Average Divergence Research Articles

Symbolic feature extraction of art graphics based on time constraint

Aiming at the problem of time-consuming and large error in current image feature extraction results, a symbolic feature extraction method for art graphics based on time constraint is proposed. Gauss filtering method is used to filter out the image noise, and Laplacian sharpening operation is used to enhance the edge of graphic symbols. According to the enhancement results, an active contour model is introduced to define a closed contour, and a driving force is used to realise the evolution of the contour to the target boundary to complete the graph segmentation. The geometric transformation of the segmentation results is analysed by translation transformation, scale transformation and rotation transformation. Based on graphics enhancement, segmentation and geometric transformation analysis, time constraints are introduced. By analysing the mean value and variance level of time-constrained laminar flow features, the average value and divergence degree of time-constrained laminar flow features are analysed, and the time-constrained symbols are regarded as a vector to realise the symbolic feature extraction of fine arts graphics. The experimental results show that the proposed method is efficient and accurate, and it is a reliable method for extracting symbolic features of fine arts graphics.

Hyperbolic Cell-centered Finite Volume Method for Obtaining Potential Magnetic Field Solutions

Abstract A hyperbolic cell-centered finite volume solver (HCCFVS) is proposed to obtain the potential magnetic field solutions prescribed by the solar observed magnetograms. By introducing solution gradients as additional unknowns and adding a pseudo-time derivative, HCCFVS transforms the second-order Poisson equation into an equivalent first-order pseudo-time-dependent hyperbolic system. Thus, instead of directly solving the Poisson equation, HCCFVS obtains the solution to the Poisson equation by achieving the steady-state solution to this first-order hyperbolic system. The code is established in Fortran 90 with Message Passing Interface parallelization. To preliminarily demonstrate the effectiveness and accuracy of the code, two test cases with exact solutions are first performed. The numerical results show its second-order convergence. Then, the code is applied to numerically solve the solar potential magnetic field problem. The solutions demonstrate the capability of HCCFVS to adequately handle the solar potential field problem, and thus it can provide a promising method of solving the same problem, except for the spherical harmonic expansion and the iterative finite difference method. Finally, by using the potential magnetic fields from HCCFVS and the spherical harmonic expansion as initial inputs, we make a comparative study on the steady-state solar corona in Carrington rotation 2098 to reaffirm the HCCFVS’s performance. Both simulations show that their modeled results are similar and capture the large-scale solar coronal structures. The average relative divergence errors, controlled by solving the Poisson equation in the projection method with HCCFVS for both simulations, are kept at an acceptable level.

Isolation and Characterization of a Novel Klebsiella pneumoniae N4-like Bacteriophage KP8.

Klebsiella pneumoniae is a common pathogen, associated with a wide spectrum of infections, and clinical isolates of K. pneumoniae often possess multiple antibiotic resistances. Here, we describe a novel lytic N4-like bacteriophage KP8, specific to K. pneumoniae, including its genome, partial structural proteome, biological properties, and proposed taxonomy. Electron microscopy revealed that KP8 belongs to the Podoviridae family. The size of the KP8 genome was 73,679 bp, and it comprised 97 putative open reading frames. Comparative genome analysis revealed that the KP8 genome possessed the highest similarity to the genomes of Enquatrovirus and Gamaleyavirus phages, which are N4-like podoviruses. In addition, the KP8 genome showed gene synteny typical of the N4-like podoviruses and contained the gene encoding a large virion-encapsulated RNA polymerase. Phylogenetic analysis of the KP8 genome revealed that the KP8 genome formed a distinct branch within the clade, which included the members of Enquatrovirus and Gamaleyavirus genera besides KP8. The average evolutionary divergences KP8/Enquatrovirus and KP8/Gamaleyavirus were 0.466 and 0.447 substitutions per site (substitutes/site), respectively, similar to that between Enquatrovirus and Gamaleyavirus genera (0.468 substitutes/site). The obtained data suggested that Klebsiella phage KP8 differs from other similar phages and may represent a new genus within the N4-like phages.

