MaRe
Application containers are emerging as key components in scientific processing, as they can improve reproducibility and standardization in-silico analysis. Chaining software tools in processing pipelines is a common practice in scientific applications and, as application containers gain momentum, workflow systems are starting to provide support for this emerging technology. Nevertheless, workflow systems fall short when it comes to data-intensive analysis, as they do not provide locality-aware scheduling for parallel workloads. To this extent, Big Data cluster-computing frameworks, such as Apache Spark, represent a natural choice. However, even though these frameworks excel at parallelizing code blocks, they do not provide any support for containerized tools parallelization. Here we introduce MaRe, which extends Apache Spark, providing an easy way to parallelize container-based analytics, with transparent management of data locality. MaRe is Docker-compliant, and it can be used as a standalone solution, as well as a workflow system add-on. We demonstrate MaRe on two data-intensive applications in virtual drug screening and in predictive toxicology, showing good scalability. MaRe is generally applicable and available as open source: https://…/MaRe …

Empusa
The RDF data model facilitates integration of diverse data available in structured and semi-structured formats. To obtain an RDF graph with a low amount of errors and internal redundancy, the chosen ontology must be consistently applied. However, with each addition of new diverse data the ontology must evolve thereby increasing its complexity, which could lead to accumulation of unintended erroneous composites. Thus, there is a need for a gatekeeping system that compares the intended content described in the ontology with the actual content of the resource. Here we present Empusa, a tool that has been developed to facilitate the creation of composite RDF resources from disparate sources. Empusa can be used to convert a schema into an associated application programming interface (API) that can be used to perform data consistency checks and generates Markdown documentation to make persistent URLs resolvable. In this way, the use of Empusa ensures consistency within and between the ontology (OWL), the Shape Expressions (ShEx) describing the graph structure, and the content of the resource. …

dipm-SC
How is popularity gained online? Is being successful strictly related to rapidly becoming viral in an online platform or is it possible to acquire popularity in a steady and disciplined fashion? What are other temporal characteristics that can unveil the popularity of online content? To answer these questions, we leverage a multi-faceted temporal analysis of the evolution of popular online contents. Here, we present dipm-SC: a multi-dimensional shape-based time-series clustering algorithm with a heuristic to find the optimal number of clusters. First, we validate the accuracy of our algorithm on synthetic datasets generated from benchmark time series models. Second, we show that dipm-SC can uncover meaningful clusters of popularity behaviors in a real-world Twitter dataset. By clustering the multidimensional time-series of the popularity of contents coupled with other domain-specific dimensions, we uncover two main patterns of popularity: bursty and steady temporal behaviors. Moreover, we find that the way popularity is gained over time has no significant impact on the final cumulative popularity. …

Mixture Generative Adversarial Network (MIXGAN)
In this work, we present an interesting attempt on mixture generation: absorbing different image concepts (e.g., content and style) from different domains and thus generating a new domain with learned concepts. In particular, we propose a mixture generative adversarial network (MIXGAN). MIXGAN learns concepts of content and style from two domains respectively, and thus can join them for mixture generation in a new domain, i.e., generating images with content from one domain and style from another. MIXGAN overcomes the limitation of current GAN-based models which either generate new images in the same domain as they observed in training stage, or require off-the-shelf content templates for transferring or translation. Extensive experimental results demonstrate the effectiveness of MIXGAN as compared to related state-of-the-art GAN-based models. …