A Survey on Compiler Autotuning using Machine Learning

Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase-ordering (choosing the order of applying optimizations). The compiler optimization space continues to grow due to the advancement of applications, increasing number of compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This survey summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (1) selecting the best optimizations and (2) the phase-ordering of optimizations. The survey highlights the approaches taken so far, the obtained results, the fine-grain classification among different approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated quarterly here (Send me your new published papers to be added in the subsequent version) History: Received November 2016; Revised August 2017; Revised February 2018; Accepted March 2018

ETL for data science?: A case study

Big data has driven data science development and research over the last years. However, there is a problem - most of the data science projects don't make it to production. This can happen because many data scientists don’t use a reference data science methodology. Another aggravating element is data itself, its quality and processing. The problem can be mitigated through research, progress and case studies documentation about the topic, fostering knowledge dissemination and reuse. Namely, data mining can benefit from other mature fields’ knowledge that explores similar matters, like data warehousing. To address the problem, this dissertation performs a case study about the project “IA-SI - Artificial Intelligence in Incentives Management”, which aims to improve the management of European grant funds through data mining. The key contributions of this study, to the academia and to the project’s development and success are: (1) A combined process model of the most used data mining process models and their tasks, extended with the ETL’s subsystems and other selected data warehousing best practices. (2) Application of this combined process model to the project and all its documentation. (3) Contribution to the project’s prototype implementation, regarding the data understanding and data preparation tasks. This study concludes that CRISP-DM is still a reference, as it includes all the other data mining process models’ tasks and detailed descriptions, and that its combination with the data warehousing best practices is useful to the project IA-SI and potentially to other data mining projects.A big data tem impulsionado o desenvolvimento e a pesquisa da ciência de dados nos últimos anos. No entanto, há um problema - a maioria dos projetos de ciência de dados não chega à produção. Isto pode acontecer porque muitos deles não usam uma metodologia de ciência de dados de referência. Outro elemento agravador são os próprios dados, a sua qualidade e o seu processamento. O problema pode ser mitigado através da documentação de estudos de caso, pesquisas e desenvolvimento da área, nomeadamente o reaproveitamento de conhecimento de outros campos maduros que exploram questões semelhantes, como data warehousing. Para resolver o problema, esta dissertação realiza um estudo de caso sobre o projeto “IA-SI - Inteligência Artificial na Gestão de Incentivos”, que visa melhorar a gestão dos fundos europeus de investimento através de data mining. As principais contribuições deste estudo, para a academia e para o desenvolvimento e sucesso do projeto são: (1) Um modelo de processo combinado dos modelos de processo de data mining mais usados e as suas tarefas, ampliado com os subsistemas de ETL e outras recomendadas práticas de data warehousing selecionadas. (2) Aplicação deste modelo de processo combinado ao projeto e toda a sua documentação. (3) Contribuição para a implementação do protótipo do projeto, relativamente a tarefas de compreensão e preparação de dados. Este estudo conclui que CRISP-DM ainda é uma referência, pois inclui todas as tarefas dos outros modelos de processos de data mining e descrições detalhadas e que a sua combinação com as melhores práticas de data warehousing é útil para o projeto IA-SI e potencialmente para outros projetos de data mining

The X-linked tumor suppressor TSPX downregulates cancer-drivers/oncogenes in prostate cancer in a C-terminal acidic domain dependent manner.

TSPX is a tumor suppressor gene located at Xp11.22, a prostate cancer susceptibility locus. It is ubiquitously expressed in most tissues but frequently downregulated in various cancers, including lung, brain, liver and prostate cancers. The C-terminal acidic domain (CAD) of TSPX is crucial for the tumor suppressor functions, such as inhibition of cyclin B/CDK1 phosphorylation and androgen receptor transactivation. Currently, the exact role of the TSPX CAD in transcriptional regulation of downstream genes is still uncertain. Using different variants of TSPX, we showed that overexpression of either TSPX, that harbors a CAD, or a CAD-truncated variant (TSPX[∆C]) drastically retarded cell proliferation in a prostate cancer cell line LNCaP, but cell death was induced only by overexpression of TSPX. Transcriptome analyses showed that TSPX or TSPX[∆C] overexpression downregulated multiple cancer-drivers/oncogenes, including MYC and MYB, in a CAD-dependent manner and upregulated various tumor suppressors in a CAD-independent manner. Datamining of transcriptomes of prostate cancer specimens in the Cancer Genome Atlas (TCGA) dataset confirmed the negative correlation between the expression level of TSPX and those of MYC and MYB in clinical prostate cancer, thereby supporting the hypothesis that the CAD of TSPX plays an important role in suppression of cancer-drivers/oncogenes in prostatic oncogenesis

Workshop Notes of the Sixth International Workshop "What can FCA do for Artificial Intelligence?"