Picking Buy-Sell Signals: A Practitioner’s Perspective on Key Technical Indicators for Selected Indian Firms

Abstract The purpose of this study is to undertake technical analysis of selected companies included in the S&P CNX Nifty 50, a leading stock market index in India. We have used the stock price data of twenty leading listed firms in India for a period from January 1, 2012 through December 31, 2017. We have applied Guppy Multiple Moving Average (GMMA), Moving Average Convergence Divergence (MACD), Stochastic Relative Strength Index (Stoch RSI) and Average Directional Index (ADX) to Heikin Ashi charts to back test and provide entry and exit points for the players in the stock market. Analysis of the price information has revealed that the GMMA and ADX are effective indicators for most of the stocks under the study but they give late signals as compared to RSI and MACD. Further, the study has shown that though RSI and MACD give early signals, yet they are risky as the number of false signals generated by them is also found out to be quite high. The study is important as the findings can be used by investors, option traders and portfolio managers to get generate profitable trading signals and obtain good risk to reward ratios.

First insights into the population genetic structure and the phylogeographic status of the Mehely’s horseshoe bat Rhinolophus mehelyi (Chiroptera: Rhinolophidae) in Iran inferred from mitochondrial genes

Mehely’s horseshoe bat, Rhinolophus mehelyi Matschie 1901, is a medium-sized rhinolophid with a discontinuous distribution from North Africa and southern Europe through Asia Minor, Anatolia, to Transcaucasia and Iran. Here, we present a detailed study of the phylogeography and population genetics of this species using 745 bp of the mitochondrial cytochrome b and 522 bp of the mitochondrial D-loop genes in 46 bats in eight localities in western and southwesternIran. Based on the mtDNA sequences, we found a low degree of genetic diversity in the Iranian populations of R. mehelyi (π = 0.0032 for Cytb and π = 0.0064 for D-loop gene), that show a close relationship among the haplotypes. With a K2P genetic distance (0.38%–0.97% on D-loop and 0.22%–0.69% on Cytb), and the phylogenetic reconstruction in the Eurasia lineage two major clades are introduced. Here, the phylogenetic trees and statistical parsimony network showed all Iranian samples were grouped in the same clade, but southern Europe and North Africa samples belonged to another clade. The average genetic divergence between reciprocally monophyletic clade of the R. mehelyi group in Iran and southern Europe and North Africa were 0.69% for Cytb and 4.09% for D-Loop respectively. The analysis of population structure suggests an incongruent pattern of genetic differentiation. Mismatch distributions and neutrality tests showed recent historical demographic events as indicated by significant deviations in Fu’s FS from neutrality. The hypothesis of sudden-expansion was not rejected by analyses of mismatch distribution as the Raggedness and SSD were insignificant (p(r) = 0.11 and p (SSD) = 0.07). Similarly, the Bayesian skyline plot suggested a population expansion scenario that took place in the past 4.000 years.

Simulation of Technical Indicators for Better Profits in the Indian Stock Market

This article designs models and uses simulation to examine optimization of technical indicators in stock market: the Moving Average Convergence Divergence (MACD) and the Relative Strength Index (RSI). Based on sector-wise Nifty 50 group of companies’ daily closing price of the stocks from the year January 2013 to September 2018. This study is to demonstrate how the simulation of technical indicators MACD and RSI helps investor in reducing the trading cycles of investment with better profits in the long run. Results concluded that the experimentation of optimization of technical indicators is one-step forward in making profitable trades as it is evident from the nifty50 stocks. Furthermore, it also proves that both the optimized MACD and RSI outperformed the standard MACD, standard RSI and Buy& Hold strategy.

