The Ravenscar Profile is an exciting development for the Ada community since it provides, for the first time in the history of our industry, support for deterministic, multi-tasking programming as an integral part of a standardized language. Despite its many advantages, the profile leaves several areas where behaviour is implementation defined and can result in run-time errors; this is unfortun...

A variety of problems in digital marketing can be modeled as Markov decision processes and solved by dynamic programming with the goal of calculating the policy that maximizes the expected discounted reward. Algorithms, such as policy iteration, require a state transition and a reward model, which can be estimated based on a given data set. In this paper, we compare the execution times for esti...

The rapidly growing size of RDF graphs in recent years necessitates distributed storage and parallel processing strategies. To obtain efficient query processing using computer clusters a wide variety of different approaches have been proposed. Related to the approach presented in the current paper are systems built on top of Hadoop HDFS, for example using Apache Accumulo or using Apache Spark. ...

Technologies for Big Data and Data Science are receiving increasing research interest nowadays. This paper introduces the prototyping architecture of a tool aimed to solve Big Data Optimization problems. Our tool combines the jMetal framework for multi-objective optimization with Apache Spark, a technology that is gaining momentum. In particular, we make use of the streaming facilities of Spark...

Translating academic medical research into new therapies is an important challenge for the biopharmaceutical industry and investment communities, which have historically favored later-stage assets with lower risk and clearer commercial value. The Stanford SPARK program is an innovative model for addressing this challenge. The program was created in 2006 to educate students and faculty about bri...

This paper describes SPARKSkein – a new reference implementation of the Skein algorithm, written and verified using the SPARK language and toolset. This paper is aimed at readers familiar with the Skein algorithm and its existing reference implementation, but who might not be familiar with SPARK. The new implementation is readable, completely portable to a wide-variety of machines of differing ...

The Apache Spark stack has enabled fast large-scale data processing. Despite a rich library of statistical models and inference algorithms, it does not give domain users the ability to develop their own models. The emergence of probabilistic programming languages has showed the promise of developing sophisticated probabilistic models in a succinct and programmatic way. These frameworks have the...

Change detection has long been a challenging problem although a lot of research has been conducted in different fields such as remote sensing and photogrammetry, computer vision, and robotics. In this paper, we blend voxel grid and Apache Spark together to propose an efficient method to address the problem in the context of big data. Voxel grid is a regular geometry representation consisting of...

Spark is a pervasively used in-memory computing framework in the era of big data, and can greatly accelerate the computation speed by wrapping the accessed data as resilient distribution datasets (RDDs) and storing these datasets in the fast accessed main memory. However, the space of main memory is limited, and Spark does not provide an intelligent mechanism to store reasonable RDDs in the lim...

We demonstrate coherent control of laser-induced optical breakdown in Ar and Xe with a femtosecond time-scale pulse train. By using a genetic algorithm to set the relative phases of seven optical sidebands that span two octaves of bandwidth, we enhance or suppress the probability of breakdown, vary the onset time of the spark, and to some extent, vary the position of the spark and the timing of...