Nash and Wardrop equilibria in aggregative games with coupling constraints

We consider the framework of aggregative games, in which the cost function of each agent depends on his own strategy and on the average population strategy. As first contribution, we investigate the relations between the concepts of Nash and Wardrop equilibrium. By exploiting a characterization of the two equilibria as solutions of variational inequalities, we bound their distance with a decreasing function of the population size. As second contribution, we propose two decentralized algorithms that converge to such equilibria and are capable of coping with constraints coupling the strategies of different agents. Finally, we study the applications of charging of electric vehicles and of route choice on a road network.Comment: IEEE Trans. on Automatic Control (Accepted without changes). The first three authors contributed equall

Mindful Awareness Training: A Pilot Study Integrating Mindfulness Practices into a Rural Jail-Based Substance Abuse Program

Mindfulness practices are now utilized in a variety of behavioral healthcare settings, including the criminal justice system. This article summarizes the findings of a pilot project incorporating mindfulness practices into a jail-based substance abuse program in a rural county jail. Participants that engaged in a psychoeducational mindfulness group that utilized practices adapted from the Mindfulness-Based Relapse Prevention (MBRP) curriculum had improved scores on measures of mindfulness, self-compassion, and quality of life. A mediated path model suggested that the length of time participants were involved in the group and their estimated amount of mindfulness practice outside the group was related to increases in mindfulness, which appeared to support subsequent increases in self-compassion and quality of life. Implications for incorporating these practices into jail-based programs and limitations are discussed

A comparison of two-coloured filter systems for treating visual reading difficulties

Copyright @ 2013 Informa UK Ltd.Purpose: Visual disturbances that make it difficult to read text are often termed “visual stress”. Coloured filters in spectacles may help some children overcome reading problems that are often caused by visual stress. It has been suggested that for optimal effect each child requires an individually prescribed colour for each eye, as determined in systems such as the “Harris Foundation” coloured filters. Alternatively, it has been argued that only blue or yellow filters, as used in the “Dyslexia Research Trust” (DRT) filter system, are necessary to affect the underlying physiology. Method: A randomised, double blind trial with 73 delayed readers, was undertaken to compare changes in reading and spelling as well as irregular and non-word reading skills after 3 months of wearing either the Harris or the DRT filters. Results: Reading improved significantly after wearing either type of filter (t = −8.4, p < 0.01), with 40% of the children improving their reading age by 6 months or more during the 3 month trial. However, spelling ability (t = 2.1, p = 0.05) and non-word reading (f = 4.7, p < 0.05) improved significantly more with the DRT than with the Harris filters. Conclusion: Education and rehabilitation professionals should therefore, consider coloured filters as an effective intervention for delayed readers experiencing visual stress

FPGA-Based Adaptive Digital Beamforming Using Machine Learning for MIMO Systems

In modern Multiple-Input and Multiple-Output (MIMO) systems, such as cellular and Wi-Fi technology, an array of antenna elements is used to spatially steer RF signals with the goal of changing the overall antenna gain pattern to achieve a higher Signal-to-interference-plus-noise ratio (SINR). Digital Beamforming (DBF) achieves this steering effect by applying weighted coefficients to antenna elements- similar to digital filtering- which adjust the phase and gain of the received, or transmitted, signals. Since real world MIMO systems are often used in dynamic environments, Adaptive Beamforming techniques have been used to overcome variable challenges to system SINR- such as dispersive channels or inter-device interference- by applying statistically-based algorithms to calculate weights adaptively. However, large element count array systems, with their high degrees of freedom (DOF), can face many challenges in real application of these adaptive algorithms. These statistical matrix methods can be either computationally prohibitive, or utilize non-optimal simplifications, in order to provide adaptive weights in time for an application, especially given a certain system's computational capability; for instance, MIMO communication devices with strict size, weight and power (SWaP) constraints often have processing limitations due to use of low-power processors or Field-Programmable Gate Arrays (FPGAs). Thus, this thesis research investigation will show novel progress in these adaptive MIMO challenges in a twofold approach. First, it will be shown that advances in Machine Learning (ML) and Deep Neural Networks (DNNs) can be directly applied to the computationally complex problem of calculating optimal adaptive beamforming weights via a custom Convolutional Neural Net (CNN). Secondly, the derived adaptive beamforming CNN will be shown to efficiently map to programmable logic FPGA resources which can update adaptive coefficients in real-time. This machine learning implementation is contrasted against the current state-of-the-art FPGA architecture for adaptive beamforming- which uses traditional, Recursive Least Squares (RLS) computation- and is shown to provide adaptive beamforming weights faster, and with fewer FPGA logic resources. The reduction in both processing latency and FPGA fabric utilization enables SWaP constrained MIMO processors to perform adaptive beamforming for higher channel count systems than currently possible with traditional computation methods

A Word to the WISE: Confusion is Unavoidable for WISE-selected Infrared Excesses

Stars with excess infrared radiation from circumstellar dust are invaluable for studies of exoplanetary systems, informing our understanding on processes of planet formation and destruction alike. All-sky photometric surveys have made the identification of dusty infrared excess candidates trivial, however, samples that rely on data from WISE are plagued with source confusion, leading to high false positive rates. Techniques to limit its contribution to WISE-selected samples have been developed, and their effectiveness is even more important as we near the end-of-life of Spitzer, the only facility capable of confirming the excess. Here, we present a Spitzer follow-up of a sample of 22 WISE-selected infrared excess candidates near the faint-end of the WISE detection limits. Eight of the 22 excesses are deemed the result of source confusion, with the remaining candidates all confirmed by the Spitzer data. We consider the efficacy of ground-based near-infrared imaging and astrometric filtering of samples to limit confusion among the sample. We find that both techniques are worthwhile for vetting candidates, but fail to identify all of the confused excesses, indicating that they cannot be used to confirm WISE-selected infrared excess candidates, but only to rule them out. This result confirms the expectation that WISE-selected infrared excess samples will always suffer from appreciable levels of contamination, and that care should be taken in their interpretation regardless of the filters applied.Comment: 13 pages, 4 Figures; Accepted for publication in Ap