A Portfolio Selection Method Based on Pattern Matching with Dual Information of Direction and Distance

Pattern matching method is one of the classic classifications of existing online portfolio selection strategies. This article aims to study the key aspects of this method—measurement of similarity and selection of similarity sets, and proposes a Portfolio Selection Method based on Pattern Matching with Dual Information of Direction and Distance (PMDI). By studying different combination methods of indicators such as Euclidean distance, Chebyshev distance, and correlation coefficient, important information such as direction and distance in stock historical price information is extracted, thereby filtering out the similarity set required for pattern matching based investment portfolio selection algorithms. A large number of experiments conducted on two datasets of real stock markets have shown that PMDI outperforms other algorithms in balancing income and risk. Therefore, it is suitable for the financial environment in the real world.

Infection of the Anther-smut Microbotryum violaceum in Populations of Silene dioica: Variation in Floral Morphology, Patterns of Spore Deposition and Pathogen-Pollinator Mediated Selection

The anther-smut Microbotryum violaceum ( Brandenburger and Schvinn) G. Deml. and Oberw.) causes a systematic infection of its host Silene dioica ( L.) Clairv., resulting in sterility and production of teliospores (dispersal propagules) in flowers. These spores are transmitted to healthy plants mainly by flower visitors. The behavioral responses of flower visitors to a variation in floral characters are not only likely to affect rates of pollen export/import, but also the rate of spore deposition and probability of disease. In a transplantation experiment, using plants from four different populations, we tested for correlation between variation in female floral morphology and patterns of spore and pollen deposition, and a resulting risk of disease. The source populations in this experiment were located on four islands in Skeppsvik archipelago in northern Sweden, and represented a gradient of disease incidence from completely healthy ( Island 1), low incidence ( Island 2) to high incidences ( Islands 3 and 4) of disease. Fifty plants from each population were transplanted to the center, of the population on Island 4. There were significant differences among the transplants in floral characters, i.e. corolla size, style length and ovule number. Plants from the non-diseased population had larger flowers and longer styles than plants from the highly diseased populations. Numbers of pollen grains and spores deposited on flowers were strongly and positively correlated. We found that plants originating from the non-diseased population captured approximately 4 times more pollen and 9 times more spores, per flower than die individuals from the resident population (Island 4, population 4). The incidences of disease among plants, from the four populations differed significantly, and was 37%, 20%, 18% and 0 for populations 1, 2, 3 and 4 respectively. In a survey of ten populations we found a significant negative correlation between the mean style length ( positively correlated with corolla size and ovule number) among healthy plants and incidence of disease in these populations. The potentiality for pathogen-pollinator mediated selection oil floral characters; and consequences for gone flow between populations of Silene dioica are discussed.

Turing pattern selection for a plant-wrack model with cross-diffusion

We investigate the Turing instability and pattern formation mechanism of a plant-wrack model with both self-diffusion and cross-diffusion terms.We first study the effect of self-diffusion on the stability of equilibrium.We then derive the conditions for the occurrence of the Turing patterns induced by cross-diffusion based on self-diffusion stability.Next,we analyze the pattern selection by using the amplitude equation and obtain the exact parameter ranges of different types of patterns,including stripe patterns,hexagonal patterns and mixed states.Finally,numerical simulations confirm the theoretical results.

Poly-view Analysis on Endogenous Innovation and Corporation’s Selection of Innovation Patterns

Today, there are still some amphibolous understanding on the concepts of endogenous innovation and its three patterns in academia field. Grasping the intension and extension of these concepts will contribute to direct the innovation undertakers engaging innovation and the governments establishing innovation system. In order to achieve such aims, we firstly introduced the concept of endogenous innovation and its three patterns. Secondly, this paper established a concept model to describe the typical route of a technology＇s forming. Based on this model, we went deep into analysis on original innovation, integrative innovation, and re-innovation after digesting the introduced technology from different views. Through these analyses, we also discussed which kind of innovation is suitable for different corporations. In the last part of this paper, we summarized our main conclusions and gave some suggestions to the corporations and governments.

Three-dimensional interfacial wave theory of dendritic growth: (Ⅱ). non-axi-symmetric global wave modes and selection criterion of pattern formation

This paper is ttie continuation of part （Ⅰ）, which completes the derivations of the 3D global wave modes solutions, yields the stability criterion and, on the basis of the results obtained, demonstrates the selection criterion of pattern formation.

