Search results for: generalization

Authors: Mohammad Younus Bhat

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The quaternion offset linear canonical transform (QOLCT), which isa time-shifted and frequency-modulated version of the quaternion linear canonical transform (QLCT), provides a more general framework of most existing signal processing tools. For the generalized QOLCT, the classical Heisenberg’s and Lieb’s uncertainty principles have been studied recently. In this paper, we first define the short-time quaternion offset linear canonical transform (ST-QOLCT) and drive its relationship with the quaternion Fourier transform (QFT). The crux of the paper lies in the generalization of several well-known uncertainty principles for the ST-QOLCT, including Donoho-Stark’s uncertainty principle, Hardy’s uncertainty principle, Beurling’s uncertainty principle, and the logarithmic uncertainty principle.

Keywords: Quaternion Fourier transform, Quaternion offset linear canonical transform, short-time quaternion offset linear canonical transform, uncertainty principle

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Authors: Nihal Murali, Kunal Gupta, Surekha Bhanot

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Training of artificial neural networks (ANNs) using reinforcement learning (RL) techniques is being widely discussed in the robot learning literature. The high model complexity of ANNs along with the model-free nature of RL algorithms provides a desirable combination for many robotics applications. There is a huge need for algorithms that generalize using raw sensory inputs, such as vision, without any hand-engineered features or domain heuristics. In this paper, the standard control problem of line following robot was used as a test-bed, and an ANN controller for the robot was trained on images from a live video feed using Q-learning. A virtual agent was first trained in simulation environment and then deployed onto a robot’s hardware. The robot successfully learns to traverse a wide range of curves and displays excellent generalization ability. Qualitative analysis of the evolution of policies, performance and weights of the network provide insights into the nature and convergence of the learning algorithm.

Keywords: artificial neural networks, q-learning, reinforcement learning, robot learning

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Authors: Raphael Naryongo, Philip Ngare, Anthony Waititu

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This paper deals with numerical simulation of Wishart processes for a single asset risky pricing model whose volatility is described by Wishart affine diffusion processes. The multi-factor specification of volatility will make the model more flexible enough to fit the stock market data for short or long maturities for better returns. The Wishart process is a stochastic process which is a positive semi-definite matrix-valued generalization of the square root process. The aim of the study is to model the log asset stock returns under the double Wishart stochastic volatility model. The solution of the log-asset return dynamics for Bi-Wishart processes will be obtained through Euler-Maruyama discretization schemes. The numerical results on the asset returns are compared to the existing models returns such as Heston stochastic volatility model and double Heston stochastic volatility model

Keywords: euler schemes, log-asset return, infinitesimal generator, wishart diffusion affine processes

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Authors: Narmina Mammadova

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In modern linguistics, the comparative study of languages is becoming increasingly popular, the typology and comparison of languages that have different structures is expanding and deepening. Of particular interest is the study of phraseological units, which makes it possible to identify the specific features of the compared languages in all their national identity. This paper gives a brief analysis of the comparative study of somatic phraseological units (SFU) of the Spanish and Turkish languages with the component "hand" in the semantic aspect; identification of equivalents, analogs and non-equivalent units, as well as a description of methods of translation of non-equivalent somatic phraseological units. Comparative study of the phraseology of unrelated languages is of particular relevance since it allows us to identify both general, universal features and differential and specific features characteristic of a particular language. Based on the results of the generalization of the study, it can be assumed that phraseological units containing a somatic component have a high interlingual phraseological activity, which contributes to an increase in the degree of interlingual equivalence.

Keywords: Linguoculturology, Turkish, Spanish, language picture of the world, phraseological units, semantic microfield

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Authors: Gaurav Sancheti

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This study presents an impacting approach of Artificial Neural Networks (ANNs) in determining the quantity of High Tensile Steel (HTS) strands required in post-tensioned (PT) slabs. Various PT slab configurations were generated by varying the span and depth of the slab. For each of these slab configurations, quantity of required HTS strands were recorded. ANNs with backpropagation algorithm and varying architectures were developed and their performance was evaluated in terms of Mean Square Error (MSE). The recorded data for the quantity of HTS strands was used as a feeder database for training the developed ANNs. The networks were validated using various validation techniques. The results show that the proposed ANNs have a great potential with good prediction and generalization capability.

