A Long Memory Process Based Parametric Modeling and Recognition of PD Signal

We address the problem of recognition and retrieval of relatively weak industrial signal such as Partial Discharges (PD) buried in excessive noise. The major bottleneck being the recognition and suppression of stochastic pulsive interference (PI) which has similar time-frequency characteristics as PD pulse. Therefore conventional frequency based DSP techniques are not useful in retrieving PD pulses. We employ statistical signal modeling based on combination of long-memory process and probabilistic principal component analysis (PPCA). An parametric analysis of the signal is exercised for extracting the features of desired pules. We incorporate a wavelet based bootstrap method for obtaining the noise training vectors from observed data. The procedure adopted in this work is completely different from the research work reported in the literature, which is generally based on deserved signal frequency and noise frequency.

Discovering injective episodes with general partial orders

Frequent episode discovery is a popular framework for temporal pattern discovery in event streams. An episode is a partially ordered set of nodes with each node associated with an event type. Currently algorithms exist for episode discovery only when the associated partial order is total order (serial episode) or trivial (parallel episode). In this paper, we propose efficient algorithms for discovering frequent episodes with unrestricted partial orders when the associated event-types are unique. These algorithms can be easily specialized to discover only serial or parallel episodes. Also, the algorithms are flexible enough to be specialized for mining in the space of certain interesting subclasses of partial orders. We point out that frequency alone is not a sufficient measure of interestingness in the context of partial order mining. We propose a new interestingness measure for episodes with unrestricted partial orders which, when used along with frequency, results in an efficient scheme of data mining. Simulations are presented to demonstrate the effectiveness of our algorithms.

Diversity in ranking via resistive graph centers

Users can rarely reveal their information need in full detail to a search engine within 1--2 words, so search engines need to "hedge their bets" and present diverse results within the precious 10 response slots. Diversity in ranking is of much recent interest. Most existing solutions estimate the marginal utility of an item given a set of items already in the response, and then use variants of greedy set cover. Others design graphs with the items as nodes and choose diverse items based on visit rates (PageRank). Here we introduce a radically new and natural formulation of diversity as finding centers in resistive graphs. Unlike in PageRank, we do not specify the edge resistances (equivalently, conductances) and ask for node visit rates. Instead, we look for a sparse set of center nodes so that the effective conductance from the center to the rest of the graph has maximum entropy. We give a cogent semantic justification for turning PageRank thus on its head. In marked deviation from prior work, our edge resistances are learnt from training data. Inference and learning are NP-hard, but we give practical solutions. In extensive experiments with subtopic retrieval, social network search, and document summarization, our approach convincingly surpasses recently-published diversity algorithms like subtopic cover, max-marginal relevance (MMR), Grasshopper, DivRank, and SVMdiv.

A game theoretic approach for feature clustering and its application to feature selection

In this paper, we develop a game theoretic approach for clustering features in a learning problem. Feature clustering can serve as an important preprocessing step in many problems such as feature selection, dimensionality reduction, etc. In this approach, we view features as rational players of a coalitional game where they form coalitions (or clusters) among themselves in order to maximize their individual payoffs. We show how Nash Stable Partition (NSP), a well known concept in the coalitional game theory, provides a natural way of clustering features. Through this approach, one can obtain some desirable properties of the clusters by choosing appropriate payoff functions. For a small number of features, the NSP based clustering can be found by solving an integer linear program (ILP). However, for large number of features, the ILP based approach does not scale well and hence we propose a hierarchical approach. Interestingly, a key result that we prove on the equivalence between a k-size NSP of a coalitional game and minimum k-cut of an appropriately constructed graph comes in handy for large scale problems. In this paper, we use feature selection problem (in a classification setting) as a running example to illustrate our approach. We conduct experiments to illustrate the efficacy of our approach.