A variant of Horn's problem and the derivative principle

Identifying the spectrum of the sum of two given Hermitian matrices with fixed eigenvalues is the famous Horn's problem. In this note, we investigate a variant of Horn's problem, i.e., we identify the probability density function (abbr. pdf) of the diagonals of the sum of two random Hermitian matrices with given spectra. We then use it to re-derive the pdf of the eigenvalues of the sum of two random Hermitian matrices with given eigenvalues via the derivative principle, a powerful tool used to get the exact probability distribution by reducing to the corresponding distribution of diagonal entries. We can also recover Jean-Bernard Zuber's recent results. Moreover, as an illustration, we derive the analytical expressions of eigenvalues of the sum of two random Hermitian matrices from GUE(n) or Wishart ensemble by the derivative principle, respectively. We also investigate the statistics of exponential of random matrices and connect them with Golden-Thompson inequality, and partially answer a question proposed by Forrester. Some potential applications in quantum information theory, such as uniform average quantum Jensen-Shannon divergence and average coherence of uniform mixture of two orbits, are discussed.

Cooperative sensor fusion in centralized sensor networks using Cauchy–Schwarz divergence

This paper presents a new solution for statistical fusion of multi-sensor information acquired from different fields of view, in a centralized sensor network. The focus is on applications that involve tracking unknown number of objects with time-varying states. Our solution is a track-to-track fusion method in which the information contents of posteriors are combined. Existing information-theoretic solutions for track-to-track fusion in sensor networks are commonly devised based on minimizing the average information divergence from the local posteriors to the fused one. A common approach is to use Generalized Covariance Intersection rule for sensor fusion. This approach works best when all the sensors detect the same object(s), and performs poorly when fields-of-view are different. We suggest Cauchy–Schwarz divergence to be used for measuring information divergence. We demonstrate that employing Cauchy–Schwarz divergence leads to fusion rules that are generally more tolerant to imperfect consensus. We show that the proposed fusion rule for multiple Poisson posteriors is the weighted arithmetic mean of the Poisson densities. Furthermore, we derive the fusion rule for labeled multi Bernoulli filter by approximating the labeled multi Bernoulli density to its first order moment. Numerical experiments show the superior performance of our solution compared to Kullback–Leibler averaging method.

Distributed Joint Sensor Registration and Multitarget Tracking via Sensor Network

This paper addresses distributed registration of a sensor network for multitarget tracking. Each sensor gets measurements of the target position in a local coordinate frame, having no knowledge about the relative positions (referred to as drift parameters) and azimuths (referred to as orientation parameters) of its neighboring nodes. The multitarget set is modeled as an independent and identically distributed (i.i.d.) cluster random finite set (RFS), and a consensus cardinality probability hypothesis density (CPHD) filter is run over the network to recursively compute in each node the posterior RFS density. Then a suitable cost function, xpressing the discrepancy between the local posteriors in terms of averaged Kullback-Leibler divergence, is minimized with respect to the drift and orientation parameters for sensor registration purposes. In this way, a computationally feasible optimization approach for joint sensor registraton and multitarget tracking is devised. Finally, the effectiveness of the proposed approach is demonstrated through simulation experiments on both tree networks and networks with cycles, as well as with both linear and nonlinear sensors.

A gradient approximation algorithm based weight momentum for restricted Boltzmann machine

Restricted Boltzmann Machine (RBM) is a powerful generative model in deep learning that can automatically conduct learning of data probability distributions without supervision. Deep architectures can effectively enhance the capability of image feature expression in image recognition. However, their learning time is long or they present poor performance given the same running time in a deep model. To address these problems, we propose a new gradient approximation algorithm called average contrastive divergence (ACD) with weight-decay momentum for training the RBM. It is an improved contrastive divergence (CD) algorithm combined with weight-decay momentum. Different combinations of the weight momentum term are added in the pre-training and fine-tuning phases of the RBM to accelerate the network convergence and improve the classification effect. Finally, the proposed algorithm is evaluated on the MNIST database, Extended Yale B and CMU-PIE face databases. The experimental results show that the proposed learning algorithm is a better approximation of the log-likelihood gradient method and outperforms the other algorithms.

