Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Understanding people through the aggregation of their digital footprints

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 160-172).Every day, millions of people encounter strangers online. We read their medical advice, buy their products, and ask them out on dates. Yet our views of them are very limited; we see individual communication acts rather than the person(s) as a whole. This thesis contends that socially-focused machine learning and visualization of archived digital footprints can improve the capacity of social media to help form impressions of online strangers. Four original designs are presented that each examine the social fabric of a different existing online world. The designs address unique perspectives on the problem of and opportunities offered by online impression formation. The first work, Is Britney Spears Span?, examines a way of prototyping strangers on first contact by modeling their past behaviors across a social network. Landscape of Words identifies cultural and topical trends in large online publics. Personas is a data portrait that characterizes individuals by collating heterogenous textual artifacts. The final design, Defuse, navigates and visualizes virtual crowds using metrics grounded in sociology. A reflection on these experimental endeavors is also presented, including a formalization of the problem and considerations for future research. A meta-critique by a panel of domain experts completes the discussion.by Aaron Robert Zinman.Ph.D

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Nonlinear Dynamics

This volume covers a diverse collection of topics dealing with some of the fundamental concepts and applications embodied in the study of nonlinear dynamics. Each of the 15 chapters contained in this compendium generally fit into one of five topical areas: physics applications, nonlinear oscillators, electrical and mechanical systems, biological and behavioral applications or random processes. The authors of these chapters have contributed a stimulating cross section of new results, which provide a fertile spectrum of ideas that will inspire both seasoned researches and students

Processes of hybrid knowledge creation in pastoralist development

This thesis addresses an under-researched disjunction surrounding knowledge creation between, and within, development and pastoralist groups. Many academics increasingly recognise pastoralist populations as creative and adaptable, yet these populations often lack the resources to develop innovations beyond the local context. Despite often being better resourced than pastoralist communities, development interventions in the Horn of Africa have achieved limited successes; an observation often linked in academic literature with a failure to rethink inappropriate established practices drawn from settled agriculture. The need to explore new ways of understanding hybrid knowledge creation in pastoralist settings emerged from the international community’s limited understanding of informal innovation processes and unique contexts of pastoralist regions, due in part to the unsuitability of current frameworks and research tools for conceptualising informal innovation in marginal settings. This study makes an original research contribution by exploring the factors that shape processes of knowledge creation between development and pastoralist groups to answer the question what factors influence innovation in pastoralist areas? An interconnected, mixed-methods research strategy was developed and applied to study the role of knowledge networks and framings in processes of knowledge creation amongst pastoralist and development actors innovating in North Horr, Kenya. The empirical data gathered throughout the research informed the development of an internally-valid analytical framework with which to explore innovation in this setting. The key findings of this study highlight the importance of the contextual and often asymmetric nature of relationships in processes of emergent knowledge creation within pastoralist development. The observations collected throughout the research process provide an empirical basis from which to discuss networks, framings, and knowledge creation in pastoralist settings; contributing to wider debates surrounding informal innovation processes and narratives of pastoralist development

EUSN 2021 Book of Abstracts, Fifth European Conference on Social Networks

Book of abstract of the fifth European conference on Social Networks EUSN 202