An overview of recent advances in structural bioinformatics of protein-protein interactions and a guide to their principles

Rich data bearing on the structural and evolutionary principles of protein protein interactions are paving the way to a better understanding of the regulation of function in the cell. This is particularly the case when these interactions are considered in the framework of key pathways. Knowledge of the interactions may provide insights into the mechanisms of crucial `driver' mutations in oncogenesis. They also provide the foundation toward the design of protein protein interfaces and inhibitors that can abrogate their formation or enhance them. The main features to learn from known 3-D structures of protein protein complexes and the extensive literature which analyzes them computationally and experimentally include the interaction details which permit undertaking structure-based drug discovery, the evolution of complexes and their interactions, the consequences of alterations such as post-translational modifications, ligand binding, disease causing mutations, host pathogen interactions, oligomerization, aggregation and the roles of disorder, dynamics, allostery and more to the protein and the cell. This review highlights some of the recent advances in these areas, including design, inhibition and prediction of protein protein complexes. The field is broad, and much work has been carried out in these areas, making it challenging to cover it in its entirety. Much of this is due to the fast increase in the number of molecules whose structures have been determined experimentally and the vast increase in computational power. Here we provide a concise overview. (C) 2014 Elsevier Ltd. All rights reserved.

Análise associativa: identificação de padrões de associação entre o perfil socioeconômico dos alunos do ensino básico e os resultados nas provas de matemática

Nos dias atuais, a maioria das operações feitas por empresas e organizações é armazenada em bancos de dados que podem ser explorados por pesquisadores com o objetivo de se obter informações úteis para auxílio da tomada de decisão. Devido ao grande volume envolvido, a extração e análise dos dados não é uma tarefa simples. O processo geral de conversão de dados brutos em informações úteis chama-se Descoberta de Conhecimento em Bancos de Dados (KDD - Knowledge Discovery in Databases). Uma das etapas deste processo é a Mineração de Dados (Data Mining), que consiste na aplicação de algoritmos e técnicas estatísticas para explorar informações contidas implicitamente em grandes bancos de dados. Muitas áreas utilizam o processo KDD para facilitar o reconhecimento de padrões ou modelos em suas bases de informações. Este trabalho apresenta uma aplicação prática do processo KDD utilizando a base de dados de alunos do 9 ano do ensino básico do Estado do Rio de Janeiro, disponibilizada no site do INEP, com o objetivo de descobrir padrões interessantes entre o perfil socioeconômico do aluno e seu desempenho obtido em Matemática na Prova Brasil 2011. Neste trabalho, utilizando-se da ferramenta chamada Weka (Waikato Environment for Knowledge Analysis), foi aplicada a tarefa de mineração de dados conhecida como associação, onde se extraiu regras por intermédio do algoritmo Apriori. Neste estudo foi possível descobrir, por exemplo, que alunos que já foram reprovados uma vez tendem a tirar uma nota inferior na prova de matemática, assim como alunos que nunca foram reprovados tiveram um melhor desempenho. Outros fatores, como a sua pretensão futura, a escolaridade dos pais, a preferência de matemática, o grupo étnico o qual o aluno pertence, se o aluno lê sites frequentemente, também influenciam positivamente ou negativamente no aprendizado do discente. Também foi feita uma análise de acordo com a infraestrutura da escola onde o aluno estuda e com isso, pôde-se afirmar que os padrões descobertos ocorrem independentemente se estes alunos estudam em escolas que possuem infraestrutura boa ou ruim. Os resultados obtidos podem ser utilizados para traçar perfis de estudantes que tem um melhor ou um pior desempenho em matemática e para a elaboração de políticas públicas na área de educação, voltadas ao ensino fundamental.

Data Analysis on Complicated Construction Data Sources: Vision, Research, and Recent Developments

Compared with construction data sources that are usually stored and analyzed in spreadsheets and single data tables, data sources with more complicated structures, such as text documents, site images, web pages, and project schedules have been less intensively studied due to additional challenges in data preparation, representation, and analysis. In this paper, our definition and vision for advanced data analysis addressing such challenges are presented, together with related research results from previous work, as well as our recent developments of data analysis on text-based, image-based, web-based, and network-based construction sources. It is shown in this paper that particular data preparation, representation, and analysis operations should be identified, and integrated with careful problem investigations and scientific validation measures in order to provide general frameworks in support of information search and knowledge discovery from such information-abundant data sources.

