Related papers: The Mellon Fedora Project: Digital Library Archite…
The looming end of Moore's Law and ascending use of deep learning drives the design of custom accelerators that are optimized for specific neural architectures. Architecture exploration for such accelerators forms a challenging constrained…
Invenio is a comprehensive web-based free digital library software suite originally developed at CERN. In order to improve its information retrieval and word similarity ranking capabilities, the goal of this thesis is to enhance Invenio by…
The paper describes an extensible model for implementing a user-centered, customizable interface to a library's collection of information resources. This model, called MyLibrary, integrates the principles of librarianship (collection,…
Multi-modal transformers mark significant progress in different domains, but siloed high-quality data hinders their further improvement. To remedy this, federated learning (FL) has emerged as a promising privacy-preserving paradigm for…
An essential capability of the Virtual Observatory is a means for describing what data and computational facilities are available where, and once identified, how to use them. The data themselves have associated metadata (e.g., FITS…
Neural network interatomic potentials (NNPs) have recently proven to be powerful tools to accurately model complex molecular systems while bypassing the high numerical cost of ab-initio molecular dynamics simulations. In recent years,…
Federated Learning with LoRA fine-tuning offers an efficient and privacy-aware solution for institutions to collaboratively leverage their large datasets to train VLLMs. However, participating institutions often possess heterogeneous…
fmeval is an open source library to evaluate large language models (LLMs) in a range of tasks. It helps practitioners evaluate their model for task performance and along multiple responsible AI dimensions. This paper presents the library…
Despite the rise of digital libraries and online reading platforms, physical libraries still offer unique benefits for education and community engagement. However, due to the convenience of digital resources, physical library visits,…
This paper describes the digital library of historical resources, a research project which involves building a testbed for the purpose of developing and testing new collaborative digital library functionality and presents an initial…
Federated learning (FL) is an emerging privacy preserving machine learning protocol that allows multiple devices to collaboratively train a shared global model without revealing their private local data. Non-parametric models like gradient…
Vertical Federated Learning (VFL) has emerged as a collaborative training paradigm that allows participants with different features of the same group of users to accomplish cooperative training without exposing their raw data or model…
Concept Analysis provides a principled approach to effective management of wide area information systems, such as the Nebula File System and Interface. This not only offers evidence to support the assertion that a digital library is a…
Open source software development, particularly within institutions such as universities and research laboratories, is often decentralized and difficult to track. Although academic teams produce many impactful scientific tools, their…
The emerging trend of Federated Cloud models enlist virtualization as a significant concept to offer a large scale distributed Infrastructure as a Service collaborative paradigm to end users. Virtualization leverage Virtual Machines (VM)…
In the emerging eScience environment, repositories of papers, datasets, software, etc., should be the foundation of a global and natively-digital scholarly communications system. The current infrastructure falls far short of this goal.…
CERN's (European Organization for Nuclear Research) WISDOM project uses XML for the replication of data between different data repositories in a heterogeneous operating system environment. For exchanging data from Web-resident databases,…
Accessing data from distributed computing is essential in many workflows, but can be complicated for users of cyberinfrastructure. They must perform multiple steps to make data available to distributed computing using unfamiliar tools.…
Digital hardware is verified by comparing its behavior against a reference model on a range of randomly generated input signals. The random generation of the inputs hopes to achieve sufficient coverage of the different parts of the design.…
Federated learning has received fast-growing interests from academia and industry to tackle the challenges of data hungriness and privacy in machine learning. A federated learning system can be viewed as a large-scale distributed system…