18,709 research outputs found
Belle II Technical Design Report
The Belle detector at the KEKB electron-positron collider has collected
almost 1 billion Y(4S) events in its decade of operation. Super-KEKB, an
upgrade of KEKB is under construction, to increase the luminosity by two orders
of magnitude during a three-year shutdown, with an ultimate goal of 8E35 /cm^2
/s luminosity. To exploit the increased luminosity, an upgrade of the Belle
detector has been proposed. A new international collaboration Belle-II, is
being formed. The Technical Design Report presents physics motivation, basic
methods of the accelerator upgrade, as well as key improvements of the
detector.Comment: Edited by: Z. Dole\v{z}al and S. Un
ASTRI SST-2M prototype and mini-array simulation chain, data reduction software, and archive in the framework of the Cherenkov Telescope Array
The Cherenkov Telescope Array (CTA) is a worldwide project aimed at building
the next-generation ground-based gamma-ray observatory. Within the CTA project,
the Italian National Institute for Astrophysics (INAF) is developing an
end-to-end prototype of the CTA Small-Size Telescopes with a dual-mirror
(SST-2M) Schwarzschild-Couder configuration. The prototype, named ASTRI SST-2M,
is located at the INAF "M.C. Fracastoro" observing station in Serra La Nave
(Mt. Etna, Sicily) and is currently in the scientific and performance
validation phase. A mini-array of (at least) nine ASTRI telescopes has been
then proposed to be deployed at the Southern CTA site, by means of a
collaborative effort carried out by institutes from Italy, Brazil, and
South-Africa. The CTA/ASTRI team is developing an end-to-end software package
for the reduction of the raw data acquired with both ASTRI SST-2M prototype and
mini-array, with the aim of actively contributing to the global ongoing
activities for the official data handling system of the CTA observatory. The
group is also undertaking a massive Monte Carlo simulation data production
using the detector Monte Carlo software adopted by the CTA consortium.
Simulated data are being used to validate the simulation chain and evaluate the
ASTRI SST-2M prototype and mini-array performance. Both activities are also
carried out in the framework of the European H2020-ASTERICS (Astronomy ESFRI
and Research Infrastructure Cluster) project. A data archiving system, for both
ASTRI SST-2M prototype and mini-array, has been also developed by the CTA/ASTRI
team, as a testbed for the scientific archive of CTA. In this contribution, we
present the main components of the ASTRI data handling systems and report the
status of their development.Comment: Proceedings of the 35th International Cosmic Ray Conference (ICRC
2017), Bexco, Busan, Korea. All CTA contributions at arXiv:1709.0348
An agent-driven semantical identifier using radial basis neural networks and reinforcement learning
Due to the huge availability of documents in digital form, and the deception
possibility raise bound to the essence of digital documents and the way they
are spread, the authorship attribution problem has constantly increased its
relevance. Nowadays, authorship attribution,for both information retrieval and
analysis, has gained great importance in the context of security, trust and
copyright preservation. This work proposes an innovative multi-agent driven
machine learning technique that has been developed for authorship attribution.
By means of a preprocessing for word-grouping and time-period related analysis
of the common lexicon, we determine a bias reference level for the recurrence
frequency of the words within analysed texts, and then train a Radial Basis
Neural Networks (RBPNN)-based classifier to identify the correct author. The
main advantage of the proposed approach lies in the generality of the semantic
analysis, which can be applied to different contexts and lexical domains,
without requiring any modification. Moreover, the proposed system is able to
incorporate an external input, meant to tune the classifier, and then
self-adjust by means of continuous learning reinforcement.Comment: Published on: Proceedings of the XV Workshop "Dagli Oggetti agli
Agenti" (WOA 2014), Catania, Italy, Sepember. 25-26, 201
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TAO Conceptual Design Report: A Precision Measurement of the Reactor Antineutrino Spectrum with Sub-percent Energy Resolution
The Taishan Antineutrino Observatory (TAO, also known as JUNO-TAO) is a
satellite experiment of the Jiangmen Underground Neutrino Observatory (JUNO). A
ton-level liquid scintillator detector will be placed at about 30 m from a core
of the Taishan Nuclear Power Plant. The reactor antineutrino spectrum will be
measured with sub-percent energy resolution, to provide a reference spectrum
for future reactor neutrino experiments, and to provide a benchmark measurement
to test nuclear databases. A spherical acrylic vessel containing 2.8 ton
gadolinium-doped liquid scintillator will be viewed by 10 m^2 Silicon
Photomultipliers (SiPMs) of >50% photon detection efficiency with almost full
coverage. The photoelectron yield is about 4500 per MeV, an order higher than
any existing large-scale liquid scintillator detectors. The detector operates
at -50 degree C to lower the dark noise of SiPMs to an acceptable level. The
detector will measure about 2000 reactor antineutrinos per day, and is designed
to be well shielded from cosmogenic backgrounds and ambient radioactivities to
have about 10% background-to-signal ratio. The experiment is expected to start
operation in 2022
Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence
IEEE Access
Volume 3, 2015, Article number 7217798, Pages 1512-1530
Open Access
Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article)
Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc
a Department of Information Engineering, University of Padua, Padua, Italy
b Department of General Psychology, University of Padua, Padua, Italy
c IRCCS San Camillo Foundation, Venice-Lido, Italy
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Abstract
In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network
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