Optimal Prediction of Foreign Exchange Market Exploring the Effect of Water Cycle Algorithm Strategy on Data Segregation

The market of foreign exchange is very huge, complex, and volatile in nature. Prediction task for this market is highly challenging as the data is highly chaotic, volatile and noisy. In this work Artificial Neural Networks (ANN), Functional Link Artificial Neural Network (FLANN), Extreme Learning Machine (ELM) are the models used to predict the price. Simple Moving Average (SMA), Stochastic Oscillator, Exponential Moving Average (EMA), Momentum, Moving Average Convergence Divergence(MACD), Average True Range (ATR), Relative Strength Index (RSI), are different technical indicators used by economists to gain an insight into the market and predict the exchange rate of currency. Generally technical indicators are calculated from price, open price, low price, high price, change percentage. The proposed network is optimized by Genetic algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Water Cycle Algorithm (WCA). The dataset collected for this experiment comprises of 4000 days of past currency exchange rates of the two currency pairs that is USA Dollar (USD) to Indian Rupees (INR) (USD\INR) and Soudi Arabia Riyal (SAR) to INR (SAR\INR). The proposed datasets are segregated into many parts and each part is trained individually. Optimization techniques such as GA, DE, PSO and WCA deployed in segregated datasets as well as the whole dataset. The experimental result shows that the segregated WCA is giving the better result when WCA applied on the whole dataset. The ELM and WCA with segregated dataset produces better result than other models what experimented in this work.

The role of uncertainty on agricultural futures markets momentum trading and volatility

Abstract This paper sheds light on the role of different sources of uncertainty on agricultural futures markets momentum trading and volatility. Momentum trading is proxied by two technical analysis indicators – the moving average convergence divergence and the relative strength index – while we also consider two different concepts of uncertainty – the CBOE volatility index of the S&P500 and daily news about the stance of economic policy in the US. To capture different effects on the transmission mechanism of uncertainty shocks, we implement a Bayesian VAR approach, which accounts for time-variation in the coefficients and the variance covariance structure of the model’s innovations. The results point in favor of a time-dependent uncertainty effect on expectations of daily momentum traders in agricultural futures markets. The corresponding trades in these periods push futures prices upwards and downwards and result in an increased volatility. Direct effects of both uncertainty sources on the volatility of agricultural futures markets confirm this view.

Complete chloroplast genomes of medicinally important Teucrium species and comparative analyses with related species from Lamiaceae.

Teucrium is one of the most economically and ecologically important genera in the Lamiaceae family; however, it is currently the least well understood at the plastome level. In the current study, we sequenced the complete chloroplast (cp) genomes of T. stocksianum subsp. stenophyllum R.A.King (TSS), T. stocksianum subsp. stocksianum Boiss. (TS) and T. mascatense Boiss. (TM) through next-generation sequencing and compared them with the cp genomes of related species in Lamiaceae (Ajuga reptans L., Caryopteris mongholica Bunge, Lamium album L., Lamium galeobdolon (L.) Crantz, and Stachys byzantina K.Koch). The results revealed that the TSS, TS and TM cp genomes have sizes of 150,087, 150,076 and 150,499 bp, respectively. Similarly, the large single-copy (LSC) regions of TSS, TS and TM had sizes of 81,707, 81,682 and 82,075 bp, respectively. The gene contents and orders of these genomes were similar to those of other angiosperm species. However, various differences were observed at the inverted repeat (IR) junctions, and the extent of the IR expansion into ψrps19 was 58 bp, 23 bp and 61 bp in TSS, TS and TM, respectively. Similarly, in all genomes, the pbsA gene was present in the LSC at varying distances from the JLA (IRa-LSC) junction. Furthermore, 89, 72, and 92 repeats were identified in the TSS, TM and TS cp genomes, respectively. The highest number of simple sequence repeats was found in TSS (128), followed by TS (127) and TM (121). Pairwise alignments of the TSS cp genome with related cp genomes showed a high degree of synteny. However, relatively lower sequence identity was observed when various coding regions were compared to those of related cp genomes. The average pairwise divergence among the complete cp genomes showed that TSS was more divergent from TM (0.018) than from TS (0.006). The current study provides valuable genomic insight into the genus Teucrium and its subspecies that may be applied to a more comprehensive study.

