Machine learning models are proposed to successfully detect heating from pressure measurements in synchrotron colliders. These models allow to analyze all the pressure measurements in the time ...available between two consecutive machine runs. The limits of simple heuristic-based algorithms arsing from noise and non-reproducibility are overcome by the proposed machine learning models. These models were trained, tested, and compared with an heuristic-based base-line approach. In particular, for the case of the CERN Large Hadron Collider (LHC), they reached better performance than base-line algorithms, both in precision and recall scores.
Machine learning (ML) systems are affected by a pervasive lack of transparency. The eXplainable Artificial Intelligence (XAI) research area addresses this problem and the related issue of explaining ...the behavior of ML systems in terms that are understandable to human beings. In many explanation of XAI approaches, the output of ML systems are explained in terms of low-level features of their inputs. However, these approaches leave a substantive explanatory burden with human users, insofar as the latter are required to map low-level properties into more salient and readily understandable parts of the input. To alleviate this cognitive burden, an alternative model-agnostic framework is proposed here. This framework is instantiated to address explanation problems in the context of ML image classification systems, without relying on pixel relevance maps and other low-level features of the input. More specifically, one obtains sets of middle-level properties of classification inputs that are perceptually salient by applying sparse dictionary learning techniques. These middle-level properties are used as building blocks for explanations of image classifications. The achieved explanations are parsimonious, for their reliance on a limited set of middle-level image properties. And they can be contrastive, because the set of middle-level image properties can be used to explain why the system advanced the proposed classification over other antagonist classifications. In view of its model-agnostic character, the proposed framework is adaptable to a variety of other ML systems and explanation problems.
Machine learning entails a broad range of techniques that have been widely used in Science and Engineering since decades. High-energy physics has also profited from the power of these tools for ...advanced analysis of colliders data. It is only up until recently that Machine Learning has started to be applied successfully in the domain of Accelerator Physics, which is testified by intense efforts deployed in this domain by several laboratories worldwide. This is also the case of CERN, where recently focused efforts have been devoted to the application of Machine Learning techniques to beam dynamics studies at the Large Hadron Collider (LHC). This implies a wide spectrum of applications from beam measurements and machine performance optimisation to analysis of numerical data from tracking simulations of non-linear beam dynamics. In this paper, the LHC-related applications that are currently pursued are presented and discussed in detail, paying also attention to future developments.
JEM-EUSO is an international program for the development of space-based Ultra-High Energy Cosmic Ray observatories. The program consists of a series of missions which are either under development or ...in the data analysis phase. All instruments are based on a wide-field-of-view telescope, which operates in the near-UV range, designed to detect the fluorescence light emitted by extensive air showers in the atmosphere. We describe the simulation software ESAF in the framework of the JEM-EUSO program and explain the physical assumptions used. We present here the implementation of the JEM-EUSO, POEMMA, K-EUSO, TUS, Mini-EUSO, EUSO-SPB1 and EUSO-TA configurations in ESAF. For the first time ESAF simulation outputs are compared with experimental data.
The notion of synergy enables one to provide simplified descriptions of hand actions. It has been used in a number of different meanings ranging from kinematic and dynamic synergies to postural and ...temporal postural synergies. However, relatively little is known about how representing an action by synergies might take into account the possibility to have a hierarchical and multiple action representation. This is a key aspect for action representation as it has been characterized by action theorists and cognitive neuroscientists. Thus, the aim of the present paper is to investigate whether and to what extent a hierarchical and multiple action representation can be obtained by a synergy approach. To this purpose, we took advantage of representing hand action as a linear combination of temporal postural synergies (TPSs), but on the assumption that TPSs have a tree-structured organization. In a tree-structured organization, a hand action representation can involve a TPS only if the ancestors of the synergy in the tree are themselves involved in the action representation. The results showed that this organization is enough to force a multiple representation of hand actions in terms of synergies which are hierarchically organized.
EUSO-SPB1 mission and science Abdellaoui, G.; Adams, J.H.; Alonso, G. ...