Keywords: artificial neural networks, back propagation, conceptual design, high tensile steel strands, post tensioned slabs, validation techniques

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Authors: Nitin Singh, Meng Ling, Talha Ahmed, Tianxia Zhao, Reinier van de Pol

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We propose the use of reinforcement learning (RL) as a viable alternative for optimizing supply chain management, particularly in scenarios with stochasticity in product demands. RL’s adaptability to changing conditions and its demonstrated success in diverse fields of sequential decision-making makes it a promising candidate for addressing supply chain problems. We investigate the impact of demand fluctuations in a multi-product supply chain system and develop RL agents with learned generalizable policies. We provide experimentation details for training RL agents and statistical analysis of the results. We study the generalization ability of RL agents for different demand uncertainty scenarios and observe superior performance compared to the agents trained with fixed demand curves. The proposed methodology has the potential to lead to cost reduction and increased profit for companies dealing with frequent inventory movement between supply and demand nodes.

Keywords: inventory management, reinforcement learning, supply chain optimization, uncertainty

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Authors: Christoph Linse, Thomas Martinetz

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Recent findings have shown that Neural Networks generalize also in over-parametrized regimes with zero training error. This is surprising, since it is completely against traditional machine learning wisdom. In our empirical study we fortify these findings in the domain of fine-grained image classification. We show that very large Convolutional Neural Networks with millions of weights do learn with only a handful of training samples and without image augmentation, explicit regularization or pretraining. We train the architectures ResNet018, ResNet101 and VGG19 on subsets of the difficult benchmark datasets Caltech101, CUB_200_2011, FGVCAircraft, Flowers102 and StanfordCars with 100 classes and more, perform a comprehensive comparative study and draw implications for the practical application of CNNs. Finally, we show that VGG19 with 140 million weights learns to distinguish airplanes and motorbikes with up to 95% accuracy using only 20 training samples per class.

Keywords: convolutional neural networks, fine-grained image classification, generalization, image recognition, over-parameterized, small data sets

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Authors: Xiaohan Bookman, Xiaoyan Zhu

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Margin-Based Principle has been proposed for a long time, it has been proved that this principle could reduce the structural risk and improve the performance in both theoretical and practical aspects. Meanwhile, feed-forward neural network is a traditional classifier, which is very hot at present with a deeper architecture. However, the training algorithm of feed-forward neural network is developed and generated from Widrow-Hoff Principle that means to minimize the squared error. In this paper, we propose a new training algorithm for feed-forward neural networks based on Margin-Based Principle, which could effectively promote the accuracy and generalization ability of neural network classifiers with less labeled samples and flexible network. We have conducted experiments on four UCI open data sets and achieved good results as expected. In conclusion, our model could handle more sparse labeled and more high-dimension data set in a high accuracy while modification from old ANN method to our method is easy and almost free of work.

Keywords: Max-Margin Principle, Feed-Forward Neural Network, classifier, structural risk

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Authors: Azeddine El-Hassouny, Chandrashekhar Meshram, Geraldin Nanfack

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In recent years, learning from data with noisy labels (Label Noise) has been a major concern in supervised learning. This problem has become even more worrying in Deep Learning, where the generalization capabilities have been questioned lately. Indeed, deep learning requires a large amount of data that is generally collected by search engines, which frequently return data with unreliable labels. In this paper, we investigate the Label Noise in Deep Learning using variational inference. Our contributions are : (1) exploiting Label Noise concept where the true labels are learnt using reparameterization variational inference, while observed labels are learnt discriminatively. (2) the noise transition matrix is learnt during the training without any particular process, neither heuristic nor preliminary phases. The theoretical results shows how true label distribution can be learned by variational inference in any discriminate neural network, and the effectiveness of our approach is proved in several target datasets, such as MNIST and CIFAR32.

Keywords: label noise, deep learning, discrete latent variable, variational inference, MNIST, CIFAR32

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Authors: Chico Horacio Jose Sambo

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Recently neural network has gained popularity when come to solve complex nonlinear problems. Permeability is one of fundamental reservoir characteristics system that are anisotropic distributed and non-linear manner. For this reason, permeability prediction from well log data is well suited by using neural networks and other computer-based techniques. The main goal of this paper is to predict reservoir permeability from well logs data by using neural network approach. A multi-layered perceptron trained by back propagation algorithm was used to build the predictive model. The performance of the model on net results was measured by correlation coefficient. The correlation coefficient from testing, training, validation and all data sets was evaluated. The results show that neural network was capable of reproducing permeability with accuracy in all cases, so that the calculated correlation coefficients for training, testing and validation permeability were 0.96273, 0.89991 and 0.87858, respectively. The generalization of the results to other field can be made after examining new data, and a regional study might be possible to study reservoir properties with cheap and very fast constructed models.

