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Scientific research increasingly relies on distributed computational resources, storage systems, networks, and instruments, ranging from HPC and cloud systems to edge devices. Event-driven architecture (EDA) benefits applications targeting…
Dynamic Programming (DP) and Constraint Programming (CP) are well-established paradigms for solving combinatorial optimization problems. Usually, these two approaches are used separately. This paper aims to show that the two can be combined…
Edge intelligence, a new paradigm to accelerate artificial intelligence (AI) applications by leveraging computing resources on the network edge, can be used to improve intelligent transportation systems (ITS). However, due to physical…
This document is one of the deliverable reports created for the ESCAPE project. ESCAPE stands for Energy-efficient Scalable Algorithms for Weather Prediction at Exascale. The project develops world-class, extreme-scale computing…
Data movement between the CPU and main memory is a first-order obstacle against improving performance, scalability, and energy efficiency in modern systems. Computer systems employ a range of techniques to reduce overheads tied to data…
Deep Neural Networks (DNNs) excel in learning hierarchical representations from raw data, such as images, audio, and text. To compute these DNN models with high performance and energy efficiency, these models are usually deployed onto…
The Payload Data Handling System (PDHS) of Gaia is a technological challenge, since it will have to process a huge amount of data with limited resources. Its main tasks include the optimal codification of science data, its packetisation and…
In the big data era of observational oceanography, passive acoustics datasets are becoming too high volume to be processed on local computers due to their processor and memory limitations. As a result there is a current need for our…
While the Machine Learning (ML) landscape is evolving rapidly, there has been a relative lag in the development of the "learning systems" needed to enable broad adoption. Furthermore, few such systems are designed to support the specialized…
Distributed Deep Learning (DDL), as a paradigm, dictates the use of GPU-based clusters as the optimal infrastructure for training large-scale Deep Neural Networks (DNNs). However, the high cost of such resources makes them inaccessible to…
DBSP_DRP is a python package that provides fully automated data reduction of data taken by the Double Spectrograph (DBSP) at the 200-inch Hale Telescope at Palomar Observatory (Oke & Gunn, 1982). The underlying data reduction functionality…
Nowadays, science has been coming into a new paradigm, called data-intensive science. While current studies of the new phenomenon focused on building up infrastructure for this new paradigm, yet a few studies concern users of scientific…
Developing software to undertake complex, compute-intensive scientific processes requires a challenging combination of both specialist domain knowledge and software development skills to convert this knowledge into efficient code. As…
DCSP (Distributed Constraint Satisfaction Problem) has been a very important research area in AI (Artificial Intelligence). There are many application problems in distributed AI that can be formalized as DSCPs. With the increasing…
Scientific advancement relies on the ability to share and reproduce results. When data analysis or calculations are carried out using software written by scientists there are special challenges around code versions, quality and code…
Managing data and code in open scientific research is complicated by two key problems: large datasets often cannot be stored alongside code in repository platforms like GitHub, and iterative analysis can lead to unnoticed changes to data,…
Software Categorization is the task of organizing software into groups that broadly describe the behavior of the software, such as "editors" or "science." Categorization plays an important role in several maintenance tasks, such as…
SkyPortal is an open-source software package designed to efficiently discover interesting transients, manage follow-up, perform characterization, and visualize the results. By enabling fast access to archival and catalog data,…
The reproducibility of scientific experiment is vital for the advancement of disciplines based on previous work. To achieve this goal, many researchers focus on complex methodology and self-invented tools which have difficulty in practical…
The NASA Astrophysics Data System (ADS) is the primary Digital Library portal for researchers in astronomy and astrophysics. Over the past 30 years, the ADS has gone from being an astronomy-focused bibliographic database to an open digital…