Tensor Analysis and Fusion of Multimodal Brain Images

Current high-throughput data acquisition technologies probe dynamical systems with different imaging modalities, generating massive data sets at different spatial and temporal resolutions posing challenging problems in multimodal data fusion. A case in point is the attempt to parse out the brain structures and networks that underpin human cognitive processes by analysis of different neuroimaging modalities (functional MRI, EEG, NIRS etc.). We emphasize that the multimodal, multi-scale nature of neuroimaging data is well reflected by a multi-way (tensor) structure where the underlying processes can be summarized by a relatively small number of components or "atoms". We introduce Markov-Penrose diagrams - an integration of Bayesian DAG and tensor network notation in order to analyze these models. These diagrams not only clarify matrix and tensor EEG and fMRI time/frequency analysis and inverse problems, but also help understand multimodal fusion via Multiway Partial Least Squares and Coupled Matrix-Tensor Factorization. We show here, for the first time, that Granger causal analysis of brain networks is a tensor regression problem, thus allowing the atomic decomposition of brain networks. Analysis of EEG and fMRI recordings shows the potential of the methods and suggests their use in other scientific domains.Comment: 23 pages, 15 figures, submitted to Proceedings of the IEE

Stochastic network formation and homophily

This is a chapter of the forthcoming Oxford Handbook on the Economics of Networks

Estudi bibliomètric any 2016. EETAC

El present document recull les publicacions indexades a la base de dades Scopus durant el període comprès entre el mesos de gener a desembre de l’any 2016, escrits per autors pertanyents a l’EETAC. Es presenten les dades recollides segons la font on s’ha publicat, els autors que han publicat, i el tipus de document publicat. S’hi inclou un annex amb la llista de totes les referències bibliogràfiques publicades.Postprint (author's final draft

Spartan Daily, May 10, 2018

Volume 150, Issue 43https://scholarworks.sjsu.edu/spartan_daily_2018/1042/thumbnail.jp

Creative Thinking and Modelling for the Decision Support in Water Management

This paper reviews the state of art in knowledge and preferences elicitation techniques. The purpose of the study was to evaluate various cognitive mapping techniques in order to conclude with the identification of the optimal technique for the NetSyMod methodology. Network Analysis – Creative System Modelling (NetSyMod) methodology has been designed for the improvement of decision support systems (DSS) with respect to the environmental problems. In the paper the difference is made between experts and stakeholders knowledge and preference elicitation methods. The suggested technique is very similar to the Nominal Group Techniques (NGT) with the external representation of the analysed problem by means of the Hodgson Hexagons. The evolving methodology is undergoing tests within several EU-funded projects such as: ITAES, IISIM, NostrumDSS.Creative modelling, Cognitive mapping, Preference elicitation techniques, Decision support

Dagstuhl Reports : Volume 1, Issue 2, February 2011

Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn

The OR/MS ecosystem: Strengths, weaknesses, opportunities, and threats

We believe that research, teaching, and practice are becoming increasingly disengaged from one another in the OR/MS ecosystem. This ecosystem comprises researchers, educators, and practitioners in its core along with end users, universities, and funding agencies. Continuing disengagement will result in OR/MS occupying only niche areas and disappearing as a distinct field even though its tools would live on. To understand the reasons for this disengagement better and to engender discussion among academics and practitioners on how to counter it, we present the ecosystem's strengths, weaknesses, opportunities, and threats. Incorporated in this paper are insights from a cluster of sessions at the 2006 INFORMS meeting in Pittsburgh (“Where Do We Want to Go in OR/MS?”) and from the literature

Education in Undergraduate Construction Management Degrees - Is it "Construction" or "Management" that is in bold type?

The proposition of this study is that the content of education in construction management degree programs has changed over time. Content has moved away from construction technology and has moved more towards generic areas of management. Here, issues arise such as prescriptive versus principle-base teaching and the degree to which experiential learning can be provided. This study explores quantifiable data to test the above proposition over an extended period of time for a selected University in Australia. The study looks at course handbook data for the construction management degree including the likes of assigned subject credit points and contact hours. From the analysis, debate and related sources of supporting information are used to extrapolate themes demonstrating the resultant changes in graduate profile arising from the analysis. Comment is also provided on the impact of such changes including the differentiation of university graduates in the past and present. The industry perspective is also canvassed in terms of how changes have affected their expectations relating to employment of University qualified graduates