The Performance of Exponential Moving Average, Moving Average Convergence-Divergence, Relative Strength Index and Momentum Trading Rules in the Pakistan Stock Market

Objective: To examine the performance of exponential moving average, moving average convergence and divergence, relative strength index and momentum rules in Pakistan stock market. Methods/Statistical Analysis: Exponential moving average, moving average convergence and divergence, relative strength index and momentum rules are applied on the daily closing prices of the KSE -100 index of Pakistan stock market covering the period 1st January 1997 to 31st December 2013. The predictability and profitability of these rules in Pakistan stock market was assessed by using simple t-test and “double or out strategy” respectively. Findings: Whole sample period results obtained by the application of EMA, MACD, RSI and MOM rules provided empirical evidence about EMA and MOM that all the variants of these rules possessed substantial prognostic capability and were able to make earnings superior to position trading. Nonetheless in the presence of transaction costs, only (1, 50, 1) and (1, 50, 0) variants of EMA, (12, 26, 9) and (8, 17, 9) variants of MACD and both variants of MOM were found to be profitable in Pakistan stock market. The division of the whole sample data on the basis of financial crisis and non-crisis eras disclosed that the applied trading rules provided more revenues during crisis than in non-crisis periods. This study is a remarkable contribution to the current literature on Pakistan stock market as previously very little research work has been carried out on this stock market. Application/Improvements: The findings of this study are novel and are hoped to be helpful for investors in Pakistan to earn maximum revenues with minimum risk of losing their hard earned money. Keywords: Exponential Moving Average, Momentum, Moving Average Convergence and Divergence, Pakistan Stock Market, Relative Strength Index

Applications of vertical cavity surface emitting lasers for low-pressure chemical vapor deposition reactors

Vertical cavity surface emitting lasers (VCSELs) with a wavelength of 980 nm were applied to design a low-pressure chemical vapor deposition (LPCVD) reactor as a promising heat source of excellent irradiation uniformity, rapid power controllability, and extended spatial scalability.The average divergence angle of the laser beam emitted from a myriad of cells was estimated from a comparison of the VCSEL emission distribution obtained from ray-tracing calculation with experimental measurements using a power meter. This property was closely related to irradiation uniformity and spatial scalability.The experimental temperature distribution of silicon wafers exposed to high power VCSEL beams was used to verify the performance of commercial codes on three-dimensional simulation models including conduction, convection and thermal radiation. For LPCVD reactor design, this code was used to predict the optimal placement of the VCSEL module for uniform irradiation on 300 mm diameter wafers.The best-fit values of the factors for the Arrhenius equation describing the deposition process of polycrystalline silicon (poly-Si) thin-film using silane gas species were obtained from a comparison of deposition rates from numerical simulations with experimental observations available in the literature. Using these factors and the optical characteristics of the VCSEL and Si, the deposition process on the wafer with 300 mm in diameter was simulated under various operation conditions. Especially, instead of the uniform surface temperature condition, the heat flux distribution estimated from experimental irradiations on the wafer was used for the boundary condition at the bottom surface of the wafer in order to realize a practical deposition process.Simulation using the realistic heat flux boundary conditions showed that deposition rates in the wafer edge-region were significantly reduced due to the severe temperature drop in that region, compared to that using the ideal uniform temperature condition. In order to minimize this wafer exclusion area, the emission power of the VCSEL emitters affecting the wafer edge-region was adjusted to slightly increase, resulting in a more uniform distribution of the deposition rate.

Topological rationality of supply chain networks

In this study, we apply a topologically distributed bounded rationality model to quantify the level of rationality in supply chain networks. We use the averaged Jensen-Shannon divergence values between Nash and Quantal Response equilibria for all inter-firm strategic interactions, which are represented as Prisoner’s Dilemma games, to characterise the average level of rationality in a given supply chain network. This is based on the game theoretic assumption that as the rationality of a particular interaction increases, it converges towards Nash equilibrium, in a certain strategic decision making scenario. Using this model, we demonstrate that hub-and-spoke topologies are collectively more rational compared to scale-free and random network topologies. Finally, we compare our theoretical results against the empirical findings reported for networked systems in various domains. In particular, it is shown that network topologies comprising higher average rationality levels emerge under increasingly competitive environments.