Nest-site selection pattern of Grus japonensis in Zhalong Nature Reserve of northeast China

Nest-site selection patterns of Red-crowned cranes（Grus japonensis） and the effects of environmental variables were studied during the years of 2002-2008 in Zhalong Nature Reserve,Qiqihar city,northeast China.The nest-site selection pattern of Red-crowned cranes included two orders and three choices：the choice of nest-site habitat type within the macro-habitat order,nest zone selection and nest-site micro-habitat selection within the micro-habitat order.Various habitats（such as Carex swamps and reed fire districts） can be selected as the nest sites for Red-crowned cranes,of which reed swamps（93.15%） are given a preference.Factor Analysis reveals that the micro-habitat selection are affected by four main factors：fire,security（concealment /disturbance）,incubation（conditions,nest-material）,and food.Further analysis reveals that Red-crowned cranes have certain adaptability to the changes of nesting habitat quality in the Zhalong wetlands.In conclusion,fire,reeds,and water were the most important variables for nest-site habitat selection of Red-crowned Cranes in Zhalong Nature Reserve.

Turing pattern selection in a reaction-diffusion epidemic model

We present Turing pattern selection in a reaction-diffusion epidemic model under zero-flux boundary conditions. The value of this study is twofold. First, it establishes the amplitude equations for the excited modes, which determines the stability of amplitudes towards uniform and inhomogeneous perturbations. Second, it illustrates all five categories of Turing patterns close to the onset of Turing bifurcation via numerical simulations which indicates that the model dynamics exhibits complex pattern replication： on increasing the control parameter v, the sequence ＂H0 hexagons → H0-hexagon-stripe mixtures →stripes → Hπ-hexagon-stripe mixtures → Hπ hexagons＂ is observed. This may enrich the pattern dynamics in a diffusive epidemic model.

Nest site selection of white-naped crane(Grus vipio) at Zhalong National Nature Reserve, Heilongjiang, China

White-naped crane （Grus vipio） is a globally threatened spe- cies. It is very important to analyze its nest site selection in circum- stances where there are multiple disturbances, and also helpful to accu- mulate valuable information about this threatened species and supply scientific suggestions for conservation and management. We studied nest site selection and the effects of environmental variables on nesting habits of white-naped crane at Zhalong National Nature Reserve, Qiqihar City, Heilongiiang, China, during March-May of 2002-2008. White-naped crane responded and adapted to changes in the quality of the spatial environments of landscape and microhabitat under multiple environ- mental disturbances. Nest site selection included two scales and two choices, namely the choice of nest site habitat type within the macro-habitat scale and nest site micro-habitat selection within the mi- cro-habitat scale. Nest sites were recorded only in reed marshes. The choice of nest site micro-habitat included three basic elements and six factors, namely incubation element （nest parameters factor, incubation temperature factor and incubation humidity factor）, safety element （pro- tection factor and concealment factor）, and food element （water factor）. Water, remnant reed clusters, and fire were major resource management challenges during the breeding period for the white-naped crane in this Reserve.

Feature Selection with Optimal Stacked Sparse Autoencoder for Data Mining

Data mining in the educational field can be used to optimize the teaching and learning performance among the students.The recently developed machine learning(ML)and deep learning(DL)approaches can be utilized to mine the data effectively.This study proposes an Improved Sailfish Optimizer-based Feature SelectionwithOptimal Stacked Sparse Autoencoder(ISOFS-OSSAE)for data mining and pattern recognition in the educational sector.The proposed ISOFS-OSSAE model aims to mine the educational data and derive decisions based on the feature selection and classification process.Moreover,the ISOFS-OSSAEmodel involves the design of the ISOFS technique to choose an optimal subset of features.Moreover,the swallow swarm optimization(SSO)with the SSAE model is derived to perform the classification process.To showcase the enhanced outcomes of the ISOFSOSSAE model,a wide range of experiments were taken place on a benchmark dataset from the University of California Irvine(UCI)Machine Learning Repository.The simulation results pointed out the improved classification performance of the ISOFS-OSSAE model over the recent state of art approaches interms of different performance measures.

Control strategy of central pattern generator gait movement under condition of attention selection

As a typical rhythmic movement, human being＇s rhythmic gait movement can be generated by a central pattern generator （CPG） located in a spinal cord by self- oscillation. Some kinds of gait movements are caused by gait frequency and amplitude variances. As an important property of human being＇s motion vision, the attention selection mechanism plays a vital part in the regulation of gait movement. In this paper, the CPG model is amended under the condition of attention selection on the theoretical basis of Matsuoka neural oscillators. Regulation of attention selection signal for the CPG model parameters and structure is studied, which consequentially causes the frequency and amplitude changes of gait movement output. Further, the control strategy of the CPG model gait movement under the condition of attention selection is discussed, showing that the attention selection model can regulate the output model of CPG gait movement in three different ways. The realization of regulation on the gait movement frequency and amplitude shows a variety of regulation on the CPG gait movement made by attention selection and enriches the controllability of CPG gait movement, which demonstrates potential influence in engineering applications.

