Mild body cooling impairs attention via distraction from skin cooling

Many contemporary workers are routinely exposed to mild cold stress, which may compromise mental function and lead to accidents. A study investigated the effect of mild body cooling of 1.0 degree C rectal temperature (Tre) on vigilance (i.e. sustained attention) and the orienting of spatial attention (i.e. spatially selective processing of visual information). Vigilance and spatial attention tests were administered to 14 healthy males and six females at four stages (pre-immersion, deltaTre = 0, -0.5 and - 1.0 degree C ) of a gradual, head-out immersion cooling session (18-25 deltaC water), and in four time-matched stages of a contrast session, in which participants sat in an empty tub and no cooling took place. In the spatial attention test, target discrimination times were similar for all stages of the contrast session, but increased significantly in the cooling phase upon immersion (deltaTre = 0 degrees C), with no further increases at deltaTre = -0.5 and - 1.0 degree C. Despite global response slowing, cooling did not affect the normal pattern of spatial orienting. In the vigilance test, the variability of detection time was adversely affected in the cooling but not the contrast trials: variability increased at immersion but did not increase further with additional cooling. These findings suggest that attentional impairments are more closely linked to the distracting effects of cold skin temperature than decreases in body core temperature.     

Exploring the Interaction between Java's Implicitly Thrown Exceptions and Instruction Scheduling

The frequent occurrence of implicitly thrown exceptions poses one of the challenges present in a Java compiler. Not only do these implicitly thrown exceptions directly affect the performance by requiring explicit checks, they also indirectly impact the performance by restricting code movement in order to satisfy the precise exception model in Java. In particular, instruction scheduling is one transformation that is restricted by implicitly thrown exceptions due to the heavy reliance on reordering instructions to exploit maximum hardware performance. The goal of this study is two-fold: first, investigate the degree to which implicitly thrown exceptions in Java hinder instruction scheduling, and second, find new techniques for allowing more efficient execution of Java programs containing implicitly thrown exceptions. Experimental results show that with aggressive scheduling techniques, such as superblock scheduling, the negative performance impact can be greatly reduced.     

A SWAT evaluation of the effect of climate change on the hydrology of the Volta River basin

The SWAT (Soil and Water Assessment Tool) was used to evaluate the impacts of a climate scenario based on IPCC A1B emissions on flows in the Volta River basin in West Africa for 2021–2050 and 2071–2100, using 1983–2012 as the reference period. Overall, the simulation indicates increased variability and a decrease of up to 40% in river flow as a consequence of decreasing rainfall and increasing temperature. In particular, the analysis shows smaller absolute but greater relative changes in the hydrology of the northern (upper) part of the basin, particularly at the end of the century.     

Multiple human tracking in high-density crowds

In this paper, we introduce a fully automatic algorithm to detect and track multiple humans in high-density crowds in the presence of extreme occlusion. Typical approaches such as background modeling and body part-based pedestrian detection fail when most of the scene is in motion and most body parts of most of the pedestrians are occluded. To overcome this problem, we integrate human detection and tracking into a single framework and introduce a confirmation-by-classification method for tracking that associates detections with tracks, tracks humans through occlusions, and eliminates false positive tracks. We use a Viola and Jones AdaBoost detection cascade, a particle filter for tracking, and color histograms for appearance modeling. To further reduce false detections due to dense features and shadows, we introduce a method for estimation and utilization of a 3D head plane that reduces false positives while preserving high detection rates. The algorithm learns the head plane from observations of human heads incrementally, without any a priori extrinsic camera calibration information, and only begins to utilize the head plane once confidence in the parameter estimates is sufficiently high. In an experimental evaluation, we show that confirmation-by-classification and head plane estimation together enable the construction of an excellent pedestrian tracker for dense crowds.     

Video gameplay, personality and academic performance

The relationship between video gameplay, video game genre preference, personality, and GPA was investigated in an online correlational study with university students. In addition to administering self-report measures of GPA and personality, we asked three different questions regarding styles of video gameplay. The first asked the average time spent playing video games per week (habitual players), the second asked the total time spent playing favorite video games (selective players), and the third asked the number of different video games played in a year (diverse players). Students who were medium in selective player style (spent 11-50 h) had significantly higher GPAs than students low on selective player style (spent 0-10 h). Students high on habitual playing style (7 or more hours a week) showed significantly lower levels of Conscientiousness compared to students low on habitual playing style (0-1 h a week). Students who were high on the diverse style (i.e., 7 or more games played a year) showed significantly higher Openness scores than students low on the diverse style (0-3 games a year). Finally, several notable relations were found between video game genre preference, GPA, and personality. Results are discussed in terms of the positive implications of video gameplay on academic performance.     

Non-linear Control of Heave for an Unmanned Helicopter Using a Neural Network

This paper describes a new non-linear control technique applied to the heave control of an unmanned rotorcraft. First a hybrid plant model consisting of exactly known dynamics is combined with a black-box representation of the unknown dynamics. Desired trajectories are calculated to smoothly achieve a sequence of random step changes in desired height according to certain optimal criterion and plant limitations. Control inputs are then determined using the MATLAB^® optimisation toolbox to achieve those desired trajectories for the plant heave model. Finally, a neural network is trained to mimic the control inputs resulting from the optimisation process. The neural network controller produces trajectories closely resembling the results from the optimisation process but with a much reduced computation time. Flight test results of control of the heave dynamics of a helicopter confirm the neural network controller’s ability to operate in high disturbance conditions and outperform a proportional-derivative (PD) controller.     

Exploring probabilistic bisimulations, part I

We take a fresh look at strong probabilistic bisimulations for processes which exhibit both non-deterministic and probabilistic behaviour. We suggest that it is natural to interpret such processes as distributions over states in a probabilistic labelled transition system, a pLTS; this enables us to adapt the standard notion of contextual equivalence to this setting. We then prove that a novel form of bisimulation equivalence between distributions are both sound and complete with respect to this contextual equivalence. We also show that a very simple extension to HML, Hennessy–Milner Logic, provides finite explanations for inequivalences between distributions. Finally we show that our bisimulations between distributions in a pLTS are simply an alternative characterisation of a standard notion of probabilistic bisimulation equivalence, defined between states in a pLTS.     

Model checking agent programming languages

In this paper we describe a verification system for multi-agent programs. This is the first comprehensive approach to the verification of programs developed using programming languages based on the BDI (belief-desire-intention) model of agency. In particular, we have developed a specific layer of abstraction, sitting between the underlying verification system and the agent programming language, that maps the semantics of agent programs into the relevant model-checking framework. Crucially, this abstraction layer is both flexible and extensible; not only can a variety of different agent programming languages be implemented and verified, but even heterogeneous multi-agent programs can be captured semantically. In addition to describing this layer, and the semantic mapping inherent within it, we describe how the underlying model-checker is driven and how agent properties are checked. We also present several examples showing how the system can be used. As this is the first system of its kind, it is relatively slow, so we also indicate further work that needs to be tackled to improve performance.