A computational model for periodic pattern perception based on frieze and wallpaper groups

We present a computational model for periodic pattern perception based on the mathematical theory of crystallographic groups. In each N-dimensional Euclidean space, a finite number of symmetry groups can characterize the structures of an infinite variety of periodic patterns. In 2D space, there are seven frieze groups describing monochrome patterns that repeat along one direction and 17 wallpaper groups for patterns that repeat along two linearly independent directions to tile the plane. We develop a set of computer algorithms that "understand" a given periodic pattern by automatically finding its underlying lattice, identifying its symmetry group, and extracting its representative motifs. We also extend this computational model for near-periodic patterns using geometric AIC. Applications of such a computational model include pattern indexing, texture synthesis, image compression, and gait analysis.     

基于不同算法的Motif预测比较分析与优化

研究转录因子结合位点（TFBs）的主要预测模型及其预测的算法,通过基于调控元件预测的3种代表性的算法MEME、Gibbs采样和Weeder预测拟南芥基因组。比较结果表明,Gibbs采样算法和Weeder算法预测长、短motif效率较高。重点分析MEME算法,提出结合不同算法查找motif的优化方法,并以实验验证该方法能有效提高预测效率。     

(l,d)-模体识别问题的遗传优化算法

转录因子结合位点识别在基因表达调控过程中起着重要的作用.文中提出了一种贝叶斯模型驱动的模体识别的遗传优化算法GOBMD(Genetic Optimization with Bayesian Model for Motif Discovery).GOBMD首先使用一个基于位置加权散列的投影过程,将输入序列中的l-mers投影到k维(k相似文献   

Methods for calculating the probabilities of finding patterns in sequences

This paper describes the use of probability-generating functions for calculating the probabilities of finding motifs in nucleic acid and protein sequences. Equations and algorithms are given for calculating the probabilities associated with nine different ways of defining motifs. Comparisons are made with searches of random sequences. A higher level structure--the pattern--is defined as a list of motifs. A pattern also specifies the permitted ranges of spacing allowed between its constituent motifs. Equations for calculating the expected numbers of matches to patterns are given.     

差分隐私DNA模体识别安全共享平台的设计与实现

在数据共享平台进行DNA模体识别的过程中,如何防止个体信息泄露已成为该领域发展的研究热点。对此,设计并实现了基于差分隐私的DNA模体识别安全共享平台,内置多种满足差分隐私保护模型的DNA模体识别算法,实现数据源选择、算法选择、隐私预算设置、结果评估、图形化结果展示等功能。同时,除具备对内置DNA数据的模体识别外,还允许科研人员自主上传、共享DNA数据库,并对共享的DNA数据进行差分隐私模体识别,为科研工作人员提供安全可靠的基因序列分析研究平台和数据共享平台。通过平台测试证明,安全共享平台能够实现DNA数据的有效识别和安全共享,设计方案有效可行。     

Equivalence of rational representations of behaviors

This article deals with the equivalence of representations of behaviors of linear differential systems. In general, the behavior of a given linear differential system has many different representations. In this paper we restrict ourselves to kernel and image representations. Two kernel representations are called equivalent if they represent one and the same behavior. For kernel representations defined by polynomial matrices, necessary and sufficient conditions for equivalence are well known. In this paper, we deal with the equivalence of rational representations, i. e. kernel and image representations that are defined in terms of rational matrices. As the first main result of this paper, we will derive a new condition for the equivalence of rational kernel representations of possibly noncontrollable behaviors. Secondly we will derive conditions for the equivalence of rational representations of a given behavior in terms of the polynomial modules generated by the rows of the rational matrices. We will also establish conditions for the equivalence of rational image representations. Finally, we will derive conditions under which a given rational kernel representation is equivalent to a given rational image representation.     

基于小波和动态时间弯曲的主题查找算法 *

针对以前查找图形主题算法的精度和时间复杂度不能兼容的问题,提出了基于小波和动态时间弯曲 （DTW）的形状主题查询算法。本算法先利用小波变换进行数据降维并剪枝,以降低计算查找主题的复杂度;再 应用DTW的高精度计算的特点来查找图形主题,同时结合v-shift公式,忽略了在垂直方向的距离,这样查找图 形主题可以解决在现实世界中因形状大小不同而相似的问题。实验结果表明该方法具有较高的匹配精度和较 低的计算代价,可以找到图形相似的主题,并不会产生漏报,具有很强的实际意义。     

