June 2016 - NumPI - Mailing list — Dipartimento di Matematica, UniPI

Call for Papers DAPS: Data Mining for Science of Success
by Luca Pappalardo 13 Jul '16

13 Jul '16

*Call for papers* *DAPS2016: The First International Workshop on Data mining for the Analysis of Performance and Success*, in conjunction with the IEEE International Conference on Data Mining (ICDM) Website: http://kdd.isti.cnr.it/daps2016/ Date: December 12, 2016, Barcelona, Spain The increasing availability of Big Data provides an unprecedented opportunity to explore the patterns underlying success. From the strategies followed by successful sportsmen to the emergence of runaway videos on YouTube, from popularity in social media to rising starts in the scientific enterprise, from widespread technologies to groundbreaking innovations, there is wealth of data that can be explored to answer common questions: How can we measure performance? What are the common patterns of success? How did successful individuals and products get to the top? What are the principles driving the dynamics of success? These are the challenging questions at the core of the emerging *Science of Success*, an interdisciplinary field that is attracting scientists from different scientific backgrounds. The purpose of this workshop is to bring together researchers from a variety of areas, all working on the problem of analyzing and understanding the patterns of success, though from different angles. The aim is to discuss: 1) the recently developed machine learning and data mining techniques that can be leveraged to address challenges in analyzing performance and success, and 2) starting from challenges in analyzing performance and success, the practical research directions in the machine learning and data mining community. The best papers of the workshop will be invited to contribute to a special issue on "Performance and Success" edited by the journal Complexity. For more information visit the workshop website: http://kdd.isti.cnr.it/daps2016, and the ICDM conference website: http://icdm2016.eurecat.org/. You can follow news about DAPS2016 on Twitter (@DAPSworkshop) and Facebook (https://www.facebook.com/DAPSworkshop/) *Important Dates:* - *Paper Submission: August 01, 2016, 23:59 PST* - Notification: September 13, 2016 - Camera ready: September 20, 2016 - Workshop: December 12, 2016 *Topics of interest:* We encourage submissions on all theoretical and practical aspects, as well as experimental studies of deployed systems. Topics of interest to the workshop include, but are not limited to, the following subject categories: - Science of Success - Big Data sources for performance and success - mathematical models of success, performance and their interplay - ranking algorithms for performance and success - patterns of success in the scientific enterprise - popularity dynamics of popularity ion social media (hashtags, viral videos, etc.) - definition and analysis of success in social networks - measures of performance and success in sports - data mining for sports analytics - anomaly, doping and fraud detection in sports performance - patterns of success in visual arts, music and writing - adoption, diffusion and success of innovations, products and technologies - novel approaches for detecting innovators and influencers - novel approaches for performance sensing and analysis - design of predictive models for performance and success - opinion mining and sentiment analysis to quantify success in social media - career success and longevity in different professions - analysis of collective success, and well-being, and development - mathematical models of success, performance and their interplay - role of networks in success *Contact information:* For problems or questions, please do not hesitate to contact the workshop organizers by email: - Luca Pappalardo: lpappalardo(a)di.unipi.it - Paolo Cintia: paolo.cintia(a)isti.cnr.it - Roberta Sinatra: sinatrar(a)ceu.edu

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Optimization & Numerical Analysis seminars: Flores-Bazan
by Federico Poloni 23 Jun '16

23 Jun '16

Speaker: Fabián Flores Bazán Affiliation: Departamento de Ingeniería Matemática, Universidad de Concepción, Chile Time and date: Monday, 4th July 2016, 15:00 Place: Sala Seminari Est, Dipartimento di Informatica, Università di Pisa Title: A semi-infinite programming approach for the circular conic StQOP: strong duality, optimality and hidden convexity Abstract: Many formulations of quadratic allocation problems, portfolio optimization problems, the maximum weight clique problem, among others, take the form as the well-known standard quadratic optimization problem, which consist in minimizing a homogeneous quadratic function on the usual simplex in the non negative orthant. We propose to analyze the same problem when the simplex is substituted by a convex and compact base of any pointed, closed, convex (possibly circular) cone. It is associated three main duals (for which a semi-infinite formulation of the primal problem is required) and establish some characterizations of strong duality with respect to each of the three duals in terms of copositivity of the Hessian of the quadratic objective function on suitable cones. Such a problem reveals a hidden convexity. Part of the results were obtained jointly with G. Cárcamo and S. Caro. This research is supported in part by CONICYT-Chile through FONDECYT 115-0973 and BASAL projects, CMM, Universidad de Chile. === Everyone is welcome! -- --federico poloni Dipartimento di Informatica, Università di Pisa http://www.di.unipi.it/~fpoloni/ tel:+39-050-2213143

