Concurrence of big data analytics and healthcare: A systematic review

Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Images

A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable? Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual's trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.

http://absimage.aps.org/image/MAR10/MWS_MAR10-2009-007155.pdf

Limits of predictability in human mobility

Current generations of NUMA node clusters feature multicore or manycore processors. Programming such architectures efficiently is a challenge because numerous hardware characteristics have to be taken into account, especially the memory hierarchy. One appealing idea to improve the performance of parallel applications is to decrease their communication costs by matching the communication pattern to the underlying hardware architecture. In this paper, we detail the algorithm and techniques proposed to achieve such a result: first, we gather both the communication pattern information and the hardware details. Then we compute a relevant reordering of the various process ranks of the application. Finally, those new ranks are used to reduce the communication costs of the application.

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Process Placement in Multicore Clusters:Algorithmic Issues and Practical Techniques

Traditional monolithic services fail to scale efficiently when a certain task within the service is flooded. They also have a long software release cycle due to the system complexity. Microservices, as a new architectural style, are built upon the idea of breaking a large service into a set of single-responsibility (along with other properties) services that are communicating over network. In such a setting, services can scale more efficiently and release cycles become shorter thanks to the continuous deployment. Also, breaking a system into microservices is a daunting task. Normally, such design decisions are made by developers or architects with some intuitions. We argue that this approach only captures the interest of a narrowed set of stakeholders and thus fails to account system-wide requirements. In this work, we propose a conceptual methodology using which security and scalability requirements are incorporated in decomposing system into microservices. Using this methodology, requirements engineers can mindfully reconcile security and scalability requirements. This initiates architectural decisions (i.e. system decomposition) at requirements engineering phase and hence expands the system architects' view. We also examine our methodology by applying it to a fictional case study.

Requirements Reconciliation for Scalable and Secure Microservice (De)composition

Tackling mode collapse in multi-generator GANs with orthogonal vectors

Interprocessor communication is an important factor in determining the performance scalability of parallel systems. The communication requirements of a parallel application can be quantified to understand its communication pattern and communication pattern similarities among applications can be determined. This is essential for the efficient mapping of applications on parallel systems and leads to better interprocessor communication implementation among others. This paper proposes a methodology to compare the communication pattern of distributed-memory programs. Communication correlation coefficient quantifies the degree of similarity between two applications based on the communication metrics selected to characterize the applications. To capture the network topology requirements, we extract the communication graph of each applications and quantities this similarity. We apply this methodology to four applications in the NAS parallel benchmark suite and evaluate the communication patterns by studying the effects of varying problem size and the number of logical processes (LPs).

/pdf/an-approach-for-matching-communication-patterns-in-parallel-478avd3vcf.pdf

An approach for matching communication patterns in parallel applications

Within the recent decades, data volume increases exponentially in different industries especially in healthcare field. Extracting the hidden value behind such massive data has become one of the hottest topics for both industry and academy. In this paper, we present an architecture for healthcare big data management and analysis. Under the guidance of the proposed architecture, a prototype system constructed based on HBase, Hive, Spark MLLib and Spark Streaming is introduced for personal health problem detection and real-time vital sign monitoring.

An Architecture for Healthcare Big Data Management and Analysis

Web services provide a complete new way of the loosely coupled service-oriented architecture and facilitate the process of application integration by encapsulating information and existing softwares. To exploit the true potential of web services, it is vital to develop methodologies and tools for automatic web service composition. Numerous web service composition description languages have been developed for professionals to study and compose web services in the past. However, current web service composition description languages like OWL-S, WSMO and METEOR-S still have many limitations such as the lack of elements to describe dynamic transformations among components of web service compositions. In this paper, we propose a Fast Web Service Composition Approach (FWSCA) which adopts the Extended OWL-S web service semantic description language(OWL-ES). OWL-ES is able to describe dynamic transformations among components of web service compositions by extending the existing process model of OWL-S. We elaborate on the design of FWSCA and discuss its usability by using an example of web service composition. For the tooling part of FWSCA, we develop an automatic web service composition Visualization and Formalization Tool (VFT) which is able to ease the process of web service composition for end-users.

An Approach for Visualization and Formalization of Web Service Composition

Time series classification (TSC) is widely used in various real-world applications such as human activity recognition, smart city governance, etc. Unfortunately, due to different reasons, only part of time series could be collected which may obviously degrade the performance of time series classifiers. To alleviate this problem, time series augmentation aims to generate synthetic time series by learning useful features from collected time series. As the popular generative model, generative adversarial networks (GAN) is regarded as a promising model for time series augmentation. However, applying GAN to the time series data suffers from a challenge in which the generated instances hold low quality but the model has gotten saturation. In this paper, for time series augmentation, we proposed TSA-GAN which is a robust GAN model with a self-adaptive recovering strategy to solve this problem. On 85 datasets of the UCR 2015 archive, our proposed TSA-GAN helps time series classifiers achieve performance improvements ranging from 8.3% to 12.5%, which is far better than the baseline.

Chao Ma

Papers

Tackling mode collapse in multi-generator GANs with orthogonal vectors

An approach for matching communication patterns in parallel applications

An Architecture for Healthcare Big Data Management and Analysis

An Approach for Visualization and Formalization of Web Service Composition

TSA-GAN: A Robust Generative Adversarial Networks for Time Series Augmentation