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This paper introduces a time-varying threshold autoregressive model (TVTAR), which is used to examine the persistence of deviations from PPP. We find support for the stationary TVTAR against the unit root hypothesis; however, for some developing countries, we do not reject the TVTAR with a unit root in the corridor regime. We calculate magnitudes, frequencies, and durations of the deviations of exchange rates from forecasted changes in exchange rates. A key result is asymmetric adjustment. In developing countries, the average cumulative deviation from forecasts during periods when exchange rates are below forecasts is twice the corresponding measure during periods when exchange rates are above forecasts.
Data Fusion is a very broad interdisciplinary technology domain. It provides techniques and methods for; integrating information from multiple sources and using the complementarities of these detections to derive maximum information about the phenomenon being observed; analyzing and deriving the meaning of these observations and predicting possible consequences of the observed state of the environment; selecting the best course of action; and controlling the actions. Here, the focus is on the more mature phase of data fusion, namely the detection and identification / classification of phenomena being observed and exploitation of the related methods for Security-Related Civil Science and Technology (SST) applications. It is necessary to; expand on the data fusion methodology pertinent to Situation Monitoring, Incident Detection, Alert and Response Management; discuss some related Cognitive Engineering and visualization issues; provide an insight into the architectures and methodologies for building a data fusion system; discuss fusion approaches to image exploitation with emphasis on security applications; discuss novel distributed tracking approaches as a necessary step of situation monitoring and incident detection; and provide examples of real situations, in which data fusion can enhance incident detection, prevention and response capability. In order to give a logical presentation of the data fusion material, first the general concepts are highlighted (Fusion Methodology, Human Computer Interactions and Systems and Architectures), closing with several applications (Data Fusion for Imagery, Tracking and Sensor Fusion and Applications and Opportunities for Fusion).
This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2010, held in Barcelona, Spain, in September 2010. The 120 revised full papers presented in three volumes, together with 12 demos (out of 24 submitted demos), were carefully reviewed and selected from 658 paper submissions. In addition, 7 ML and 7 DM papers were distinguished by the program chairs on the basis of their exceptional scientific quality and high impact on the field. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. A topic widely explored from both ML and DM perspectives was graphs, with motivations ranging from molecular chemistry to social networks.
Adaptation and Learning in Automatic Systems
The two volume set, LNCS 9886 + 9887, constitutes the proceedings of the 25th International Conference on Artificial Neural Networks, ICANN 2016, held in Barcelona, Spain, in September 2016. The 121 full papers included in this volume were carefully reviewed and selected from 227 submissions. They were organized in topical sections named: from neurons to networks; networks and dynamics; higher nervous functions; neuronal hardware; learning foundations; deep learning; classifications and forecasting; and recognition and navigation. There are 47 short paper abstracts that are included in the back matter of the volume.
Naming What We Know examines the core principles of knowledge in the discipline of writing studies using the lens of “threshold concepts”—concepts that are critical for epistemological participation in a discipline. The first part of the book defines and describes thirty-seven threshold concepts of the discipline in entries written by some of the field’s most active researchers and teachers, all of whom participated in a collaborative wiki discussion guided by the editors. These entries are clear and accessible, written for an audience of writing scholars, students, and colleagues in other disciplines and policy makers outside the academy. Contributors describe the conceptual background of the field and the principles that run throughout practice, whether in research, teaching, assessment, or public work around writing. Chapters in the second part of the book describe the benefits and challenges of using threshold concepts in specific sites—first-year writing programs, WAC/WID programs, writing centers, writing majors—and for professional development to present this framework in action. Naming What We Know opens a dialogue about the concepts that writing scholars and teachers agree are critical and about why those concepts should and do matter to people outside the field.
Over the last decade the notion of ‘threshold concepts’ has proved influential around the world as a powerful means of exploring and discussing the key points of transformation that students experience in their higher education courses and the ‘troublesome knowledge’ that these often present.