Digits speech recognition based on geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems.We observe that this may be true for a recognition tasks based on geometrical learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions via the Hilbert transform. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy, Experiments show method based on ICA and geometrical learning outperforms HMM in different number of train samples.

Continuous speech recognition based on ICA and geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems. We observe that this may be true for recognition tasks based on Geometrical Learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy. Experiments show that the method based on ICA and Geometrical Learning outperforms HMM in a different number of training samples.

Continuous speech recognition based on ICA and geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems. We observe that this may be true for recognition tasks based on Geometrical Learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy. Experiments show that the method based on ICA and Geometrical Learning outperforms HMM in a different number of training samples.

汉语文本语篇语句焦点确定及焦点与重音关系研究

This study investigated the method of the focus identification in Chinese text discourse and the relationship between accent and focus, large corpus analysis and decision tree were used in the research. The main results are: 1. Based on the concept of the Focus and understanding of the discourse, Foci identification is consistent and steady; 2. Special Focus markers and specific Focus constructions have greater influence than special constituent order on identifying Focus in Chinese discourse; while information states also have great influence on focus identifying; part of speech，information state, the relative position in the sentence, focus-sensitive operator, specific Focus constructions, contrast relations, relations between the sentences are important factors to focus identifying; 3. Using multi-dimensional tagging and knowledge discovery, it is a feasible way to construct and employ decision trees by computing tagging results to identify Focus; 4. Focus predicting also depends on literal types and styles of the discourse, several types of decision trees should be constructed for different literal types; 5. In the monologue discourse, the most prominent accent is located on the Focus word or in the scope of the Focus; there are some kinds of rules on accent assignment in broad Focus; it is necessary to analyze and classify focus structure for the research of relations between accent and Focus.

Data Reduction with Rough Sets

R. Jensen, Q. Shen, Data Reduction with Rough Sets, In: Encyclopedia of Data Warehousing and Mining - 2nd Edition, Vol. II, 2008.

An ontological characterization of time-series and state-sequences for data mining

Time-series and sequences are important patterns in data mining. Based on an ontology of time-elements, this paper presents a formal characterization of time-series and state-sequences, where a state denotes a collection of data whose validation is dependent on time. While a time-series is formalized as a vector of time-elements temporally ordered one after another, a state-sequence is denoted as a list of states correspondingly ordered by a time-series. In general, a time-series and a state-sequence can be incomplete in various ways. This leads to the distinction between complete and incomplete time-series, and between complete and incomplete state-sequences, which allows the expression of both absolute and relative temporal knowledge in data mining.

Temporal pattern recognition in video clips detection

Temporal representation and reasoning plays an important role in Data Mining and Knowledge Discovery, particularly, in mining and recognizing patterns with rich temporal information. Based on a formal characterization of time-series and state-sequences, this paper presents the computational technique and algorithm for matching state-based temporal patterns. As a case study of real-life applications, zone-defense pattern recognition in basketball games is specially examined as an illustrating example. Experimental results demonstrate that it provides a formal and comprehensive temporal ontology for research and applications in video events detection.

Conference report: 7th Annual Congress of International Drug Discovery Science and Technology

This is a report on the 7th Annual Congress of International Drug Discovery Science and Technology held in Shanghai, China from 22–25 October, 2009. The conference, organized by BIT Life Sciences, comprised several parallel sessions, keynote presentations and a selection of selection of 20-minute presentations covering a range of therapeutic areas, including general medicinal chemistry, oncology, inflammation, receptors and ion channels, drug, metabolism and pharmokinetics, and fragment-based drug discovery. There were also sessions devoted to genomics, biomarkers, immunology, cell biology, molecular imaging and biochips. Supported by an exhibition of services/products and posters, the conference underlined the marked presence of Asian CROs.