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Optimization & Numerical Analysis seminars: Romani
by Federico Poloni 20 Jun '16

20 Jun '16

Speaker: Francesco Romani Affiliation: Scuola Normale Superiore, Pisa Time: Monday, 20 June 2016, h. 10:00 Place: Sala Seminari Ovest, Dipartimento di Informatica, Università di Pisa Title: Algoritmi adattivi per il Clustering convesso e non convesso basato sulla NMF simmetrica Abstract: Vengono presentati algoritmi adattivi basati sulla fattorizzazione simmetrica non negativa di matrici sia per il caso di cluster con inviluppo convesso che di cluster qualsiasi. A parte il calcolo delle distanze tra i punti, gli algoritmi non fanno uso di nozioni geometriche. Lavoro congiunto con Paola Favati (CNR), Grazia Lotti (Università di Parma), Ornella Menchi http://pages.di.unipi.it/fpoloni/files/seminari/romani.doc === Everyone is welcome! -- --federico poloni Dipartimento di Informatica, Università di Pisa http://www.di.unipi.it/~fpoloni/ tel:+39-050-2213143

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Reminder: Seminario Clustering
by Francesco Romani 20 Jun '16

20 Jun '16

Speaker: Francesco Romani Time: Monday, 20 June 2016, h. 10:00 Place: Sala Seminari Ovest, Dipartimento di Informatica, Università di Pisa Title: Algoritmi adattivi per il Clustering convesso e non convesso basati sulla NMF simmetrica Lavoro congiunto con Paola Favati (CNR), Grazia Lotti (Università di Parma), Ornella Menchi ____________________________ Prof. Francesco Romani Dipartimento di Informatica Via Buonarroti 2, 56127 PISA, ITALY Tel +39-050-2212734 FAX +39-050-2212726 http://www.di.unipi.it/~romani

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Fwd: Reminder: Seminario Clustering
by Francesco Romani 20 Jun '16

20 Jun '16

Speaker: Francesco Romani Time: Monday, 20 June 2016, h. 10:00 Place: Sala Seminari Ovest, Dipartimento di Informatica, Università di Pisa Title: Algoritmi adattivi per il Clustering convesso e non convesso basati sulla NMF simmetrica Lavoro congiunto con Paola Favati (CNR), Grazia Lotti (Università di Parma), Ornella Menchi

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Reminder: Optimization & Numerical Analysis seminars: Brugiapaglia
by Federico Poloni 20 Jun '16

20 Jun '16

Speaker: Simone Brugiapaglia Affiliation: Politecnico di Milano --- Laboratory for Modeling and Scientific Computing MOX Time: Tuesday, December 15, 11 am Place: Sala Seminari Est, Dipartimento di Informatica, Università di Pisa Title: CORSING: Sparse approximation of PDEs based on Compressed Sensing Abstract: We present a novel method for the numerical approximation of PDEs, motivated by recent developments in sparse representation, and particularly by compressed sensing. We named this approach CORSING (COmpRessed SolvING). Establishing an analogy between the sampling of a signal and the Petrov-Galerkin discretization of a PDE, the CORSING method can recover the best s-term approximation to the solution with respect to N suitable trial functions, with s<<N, by evaluating the bilinear form associated with the PDE against a randomized choice of m<<N test functions. This yields an underdetermined m x N linear system, that is solved by means of sparse optimization techniques. A theoretical analysis of the CORSING procedure is presented, based on the concepts of local a-coherence and restricted inf-sup property, along with numerical experiments that confirm the robustness and reliability of the proposed strategy. === Everyone is welcome! -- --federico poloni

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