Memory-augmented Dense Predictive Coding for Video Representation Learning

The objective of this paper is self-supervised learning from video, in particular for representations for action recognition. We make the following contributions: (i) We propose a new architecture and learning framework Memory-augmented Dense Predictive Coding (MemDPC) for the task. It is trained with a predictive attention mechanism over the set of compressed memories, such that any future states can always be constructed by a convex combination of the condense representations, allowing to make multiple hypotheses efficiently. (ii) We investigate visual-only self-supervised video representation learning from RGB frames, or from unsupervised optical flow, or both. (iii) We thoroughly evaluate the quality of learnt representation on four different downstream tasks: action recognition, video retrieval, learning with scarce annotations, and unintentional action classification. In all cases, we demonstrate state-of-the-art or comparable performance over other approaches with orders of magnitude fewer training data.Comment: ECCV2020, Spotligh

Smart Tourism Destinations: Can the Destination Management Organizations Exploit Benefits of the ICTs? Evidences from a Multiple Case Study

Recent developments of ICTs enable new ways to experience tourism and conducted to the concept of smart tourism. The adoption of cutting-edge technologies and its combination with innovative organizational models fosters cooperation, knowledge sharing, and open innovation among service providers in tourism destination. Moreover, it offers innovative services to visitors. In few words, they become smart tourism destinations. In this paper, we report first results of the SMARTCAL project aimed at conceiving a digital platform assisting Destination Management Organizations (DMOs) in providing smart tourism services. A DMO is the organization charged with managing the tourism offer of a collaborative network, made up of service providers acting in a destination. In this paper, we adopted a multiple case studies approach to analyze five Italian DMOs. Our aims were to investigate (1) if, and how, successful DMOs were able to offer smart tourism services to visitors; (2) if the ICTs adoption level was related to the collaboration level among DMO partners. First results highlighted that use of smart technologies was still in an embryonic stage of development, and it did not depend from collaboration levels

Panorametry: suggestion of a method for mandibular measurements on panoramic radiographs

<p>Abstract</p> <p>Background</p> <p>Orthopantomography (panoramic radiography) has been used for the study of measurements involving particularly the prediction of the eruption of impacted lower third molars and analyses of measurements of the ramus and head of mandible. The discrepancies involved with the projection of this radiographic image has stimulated the search for further ways to use it, particularly in orthodontic treatments and oral and maxillofacial surgeries. The author proposes a graphimetric method for the mandible, based on panoramic radiography. The results are expressed in linear and angular measurements, aiming at bilateral comparisons as well as the determination of the proportion of skeletal and dental structures, individually and among themselves as a whole. The method has been named Panorametry, and allows measurement of the mandible (Mandibular Panorametry) or the posterior mandibular teeth (Dental Panorametry). When combining mandible and maxilla, it should be referred to as Total Panorametry. It may also be used, in the future, with Cone Beam computed tomography (CT) images, and in this case it may be mentioned as CT Panorametry.</p

GROWTH on S190510g: DECam Observation Planning and Follow-Up of a Distant Binary Neutron Star Merger Candidate

The first two months of the third Advanced LIGO and Virgo observing run (2019 April–May) showed that distant gravitational-wave (GW) events can now be readily detected. Three candidate mergers containing neutron stars (NS) were reported in a span of 15 days, all likely located more than 100 Mpc away. However, distant events such as the three new NS mergers are likely to be coarsely localized, which highlights the importance of facilities and scheduling systems that enable deep observations over hundreds to thousands of square degrees to detect the electromagnetic counterparts. On 2019 May 10 02:59:39.292 UT the GW candidate S190510g was discovered and initially classified as a binary neutron star (BNS) merger with 98% probability. The GW event was localized within an area of 3462 deg^2, later refined to 1166 deg^2 (90%) at a distance of 227 ± 92 Mpc. We triggered Target-of-Opportunity observations with the Dark Energy Camera (DECam), a wide-field optical imager mounted at the prime focus of the 4 m Blanco Telescope at Cerro Tololo Inter-American Observatory in Chile. This Letter describes our DECam observations and our real-time analysis results, focusing in particular on the design and implementation of the observing strategy. Within 24 hr of the merger time, we observed 65% of the total enclosed probability of the final skymap with an observing efficiency of 94%. We identified and publicly announced 13 candidate counterparts. S190510g was reclassified 1.7 days after the merger, after our observations were completed, with a "BNS merger" probability reduced from 98% to 42% in favor of a "terrestrial classification

GRB 140606B/iPTF14bfu: Detection of shock-breakout emission from a cosmological γ -ray burst?

We present optical and near-infrared photometry of GRB 140606B (z = 0.384), and optical photometry and spectroscopy of its associated supernova (SN). The results of our modelling indicate that the bolometric properties of the SN (MNi = 0.4 ± 0.2 M·, Mej = 5 ± 2 M·, and EK = 2 ± 1 × 1052 erg) are fully consistent with the statistical averages determined for other γ -ray burst (GRB)-SNe. However, in terms of its γ -ray emission, GRB 140606B is an outlier of the Amati relation, and occupies the same region as low luminosity (ll) and short GRBs. The γ -ray emission in llGRBs is thought to arise in some or all events from a shock breakout (SBO), rather than from a jet. The measured peak photon energy (Ep ≈ 800 keV) is close to that expected for γ -rays created by an SBO (≳ 1 MeV). Moreover, based on its position in theMV ,p-Liso,γ plane and the EK-Γβ plane, GRB 140606B has properties similar to both SBO-GRBs and jetted-GRBs. Additionally, we searched for correlations between the isotropic γ -ray emission and the bolometric properties of a sample of GRB-SNe, finding that no statistically significant correlation is present. The average kinetic energy of the sample is ĒK = 2.1 × 1052 erg. All of the GRB-SNe in our sample, with the exception of SN 2006aj, are within this range, which has implications for the total energy budget available to power both the relativistic and non-relativistic components in a GRB-SN event. © 2015 The Authors

Machine learning for the Zwicky transient facility

The Zwicky Transient Facility is a large optical survey in multiple filters producing hundreds of thousands of transient alerts per night. We describe here various machine learning (ML) implementations and plans to make the maximal use of the large data set by taking advantage of the temporal nature of the data, and further combining it with other data sets. We start with the initial steps of separating bogus candidates from real ones, separating stars and galaxies, and go on to the classification of real objects into various classes. Besides the usual methods (e.g., based on features extracted from light curves) we also describe early plans for alternate methods including the use of domain adaptation, and deep learning. In a similar fashion we describe efforts to detect fast moving asteroids. We also describe the use of the Zooniverse platform for helping with classifications through the creation of training samples, and active learning. Finally we mention the synergistic aspects of ZTF and LSST from the ML perspective