Keywords: neural network, permeability, multilayer perceptron, well log

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Authors: Redouane Tlemsani, Redouane, Belkacem Kouninef, Abdelkader Benyettou

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In this paper, our system is a Markovien system which we can see it like a Dynamic Bayesian Networks. One of the major interests of these systems resides in the complete training of the models (topology and parameters) starting from training data. The Bayesian Networks are representing models of dubious knowledge on complex phenomena. They are a union between the theory of probability and the graph theory in order to give effective tools to represent a joined probability distribution on a set of random variables. The representation of knowledge bases on description, by graphs, relations of causality existing between the variables defining the field of study. The theory of Dynamic Bayesian Networks is a generalization of the Bayesians networks to the dynamic processes. Our objective amounts finding the better structure which represents the relationships (dependencies) between the variables of a dynamic bayesian network. In applications in pattern recognition, one will carry out the fixing of the structure which obliges us to admit some strong assumptions (for example independence between some variables).

Keywords: Arabic on line character recognition, dynamic Bayesian network, pattern recognition, networks

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Authors: Eusebio Jr. Lina, Ederlina Nocon

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Cyclic codes are a fundamental subclass of linear codes that enjoy a very interesting algebraic structure. The class of skew cyclic codes (or θ-cyclic codes) is a generalization of the notion of cyclic codes. This a very large class of linear codes which can be used to systematically search for codes with good properties. A linear code with complementary dual (LCD code) is a linear code C satisfying C ∩ C^⊥ = {0}. This subclass of linear codes provides an optimum linear coding solution for a two-user binary adder channel and plays an important role in countermeasures to passive and active side-channel analyses on embedded cryptosystems. This paper aims to identify LCD codes from the class of skew cyclic codes. Let F_q be a finite field of order q, and θ be an automorphism of F_q. Some conditions for a skew cyclic code to be LCD were given. To this end, the properties of a noncommutative skew polynomial ring F_q[x, θ] of automorphism type were revisited, and the algebraic structure of skew cyclic code using its skew polynomial representation was examined. Using the result that skew cyclic codes are left ideals of the ring F_q[x, θ]/〈x^n-1〉, a characterization of a skew cyclic LCD code of length n was derived. A necessary condition for a skew cyclic code to be LCD was also given.

Keywords: LCD cyclic codes, skew cyclic LCD codes, skew cyclic complementary dual codes, theta-cyclic codes with complementary duals

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Authors: Manzurul Alam, John Griffiths, David Holloway, Megan Paull, Anne Clear

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Background: This paper investigates the nature of risk management practices in non-profit organizations. It is argued here that the risk exposure of these organizations has increased as a result of their entrepreneurial activities. This study explores how a particular non-profit organization formulates its risk strategies in the face funding restrictions. Design/Method/Approach: The study adopts a case study approach to report the results on how a non-profit organization diversifies its activities, tackles risks arising from such activities and improves performance. Results: The findings show that the organization made structural adjustments and leadership changes which helped to adjust their risk strategies. It also reports the organizational processes to deal with risks arising from both related and unrelated diversification strategies. Implications: Any generalization from this case example needs to be taken with caution as there are significant differences between non-profit organizations operating in different sectors. Originality: The paper makes a significant contribution to the non-profit literature by highlighting the diversification strategies along with risk performance.

Keywords: risk management, performance management, non-profit organizations, financial management

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Authors: Ashkan Golgoon, Souhayl Sadik, Arash Yavari

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Eigenstrains in nonlinear solids are created due to anelastic effects such as non-uniform temperature distributions, growth, remodeling, and defects. Eigenstrains understanding is indispensable, as they can generate residual stresses and strongly affect the overall response of solids. Here, we study the residual stress and deformation fields of an incompressible isotropic infinite wedge with a circumferentially-symmetric distribution of finite eigenstrains. We construct a material manifold, whose Riemannian metric explicitly depends on the eigenstrain distribution, thereby we turn the problem into a classical nonlinear elasticity problem, where we find an embedding of the Riemannian material manifold into the ambient Euclidean space. In particular, we find exact solutions for the residual stress and deformation fields of a neo-Hookean wedge having a symmetric inclusion with finite radial and circumferential eigenstrains. Moreover, we numerically solve a similar problem when a symmetric Mooney-Rivlin inhomogeneity with finite eigenstrains is placed in a neo-Hookean wedge. Generalization of the eigenstrain problem to other geometries are also discussed.

