorization during training, to compress the word embedding layer which represents the size bottleneck for most NLP models. Our models are trained, compressed and then further re-trained on the downstream task to recover accuracy while maintaining the reduced size. Empirically, we show that the proposed method can achieve 90% compression with minimal impact in accuracy for sentence classification tasks, and outperforms alternative methods like fixed-point quantization or offline word embedding compression. We also analyze the inference time and storage space for our method through FLOP calculations, showing that we can compress DNN models by a configurable ratio and regain accuracy loss without introducing additional latency compared to fixed point quantization. Finally, we introduce a novel learning rate schedule, the Cyclically Annealed Learning Rate (CALR), which we empirically demonstrate to outperform other popular adaptive learning rate algorithms on a sentence classification benchmark.
Deep Short Text Classification with Knowledge Powered Attention
Authors: Chen Jindong , Hu Yizhou , Liu Jingping , Xiao Yanghua , Jiang Haiyun
Published: August 2019

Short text classification is one of important tasks in Natural Language Processing (NLP). Unlike paragraphs or documents, short texts are more ambiguous since they have not enough contextual information, which poses a great challenge for classification. In this paper, we retrieve knowledge from external knowledge source to enhance the semantic representation of short texts. We take conceptual information as a kind of knowledge and incorporate it into deep neural networks. For the purpose of measuring the importance of knowledge, we introduce attention mechanisms and propose deep Short Text Classification with Knowledge powered Attention (STCKA). We utilize Concept towards Short Text (CST) attention and Concept towards Concept Set (C-CS) attention to acquire the weight of concepts from two aspects. And we classify a short text with the help of conceptual information. Unlike traditional approaches, our model acts like a human being who has intrinsic ability to make decisions based on observation (i.e., training data for machines) and pays more attention to important knowledge. We also conduct extensive experiments on four public datasets for different tasks. The experimental results and case studies show that our model outperforms the state-of-the-art methods, justifying the effectiveness of knowledge powered attention.
Transfer Learning for Sequence Labeling Using Source Model and Target Data
Authors: Chen Lingzhen , Moschitti Alessandro
Published: August 2019

In this paper, we propose an approach for transferring the knowledge of a neural model for sequence labeling, learned from the source domain, to a new model trained on a target domain, where new label categories appear. Our transfer learning (TL) techniques enable to adapt the source model using the target data and new categories, without accessing to the source data. Our solution consists in adding new neurons in the output layer of the target model and transferring parameters from the source model, which are then fine-tuned with the target data. Additionally, we propose a neural adapter to learn the difference between the source and the target label distribution, which provides additional important information to the target model. Our experiments on Named Entity Recognition show that (i) the learned knowledge in the source model can be effectively transferred when the target data contains new categories and (ii) our neural adapter further improves such transfer.
Implicit Argument Prediction as Reading Comprehension
Authors: Cheng Pengxiang , Erk Katrin
Published: August 2019

Implicit arguments, which cannot be detected solely through syntactic cues, make it harder to extract predicate-argument tuples. We present a new model for implicit argument prediction that draws on reading comprehension, casting the predicate-argument tuple with the missing argument as a query. We also draw on pointer networks and multi-hop computation. Our model shows good performance on an argument cloze task as well as on a nominal implicit argument prediction task.
Joint Extraction of Entities and Overlapping Relations Using Position-Attentive Sequence Labeling
Authors: Dai Dai , Xiao Xinyan , Lyu Yajuan , Dou Shan , She Qiaoqiao , Wang Haifeng
Published: August 2019

Joint entity and relation extraction is to detect entity and relation using a single model. In this paper, we present a novel unified joint extraction model which directly tags entity and relation labels according to a query word position p, i.e., detecting entity at p, and identifying entities at other positions that have relationship with the former. To this end, we first design a tagging scheme to generate n tag sequences for an n-word sentence. Then a position-attention mechanism is introduced to produce different sentence representations for every query position to model these n tag sequences. In this way, our method can simultaneously extract all entities and their type, as well as all overlapping relations. Experiment results show that our framework performances significantly better on extracting overlapping relations as well as detecting long-range relation, and thus we achieve state-of-the-art performance on two public datasets.
EA Reader: Enhance Attentive Reader for Cloze-Style Question Answering via Multi-Space Context Fusion
Authors: Fu Chengzhen , Zhang Yan
Published: August 2019

