1,721,237 research outputs found
The 1st International Workshop on the Environmental Sustainability of High-Performance Software (SHiPS)
No full textProceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysi
Efficienza e gestione dei mercati all'ingrosso / I.F.O.R., Istituto per la formazione imprenditoriale nel commercio
No full textCOVID-19 outbreak in Italy: experimental chest X-ray scoring system for quantifying and monitoring disease progression
No full textSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a new virus recently isolated from humans. SARS-CoV-2 was discovered to be the pathogen responsible for a cluster of pneumonia cases associated with severe respiratory disease that occurred in December 2019 in China. This novel pulmonary infection, formally called Coronavirus Disease 2019 (COVID-19), has spread rapidly in China and beyond. On 8 March 2020, the number of Italians with SARS-CoV-2 infection was 7375 with a 48% hospitalization rate. At present, chest-computed tomography imaging is considered the most effective method for the detection of lung abnormalities in early-stage disease and quantitative assessment of severity and progression of COVID-19 pneumonia. Although chest X-ray (CXR) is considered not sensitive for the detection of pulmonary involvement in the early stage of the disease, we believe that, in the current emergency setting, CXR can be a useful diagnostic tool for monitoring the rapid progression of lung abnormalities in infected patients, particularly in intensive care units. In this short communication, we present our experimental CXR scoring system that we are applying to hospitalized patients with COVID-19 pneumonia to quantify and monitor the severity and progression of this new infectious disease. We also present the results of our preliminary validation study on a sample of 100 hospitalized patients with SARS-CoV-2 infection for whom the final outcome (recovery or death) was available
Unilateral triple mandibular canal with double mandibular foramen: cone-beam computed tomography findings of an unexpected anatomical variant
Full text linkThe mandibular canal is a bony channel located within the spongiosa of the mandible. The main structure contained in the mandibular canal is the inferior alveolar nerve (IAN). The IAN is a very important structure that requires due consideration during dental or surgical procedures involving the mandible. Therefore, a detailed morphological analysis of the mandibular canal should be carried out before any surgical procedure in the mandibular region in order to avoid complications and to reduce the risk of inadequate local nerve blocking. The human mandible typically has a single mandibular canal on each side; however, accessory mandibular canals have been described previously in the literature. The most common variant of the mandibular canal is the bifid mandibular canal, which has a prevalence ranging from 10-66% on cone-beam computed tomography (CBCT) examinations. A rare variant of bifid mandibular canal is the trifid canal, accounting for less than 6% of all bifid canals. In some cases, the bifid and trifid mandibular canals are associated with a double mandibular foramen, which is a rare anatomical variant with a reported incidence of 1.35% on CBCT images. Herein, we present the interesting CBCT images of an unexpected anatomical variant characterized by unilateral triple mandibular canal with double mandibular foramen in a young Caucasian woman
Constrained Hardware Dimensioning for AI Algorithms
Full text linkGiven the diffusion of Artificial Intelligence (AI) in numerous domains,
experts and practitioners are faced with the challenge of finding the optimal hardware
(HW) resources and configuration (hardware dimensioning) under different con-
straints and objectives (e.g., budget, time, solution quality). To tackle this challenge,
we propose an automated tool for HArdware Dimensioning of (AI) Algorithms
(HADA), an approach relying on the integration of Machine Learning (ML) models
together into an optimization problem, where experts domain knowledge can be
injected as well. The ML models encapsulate the data-driven knowledge about
the relationships between HW requirements and AI algorithm performances. We
show how HADA can be employed to find the best HW configuration that respects
user-defined constraints in three different domains
Studio della variazione della linea di costa a Nord dei Lidi Ferraresi
No full textSi riportano i risultati di uno studio sulla variazione della linea di costa a nord dei lidi ferraresi, integrando misure in sito sia tradiizonali che GPS e rilievi fotogrammetrici
Towards Intelligent Music Production: A Sample-based Approach
Full text linkTechnological advances have always played a central role in shaping the production of popular music. Over the past few years, music generation systems started to attract considerable interest within the academic community, although the proposed prototypes rarely managed to emerge and be adopted by producers in their professional workflows. We argue that a major cause of that is the inherent complexity of integrating those systems into well-established music production pipelines, especially given that most of them are designed with the intent of replacing human creativity rather than assisting it. To this end, we discuss our proposal for a novel approach for Intelligent Music Production based on samples arrangement. Such a tools could offer several potential benefits in enhancing human creativity, as they provide the opportunity to keep human artists in the creative loop as well as to reduce computational costs and hardware requirements, making music production more accessible. As a first step towards this direction, we eventually present MusiComb, a prototype for sample-based music generation. Alongside, we report how this relatively simple system has demonstrated its ability to produce realistic tracks in few seconds while adhering to user-defined constraints
Vaccination and Reduced Severity of COVID-19 Pneumonia Visualized with Chest Radiography
No full textRilevamento topografico della Sacca di Goro
No full textSi riporta una descrizione delle campagne di misure topografiche tradizionali e satellitari, per lo studio dell'evoluzione morfodinamica della Sacca di Goro
Online Job Failure Prediction in an HPC System
No full textModern High Performance Computing (HPC) systems are complex machines, with major impacts on economy and society. Along with their computational capability, their energy consumption is also steadily raising, representing a critical issue given the ongoing environmental and energetic crisis. Therefore, developing strategies to optimize HPC system management has paramount importance, both to guarantee top-tier performance and to improve energy efficiency. One strategy is to act at the workload level and highlight the jobs that are most likely to fail, prior to their execution on the system. Jobs failing during their execution unnecessarily occupy resources which could delay other jobs, adversely affecting the system performance and energy consumption. In this paper, we study job failure prediction at submit-time using classical machine learning algorithms. Our novelty lies in (i) the combination of these algorithms with Natural Language Processing (NLP) tools to represent jobs and (ii) the design of the approach to work in an online fashion in a real system. The study is based on a dataset extracted from a production machine hosted at the HPC centre CINECA in Italy. Experimental results show that our approach is promising
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