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作者： Freitas, André Da Silva, João C.P. Curry, Edward Buitelaar, Paul Department of Computer Science and Mathematics University of Passau Dr-Hans-Kapfinger Strasse 12 R.109 Passau94032 Germany Insight Centre for Data Analytics National University of Ireland Insight IDA Buisiness Park Lower Dangan Galway Ireland Computer Science Department Federal University of Rio de Janeiro CCMN-UFRJ Caixa-Postal 68530 Rio de Janeiro RJCEP: 21941-916 Brazil College of Graduate Studies University of South Africa Pretoria South Africa

Tasks such as question answering and semantic search are dependent on the ability of querying and reasoning over large-scale commonsense knowledge bases (KBs). However, dealing with commonsense data demands coping with problems such as the increase in schema complexity, semantic inconsistency, incompleteness and scalability. This paper proposes a selective graph navigation mechanism based on a distributional relational semantic model which can be applied to querying and reasoning over heterogeneous knowledge bases (KBs). The approach can be used for approximative reasoning, querying and associational knowledge discovery. In this paper we focus on commonsense reasoning as the main motivational scenario for the approach. The approach focuses on addressing the following problems: (i) providing a semantic selection mechanism for facts which are relevant and meaningful in a specific reasoning and querying context and (ii) allowing coping with information incompleteness in large KBs. The approach is evaluated using ConceptNet as a commonsense KB, and achieved high selectivity, high selectivity scalability and high accuracy in the selection of meaningful navigational paths. Distributional semantics is also used as a principled mechanism to cope with information incompleteness. © 2015 Elsevier B.V. All rights reserved.

关键词： Scalability

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Author Correction: Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors

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Nature genetics 2018年第12期50卷 1754页

作者： Huipeng Li Elise T Courtois Debarka Sengupta Yuliana Tan Kok Hao Chen Jolene Jie Lin Goh Say Li Kong Clarinda Chua Lim Kiat Hon Wah Siew Tan Mark Wong Igor Cima Min-Han Tan Lawrence J K Wee Axel M Hillmer Iain Beehuat Tan Paul Robson Shyam Prabhakar Computational and Systems Biology Genome Institute of Singapore Singapore Singapore. Developmental Cellomics Laboratory Genome Institute of Singapore Singapore Singapore. Department of Computer Science and Engineering and Center for Computational Biology Indraprastha Institute of Information Technology Delhi India. Synthetic Biology Genome Institute of Singapore Singapore Singapore. Cancer Therapeutics and Stratified Oncology Genome Institute of Singapore Singapore Singapore. Department of Medical Oncology National Cancer Centre Singapore Singapore Singapore. Department of Pathology Singapore General Hospital Singapore Singapore. Department of Colorectal Surgery Singapore General Hospital Singapore Singapore. Institute of Bioengineering and Nanotechnology Singapore Singapore. Data Analytics Department Institute for Infocomm Research Singapore Singapore. Cancer Therapeutics and Stratified Oncology Genome Institute of Singapore Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Department of Medical Oncology National Cancer Centre Singapore Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Program in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Developmental Cellomics Laboratory Genome Institute of Singapore Singapore Singapore. paul.robson@***. The Jackson Laboratory for Genomic Medicine Farmington Connecticut USA. paul.robson@***. Department of Genetics and Genome Sciences Institute for Systems Genomics University of Connecticut Farmington Connecticut USA. paul.robson@***. Department of Biological Sciences National University of Singapore Singapore Singapore. paul.robson@***. Computational and Systems Biology Genome Institute of Singapore Singapore Singapore. prabhakars@gis.a-star.edu.sg.

In the version of the article published, the author list is not accurate. Igor Cima and Min-Han Tan should have been authors, appearing after Mark Wong in the author list, while Paul Jongjoon Choi should not have been listed as an author. Igor Cima and Min-Han Tan both have the affiliation Institute of Bioengineering and Nanotechnology, Singapore, Singapore, and their contributions should have been noted in the Author Contributions section as "I.C. preprocessed Primary Cell Atlas data with inputs from M.-H.T." The following description of the contribution of Paul Jongjoon Choi should not have appeared: "P.J.C. supported the smFISH experiments." In the 'RCA: global panel' section of the Online Methods, the following sentence should have appeared as the second sentence, "An expression atlas of human primary cells (the Primary Cell Atlas) was preprocessed similarly to in ref. 55," with new reference 55 (Cima, I. et al. Tumor-derived circulating endothelial cell clusters in colorectal cancer. science Transl. Med. 8, 345ra89, 2016).

