Recent Developments of NEMO: Detection of Solar Eruptions Characteristics

The recent developments in space instrumentation for solar observations and telemetry have caused the necessity of advanced pattern recognition tools for the different classes of solar events. The Extreme ultraviolet Imaging Telescope (EIT) of solar corona on-board SOHO spacecraft has uncovered a new class of eruptive events which are often identified as signatures of Coronal Mass Ejection (CME) initiations on solar disk. It is evident that a crucial task is the development of an automatic detection tool of CMEs precursors. The Novel EIT wave Machine Observing (NEMO) (http://sidc.be/nemo) code is an operational tool that detects automatically solar eruptions using EIT image sequences. NEMO applies techniques based on the general statistical properties of the underlying physical mechanisms of eruptive events on the solar disc. In this work, the most recent updates of NEMO code - that have resulted to the increase of the recognition efficiency of solar eruptions linked to CMEs - are presented. These updates provide calculations of the surface of the dimming region, implement novel clustering technique for the dimmings and set new criteria to flag the eruptive dimmings based on their complex characteristics. The efficiency of NEMO has been increased significantly resulting to the extraction of dimmings observed near the solar limb and to the detection of small-scale events as well. As a consequence, the detection efficiency of CMEs precursors and the forecasts of CMEs have been drastically improved. Furthermore, the catalogues of solar eruptive events that can be constructed by NEMO may include larger number of physical parameters associated to the dimming regions.Comment: 12 Pages, 5 figures, submitted to Solar Physic

Integrating social media with existing knowledge and information for crisis response

Existing studies on social media in the context of crisis have studied the content of items and their patterns of transmission. However, social media content generated during a crisis will generally be unstructured and only reflect the immediate experiences of the authors, while the volumes of data created can make rapid interpretation very challenging. Crisis situations can be characterized with various expected attributes. In many situations there will be large amounts of information relevant to the situation already available. We argue that existing natural language engineering technologies can be integrated with emerging social media content utilization techniques for more powerful exploitation of social media content in crisis response

On gait as a biometric: progress and prospects

There is increasing interest in automatic recognition by gait given its unique capability to recognize people at a distance when other biometrics are obscured. Application domains are those of any noninvasive biometric, but with particular advantage in surveillance scenarios. Its recognition capability is supported by studies in other domains such as medicine (biomechanics), mathematics and psychology which also suggest that gait is unique. Further, examples of recognition by gait can be found in literature, with early reference by Shakespeare concerning recognition by the way people walk. Many of the current approaches confirm the early results that suggested gait could be used for identification, and now on much larger databases. This has been especially influenced by DARPA’s Human ID at a Distance research program with its wide scenario of data and approaches. Gait has benefited from the developments in other biometrics and has led to new insight particularly in view of covariates. Equally, gait-recognition approaches concern extraction and description of moving articulated shapes and this has wider implications than just in biometrics

Automated Markerless Extraction of Walking People Using Deformable Contour Models

We develop a new automated markerless motion capture system for the analysis of walking people. We employ global evidence gathering techniques guided by biomechanical analysis to robustly extract articulated motion. This forms a basis for new deformable contour models, using local image cues to capture shape and motion at a more detailed level. We extend the greedy snake formulation to include temporal constraints and occlusion modelling, increasing the capability of this technique when dealing with cluttered and self-occluding extraction targets. This approach is evaluated on a large database of indoor and outdoor video data, demonstrating fast and autonomous motion capture for walking people

Thinking through the implications of neural reuse for the additive factors method

One method for uncovering the subprocesses of mental processes is the “Additive Factors Method” (AFM). The AFM uses reaction time data from factorial experiments to infer the presence of separate processing stages. This paper investigates the conceptual status of the AFM. It argues that one of the AFM’s underlying assumptions is problematic in light of recent developments in cognitive neuroscience. Discussion begins by laying out the basic logic of the AFM, followed by an analysis of the challenge presented by neural reuse. Following this, implications are analysed and avenues of response considered

Generating Navigable Semantic Maps from Social Sciences Corpora

It is now commonplace to observe that we are facing a deluge of online information. Researchers have of course long acknowledged the potential value of this information since digital traces make it possible to directly observe, describe and analyze social facts, and above all the co-evolution of ideas and communities over time. However, most online information is expressed through text, which means it is not directly usable by machines, since computers require structured, organized and typed information in order to be able to manipulate it. Our goal is thus twofold: 1. Provide new natural language processing techniques aiming at automatically extracting relevant information from texts, especially in the context of social sciences, and connect these pieces of information so as to obtain relevant socio-semantic networks; 2. Provide new ways of exploring these socio-semantic networks, thanks to tools allowing one to dynamically navigate these networks, de-construct and re-construct them interactively, from different points of view following the needs expressed by domain experts.Comment: in Digital Humanities 2015, Jun 2015, Sydney, Australia. Actes de la Conf{\'e}rence Digital Humanities 2015. arXiv admin note: text overlap with arXiv:1406.421

Dimensions of Neural-symbolic Integration - A Structured Survey

Research on integrated neural-symbolic systems has made significant progress in the recent past. In particular the understanding of ways to deal with symbolic knowledge within connectionist systems (also called artificial neural networks) has reached a critical mass which enables the community to strive for applicable implementations and use cases. Recent work has covered a great variety of logics used in artificial intelligence and provides a multitude of techniques for dealing with them within the context of artificial neural networks. We present a comprehensive survey of the field of neural-symbolic integration, including a new classification of system according to their architectures and abilities.Comment: 28 page