Keywords: finite eigenstrains, geometric mechanics, inclusion, inhomogeneity, nonlinear elasticity

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Authors: Mohammad Farid

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In this paper, we introduce and study an explicit iterative method based on hybrid extragradient method to approximate a common solution of generalized mixed equilibrium problem and fixed point problem for a nonexpansive semigroup in Hilbert space. Further, we prove that the sequence generated by the proposed iterative scheme converge strongly to the common solution of generalized mixed equilibrium problem and fixed point problem for a nonexpansive semigroup. This common solution is the unique solution of a variational inequality problem and is the optimality condition for a minimization problem. The results presented in this paper are the supplement, extension and generalization of the previously known results in this area.

Keywords: generalized mixed equilibrium problem, fixed-point problem, nonexpansive semigroup, variational inequality problem, iterative algorithms, hybrid extragradient method

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Authors: Redouane Tlemsani, Abdelkader Benyettou

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Work is in on line Arabic character recognition and the principal motivation is to study the Arab manuscript with on line technology. This system is a Markovian system, which one can see as like a Dynamic Bayesian Network (DBN). One of the major interests of these systems resides in the complete models training (topology and parameters) starting from training data. Our approach is based on the dynamic Bayesian Networks formalism. The DBNs theory is a Bayesians networks generalization to the dynamic processes. Among our objective, amounts finding better parameters, which represent the links (dependences) between dynamic network variables. In applications in pattern recognition, one will carry out the fixing of the structure, which obliges us to admit some strong assumptions (for example independence between some variables). Our application will relate to the Arabic isolated characters on line recognition using our laboratory database: NOUN. A neural tester proposed for DBN external optimization. The DBN scores and DBN mixed are respectively 70.24% and 62.50%, which lets predict their further development; other approaches taking account time were considered and implemented until obtaining a significant recognition rate 94.79%.

Keywords: Arabic on line character recognition, dynamic Bayesian network, pattern recognition, computer vision

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Authors: Samson Henry Dogo, Allan Clark, Elena Kulinskaya

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The objective in meta-analysis is to combine results from several independent studies in order to create generalization and provide evidence based for decision making. But recent studies show that the magnitude of effect size estimates reported in many areas of research finding changed with year publication and this can impair the results and conclusions of meta-analysis. A number of sequential methods have been proposed for monitoring the effect size estimates in meta-analysis. However they are based on statistical theory applicable to fixed effect model (FEM). For random-effects model (REM), the analysis incorporates the heterogeneity variance, tau-squared and its estimation create complications. In this paper proposed the use of Gombay and Serbian (2005) truncated CUSUM-type test with asymptotically valid critical values for sequential monitoring of REM. Simulation results show that the test does not control the Type I error well, and is not recommended. Further work required to derive an appropriate test in this important area of application.

Keywords: meta-analysis, random-effects model, sequential test, temporal changes in effect sizes

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Authors: C. Cohen, O. Levi

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This work introduces a generalization of the classical Tullock’s model of one-stage contests under complete information with multiple unlimited numbers of contestants. In classical Tullock’s model, the contest winner is not necessarily the highest bidder. Instead, the winner is determined according to a draw in which the winning probabilities are the relative contestants’ efforts. The Tullock modeling fits well political contests, in which the winner is not necessarily the highest effort contestant. This work presents a modified model which uses a simple non-discriminating rule, namely, a parameter to influence the total costs planned for an election, for example, the contest designer can control the contestants' efforts. The winner pays a fee, and the losers are reimbursed the same amount. Our proposed model includes a mechanism that controls the efforts exerted and balances competition, creating a tighter, less predictable and more interesting contest. Additionally, the proposed model follows the fairness criterion in the sense that it does not alter the contestants' probabilities of winning compared to the classic Tullock’s model. We provide an analytic solution for the contestant's optimal effort and expected reward.

