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Open Science, Reproducible Research, Findable, Accessible, Interoperable and Reusable (FAIR) data principles are long term goals for scientific dissemination. However, the implementation of these principles calls for a reinspection of our…
Provenance has been thought of a mechanism to verify a workflow and to provide workflow reproducibility. This provenance of scientific workflows has been effectively carried out in Grid based scientific workflow systems. However, recent…
With the advent of Open Science, researchers have started to publish their research artefacts (i. e., data, software, and other products of the investigations) in order to allow others to reproduce their investigations. While this…
Interactive notebook programming is universal in modern ML and AI workflows, with interactive deep learning training (IDLT) emerging as a dominant use case. To ensure responsiveness, platforms like Jupyter and Colab reserve GPUs for…
The scalability of pool-based active learning is limited by the computational cost of evaluating large unlabeled datasets, a challenge that is particularly acute in virtual screening for drug discovery. While active learning strategies such…
Computational physics increasingly depends on large simulation datasets generated by software that remains under active development for many years. In such settings, reproducibility requires not only well documented data but also explicit…
In software engineering, numerous studies have focused on the analysis of fine-grained logs, leading to significant innovations in areas such as refactoring, security, and code completion. However, no similar studies have been conducted for…
Effective provenance tracking enhances reproducibility, governance, and data quality in array workflows. However, significant challenges arise in capturing this provenance, including: (1) rapidly evolving APIs, (2) diverse operation types,…
Learning to flexibly follow task instructions in dynamic environments poses interesting challenges for reinforcement learning agents. We focus here on the problem of learning control flow that deviates from a strict step-by-step execution…
Reproducibility is a fundamental requirement of the scientific process since it enables outcomes to be replicated and verified. Computational scientific experiments can benefit from improved reproducibility for many reasons, including…
Reliable storage emulations from fault-prone components have established themselves as an algorithmic foundation of modern storage services and applications. Most existing reliable storage emulations are built from storage services…
Among the multiple causes of high error rates in spreadsheets, lack of proper training and of deep understanding of the computational model upon which spreadsheet computations rest might not be the least issue. The paper addresses this…
A fundamental challenge in Continual Learning (CL) is catastrophic forgetting, where adapting to new tasks degrades the performance on previous ones. While the field has evolved with diverse methods, this rapid surge in diverse…
Training expressive flow-based policies with off-policy reinforcement learning is notoriously unstable due to gradient pathologies in the multi-step action sampling process. We trace this instability to a fundamental connection: the flow…
Computational notebooks, which integrate code, documentation, tags, and visualizations into a single document, have become increasingly popular for data analysis tasks. With the advent of immersive technologies, these notebooks have evolved…
Jupyter notebooks represent a unique format for programming - a combination of code and Markdown with rich formatting, separated into individual cells. We propose to perceive a Jupyter Notebook cell as a simplified and raw version of a…
As we approach the physical limits of CMOS technology, advances in materials science and nanotechnology are making available a variety of unconventional computing substrates that can potentially replace top-down-designed silicon-based…
One-shot imitation is the vision of robot programming from a single demonstration, rather than by tedious construction of computer code. We present a practical method for realizing one-shot imitation for manipulation tasks, exploiting…
Interactive computing notebooks, such as Jupyter notebooks, have become a popular tool for developing and improving data-driven models. Such notebooks tend to be executed either in the user's own machine or in a cloud environment, having…
Computational reproducibility is central to scientific credibility, yet verifying published results at scale remains costly. We develop an AI-assisted workflow for automated full-paper replication -- retrieving materials, reconstructing…