Contrasting endemism in pond-dwelling cyclic parthenogens: the Daphnia curvirostris species group (Crustacea: Cladocera)

Pond-dwelling cyclic parthenogens are often proposed to be highly vagile. However, the Holarctic biogeography of parthenogens has been hampered by very limited sampling in the eastern Palearctic. Here we examine the geographic boundaries, diversity, and connectivity across the Palearctic for the Daphnia curvirostris complex (Cladocera: Daphniidae). Nuclear (HSP90) and mitochondrial (ND2) sequence data supported the existence of five main clades (most of which corresponded to presumptive species) with one eastern Palearctic clade being novel to this study (the average mitochondrial genetic divergence from known species was 19.2%). D. curvirostris s.s. was geographically widespread in the Palearctic, with a population genetic signature consistent with postglacial expansion. The Eastern Palearctic had local nine endemic species and/or subclades (other Holarctic regions lacked more than one endemic subclade). Even though several endemic species appeared to have survived Pleistocene glaciation in the eastern Palearctic, much of the Palearctic has been recolonized by D. curvirostris s.str. from a Western Palearctic refugium. A disjunct population in Mexico also shared its haplotypes with D. curvirostris s.str., consistent with a recent introduction. The only apparently endemic North American lineage was detected in a thermally disturbed pond system in northwestern Alaska. Our results for pond-dwelling cyclic parthenogens further support the hypothesis that the Eastern Palearctic is a diversity hotspot for freshwater invertebrates.

Generating Trading Strategies Based on Candlestick Chart Pattern Characteristics

Candlestick chart patterns are widely used in stock trading decisions. Patterns of candlestick series are found to provide hints for the price of the next one. This research proposes a technique to generate stock trading strategies employing features which are shown to effectively recognize patterns in candlestick charts. The features are combined into a tree-like trading strategy using the Chi-square Automatic Interaction Detector algorithm. The technique is evaluated using actual stocks from Stock Exchange of Thailand. The results show that the generated strategies are more profitable than other popular trading techniques, such as moving average convergence divergence, exponential moving average, relative strength index, stochastic oscillator and average directional index.

A Real-Time Twitter Trend Analysis and Visualization Framework

Trend analysis over Twitter offers organizations a fast and effective way of predicting the future trends. In the recent years, a wide range of indicators and methods were used for predicting the trend on Twitter with varying results, unfortunately most of the research focused only on the emerging trends which has gained long-term attention on the Twitter platform. This article depicts trend variations, i.e. to predict whether the trend on Twitter will gain attention or not in the next few hours. Hence a novel method called: “Twitter Trend Momentum (TTM)” is introduced for trend prediction which is the enhancement of a well-known stock market indicator called moving average convergence divergence (MACD). Reason analysis for trend variation is also carried out as an extension to the authors' research work. An evaluation of the framework showed the best results which are applied to build a real-time web application called “TwitTrend.” The application acts as a real-time update and recommendation system of top trends to users.

Multigene barcoding and phylogeny of selected Engraulidae species

Anchovies (Engraulidae) are one of the ecologically important groups and often difficult to identify due to their small size and overlapping morphological characters. In the present study, reference DNA barcodes were generated for 82 individuals representing 13 species of Engraulidae family using mitochondrial Cytochrome c oxidase subunit I (COI) and 16S rRNA genes. The average genetic distance value of COI gene for conspecific, congeneric and confamilial is 0.25, 20.45 and 22.28%, respectively. Mitochondrial 16S rRNA showed an average divergence value of 0.60, 10.28 and 14.37% for within species, between species and within families, respectively. Comparison of the present study reference barcodes with the reported sequences revealed high frequency of misidentification of species and possible occurrence of cryptic species in this family. Phylogenetic tree reconstructed using different methodologies revealed monophyletic nature of genus Stolephorus and the evolutionary relationship within genus Stolephorus is defined as ([S. insularis: S. tamilensis] S. dubiosus (S. waitei [S. commersonnii: S. indicus])).