Increasing information accessibility on the Web: a rating system for specialized dictionaries

The paper illustrates the features of the WLR (Web Linguistic Resources) portal, which collects specialized online dictionaries and asses their suitability for different functions using a specifically designed rating system. The contribution aims to demonstrate how the existing tool has improved the usefulness of lexico-graphical portals and how its effectiveness can be further increased by transforming the portal into a collaborative resource.Questo contributo descrive le caratteristiche del portale WLR (Web Linguistic Resources) che raccoglie dizionari specialistici della Rete e ne stima l’utilizzabilità per diverse funzioni, avvalendosi di uno specifico sistema di valutazione. Viene quindi mostrato come questo strumento incrementi l’utilizzabilità dei portali lessicografici finora sviluppati e come la sua efficacia possa essere ulteriormente migliorata trasformandolo in risorsa collaborativa

Performance of the CMS Cathode Strip Chambers with Cosmic Rays

The Cathode Strip Chambers (CSCs) constitute the primary muon tracking device in the CMS endcaps. Their performance has been evaluated using data taken during a cosmic ray run in fall 2008. Measured noise levels are low, with the number of noisy channels well below 1%. Coordinate resolution was measured for all types of chambers, and fall in the range 47 microns to 243 microns. The efficiencies for local charged track triggers, for hit and for segments reconstruction were measured, and are above 99%. The timing resolution per layer is approximately 5 ns

Aligning the CMS Muon Chambers with the Muon Alignment System during an Extended Cosmic Ray Run

Peer reviewe

Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects

Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M>70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0<e≤0.3 at 0.33 Gpc−3 yr−1 at 90\% confidence level

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

TOWARDS COST-PERFORMANCE AWARENESS IN FOG-BASED DATA MANAGEMENT ARCHITECTURES

The evolution and diffusion of the latest Information and Communication Technologies (ICT) allowed for the constitution of huge intelligent environments to improve and facilitate users’ lives by providing innovative services and information. Underlying these environments is the Internet of Things (IoT), which has been identified as the key driver for implementing intelligence in any environment, such as factories, cities, or homes. Such a rapid technological evolution combined with easy access to an internet connection had various effects. First, it dramatically increased the amount of data collected by IoT sensor networks in the form of time series. Then, it made it easier for users to access data and services. This has put a strain on the DBMSs and the existing architectures designed to manage IoT data flows, resulting in increased latencies and network traffic. New databases for temporal data management have been designed and combined with consolidated Cloud-centric architectures to face the problems mentioned above. Nevertheless, as Cloud computing demonstrated its limits in the presence of time-sensitive tasks, new computational paradigms like Fog computing have been introduced to overcome the limits deriving from Cloud-centric data management. Introducing new solutions based on these technologies has brought numerous advantages and challenges. For example, classic distributed data management approaches have proven ineffective in some cases, leading to cost/performance inefficiencies. Likewise, abuse or misuse of the Fog paradigm can lead to resource waste or even performance deterioration. The objective of the thesis is to provide a hybrid Cloud-Fog architecture, which allows reducing the costs of data management compared to Cloud-based architectures without excessively reducing performance. To this end, a Fog level is introduced, which assumes a more active role in data management, to use the intrinsic knowledge of the area served and the related analytical workloads. The effectiveness of the architecture is shown using real data and typical analytical configurations with state-of-the-art databases. However, we designed and developed an extensible simulation tool due to the lack of appropriate tools for evaluating similar architectures. This tool aims to provide a solution architect with the basic functionalities both to evaluate the introduction of a Fog layer and evaluate the efficiency of the different strategies. Finally, we propose a new evaluation metric that provides an overview of the cost-performance efficiency for the considered architecture

An Haptic Interface for Industrial High-Precision Manufacturing Tasks

Within the Industry 4.0 context, a great number of machineries has been equipped with multiple sensors collecting vast amounts of heterogeneous data, including multimedia ones. In the context of high-precision industrial manufacturing, the output of these sensors can be exploited to leverage on human intelligence for monitoring the quality of the production. Nevertheless, in complex scenarios, the amount of sensed data could lead to a visual and acoustic overload for the Decision Maker. In this poster we propose a multi-modal user interface (UI) we devised to support the Decision Maker in monitoring the outcome of high-precision manufacturing machineries. In particular, to mitigate the acoustic and visual overloads, we propose the use of the haptic channel, both to control the playback of the collected data stream, and to get feedbacks about anomalous situations