18,477 research outputs found
RD53 Collaboration and CHIPIX65 Project for the development of an innovative Pixel Front End Chip for HL-LHC
Full text linkBusiness Intelligence INFN: Introduzione ai Nuovi Report di Bilancio INFN
Full text link<p>L’obiettivo di questo elaborato è descrivere le scelte progettuali che hanno portato nel 2020 alla ristrutturazione della reportistica dedicata al Bilancio INFN presente sull’impianto di Business Intelligence (BI) INFN. Nello specifico vengono presentate le motivazioni della dismissione dei vecchi report multidimensionali basati su Viste OLAP in favore degli attuali report JRXML. Vengono anche presentati alcuni aspetti peculiari delle soluzioni implementate sul data warehouse che offre funzionalità di backend per l’impianto volte a garantire il giusto layer autorizzativo nell’accesso ai dati ed alle risorse.</p>
Updates on the INFN Open Access Repository
Full text link<p>Research organisations are moving to new models of sharing publications and data among communities in order to overcome limitations of current publishing systems: free and open access, data and publication associations, etc.<br>\nINFN and other organisations, both public and private, have signed a global initiative launched by <em>Science Europe</em>, named <strong>Plan S</strong>, aimed at moving the state funded research works in open repositories or journal available to all.</p>\n\n<p>In this context, we have updated the pilot of the INFN Open Access Repository, that is operational since 2014, to a version that is compliant with Plan S requirements. Starting from Zenodo code, that powers the EC flagship repository with the same name, developed by CERN in the context of the OpenAIRE series of projects, we customised the implementation to add<br>\nfeatures useful for INFN.</p>\n\n<p>These include the integration with INFN-AAI for the authentication, configurable look and feel, data migration from previous repository and some fixes. Additionally, we have developed yaml files describing all micro services behind Zenodo for an automated deployment on a Kubernetes-based<br>\ninfrastructure.</p>\n\n<p>The repository is open for testing by all INFN staff and associated researchers and people from other organisations are also investigating it, already. We are currently preparing a Conceptual Design Report for the updater repository for evaluation by the INFN management and we will report<br>\non it.</p>
Updates on the INFN Open Access Repository
Full text link<p>Research organisations are moving to new models of sharing publications and data among communities in order to overcome limitations of current publishing systems: free and open access, data and publication associations, etc.<br>\nINFN and other organisations, both public and private, have signed a global initiative launched by <em>Science Europe</em>, named <strong>Plan S</strong>, aimed at moving the state funded research works in open repositories or journal available to all.</p>\n\n<p>In this context, we have updated the pilot of the INFN Open Access Repository, that is operational since 2014, to a version that is compliant with Plan S requirements. Starting from Zenodo code, that powers the EC flagship repository with the same name, developed by CERN in the context of the OpenAIRE series of projects, we customised the implementation to add<br>\nfeatures useful for INFN.</p>\n\n<p>These include the integration with INFN-AAI for the authentication, configurable look and feel, data migration from previous repository and some fixes. Additionally, we have developed yaml files describing all micro services behind Zenodo for an automated deployment on a Kubernetes-based<br>\ninfrastructure.</p>\n\n<p>The repository is open for testing by all INFN staff and associated researchers and people from other organisations are also investigating it, already. We are currently preparing a Conceptual Design Report for the updater repository for evaluation by the INFN management and we will report<br>\non it.</p>
INFN-CNAF Annual Report 2013
No full text<p>This first CNAF Annual Report is dedicated to CNAF itself: to its first 50 years, to those who founded<br>it, to those who contributed to improving it until it became one of the main data centers for experimental<br>physics and particle physics especially, to those who succeeded in attracting copious external funding to<br>our Institute. European funds, regional funds and prize funds have in fact allowed us to inject our center<br>with innovation and development, to remain technologically advanced and to face with adequate tools<br>the increasingly engaging challenges our experiments require. But this first activity report is primarily<br>dedicated to all CNAF personnel, both staff and temporary collaborators, who day by day make it<br>happen.</p>
