Performance Optimization of a Parallel Error Correction Tool

Due to the continuous development in the field of Next Generation Sequencing (NGS) technologies that have allowed researchers to take advantage of greater genetic samples in less time, it is a matter of relevance to improve the existing algorithms aimed at the enhancement of the quality of those generated reads. In this work, we present a Big Data tool implemented upon the open-source Apache Spark framework that is able to execute validated error-correction algorithms at an improved performance. The experimental evaluation conducted on a multi-core cluster has shown significant improvements in execution times, providing a maximum speedup of 9.5 over existing error correction tools when processing an NGS dataset with 25 million reads.

Validity Evidence for the Medical Student Core Physical Examination

Background The Core Physical Exam (CPE) has been proposed as a set of key physical exam (PE) items for teaching and assessing PE skills in medical students, and as the basis of a Core + Cluster curriculum. Beyond the initial development of the CPE and proposal of the CPE and the Core + Cluster curriculum, no additional validity evidence has been presented for use of the CPE to teach or assess PE skills of medical students. As a result, a modified version of the CPE was developed by faculty at the University of Colorado School of Medicine (UCSOM) and implemented in the school’s clinical skills course in the context of an evolving Core + Cluster curriculum. Methods Validity evidence for the 25-item University of Colorado School of Medicine (UCSOM) CPE was analyzed using longitudinal assessment data from 366 medical students (Classes of 2019 and 2020), obtained from September 2015 through December 2019. Using Messick's unified validity framework, validity evidence specific to content, response process, internal structure, relationship to other variables, and consequences was gathered. Results Content and response process validity evidence included expert content review and rater training. For internal structure, a generalizability study phi coefficient of 0.258 suggests low reliability for a single assessment due to variability in learner performance by occasion and CPE items. Correlations of performance on the UCSOM CPE with other PE assessments were low, ranging from .00-.34. Consequences were explored through determination of a pass-fail cut score. Following a modified Angoff process, clinical skills course directors selected a consensus pass-fail cut score of 80% as a defensible and practical threshold for entry into precepted clinical experiences. Conclusions Validity evidence supports the use of the UCSOM CPE as an instructional strategy for teaching PE skills and as a formative assessment of readiness for precepted clinical experiences. The low generalizability coefficient suggests that inferences about PE skills based on the UCSOM CPE alone should be made with caution, and that the UCSOM CPE in isolation should be used primarily as a formative assessment.

Detection of Representative Variables in Complex Systems with Interpretable Rules Using Core-Clusters

In this paper, we present a new framework dedicated to the robust detection of representative variables in high dimensional spaces with a potentially limited number of observations. Representative variables are selected by using an original regularization strategy: they are the center of specific variable clusters, denoted CORE-clusters, which respect fully interpretable constraints. Each CORE-cluster indeed contains more than a predefined amount of variables and each pair of its variables has a coherent behavior in the observed data. The key advantage of our regularization strategy is therefore that it only requires to tune two intuitive parameters: the minimal dimension of the CORE-clusters and the minimum level of similarity which gathers their variables. Interpreting the role played by a selected representative variable is additionally obvious as it has a similar observed behaviour as a controlled number of other variables. After introducing and justifying this variable selection formalism, we propose two algorithmic strategies to detect the CORE-clusters, one of them scaling particularly well to high-dimensional data. Results obtained on synthetic as well as real data are finally presented.

Accelerating and Improving Simulation Performance in Communication Systems Modelling through Parallel Computing and Clustering

Existing 5G communication systems suffer from two major problems: the need for better spectrum efficiency and lesser adjacent channel interference. Thus, development of novel waveform techniques to overcome these problems is a major topic of research amongst scholars and it requires carrying out Monte Carlo simulations in MATLAB© (by MathWorks) to measure the Bit Error Rate (BER) of these communication models. As most of these simulations require millions of computations, they take a significantly long time to run (for example, days) as they run on single-core machines and carry out the computations serially. The main objective of this research is to reimplement current scripts using various parallel computing techniques in MATLAB to study which one is the best suited for this particular type of simulations while also scaling these scripts onto a multi-core cluster to further improve the execution time.

Effective attention-based network for syndrome differentiation of AIDS

Background Syndrome differentiation aims at dividing patients into several types according to their clinical symptoms and signs, which is essential for traditional Chinese medicine (TCM). Several previous works were devoted to employing the classical algorithms to classify the syndrome and achieved delightful results. However, the presence of ambiguous symptoms substantially disturbed the performance of syndrome differentiation, This disturbance is always due to the diversity and complexity of the patients’ symptoms. Methods To alleviate this issue, we proposed an algorithm based on the multilayer perceptron model with an attention mechanism (ATT-MLP). In particular, we first introduced an attention mechanism to assign different weights for different symptoms among the symptomatic features. In this manner, the symptoms of major significance were highlighted and ambiguous symptoms were restrained. Subsequently, those weighted features were further fed into an MLP to predict the syndrome type of AIDS. Results Experimental results for a real-world AIDS dataset show that our framework achieves significant and consistent improvements compared to other methods. Besides, our model can also capture the key symptoms corresponding to each type of syndrome. Conclusion In conclusion, our proposed method can learn these intrinsic correlations between symptoms and types of syndromes. Our model is able to learn the core cluster of symptoms for each type of syndrome from limited data, while assisting medical doctors to diagnose patients efficiently.

On the relation between mini-halos and AGN feedback in clusters of galaxies

ABSTRACT A variety of large-scale diffuse radio structures have been identified in many clusters with the advent of new state-of-the-art facilities in radio astronomy. Among these diffuse radio structures, radio mini-halos are found in the central regions of cool core clusters. Their origin is still unknown and they are challenging to discover; less than 30 have been published to date. Based on new VLA observations, we confirmed the mini-halo in the massive strong cool core cluster PKS 0745−191 (z = 0.1028) and discovered one in the massive cool core cluster MACS J1447.4+0827 (z = 0.3755). Furthermore, using a detailed analysis of all known mini-halos, we explore the relation between mini-halos and active galactic nucleus (AGN) feedback processes from the central galaxy. We find evidence of strong, previously unknown correlations between mini-halo radio power and X-ray cavity power, and between mini-halo and the central galaxy radio power related to the relativistic jets when spectrally decomposing the AGN radio emission into a component for past outbursts and one for ongoing accretion. Overall, our study indicates that mini-halos are directly connected to the central AGN in clusters, following previous suppositions. We hypothesize that AGN feedback may be one of the dominant mechanisms giving rise to mini-halos by injecting energy into the intra-cluster medium and reaccelerating an old population of particles, while sloshing motion may drive the overall shape of mini-halos inside cold fronts. AGN feedback may therefore not only play a vital role in offsetting cooling in cool core clusters, but may also play a fundamental role in re-energizing non-thermal particles in clusters.