Maximal Common Subsequences and Minimal Common Supersequences

The problems of finding a longest common subsequence and a shortest common supersequence of a set of strings are well known. They can be solved in polynomial time for two strings (in fact the problems are dual in this case), or for any fixed number of strings, by dynamic programming. But both problems are NP-hard in general for an arbitrary numberkof strings. Here we study the related problems of finding a shortest maximal common subsequence and a longest minimal common supersequence. We describe dynamic programming algorithms for the case of two strings (for which case the problems are no longer dual), which can be extended to any fixed number of strings. We also show that both problems are NP-hard in general forkstrings, although the latter problem, unlike shortest common supersequence, is solvable in polynomial time for strings of length 2. Finally, we prove a strong negative approximability result for the shortest maximal common subsequence problem.     

Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC)

Block matching (BM) motion estimation plays a very important role in video coding. In a BM approach, image frames in a video sequence are divided into blocks. For each block in the current frame, the best matching block is identified inside a region of the previous frame, aiming to minimize the sum of absolute differences (SAD). Unfortunately, the SAD evaluation is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be approached as an optimization problem, where the goal is to find the best matching block within a search space. The simplest available BM method is the full search algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of SAD values for all elements of the search window. Recently, several fast BM algorithms have been proposed to reduce the number of SAD operations by calculating only a fixed subset of search locations at the price of poor accuracy. In this paper, a new algorithm based on Artificial Bee Colony (ABC) optimization is proposed to reduce the number of search locations in the BM process. In our algorithm, the computation of search locations is drastically reduced by considering a fitness calculation strategy which indicates when it is feasible to calculate or only estimate new search locations. Since the proposed algorithm does not consider any fixed search pattern or any other movement assumption as most of other BM approaches do, a high probability for finding the true minimum (accurate motion vector) is expected. Conducted simulations show that the proposed method achieves the best balance over other fast BM algorithms, in terms of both estimation accuracy and computational cost.     

ptGAs--genetic algorithms evolving noncoding segments by means of promoter/terminator sequences

In this article we present work on chromosome structures for genetic algorithms (GAs) based on biological principles. Mainly, the influence of noncoding segments on GA behavior and performance is investigated. We compare representations with noncoding sequences at predefined, fixed locations with "junk" code induced by the use of promoter/terminator sequences (ptGAs) that define start and end of a coding sequence, respectively. As one of the advantages of noncoding segments a few researchers have identified the reduction of the disruptive effects of crossover, and we solidify this argument by a formal analysis of crossover disruption probabilities for noncoding segments at fixed locations. The additional use of promoter/terminator sequences not only enables evolution of parameter values, but also allows for adaptation of number, size, and location of genes (problem parameters) on an artificial chromosome. Randomly generated chromosomes of fixed length carry different numbers of promoter/terminator sequences resulting in genes of varying size and location. Evolution of these ptGA chromosomes drives the number of parameters and their values to (sub)optimal solutions. Moreover, the formation of tightly linked building blocks is enhanced by self-organization of gene locations. We also introduce a new, nondisruptive crossover operator emerging from the ptGA gene structure with adaptive crossover rate, location, and number of crossover sites. For experimental comparisons of this genetic operator to conventional crossover in GAs, as well as properties of different ptGA chromosome structures, an artificial problem from the literature is utilized. Finally, the potential of ptGA is demonstrated on an NP-complete combinatorial optimization problem.     

Topological invariants for lines

A set of topological invariants for relations between lines embedded in the 2-dimensional Euclidean space is given. The set of invariants is proven to be necessary and sufficient to characterize topological equivalence classes of binary relations between simple lines. The topology of arbitrarily complex geometric scenes is described with a variation of the same set of invariants. Polynomial time algorithms are given to assess topological equivalence of two scenes. Invariants and efficient algorithms is due to application areas of spatial database systems where a model for describing topological relations between planar features is sought     

Heuristics for the minimum project-duration problem with minimal and maximal time lags under fixed resource constraints

The authors consider the problem of minimizing the duration of a project under fixed resource constraints. For the case that there are only minimal time lags between the start of successive activities and that therefore the project can be described by an acyclic network, a large number of exact and heuristic algorithms can be found in the literature. In this paper, the authors permit both minimal and maximal time lags between activities. The project can then be modelled by an activityon-node network containing cycles. Efficient priority-rule heuristics are presented for solving the resource-constrained project-scheduling problem. Computational results are discussed for projects containing up to 1000 activities and several resources.     