CovidStat @ INFN Open Access Repository
Full text link<p>La presentazione mostra il modello dei dati e dei metadati per l'integrazione dei prodotti della ricerca dell'applicazione CovidStat (https://covid19.infn.it) con l'INFN Open Access Repository, in ottemperanza ai principi FAIR (www.go-fair.org/fair-principles/).</p>
AI Playground for INFN Scientific Use Cases
Full text link<p>Talk given at "Workshop sul Calcolo nell'INFN", Palau (Sassari), 20-24/05/2024</p>\n\n<p>Title: </p>\n\n<p><strong>AI Playground:</strong> a curated collection of technologies offered “as a Service” on top of INFN Cloud for fast prototyping Machine Learning solutions across INFN research areas. </p>\n\n<p>Abstract: </p>\n\n<p>The introduction of <strong>ChatGPT</strong> in November 2022 has gained widespread attention and significantly boosted <strong>Generative AI </strong>adoption in technological solutions, highlighting the <strong>potential of AI</strong> to automate tasks, analyze large datasets, and make predictions with high accuracy. </p>\n\n<p>The fast-paced adoption of AI techniques has also been possible by the development and general availability of <strong>AI frameworks, libraries and platforms</strong> that provide structured approaches that make it easier to implement AI solutions. </p>\n\n<p>The integration of AI and Machine Learning (ML) in the <strong>Physics </strong>domain is also becoming increasingly pervasive, transforming the way scientists approach and solve complex problems. For instance, ML techniques in the High Energy Physics (HEP) domain are ubiquitous, successfully used in many areas and are playing a significant role in LHC Run 3 and in the future High-Luminosity LHC upgrade. </p>\n\n<p><strong>INFN</strong> always stands at the frontier’s edge of the most innovative technological advancements, hence supporting AI as a promising approach across the diverse research areas. </p>\n\n<p>However, AI adoption requires researchers at INFN to solve not only problems related to the specificity of the application/experiment (e.g., tailored models and specialized domain knowledge), but also requires solving general infrastructure-level and ML-workflow related problems. </p>\n\n<p>In this regard, we introduce <strong>AI Playground</strong>, a curated collection of technologies offered “<strong>as a Service</strong>” on top of INFN Cloud, for fast prototyping Machine Learning solutions across INFN research areas. </p>\n\n<p>AI Playground leverages <strong>INFN Cloud</strong> resources and principles by providing an open-source solution to INFN users that can be deployed through the INFN Cloud Dashboard. </p>\n\n<p>The general idea behind the design of AI Playground is to address common use cases within the institute, collect reliable and consolidated technologies to solve these problems, then offer these technologies within the playground so that scientists can easily <strong>prototype</strong> their AI solutions for use cases that benefit of the same technologies. </p>\n\n<p>In this contribution we introduce the principles and high-level architecture of AI Playground and address two use cases in different domains that have been prototyped within the playground. </p>\n\n<p>The first use case is in the <strong>NLP</strong> domain: we expose through an INFN Cloud HTTP endpoint a RAG (Retrieval Augmented Generation) pipeline: RAG is a popular technique for injecting knowledge into a Large Language Model (LLM). We describe the RAG pipeline implemented through on-premises model serving of open source LLMs. </p>\n\n<p>The second use case is in the <strong>HEP </strong>domain: we expose through an INFN Cloud HTTP endpoint a model for inference related to a signal-vs-noise discrimination problem about data generated by particle collisions. </p>\n\n<p>The two use cases belong to different research areas but leverage the same AI Playground technologies. </p>\n\n<p>AI Playground is currently a work in progress, the aim is that its <strong>application-agnostic</strong> nature will serve as a unified ecosystem where developers, data scientists, and domain experts can leverage a standardized framework for ML model development and deployment. Hopefully, the playground will eliminate the need for extensive domain expertise in every application area, empowering a broader audience to leverage the benefits of machine learning, breaking down barriers and fostering innovation across diverse research domains.</p>
INFN use-case: Open Access Repository
No full text<p>INFN OAR Presentation at the InvenioRDM Project Meeting held at CERN 20-24 January 2020.</p>
Brochure TT INFN
No full text<p>Brochure sul Trasferimento Tecnologico di INFN che mostra esempi del portfolio tecnologico dell'Istituto</p>
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