Unsupervised Feature Selection for Latent Dirichlet Allocation

As a generative model,Latent Dirichlet Allocation Model,which lacks optimization of topics' discrimination capability focuses on how to generate data,This paper aims to improve the discrimination capability through unsupervised feature selection.Theoretical analysis shows that the discrimination capability of a topic is limited by the discrimination capability of its representative words.The discrimination capability of a word is approximated by the Information Gain of the word for topics,which is used to distinguish between "general word" and "special word" in LDA topics.Therefore,we add a constraint to the LDA objective function to let the "general words" only happen in "general topics" other than "special topics".Then a heuristic algorithm is presented to get the solution.Experiments show that this method can not only improve the information gain of topics,but also make the topics easier to understand by human.

Feature selection algorithm for text classification based on improved mutual information

In order to solve the poor performance in text classification when using traditional formula of mutual information （MI） , a feature selection algorithm were proposed based on improved mutual information. The improved mutual information algorithm, which is on the basis of traditional improved mutual information methods that enbance the MI value of negative characteristics and feature＇ s frequency, supports the concept of concentration degree and dispersion degree. In accordance with the concept of concentration degree and dispersion degree, formulas which embody concentration degree and dispersion degree were constructed and the improved mutual information was implemented based on these. In this paper, the feature selection algorithm was applied based on improved mutual information to a text classifier based on Biomimetic Pattern Recognition and it was compared with several other feature selection methods. The experimental results showed that the improved mutu- al information feature selection method greatly enhances the performance compared with traditional mutual information feature selection methods and the performance is better than that of information gain. Through the introduction of the concept of concentration degree and dispersion degree, the improved mutual information feature selection method greatly improves the performance of text classification system.

Pattern selection in a predation model with self and cross diffusion

In this paper, we present the amplitude equations for the excited modes in a cross-diffusive predator-prey model with zero-flux boundary conditions. From these equations, the stability of patterns towards uniform and inhomogenous perturbations is determined. Furthermore, we present novel numerical evidence of six typical turing patterns, and find that the model dynamics exhibits complex pattern replications： for μ1 〈μ ≤μ2, the steady state is the only stable solution of the model; for μ2 〈 μ ≤ μ4, by increasing the control parameter μ, the sequence Hπ-hexagons→ Hπ- hexagon-stripe mixtures → stripes → H0-hexagon-stripe mixtures → H0-hexagons is observed; for μ 〉 μ4, the stripe pattern emerges. This may enrich the pattern formation in the cross-diffusive predatorprey model.

Bidirectional Automated Branch and Bound Algorithm for Feature Selection

Feature selection is a process where a minimal feature subset is selected from an original feature set according to a certain measure. In this paper, feature relevancy is defined by an inconsistency rate. A bidirectional automated branch and bound algorithm is presented. It is a new complete search algorithm for feature selection, which performs feature deletion and feature addition in parallel. Its bound is determined by inconsistency rate of the original feature set, hence termed as ‘automated’. Experimental study shows that it is fit for feature selection.

A FEATURE SELECTION ALGORITHM DESIGN AND ITS IMPLEMENTATION IN INTRUSION DETECTION SYSTEM

Objective Present a new features selection algorithm. Methods based on rule induction and field knowledge. Results This algorithm can be applied in catching dataflow when detecting network intrusions, only the sub dataset including discriminating features is catched. Then the time spend in following behavior patterns mining is reduced and the patterns mined are more precise. Conclusion The experiment results show that the feature subset catched by this algorithm is more informative and the dataset’s quantity is reduced significantly.

A New Hybrid Feature Selection Method Using T-test and Fitness Function

Feature selection(FS)(or feature dimensional reduction,or feature optimization)is an essential process in pattern recognition and machine learning because of its enhanced classification speed and accuracy and reduced system complexity.FS reduces the number of features extracted in the feature extraction phase by reducing highly correlated features,retaining features with high information gain,and removing features with no weights in classification.In this work,an FS filter-type statistical method is designed and implemented,utilizing a t-test to decrease the convergence between feature subsets by calculating the quality of performance value(QoPV).The approach utilizes the well-designed fitness function to calculate the strength of recognition value(SoRV).The two values are used to rank all features according to the final weight(FW)calculated for each feature subset using a function that prioritizes feature subsets with high SoRV values.An FW is assigned to each feature subset,and those with FWs less than a predefined threshold are removed from the feature subset domain.Experiments are implemented on three datasets:Ryerson Audio-Visual Database of Emotional Speech and Song,Berlin,and Surrey Audio-Visual Expressed Emotion.The performance of the F-test and F-score FS methods are compared to those of the proposed method.Tests are also conducted on a system before and after deploying the FS methods.Results demonstrate the comparative efficiency of the proposed method.The complexity of the system is calculated based on the time overhead required before and after FS.Results show that the proposed method can reduce system complexity.