A note on representations and variation operators

Consideration is given to the effects of representations and operators in evolutionary algorithms. In particular, theorems are presented which establish, under some general assumptions, that no choice of cardinality of a representation offers any intrinsic advantage over another. Functionally equivalent algorithms can be constructed regardless of the chosen representation. Further, a similar effective equivalence of variation operators is shown such that no intrinsic advantage accrues to any particular one-parent operator or any particular two-parent operator     

An algebraic characterisation of elementary net system (observable) state space

The paper proposes a characterisation of the state space ofan Elementary Net (EN) system in terms of a relational algebraic structure, called Local State Transformation (LST) algebra. The latter allows one to take into consideration in an explicit way local state transformations performed by the occurrence of events. A class of injective morphisms between LST algebras allows one to compare system behaviours at different levels of abstraction and to introduce a notion of preorder in the class of EN systems. This leads in a natural way to a notion of equivalence which preserves the system degree of concurrency, and whose equivalence classes have a unique (up to isomorphism) canonical representative which is minimal with respect to net elements. Moreover, the class of morphisms allows the definition of a notion of state observability and of a notion of equivalence between Observable Elementary Net Systems. All these notions provide the system designer with a set of useful tools for an incremental design process, in which the (concurrent and nondeterministic) control of the considered system need not be fixed ‘a priori’.     

Sequential Association Rule Mining with Time Lags

This paper presents MOWCATL, an efficient method for mining frequent association rules from multiple sequential data sets. Our goal is to find patterns in one or more sequences that precede the occurrence of patterns in other sequences. Recent work has highlighted the importance of using constraints to focus the mining process on the association rules relevant to the user. To refine the data mining process, this approach introduces the use of separate antecedent and consequent inclusion constraints, in addition to the traditional frequency and support constraints in sequential data mining. Moreover, separate antecedent and consequent maximum window widths are used to specify the antecedent and consequent patterns that are separated by either a maximal width time lag or a fixed width time lag.Multiple time series drought risk management data are used to show that our approach can be effectively employed in real-life problems. This approach is compared to existing methods to show how they complement each other to discover associations in the drought risk management domain. The experimental results validate the superior performance of our method for efficiently finding relationships between global climatic episodes and local drought conditions. Both the maximal and fixed width time lags are shown to be useful when finding interesting associations.     

Efficient morphological shape representation by varying overlapping levels among representative disks

The morphological skeleton transform, the morphological shape decomposition, and the overlapped morphological shape decomposition are three basic morphological shape representation schemes. In this paper, we propose a new way of generalizing these basic representation algorithms to improve representational efficiency. In all three basic algorithms, a fixed overlapping policy is used to control the overlapping relationships among representative disks of different sizes. In our new algorithm, different overlapping policies are used to generate shape components that have different overlapping relationships among themselves. The overlapping policy is selected dynamically according to local shape features. Experiments show that compared to the three basic algorithms, our algorithm produces more efficient representations with lower numbers of representative points.     

基于Matrix Profile的时间序列变长模体挖掘

已有的变长模体发现算法存在速度慢、可扩展性较差,且结果中包含过短、过长和平凡匹配等无意义模体的问题。本文提出一种基于Matrix Profile的时间序列变长模体挖掘算法。该算法使用STOMP算法作为子程序,使用结合了增量计算的下界距离来加速候选模体提取过程;采用长度相似性条件和模体分组等价类方法踢除过短、过长和平凡匹配等无意义的模体。在数据集UCR上的实验表明,提出的算法在发现变长模体时,能够有效地过滤无意义模体,且具有较高的效率和准确率。     

Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization

The MEME algorithm extends the expectation maximization (EM) algorithm for identifying motifs in unaligned biopolymer sequences. The aim of MEME is to discover new motifs in a set of biopolymer sequences where little or nothing is known in advance about any motifs that may be present. MEME innovations expand the range of problems which can be solved using EM and increase the chance of finding good solutions. First, subsequences which actually occur in the biopolymer sequences are used as starting points for the EM algorithm to increase the probability of finding globally optimal motifs. Second, the assumption that each sequence contains exactly one occurrence of the shared motif is removed. This allows multiple appearances of a motif to occur in any sequence and permits the algorithm to ignore sequences with no appearance of the shared motif, increasing its resistance to noisy data. Third, a method for probabilistically erasing shared motifs after they are found is incorporated so that several distinct motifs can be found in the same set of sequences, both when different motifs appear in different sequences and when a single sequence may contain multiple motifs. Experiments show that MEME can discover both the CRP and LexA binding sites from a set of sequences which contain one or both sites, and that MEME can discover both the –10 and –35 promoter regions in a set of E. coli sequences.