Astroparticle physics,
January 2024, 2024-01-00, 2024, Letnik:
154
Journal Article
Recenzirano
Odprti dostop
The Extreme Universe Space Observatory on a Super Pressure Balloon 1 (EUSO-SPB1) was launched in 2017 April from Wanaka, New Zealand. The plan of this mission of opportunity on a NASA super pressure ...balloon test flight was to circle the southern hemisphere. The primary scientific goal was to make the first observations of ultra-high-energy cosmic-ray extensive air showers (EASs) by looking down on the atmosphere with an ultraviolet (UV) fluorescence telescope from suborbital altitude (33 km). After 12 days and 4 h aloft, the flight was terminated prematurely in the Pacific Ocean. Before the flight, the instrument was tested extensively in the West Desert of Utah, USA, with UV point sources and lasers. The test results indicated that the instrument had sensitivity to EASs of ⪆3 EeV. Simulations of the telescope system, telescope on time, and realized flight trajectory predicted an observation of about 1 event assuming clear sky conditions. The effects of high clouds were estimated to reduce this value by approximately a factor of 2. A manual search and a machine-learning-based search did not find any EAS signals in these data. Here we review the EUSO-SPB1 instrument and flight and the EAS search.
Abstract This paper addresses the problem of extracting view-invariant visual features for the recognition of object-directed actions and introduces a computational model of how these visual features ...are processed in the brain. In particular, in the test-bed setting of reach-to-grasp actions, grip aperture is identified as a good candidate for inclusion into a parsimonious set of hand high-level features describing overall hand movement during reach-to-grasp actions. The computational model NeGOI (neural network architecture for measuring grip aperture in an observer-independent way) for extracting grip aperture in a view-independent fashion was developed on the basis of functional hypotheses about cortical areas that are involved in visual processing. An assumption built into NeGOI is that grip aperture can be measured from the superposition of a small number of prototypical hand shapes corresponding to predefined grip-aperture sizes. The key idea underlying the NeGOI model is to introduce view-independent units ( VIP units) that are selective for prototypical hand shapes, and to integrate the output of VIP units in order to compute grip aperture. The distinguishing traits of the NEGOI architecture are discussed together with results of tests concerning its view-independence and grip-aperture recognition properties. The overall functional organization of NEGOI model is shown to be coherent with current functional models of the ventral visual stream, up to and including temporal area STS. Finally, the functional role of the NeGOI model is examined from the perspective of a biologically plausible architecture which provides a parsimonious set of high-level and view-independent visual features as input to mirror systems.
Nowadays the measurement of the nuchal translucency thickness is being used as part of routine ultrasound scanning during the end of the first trimester of pregnancy, for the screening of chromosomal ...defects, as trisomy 21. Currently, the measurement is being performed manually by physicians. The measurement can take a long time for being accomplished, needs to be performed by highly skilled operators, and is prone to errors. Semi-automated methods requires that the user manually selects a region of the image containing the nuchal translucency, procedure that is somewhat time consuming. In this paper we present a complete system prototype that is able to perform the measurement of the nuchal translucency thickness without any manual intervention from the operator, operating on the video stream coming out from the ultrasound machine.
The MAGIC-5 Project aims at developing computer aided detection (CAD) software for medical applications on distributed databases by means of a GRID infrastructure connection. The use of automatic ...systems for analyzing medical images is of paramount importance in the screening programs, due to the huge amount of data to check. Examples are: mammographies for breast cancer detection, computed-tomography (CT) images for lung cancer analysis, and the positron emission tomography (PET) imaging for the early diagnosis of the Alzheimer disease. The need for acquiring and analyzing data stored in different locations requires a GRID approach of distributed computing system and associated data management. The GRID technologies allow remote image analysis and interactive online diagnosis, with a relevant reduction of the delays actually associated to the screening programs. From this point of view, the MAGIC-5 Collaboration can be seen as a group of distributed users sharing their resources for implementing different virtual organizations (VO), each one aiming at developing screening programs, tele-training, tele-diagnosis and epidemiologic studies for a particular pathology.
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