Keywords: contests, Tullock's model, political elections, control expenses

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Authors: Mohd Rashid bin Ab Hamid, Noor Azura binti Omar, Zainol Bin Mustafa

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Purpose: The aim of this paper is to study the encouraging factors for scholarly works contribution towards among academicians at Malaysian university. Methods: This paper uses questionnaire for data collection on the respondents’ perceptional level on the institutional repository efforts in one of the university under study. Several encouraging factors have been identified and to be measured using descriptive statistics. The factors are related to content contribution, i.e. personal factor, professional factor, organizational factor and technological factor. Findings: The study found that all these four encouraging factors did have a relation to the contribution of scholarly works in the university by the academician. Research Limitations: This study used a case study and generalization to all Malaysian universities should be well taken care of. Practical implications: The library at the university should look into these four encouraging factors in order to enhance the contribution from academician towards the repository. Originality/value: This research paper provides basic information for the knowledge management officers in the university by endeavouring more efforts in order to attract more contributions.

Keywords: institutional repository, information retrieval, information storage and retrieval

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Authors: Aghbalou Nihad, Charki Abderafi, Saida Rahali, Reklaoui Kamal

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Maximize the benefit from the wind energy potential is the most interest of the wind power stakeholders. As a result, the wind tower size is radically increasing. Nevertheless, choosing an appropriate wind turbine for a selected site require an accurate estimate of vertical wind profile. It is also imperative from cost and maintenance strategy point of view. Then, installing tall towers or even more expensive devices such as LIDAR or SODAR raises the costs of a wind power project. Various models were developed coming within this framework. However, they suffer from complexity, generalization and lacks accuracy. In this work, we aim to investigate the ability of neural network trained using the Bayesian Regularization technique to estimate wind speed profile up to height of 100 m based on knowledge of wind speed lower heights. Results show that the proposed approach can achieve satisfactory predictions and proof the suitability of the proposed method for generating wind speed profile and probability distributions based on knowledge of wind speed at lower heights.

Keywords: bayesian regularization, neural network, wind shear, accuracy

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Authors: Amaal Almigal

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Technology has been argued to offer distinct advantages and benefits for teaching children with autism spectrum disorder (ASD) to communicate. One aspect of this technology is augmentative and alternative communication (AAC) systems such as picture exchange or speech generation devices. Whilst there has been significant progress in teaching these children to request their wants and needs with AAC, there remains a need for developing technologies that can really make a difference in teaching them to ask questions. iPad-based AAC can be effective for communication. However, the effectiveness of this type of AAC in teaching children to ask questions needs to be examined. Thus, in order to examine the effectiveness of iPad-based AAC in teaching children with ASD to ask questions, This research will test whether iPad leads to more learning than a traditional approach picture and text cards does. Two groups of children who use AAC will be taught to ask ‘What is it?’ questions. With the first group, low-tech AAC picture and text cards will be used, while an iPad-based AAC application called Proloquo2Go will be used with the second group. Interviews with teachers and parents will be conducted before and after the experiment. The children’s perspectives will also be considered. The initial outcomes of this research indicate that iPad can be an effective tool to help children with autism to ask questions.

Keywords: autism, communication, information, iPad, pictures, requesting

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Authors: Gaye Ibrahima, Mendy Gervais, Seck Diaraf, Ouya Samuel

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In this paper, we consider the influence maximization in social networks. Here we give importance to initial diffuser called the seeds. The goal is to find efficiently a subset of k elements in the social network that will begin and maximize the information diffusion process. A new approach which treats the social network before to determine the seeds, is proposed. This treatment eliminates the information feedback toward a considered element as seed by extracting an acyclic spanning social network. At first, we propose two algorithm versions called SCG − algoritm (v1 and v2) (Spanning Connected Graphalgorithm). This algorithm takes as input data a connected social network directed or no. And finally, a generalization of the SCG − algoritm is proposed. It is called SG − algoritm (Spanning Graph-algorithm) and takes as input data any graph. These two algorithms are effective and have each one a polynomial complexity. To show the pertinence of our approach, two seeds set are determined and those given by our approach give a better results. The performances of this approach are very perceptible through the simulation carried out by the R software and the igraph package.

Keywords: acyclic spanning graph, centrality measures, information feedback, influence maximization, social network

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Authors: Romisaa Ali

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This paper presents a comparison between twin-delayed Deep Deterministic Policy Gradient (TD3) and Soft Actor-Critic (SAC) reinforcement learning algorithms in the context of training robust navigation policies for Jackal robots. By leveraging an open-source framework and custom motion control environments, the study evaluates the performance, robustness, and transferability of the trained policies across a range of scenarios. The primary focus of the experiments is to assess the training process, the adaptability of the algorithms, and the robot’s ability to navigate in previously unseen environments. Moreover, the paper examines the influence of varying environmental complexities on the learning process and the generalization capabilities of the resulting policies. The results of this study aim to inform and guide the development of more efficient and practical reinforcement learning-based navigation policies for Jackal robots in real-world scenarios.

