ILP heuristics and a new exact method for bi-objective 0/1 ILPs: Application to FTTx-network design

Heuristics and metaheuristics are inevitable ingredients of most of the general purpose ILP solvers today, because of their contribution to the significant boost of the performance of exact methods. In the field of bi/multi-objective optimization, to the best of our knowledge, it is still not very common to integrate ILP heuristics into exact solution frameworks. This paper aims to bring a stronger attention of both the exact and metaheuristic communities to still unexplored possibilities for performance improvements of exact and heuristic multi-objective optimization algorithms.We focus on bi-objective optimization problems whose feasible solutions can be described as 0/1 integer linear programs and propose two ILP heuristics, boundary induced neighborhood search (BINS) and directional local branching. Their main idea is to combine the features and explore the neighborhoods of solutions that are relatively close in the objective space. A two-phase ILP-based heuristic framework relying on BINS and directional local branching is introduced. Moreover, a new exact method called adaptive search in objective space (ASOS) is also proposed. ASOS combines features of the ϵ-constraint method with the binary search in the objective space and uses heuristic solutions produced by BINS for guidance. Our new methods are computationally evaluated on two problems of particular relevance for the design of FTTx-networks. Comparison with other known exact methods (relying on the exploration of the objective space) is conducted on a set of realistic benchmark instances representing telecommunication access networks from Germany.     

Finger texture biometric verification exploiting Multi-scale Sobel Angles Local Binary Pattern features and score-based fusion

In this paper a new feature extraction method called Multi-scale Sobel Angles Local Binary Pattern (MSALBP) is proposed for application in personal verification using biometric Finger Texture (FT) patterns. This method combines Sobel direction angles with the Multi-Scale Local Binary Pattern (MSLBP). The resulting characteristics are formed into non-overlapping blocks and statistical calculations are implemented to form a texture vector as an input to an Artificial Neural Network (ANN). A Probabilistic Neural Network (PNN) is applied as a multi-classifier to perform the verification. In addition, an innovative method for FT fusion based on individual finger contributions is suggested. This method is considered as a multi-object verification, where a finger fusion method named the Finger Contribution Fusion Neural Network (FCFNN) is employed for the five fingers. Two databases have been employed in this paper: PolyU3D2D and Spectral 460 nm (S460) from CASIA Multi-Spectral (CASIA-MS) images. The MSALBP feature extraction method has been examined and compared with different Local Binary Pattern (LBP) types; in classification it yields the lowest Equal Error Rate (EER) of 0.68% and 2% for PolyU3D2D and CASIA-MS (S460) databases, respectively. Moreover, the experimental results revealed that our proposed finger fusion method achieved superior performance for the PolyU3D2D database with an EER of 0.23% and consistent performance for the CASIA-MS (S460) database with an EER of 2%.     

基于全局和局部路径规划的人群运动研究与仿真

虚拟现实技术是多媒体技术广泛应用后兴起的计算机高新技术。大规模虚拟人群仿真得到了国内外很多学者的关注。大规模的虚拟人群仿真又分为人群绘制和路径规划两个研究方向。本文在复杂的环境中利用全局和局部路径二级规划算法,为大规模的虚拟人群实时规划出一条无碰撞的路径。     

基于零空间分析的张量局部Fisher判别方法

结合局部Fisher判别、张量子空间学习和零空间分析等技术的优点,提出了一种基于零空间分析的张量局部Fisher判别算法,其特点包括:i) 引入类间判别信息,对局部Fisher判别技术进行调整,提升了算法识别性能并且降低了计算时间复杂度;ii) 通过张量型降维思想对输入样本进行双边投影变换而非单边投影,获得了更高的信息压缩率;iii) 随着训练样本量的变化,可采用基于零空间分析的求解方法和传统的直接迭代更新计算方法。通过ORL、Yale和ExYaleB 3个人脸数据库验证了所提算法的性能。     