International audienc

Forum Session at the First International Conference on Service Oriented Computing (ICSOC03)

The First International Conference on Service Oriented Computing (ICSOC) was held in Trento, December 15-18, 2003. The focus of the conference ---Service Oriented Computing (SOC)--- is the new emerging paradigm for distributed computing and e-business processing that has evolved from object-oriented and component computing to enable building agile networks of collaborating business applications distributed within and across organizational boundaries. Of the 181 papers submitted to the ICSOC conference, 10 were selected for the forum session which took place on December the 16th, 2003. The papers were chosen based on their technical quality, originality, relevance to SOC and for their nature of being best suited for a poster presentation or a demonstration. This technical report contains the 10 papers presented during the forum session at the ICSOC conference. In particular, the last two papers in the report ere submitted as industrial papers

Knowledge discovery for moderating collaborative projects

In today's global market environment, enterprises are increasingly turning towards collaboration in projects to leverage their resources, skills and expertise, and simultaneously address the challenges posed in diverse and competitive markets. Moderators, which are knowledge based systems have successfully been used to support collaborative teams by raising awareness of problems or conflicts. However, the functioning of a moderator is limited to the knowledge it has about the team members. Knowledge acquisition, learning and updating of knowledge are the major challenges for a Moderator's implementation. To address these challenges a Knowledge discOvery And daTa minINg inteGrated (KOATING) framework is presented for Moderators to enable them to continuously learn from the operational databases of the company and semi-automatically update the corresponding expert module. The architecture for the Universal Knowledge Moderator (UKM) shows how the existing moderators can be extended to support global manufacturing. A method for designing and developing the knowledge acquisition module of the Moderator for manual and semi-automatic update of knowledge is documented using the Unified Modelling Language (UML). UML has been used to explore the static structure and dynamic behaviour, and describe the system analysis, system design and system development aspects of the proposed KOATING framework. The proof of design has been presented using a case study for a collaborative project in the form of construction project supply chain. It has been shown that Moderators can "learn" by extracting various kinds of knowledge from Post Project Reports (PPRs) using different types of text mining techniques. Furthermore, it also proposed that the knowledge discovery integrated moderators can be used to support and enhance collaboration by identifying appropriate business opportunities and identifying corresponding partners for creation of a virtual organization. A case study is presented in the context of a UK based SME. Finally, this thesis concludes by summarizing the thesis, outlining its novelties and contributions, and recommending future research

Lessons learned from additional research analyses of unsolved clinical exome cases

BACKGROUND: Given the rarity of most single-gene Mendelian disorders, concerted efforts of data exchange between clinical and scientific communities are critical to optimize molecular diagnosis and novel disease gene discovery. METHODS: We designed and implemented protocols for the study of cases for which a plausible molecular diagnosis was not achieved in a clinical genomics diagnostic laboratory (i.e. unsolved clinical exomes). Such cases were recruited to a research laboratory for further analyses, in order to potentially: (1) accelerate novel disease gene discovery; (2) increase the molecular diagnostic yield of whole exome sequencing (WES); and (3) gain insight into the genetic mechanisms of disease. Pilot project data included 74 families, consisting mostly of parent-offspring trios. Analyses performed on a research basis employed both WES from additional family members and complementary bioinformatics approaches and protocols. RESULTS: Analysis of all possible modes of Mendelian inheritance, focusing on both single nucleotide variants (SNV) and copy number variant (CNV) alleles, yielded a likely contributory variant in 36% (27/74) of cases. If one includes candidate genes with variants identified within a single family, a potential contributory variant was identified in a total of ~51% (38/74) of cases enrolled in this pilot study. The molecular diagnosis was achieved in 30/63 trios (47.6%). Besides this, the analysis workflow yielded evidence for pathogenic variants in disease-associated genes in 4/6 singleton cases (66.6%), 1/1 multiplex family involving three affected siblings, and 3/4 (75%) quartet families. Both the analytical pipeline and the collaborative efforts between the diagnostic and research laboratories provided insights that allowed recent disease gene discoveries (PURA, TANGO2, EMC1, GNB5, ATAD3A, and MIPEP) and increased the number of novel genes, defined in this study as genes identified in more than one family (DHX30 and EBF3). CONCLUSION: An efficient genomics pipeline in which clinical sequencing in a diagnostic laboratory is followed by the detailed reanalysis of unsolved cases in a research environment, supplemented with WES data from additional family members, and subject to adjuvant bioinformatics analyses including relaxed variant filtering parameters in informatics pipelines, can enhance the molecular diagnostic yield and provide mechanistic insights into Mendelian disorders. Implementing these approaches requires collaborative clinical molecular diagnostic and research efforts

Structural Characterization of Potential Cancer Biomarker Proteins

abstract: Cancer claims hundreds of thousands of lives every year in US alone. Finding ways for early detection of cancer onset is crucial for better management and treatment of cancer. Thus, biomarkers especially protein biomarkers, being the functional units which reflect dynamic physiological changes, need to be discovered. Though important, there are only a few approved protein cancer biomarkers till date. To accelerate this process, fast, comprehensive and affordable assays are required which can be applied to large population studies. For this, these assays should be able to comprehensively characterize and explore the molecular diversity of nominally "single" proteins across populations. This information is usually unavailable with commonly used immunoassays such as ELISA (enzyme linked immunosorbent assay) which either ignore protein microheterogeneity, or are confounded by it. To this end, mass spectrometric immuno assays (MSIA) for three different human plasma proteins have been developed. These proteins viz. IGF-1, hemopexin and tetranectin have been found in reported literature to show correlations with many diseases along with several carcinomas. Developed assays were used to extract entire proteins from plasma samples and subsequently analyzed on mass spectrometric platforms. Matrix assisted laser desorption ionization (MALDI) and electrospray ionization (ESI) mass spectrometric techniques where used due to their availability and suitability for the analysis. This resulted in visibility of different structural forms of these proteins showing their structural micro-heterogeneity which is invisible to commonly used immunoassays. These assays are fast, comprehensive and can be applied in large sample studies to analyze proteins for biomarker discovery.Dissertation/ThesisM.S. Biochemistry 201