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Latent features for algorithm selection 7

Latent features for algorithm selection

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7th Annual Symposium on Combinatorial Search, SoCS 2014

作者： Malitsky, Yuri O’Sullivan, Barry Insight Centre for Data Analytics Department of Computer Science University College Cork Ireland

ISBN: (纸本)9781577356769

The success and power of algorithm selection techniques has been empirically demonstrated on numerous occasions, most noticeably in the competition settings like those for SAT, CSP, MaxSAT, QBF, etc. Yet while there is now a plethora of competing approaches, all of them are dependent on the quality of a set of structural features they use to distinguish amongst the instances. Over the years, each domain has defined and refined its own set of features, yet at their core they are mostly a collection of everything that was considered useful in the past. As an alternative to this shotgun generation of features, this paper instead proposes a more systematic approach. Specifically, the paper shows how latent features gathered from matrix decomposition are enough for a linear model to achieve a level of performance comparable to a perfect Oracle portfolio. This information can, in turn, help guide researchers to the kinds of structural features they should be looking for, or even just identifying when such features are missing. Copyright © 2014, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Artificial intelligence

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A Note on the Evaluation of Mutation Prioritization Algorithms

A Note on the Evaluation of Mutation Prioritization Algorith...

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IEEE Symposium Series on Computational Intelligence

作者： Dusan Popovic Jesse Davis Alejandro Sifrim Bart De Moor STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Department of Electrical Engineering (ESAT) KU Leuven Department of Computer Science KU Leuven Wellcome Trust Sanger Institute Wellcome Trust Genome Campus

ISBN: (纸本)9781479975617

Recent developments in the field of gene sequencing technology greatly accelerated discovery of mutations that cause various genetic disorders. At the same time, a typical sequencing experiment generates a large number of candidate mutations, hence detecting single or few causative variants is still a formidable problem. Many computational methods have been proposed to assist this process, from which a large portion employ statistical learning in some form. Consequently, each newly designed algorithm is routinely compared to other competing systems in hope to demonstrate advantageous performance. In this work we review and discuss several issues related to the current practice of evaluation of mutation prioritization algorithms and suggest possible directions for improvements.

关键词： Statistical learning Hereditary Diseases Mutation Hopefulness algorithms Base Sequence Design Algorithm Note Taking computational methods

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Proteus: A hierarchical portfolio of solvers and transformations

Proteus: A hierarchical portfolio of solvers and transformat...

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11th International Conference on the Integration of Artificial Intelligence (AI) and Operations Research (OR) Techniques in Constraint Programming, CPAIOR 2014

作者： Hurley, Barry Kotthoff, Lars Malitsky, Yuri O'Sullivan, Barry Insight Centre for Data Analytics Department of Computer Science University College Cork Ireland

ISBN: (纸本)9783319070452

In recent years, portfolio approaches to solving SAT problems and CSPs have become increasingly common. There are also a number of different encodings for representing CSPs as SAT instances. In this paper, we leverage advances in both SAT and CSP solving to present a novel hierarchical portfolio-based approach to CSP solving, which we call Proteus, that does not rely purely on CSP solvers. Instead, it may decide that it is best to encode a CSP problem instance into SAT, selecting an appropriate encoding and a corresponding SAT solver. Our experimental evaluation used an instance of Proteus that involved four CSP solvers, three SAT encodings, and six SAT solvers, evaluated on the most challenging problem instances from the CSP solver competitions, involving global and intensional constraints. We show that significant performance improvements can be achieved by Proteus obtained by exploiting alternative view-points and solvers for combinatorial problem-solving. © 2014 Springer International Publishing.

关键词： Problem solving

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Parallelizing OPTICS for multicore systems 14

Parallelizing OPTICS for multicore systems

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7th ACM India Computing Conference, COMPUTE 2014

作者： Goyal, Poonam Kumari, Sonal Kumar, Dhruv Balasubramaniam, Sundar Goyal, Navneet Advanced Data Analytics and Parallel Technologies Laboratory Department of Computer Science and Information Systems BITS-Pilani Pilani India

ISBN: (纸本)9781605588148

Parallelizing algorithms to leverage multiple cores in a processor or multiple nodes in a cluster setup is the only way forward to handle ever-increasing volumes of data. OPTICS is a well-known density based clustering algorithm to identify arbitrary shaped clusters. Since, hierarchical cluster ordering of OPTICS is sensitive to the order in which data is processed, typically a priority queue is used to maintain the order. This sequential access order makes it difficult to parallelize OPTICS. Moreover, the execution time of OPTICS increases with increase in density of data. We propose a parallel version of OPTICS for shared memory multi-core systems using a master-slave pattern for parallelization. The master runs concurrently with the slaves and distributes data to the slaves. Each slave performs neighborhood queries for a subset of data. Our approach ensures that cluster ordering matches with that of the classical OPTICS. Our solution runs in a mostly data parallel mode yielding scalable performance. We also argue that our approach is well suited for dense datasets in particular. Copyright 2014 ACM.