Keywords: Jackal robot environments, reinforcement learning, TD3, SAC, robust navigation, transferability, custom environment

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Authors: Guyue Zhu, Liangping Hong

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Chinese overall urban design faces a large number of problems such as the neglect of urban characteristics, generalization of content, and difficulty in implementation. Focusing on these issues, this paper proposes the main points of shaping urban characteristics in overall urban design: focuses on core problems in city function and scale, landscape pattern, historical culture, social resources and modern city style and digs the urban characteristic genes. Then, we put forward “core problem location and characteristic gene enhancement” as a kind of overall urban design technical method. Firstly, based on the main problems in urban space as a whole, for the operability goal, the method extracts the key genes and integrates into the multi-dimension system in a targeted manner. Secondly, hierarchical management and guidance system is established which may be in line with administrative management. Finally, by converting the results, action plan is drawn up that can be dynamically implemented. Based on the above idea and method, a practical exploration has been performed in the case of Xiangyang central city.

Keywords: city characteristics, overall urban design, planning implementation, Xiangyang central city

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Authors: Nicolae Pascu

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Aside from being a fundamental tool in Complex analysis, Schwarz Lemma-which was finalized in its most complete form at the beginning of the last century-generated an important area of research in various fields of mathematics, which continues to advance even today. We present some properties of analytic functions in the half-plane which satisfy the conditions of the classical Schwarz Lemma (Carathéodory functions) and obtain a generalization of the well-known Aleksandrov-Sobolev Lemma for analytic functions in the half-plane (the correspondent of Schwarz-Pick Lemma from the unit disk). Using this Schwarz-type lemma, we obtain a characterization for the entire class of Carathéodory functions, which might be of independent interest. We prove two monotonicity properties for Carathéodory functions that do not depend upon their normalization at infinity (the hydrodynamic normalization). The method is based on conformal mapping arguments for analytic functions in the half-plane satisfying appropriate conditions, in the spirit of Schwarz lemma. According to the research findings in this paper, our main results give estimates for the modulus and the argument for the entire class of Carathéodory functions. As applications, we give several extensions of Julia-Wolf-Carathéodory Lemma in a half-strip and show that our results are sharp.

Keywords: schwarz lemma, Julia-wolf-caratéodory lemma, analytic function, normalization condition, caratéodory function

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Authors: Rania Kassir, Christophe Dos Santos, Halim Abboud, Olivier Godefroy

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The ability to communicate in at least two different languages is shared by a growing number of humans. Recently, many researchers have been studying the elderly bilingual population around the world in neuroscience, and yet, until today there’s no accurate nor universal measure or methodology used to examine bilingualism across these studies which constitute a real challenge for results generalization. This study contributes to the quest of a multidimensional bilingualism index and language proficiency literature by investigating a new bilingualism index from a reliable subjective questionnaire the Language Experience and Proficiency Questionnaire (LEAP-Q), multi-linguistic tests, and a diverse bilingual population all featured in one analysis and one index. One hundred Lebanese subjects aged between 55 and 92 years old divided into three different bilingualism subgroups (Arabic prominent, balanced, and French prominent) were recruited and underwent the LEAP-Q with a set of linguistic and cognitive tests. The analysis of the collected data led to the creation of a robust bilingualism index from speaking and oral understanding scores that underline specifically bilingualism subtype according to cutoffs scored. The practice implications of this index, particularly its use within bilingual populations, are addressed in the conclusion of this work.