A compact hybrid feature vector for an accurate secondary structure prediction

Amino acid propensity score is one of the earliest successful methods used in protein secondary structure prediction. However, the score performs poorly on small-sized datasets and low-identity protein sequences. Based on current in silico method, secondary structure can be predicted from local folds or local protein structure. In biology, the evolution of secondary structure produces local protein structure with different lengths. To precisely predict secondary structures, we propose a derivative feature vector, DPS that utilizes the optimal length of the local protein structure. DPS is the unification of amino acid propensity score and dihedral angle score. This new feature vector is further normalized to level the edges. Prediction is performed by support vector machines (SVM) over the DPS feature vectors with class labels generated by secondary structure assignment method (SSAM) and secondary structure prediction method (SSPM). All experiments are carried out on RS126 sequences. The results from this proposed method also highlight the overall accuracy of our method compared to other state-of-the-art methods. The performance of our method was acceptable specifically in dealing with low number and low identity sequences.     

A structure-preserved local matching approach for face recognition

In this paper, a novel local matching method called structure-preserved projections (SPP) is proposed for face recognition. Unlike most existing local matching methods which neglect the interactions of different sub-pattern sets during feature extraction, i.e., they assume different sub-pattern sets are independent; SPP takes the holistic context of the face into account and can preserve the configural structure of each face image in subspace. Moreover, the intrinsic manifold structure of the sub-pattern sets can also be preserved in our method. With SPP, all sub-patterns partitioned from the original face images are trained to obtain a unified subspace, in which recognition can be performed. The efficiency of the proposed algorithm is demonstrated by extensive experiments on three standard face databases (Yale, Extended YaleB and PIE). Experimental results show that SPP outperforms other holistic and local matching methods.     

An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows

Binary image representation is essential format for document analysis. In general, different available binarization techniques are implemented for different types of binarization problems. The majority of binarization techniques are complex and are compounded from filters and existing operations. However, the few simple thresholding methods available cannot be applied to many binarization problems. In this paper, we propose a local binarization method based on a simple, novel thresholding method with dynamic and flexible windows. The proposed method is tested on selected samples called the DIBCO 2009 benchmark dataset using specialized evaluation techniques for binarization processes. To evaluate the performance of our proposed method, we compared it with the Niblack, Sauvola and NICK methods. The results of the experiments show that the proposed method adapts well to all types of binarization challenges, can deal with higher numbers of binarization problems and boosts the overall performance of the binarization.     

Estimating inter-group interaction radius for point processes with nested spatial structures

A statistical procedure is proposed in order to estimate the interaction radius between points of a non-stationary point process when the process can present local aggregated and regular patterns. The model under consideration is a hierarchical process with two levels, points and clusters of points. Points will represent individuals, clusters will represent groups of individuals. Points or clusters do not interact as soon as they are located beyond a given interaction radius, and are assumed to interact if their distance is less than this interaction radius. Interaction radius estimation is performed in the following way. For a given distance, observations are split into several clusters whose in-between distances are larger than this distance. For each cluster, a neighbourhood and an area in which this cluster is randomly located is defined under the assumption that the distance between the cluster and its neighbourhood is larger than the interaction radius. The p-value of a test of this assumption is then computed for each cluster. Modelling the expectation of this p-value as a function of the distance leads to an estimate of the interaction radius by a least-square method. This approach is shown to be robust against non-stationarity. Unlike most classical approaches, this method makes no assumption on the point spatial distribution inside the clusters. Two applications are presented in animal and plant ecology.     

Patterns for multigrid equidistributed functions: Application to general parabolas and length estimation

We show that for some special functions (called k-multigrid equidistributed functions), we can compute the limit of the frequency of patterns in the discretization of their graph, when the resolution tends to zero. This result is applied to parabolas. We deduce also that local length estimators almost never converge to the length for the parabolas.     

基于理论线性溶剂化能关系研究两类局部麻醉药物的疏水性质

用理论线性溶剂化能关系建立了两类局部麻醉药物 (咪唑烷二酮类和苄醇类 )的结构—疏水性关系。结果表明 :分子的体积越大 ,两类化合物的亲脂性越强。另外 ,对咪唑烷二酮而言 ,分子的氢键碱度与其LogP值呈负相关 ,而苄醇类化合物的氢键酸度越大 ,对亲脂性越有利。