Disruption of White Matter Integrity in Bipolar Depression as a Possible Structural Marker of Illness

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Lockheed Martin And The Controversial F-35

Over the course several years - from late in the 1950s to the early 1970s - Lockheed was embroiled in the “Deal of the Century” where the company was found guilty of bribing foreign countries’ governments to select the Lockheed F-104 Starfighter over arguably better alternatives in addressing those nations’ respective air force needs.  Today, Lockheed Martin is the leading contractor on the F-35 Lightning II project, which is intended to produce the world’s most advanced stealth strike-fighter for US armed forces and those of several allied foreign nations.  However, the F-35 program has become the single most expensive project in American history and has been beset with serious technical problems, major schedule delays, cost overruns, and the F-35’s performance capabilities are seen by many as inferior to the nation’s potential adversaries’ fighter aircraft, if not inferior to the aircraft it is intended to replace.

Corpus-Based Approaches to Figurative Language: Metaphor and Austerity

Austerity is a by-product of the ongoing financial crisis. As Kitson et al (2001) explain, what was a \u201cNICE\u201d (\u2018non-inflationary consistent expansion\u2019) economy has turned \u201cVILE\u201d (\u2018volatile inflation, little expansion\u2019), and the economic and social fall-out is now becoming visible. Unemployment, redundancy, inflation, recession, insecurity, and poverty all loom, causing governments, businesses and individuals to reevaluate their priorities. A changing world changes attitudes, and the earliest manifestations of such change can often be found in figurative language. Political rhetoric attempts to sweeten the bitter pill that nations have no choice but to swallow; all are invited to share the pain, make sacrifices for the common good, and weather the storm. But more sinister undertones can also be perceived. In times of social and financial dire straits, scapegoats are sought and mercilessly pursued in the press. The elderly, unemployed, and disabled are under fire for \u201csponging off the state\u201d; and as jobs become scarcer and the tax bill rises, migrant populations and asylum seekers are viewed with increasing suspicion and resentment. Calls for a \u201cbig society\u201d fall on deaf ears. Society, it seems, is shrinking as self-preservation takes hold. Austerity is a timely area of study: although austerity measures have been implemented in the past, most of the contributions here address the current political and economic situation, which means that some of the studies reported are work in progress while others look at particular \u201cwindows\u201d of language output from the recent past. Whichever their focus, the papers presented here feature up-to-the-minute research into the metaphors being used to comment upon our current socioeconomic situation. The picture of austerity that emerges from these snapshots is a complex one, and one which is likely to be developed further and more widely in the coming future

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 218, April 1981

This bibliography lists 161 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1981

enhancing the circular economy with nature based solutions in the built urban environment green building materials systems and sites

Abstract The objective of this review paper is to survey the state of the art on nature-based solutions (NBS) in the built environment, which can contribute to a circular economy (CE) and counter the negative impacts of urbanization through the provision of ecosystem services. NBS are discussed here at three different levels: (i) green building materials, including biocomposites with plant-based aggregates; (ii) green building systems, employed for the greening of buildings by incorporating vegetation in their envelope; and (iii) green building sites, emphasizing the value of vegetated open spaces and water-sensitive urban design. After introducing the central concepts of NBS and CE as they are manifested in the built environment, we examine the impacts of urban development and the historical use of materials, systems and sites which can offer solutions to these problems. In the central section of the paper we present a series of case studies illustrating the development and implementation of such solutions in recent years. Finally, in a brief critical analysis we look at the ecosystem services and disservices provided by NBS in the built environment, and examine the policy instruments which can be leveraged to promote them in the most effective manner – facilitating the future transition to fully circular cities

Automatic Detection of Reflective Thinking in Mathematical Problem Solving based on Unconstrained Bodily Exploration

For technology (like serious games) that aims to deliver interactive learning, it is important to address relevant mental experiences such as reflective thinking during problem solving. To facilitate research in this direction, we present the weDraw-1 Movement Dataset of body movement sensor data and reflective thinking labels for 26 children solving mathematical problems in unconstrained settings where the body (full or parts) was required to explore these problems. Further, we provide qualitative analysis of behaviours that observers used in identifying reflective thinking moments in these sessions. The body movement cues from our compilation informed features that lead to average F1 score of 0.73 for binary classification of problem-solving episodes by reflective thinking based on Long Short-Term Memory neural networks. We further obtained 0.79 average F1 score for end-to-end classification, i.e. based on raw sensor data. Finally, the algorithms resulted in 0.64 average F1 score for subsegments of these episodes as short as 4 seconds. Overall, our results show the possibility of detecting reflective thinking moments from body movement behaviours of a child exploring mathematical concepts bodily, such as within serious game play

Personalized face and gesture analysis using hierarchical neural networks

The video-based computational analyses of human face and gesture signals encompass a myriad of challenging research problems involving computer vision, machine learning and human computer interaction. In this thesis, we focus on the following challenges: a) the classification of hand and body gestures along with the temporal localization of their occurrence in a continuous stream, b) the recognition of facial expressivity levels in people with Parkinson's Disease using multimodal feature representations, c) the prediction of student learning outcomes in intelligent tutoring systems using affect signals, and d) the personalization of machine learning models, which can adapt to subject and group-specific nuances in facial and gestural behavior. Specifically, we first conduct a quantitative comparison of two approaches to the problem of segmenting and classifying gestures on two benchmark gesture datasets: a method that simultaneously segments and classifies gestures versus a cascaded method that performs the tasks sequentially. Second, we introduce a framework that computationally predicts an accurate score for facial expressivity and validate it on a dataset of interview videos of people with Parkinson's disease. Third, based on a unique dataset of videos of students interacting with MathSpring, an intelligent tutoring system, collected by our collaborative research team, we build models to predict learning outcomes from their facial affect signals. Finally, we propose a novel solution to a relatively unexplored area in automatic face and gesture analysis research: personalization of models to individuals and groups. We develop hierarchical Bayesian neural networks to overcome the challenges posed by group or subject-specific variations in face and gesture signals. We successfully validate our formulation on the problems of personalized subject-specific gesture classification, context-specific facial expressivity recognition and student-specific learning outcome prediction. We demonstrate the flexibility of our hierarchical framework by validating the utility of both fully connected and recurrent neural architectures