Keywords: bilingualism, language dominance, bilingualism index, balanced bilingualism, Arabic first language, Lebanese, Arabic-French bilingualism

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Authors: Feng Wang, Shencheng Ni, Shuying Han, Shanhong You

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With the development of optical communication, optical performance monitoring (OPM) has received more and more attentions. Since optical signal-to-noise ratio (OSNR) is directly related to bit error rate (BER), it is one of the important parameters in optical networks. Recently, artificial neural network (ANN) has been greatly developed. ANN has strong learning and generalization ability. In this paper, a method of OSNR monitoring based on delay-tap sampling (DTS) and ANN has been proposed. DTS technique is used to extract the eigenvalues of the signal. Then, the eigenvalues are input into the ANN to realize the OSNR monitoring. The experiments of 10 Gb/s non-return-to-zero (NRZ) on–off keying (OOK), 20 Gb/s pulse amplitude modulation (PAM4) and 20 Gb/s return-to-zero (RZ) differential phase-shift keying (DPSK) systems are demonstrated for the OSNR monitoring based on the proposed method. The experimental results show that the range of OSNR monitoring is from 15 to 30 dB and the root-mean-square errors (RMSEs) for 10 Gb/s NRZ-OOK, 20 Gb/s PAM4 and 20 Gb/s RZ-DPSK systems are 0.36 dB, 0.45 dB and 0.48 dB respectively. The impact of chromatic dispersion (CD) on the accuracy of OSNR monitoring is also investigated in the three experimental systems mentioned above.

Keywords: artificial neural network (ANN), chromatic dispersion (CD), delay-tap sampling (DTS), optical signal-to-noise ratio (OSNR)

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Authors: Daniel Marra

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This paper analyses from a semantic-diachronic viewpoint the change of meanings that two lexical items of Brazilian-Portuguese language have gone through. Cerrado and Vereda designate currently the second largest Brazilian biome and one of its most important subsystems. Nevertheless, these two words have long individual histories that can be traced back to their Latin etymons. Therefore, the purpose of this work is to highlight the process by which meaning instantiated itself in these words’ formation and to discuss how semantic change installed subsequently in them. As this paper shows, the aforementioned words have been, in different past, synchronizes, created, and undergone changes of meanings by metaphor and metonymy. Besides, it is argued here that semantic change takes place due to external causes, such as generalization and specialization of meaning. It happens when a specialized use of a lexical item, restricted to a particular linguistic group, is adopted by other groups, having its meaning generalized by them. In these processes, the etymological idea of the word is generally lost, which gains, in the new group, less specific meaning in relation to its etymology, sometimes with no relation to the original idea. As a final point, it is claimed that both the creation of a lexical item and its change of meaning involve pragmatic goals, such as the need the language users have to express a new meaning related to a certain reality in the empirical world.

Keywords: Brazilian biomes, metaphor and metonymy, Portuguese lexicon, semantic change

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Authors: Givi Kupatadze

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Backward economies are facing a challenge of achieving sustainable high economic growth rate. Dominant growth models represent a roadmap in framing economic development strategy. This paper examines a relevance of the dominant growth models for backward economies. Cobb-Douglas production function, the Harrod-Domar model of economic growth, the Solow growth model and general formula of gross domestic product are examined to undertake a comprehensive study of the dominant growth models. Deductive research method allows to uncover major weaknesses of the dominant growth models and to come up with practical implications for economic development strategy. The key finding of the paper shows, contrary to what used to be taught by textbooks of economics, that constant returns to scale property of the dominant growth models are a mere coincidence and its generalization over space and time can be regarded as one of the most unfortunate mistakes in the whole field of political economy. The major suggestion of the paper for backward economies is that understanding and considering taxonomy of economic activities based on increasing and diminishing returns to scale represent a cornerstone of successful economic development strategy.

Keywords: backward economies, constant returns to scale, dominant growth models, taxonomy of economic activities

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Authors: Ana Serafimovic, Karthik Devarajan

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Non-negative matrix factorization approximates a high dimensional non-negative matrix V as the product of two non-negative matrices, W and H, and allows only additive linear combinations of data, enabling it to learn parts with representations in reality. It has been successfully applied in the analysis and interpretation of high dimensional data arising in neuroscience, computational biology, and natural language processing, to name a few. The objective of this paper is to assess a hybrid algorithm for non-negative matrix factorization with multiplicative updates. The method aims to minimize the symmetric version of Kullback-Leibler divergence known as intrinsic information and assumes that the noise is signal-dependent and that it originates from an arbitrary distribution from the exponential family. It is a generalization of currently available algorithms for Gaussian, Poisson, gamma and inverse Gaussian noise. We demonstrate the potential usefulness of the new generalized algorithm by comparing its performance to the baseline methods which also aim to minimize symmetric divergence measures.

Keywords: non-negative matrix factorization, dimension reduction, clustering, intrinsic information, symmetric information divergence, signal-dependent noise, exponential family, generalized Kullback-Leibler divergence, dual divergence

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