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In this article, we describe the iterative participatory design of SOPHIE, an online virtual patient for feedback-based practice of sensitive patient-physician conversations, and discuss an initial qualitative evaluation of the system by professional end users. The design of SOPHIE was motivated from a computational linguistic analysis of the transcripts of 383 patient-physician conversations from an essential office visit of late stage cancer patients with their oncologists. We developed methods for the automatic detection of two behavioral paradigms, lecturing and positive language usage patterns (sentiment trajectory of conversation), that are shown to be significantly associated with patient prognosis understanding. These automated metrics associated with effective communication were incorporated into SOPHIE, and a pilot user study identified that SOPHIE was favorably reviewed by a user group of practicing physicians. [ABSTRACT FROM AUTHOR]

In this paper, we consider the pair-wise semiglobal sequence alignment problem with gaps, which is motivated by the re-sequencing problem that requires to assemble short reads sequences into a genome sequence by referring to a reference sequence. The problem has been studied before for single gap and bounded number of gaps. For single gap, there is a GPU-based algorithm proposed (Barton et al., 2015). In our work, we propose a GPU-based algorithm for the bounded number of gaps case, called $\mathtt {GPUGapsMis}$ GPUGapsMis . We implement the algorithm and compare the performance with the CPU-based algorithm, called $\mathtt {CPUGapsMis}$ CPUGapsMis . The algorithm has two distinct stages: the alignment phase, and the backtrack phase. We investigate several different approaches, in order to determine the most favorable for this problem, by means of a Hybrid model or a wholly-GPU based model, as well as the alignment of single text sequences or multiple text sequences on the GPU at a time. We show that the alignment phase of the algorithm is a good candidate for parallelization, with peak speedup of 11 times. We show that although the backtracking phase is sequential, it is more beneficial to perform it on the GPU, as opposed to returning to the CPU and performing there. When performing both phases on the GPU, $\mathtt {GPUGapsMis}$ GPUGapsMis achieves a peak speedup of 10.4 times against CPUGapsMis. Our data parallel GPU algorithm achieves results which are an improvement on those of an existing GPU data parallel implementation (Ojiaku, 2014). [ABSTRACT FROM AUTHOR]

The scheduling of arbitrarily divisible loads on a distributed system is studied by Divisible Load Theory (DLT). DLT has the underlying assumption that the processors will not cheat. In the real world, this assumption is unrealistic as the processors are owned and operated by autonomous rational organizations that have no a priori motivation for cooperation. Consequently, they will manipulate the algorithms if it benefits them to do so. In this work, we propose strategy proof mechanisms for scheduling divisible loads on three types of bus-connected distributed systems. These mechanisms provide incentives to the processors to obey the prescribed algorithms and to truthfully report their parameters, leading to an efficient load allocation and execution. [ABSTRACT FROM AUTHOR]

Discusses the possibility of using the unstable periodic orbits (UPO) from a chaotic system to approximate a chaotic signal from that system. Discussion on constructing UPO sequences; Application of the chaotic system to a chaotic flow; Interference from other chaotic signals; Possible improvements; Conclusion.

Provides information on how to synthesize chaotic systems that improve characteristics for use in communications. Identification of chaotic systems; Description of a chaotic circuit; Construction of an analog map circuit; Potential of an analog circuit.