Query-document semantic interactions are essential for the success of many cloze-style question answering models. Recently, researchers have proposed several attention-based methods to predict the answer by focusing on appropriate subparts of the context document. In this paper, we design a novel module to produce the query-aware context vector, named Multi-Space based Context Fusion (MSCF), with the following considerations: (1) interactions are applied across multiple latent semantic spaces; (2) attention is measured at bit level, not at token level. Moreover, we extend MSCF to the multi-hop architecture. This unified model is called Enhanced Attentive Reader (EA Reader). During the iterative inference process, the reader is equipped with a novel memory update rule and maintains the understanding of documents through read, update and write operations. We conduct extensive experiments on four real-world datasets. Our results demonstrate that EA Reader outperforms state-of-the-art models.
Predicting and Analyzing Language Specificity in Social Media Posts
Authors: Gao Yifan , Zhong Yang , Preoţiuc-Pietro Daniel , Li Junyi Jessy
Published: August 2019

In computational linguistics, specificity quantifies how much detail is engaged in text. It is an important characteristic of speaker intention and language style, and is useful in NLP applications such as summarization and argumentation mining. Yet to date, expert-annotated data for sentence-level specificity are scarce and confined to the news genre. In addition, systems that predict sentence specificity are classifiers trained to produce binary labels (general or specific).We collect a dataset of over 7,000 tweets annotated with specificity on a fine-grained scale. Using this dataset, we train a supervised regression model that accurately estimates specificity in social media posts, reaching a mean absolute error of 0.3578 (for ratings on a scale of 1-5) and 0.73 Pearson correlation, significantly improving over baselines and previous sentence specificity prediction systems. We also present the first large-scale study revealing the social, temporal and mental health factors underlying language specificity on social media.
Kernelized Hashcode Representations for Relation Extraction
Authors: Garg Sahil , Galstyan Aram , Steeg Greg Ver , Rish Irina , Cecchi Guillermo , Gao Shuyang
Published: August 2019

Kernel methods have produced state-of-the-art results for a number of NLP tasks such as relation extraction, but suffer from poor scalability due to the high cost of computing kernel similarities between natural language structures. A recently proposed technique, kernelized locality-sensitive hashing (KLSH), can significantly reduce the computational cost, but is only applicable to classifiers operating on kNN graphs. Here we propose to use random subspaces of KLSH codes for efficiently constructing an explicit representation of NLP structures suitable for general classification methods. Further, we propose an approach for optimizing the KLSH model for classification problems by maximizing an approximation of mutual information between the KLSH codes (feature vectors) and the class labels. We evaluate the proposed approach on biomedical relation extraction datasets, and observe significant and robust improvements in accuracy w.r.t. state-ofthe-art classifiers, along with drastic (orders-of-magnitude) speedup compared to conventional kernel methods.
Sentence-Wise Smooth Regularization for Sequence to Sequence Learning
Authors: Gong Chengyue , Tan Xu , He Di , Qin Tao
Published: August 2019

Maximum-likelihood estimation (MLE) is widely used in sequence to sequence tasks for model training. It uniformly treats the generation/prediction of each target token as multiclass classification, and yields non-smooth prediction probabilities: in a target sequence, some tokens are predicted with small probabilities while other tokens are with large probabilities. According to our empirical study, we find that the non-smoothness of the probabilities results in low quality of generated sequences. In this paper, we propose a sentence-wise regularization method which aims to output smooth prediction probabilities for all the tokens in the target sequence. Our proposed method can automatically adjust the weights and gradients of each token in one sentence to ensure the predictions in a sequence uniformly well. Experiments on three neural machine translation tasks and one text summarization task show that our method outperforms conventional MLE loss on all these tasks and achieves promising BLEU scores on WMT14 English-German and WMT17 Chinese-English translation task.
Switch-LSTMs for Multi-Criteria Chinese Word Segmentation
Authors: Gong Jingjing , Chen Xinchi , Gui Tao , Qiu Xipeng
Published: August 2019

Multi-criteria Chinese word segmentation is a promising but challenging task, which exploits several different segmentation criteria and mines their common underlying knowledge. In this paper, we propose a flexible multi-criteria learning for Chinese word segmentation. Usually, a segmentation criterion could be decomposed into multiple sub-criteria, which are shareable with other segmentation criteria. The process of word segmentation is a routing among these sub-criteria. From this perspective, we present Switch-LSTMs to segment words, which consist of several long short-term memory neural networks (LSTM), and a switcher to automatically switch the routing among these LSTMs. With these auto-switched LSTMs, our model provides a more flexible solution for multi-criteria CWS, which is also easy to transfer the learned knowledge to new criteria. Experiments show that our model obtains significant improvements on eight corpora with heterogeneous segmentation criteria, compared to the previous method and single-criterion learning.
Story Ending Generation with Incremental Encoding and Commonsense Knowledge
Authors: Guan Jian , Wang Yansen , Huang Minlie
Published: August 2019