关键词： Hierarchical systems

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Random manhattan indexing 25

Random manhattan indexing

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25th International Workshop on database and Expert Systems Applications, DEXA 2014

作者： Zadeh, Behrang Q. Handschuh, Siegfried Insight Centre for Data Analytics National University of Ireland Galway Ireland Department of Computer Science and Mathematics University of Passau Germany

ISBN: (纸本)9781479957224

Vector space models (VSMs) are mathematically well-defined frameworks that have been widely used in text processing. In these models, high-dimensional, often sparse vectors represent text units. In an application, the similarity of vectors - and hence the text units that they represent - is computed by a distance formula. The high dimensionality of vectors, however, is a barrier to the performance of methods that employ VSMs. Consequently, a dimensionality reduction technique is employed to alleviate this problem. This paper introduces a new method, called Random Manhattan Indexing (RMI), for the construction of L1 normed VSMs at reduced dimensionality. RMI combines the construction of a VSM and dimension reduction into an incremental, and thus scalable, procedure. In order to attain its goal, RMI employs the sparse Cauchy random projections. © 2014 IEEE.

关键词： Vectors

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A decomposition approach for discovering discriminative motifs in a sequence database 10

A decomposition approach for discovering discriminative moti...

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Dixiemes Journees Francophones de Programmation par Contraintes, JFPC 2014 - 10th French-Speaking Conference on Constraint Programming, JFPC 2014

作者： Vigneron, Vincent Lesaint, David Mehta, Deepak O'Sullivan, Barry LERIA Université d'Angers France INSIGHT Centre for Data Analytics Department of Computer Science University College Cork Ireland

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ReACT: Real-time algorithm configuration through tournaments 7

ReACT: Real-time algorithm configuration through tournaments

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7th Annual Symposium on Combinatorial Search, SoCS 2014

作者： Fitzgerald, Tadhg Malitsky, Yuri O’Sullivan, Barry Tierney, Kevin Insight Centre for Data Analytics Department of Computer Science University College Cork Ireland Department of Business Information Systems University of Paderborn Germany

ISBN: (纸本)9781577356769

The success or failure of a solver is oftentimes closely tied to the proper configuration of the solver’s parameters. However, tuning such parameters by hand requires expert knowledge, is time consuming, and is error-prone. In recent years, automatic algorithm configuration tools have made significant advances and can nearly always find better parameters than those found through hand tuning. However, current approaches require significant offline computational resources, and follow a train-once methodology that is unable to later adapt to changes in the type of problem solved. To this end, this paper presents Real-time Algorithm Configuration through Tournaments (ReACT), a method that does not require any offline training to perform algorithm configuration. ReACT exploits the multi-core infrastructure available on most modern machines to create a system that continuously searches for improving parameterizations, while guaranteeing a particular level of performance. The experimental results show that, despite the simplicity of the approach, ReACT quickly finds a set of parameters that is better than the default parameters and is competitive with state-of-the-art algorithm configurators. Copyright © 2014.

关键词： Scattering parameters

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SAFE: Policy aware SPARQL query federation over RDF data cubes 7

SAFE: Policy aware SPARQL query federation over RDF data cub...

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7th International Workshop on Semantic Web Applications and Tools for Life sciences, SWAT4LS 2014

作者： Khan, Yasar Saleem, Muhammad Iqbal, Aftab Mehdi, Muntazir Hogan, Aidan Hasapis, Panagiotis Ngomo, Axel-Cyrille Ngonga Decker, Stefan Sahay, Ratnesh Insight Centre for Data Analytics NUI Galway Ireland AKSW University of Leipzig Germany Department of Computer Science Universidad de Chile Chile INTRASOFT International Luxembourg

Several query federation engines have been proposed for accessing public Linked Open data sources. However, in many domains, resources are sensitive and access to these resources is tightly controlled by stakeholders;consequently, privacy is a major concern when federating queries over such datasets. In this paper, we present SAFE, a SPARQL query federation engine that enables decentralised, policy-aware access to clinical information represented as RDF data cubes. We validate the performance of the system with experiments over real-world datasets provided by three clinical organisations as well as legacy Linked datasets. In our evaluation, we show that SAFE enables granular graph-level access control over distributed clinical RDF data cubes and efficiently reduces the query execution time when compared with general-purpose SPARQL query federation engine.

关键词： Engines

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