A bidirectional feature selection method based on mutual information and redundancy-synergy coefficient

Feature subset selection is a fundamental problem of data mining. The mutual information of feature subset is a measure for feature subset containing class feature information. A hashing mechanism is proposed to calculate the mutual information of feature subset. The feature relevancy is defined by mutual information. Redundancy-synergy coefficient, a novel redundancy and synergy measure for features to describe the class feature, is defined. In terms of information maximization rule, a bidirectional heuristic feature subset selection method based on mutual information and redundancy-synergy coefficient is presented. This study’s experiments show the good performance of the new method.

An enhanced PSO-DEFS based feature selection with biometric authentication for identi¯cation of diabetic retinopathy

Recently,automatic diagnosis of diabetic retinopathy(DR)from the retinal image is the most significant ressearch topic in the medical applications.Diabetic macular edema(DME)is the.major reason for the loss of vision in patients suffering fom DR.Early identification of the DR enables to prevent the vision loss and encourage diabetic control activities.Many techniques are.developed to diagnose the DR.The major drawbacks of the existing techniques are low accuracy and high time complexity.To owercome these issues,this paper propases an enhanced particle swarm optimization differential evolution feature selection(PSO DEFS)based feature selection approach with biometric aut hentication for the identification of DR.Initially,a hybrid median filter(HMF)is used for pre processing the input images.Then,the pre-processed images are embedded with each other by using least significant bit(LSB)for authentication purpose.Si-multaneously,the image features are extracted using convoluted local tetra pattern(CLTrP)and Tamura features.Feature selection is performed using PSO DEFS and PSO-gravitational search algorithm(PSO GSA)to reduce time complexity.Based on some performance metrics,the PSO-DEFS is chosen as a better choice for feature selection.The feature selection is performed based on the fitness value.A multi-relevance vector machine(M-RVM)is introduced to dlassify the 13 normal and 62 abnormal images among 75 images from 60 patients.Finally,the DR patients are further dassified by M-RVM.The experimental results exhibit that the proposed approach achieves better accuracy,sensitivity,and specificity than the exist ing techniques.

Antenna selection for MIMO system based on pattern recognition

This paper proposes a novel Multiple-Input Multiple-Output (MIMO) transmission scheme based on Pattern Recognition (PR), which is termed as the PR aided Transmission Antenna Selection MIMO (PR-TAS aided MIMO). As the conventional TAS algorithms need to search all possible legitimate antenna subsets, they may impose some redundant calculations. In order to avoid this problem, we employ some pattern recognition methods to carry out the TAS algorithm in this paper. To be specific, two PR algorithms, namely the K-Nearest Neighbor (KNN) algorithm and the Support Vector Machine (SVM) algorithm, are introduced and redesigned to obtain a TAS with lower complexity but higher efficiency. Moreover, in order to improve the performance of the SVM, we propose a new feature extraction of channel matrix for the TAS. Our simulation results show that the proposed KNN and SVM based PR-TAS algorithms are capable of striking a flexible tradeoff between the complexity and the Bit Error Rate (BER), and the new feature can effectively improve the BER performance compared with the conventional feature extraction method.

A numerical study on pattern selection in crystal growth by using anisotropic lattice Boltzmann-phase field method

Pattern selection during crystal growth is studied by using the anisotropic lattice Boltzmann-phase field model.In the model,the phase transition,melt flows,and heat transfer are coupled and mathematically described by using the lattice Boltzmann(LB)scheme.The anisotropic streaming-relaxation operation fitting into the LB framework is implemented to model interface advancing with various preferred orientations.Crystal pattern evolutions are then numerically investigated in the conditions of with and without melt flows.It is found that melt flows can significantly influence heat transfer,crystal growth behavior,and phase distributions.The crystal morphological transition from dendrite,seaweed to cauliflower-like patterns occurs with the increase of undercoolings.The interface normal angles and curvature distributions are proposed to quantitatively characterize crystal patterns.The results demonstrate that the distributions are corresponding to crystal morphological features,and they can be therefore used to describe the evolution of crystal patterns in a quantitative way.