Generating a reasonable ending for a given story context, i.e., story ending generation, is a strong indication of story comprehension. This task requires not only to understand the context clues which play an important role in planning the plot, but also to handle implicit knowledge to make a reasonable, coherent story. In this paper, we devise a novel model for story ending generation. The model adopts an incremental encoding scheme to represent context clues which are spanning in the story context. In addition, commonsense knowledge is applied through multi-source attention to facilitate story comprehension, and thus to help generate coherent and reasonable endings. Through building context clues and using implicit knowledge, the model is able to produce reasonable story endings. Automatic and manual evaluation shows that our model can generate more reasonable story endings than state-of-the-art baselines1.
1q21 Gain Combined with High-Risk Factors Is a Heterogeneous Prognostic Factor in Newly Diagnosed Multiple Myeloma: A Multicenter Study in China
Authors: Li Xiaozhe , Chen Wenming , Wu Yin , Li Jianyong , Chen Lijuan , Fang Baijun , Feng Ying , Liu Junru , Chen Meilan , Gu Jingli , Huang Beihui , Li Juan
Published: August 2019

Background

The prognostic value of 1q21 gain in newly diagnosed multiple myeloma (NDMM) remains controversial. Our aim was to investigate the prognostic value of 1q21 gain in a Chinese population.

Materials and Methods

We retrospectively identified 565 patients with NDMM from multiple centers in China.

Results

We detected 1q21 gain in 222 (39.3%) patients, among whom 144 had three copies of 1q21, 57 had four copies of 1q21, and 21 had at least five copies of 1q21. Copy number variation did not show any effect on the disease outcome. Multivariate analysis indicated that 1q21 gain was an independent factor for poor prognosis, but we found that 1q21 gain was strongly associated with other high-risk factors, such as del(17p), t(4;14), t(14;16), lactate dehydrogenase (LDH) level >300 U/L and International Scoring System (ISS) stage II–III (p < .001). Further analysis revealed that in the absence of other high-risk factors, isolated 1q21 gain resulted in similar progression-free survival (PFS; 52.0 vs. 52.8 months, p = .810) and overall survival (OS; not reached vs. not reached, p = .833); additionally, when present with other high-risk cytogenetic abnormalities or increased LDH levels, 1q21 gain lost its prognostic power. However, the presence of 1q21 gain increased the adverse impact of ISS stage. Furthermore, 1q21 gain predicted poor PFS and OS in patients who received bortezomib-based regimens. Moreover, autologous stem cell transplantation reversed the poor prognosis in patients with 1q21 gain.

Conclusion

Our results show that heterogeneity exists among patients with 1q21 gain and suggest that we should assess the impact of 1q21 gain on prognosis according to different treatment regimens and accompanying high-risk factors.

Implications for Practice

1q21 gain is one of the most common chromosomal aberrations in multiple myeloma (MM); however, the prognostic value of 1q21 gain remains controversial. This study investigated the prognostic value of 1q21 gain in a Chinese population with newly diagnosed MM. The results showed that heterogeneity exists among patients with 1q21 gain and suggested that the impact of 1q21 gain on prognosis should be assessed according to different treatment regimens and accompanying high-risk factors. These results could help stratify risk in patients with MM and guide treatment decisions.
Heat flux and convective boundary influence on nanofluid-filled cavity
Authors: Kosti Siddhartha
Published: August 2019

Electronic devices release heat to the atmosphere from the walls, and system miniaturisation results in exponential increase in the heat associated with these high-heat-generating/electronic devices. To analyse this numerically, heat-flux and convective types of boundary condition are considered at the walls, which are the most realistic boundary condition type. In present work, the physical model consists of nanofluid inside an enclosure with one side subjected to constant heat-flux and the other sides exposed to convective boundaries, is solved. The nanofluid is completely confined within the enclosure and flows due to natural convection. Considering the heat-flux varying from 100 W/m2to 10 kW/m2, three (Ra) Rayleigh numbers are calculated. Results are validated with experimental results also. Results show that increasing Ra and copper nanoparticle concentration results in strengthening the heat transfer and average Nusselt number. Results also show that the thermal boundary layer thickness increases with aspect ratio (AR). Streamline contours show that natural convection strength is higher for low ARs compared to high ARs. For a low Ra (2 × 104), viscosity model is more sensitive than the thermal conductivity model, and for high Ra (2 × 106), the thermal conductivity model is more sensitive than the viscosity model.