Efficacy of Audiovisual Distraction in the Reduction of Dental Anxiety During Endodontic Treatment

Master of ScienceDentistryEndodonticsUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/91618/1/DeNitto-Efficacy_of_Audiovisul_Distraction.pd

Unsupervised activity recognition for autonomous water drones

We propose an automatic system aimed at discovering relevant activities for aquatic drones employed in water monitoring applications. The methodology exploits unsupervised time series segmentation to pursue two main goals: i) to support on-line decision making of drones and operators, ii) to support off-line analysis of large datasets collected by drones. The main novelty of our approach consists of its unsupervised nature, which enables to analyze unlabeled data. We investigate different variants of the proposed approach and validate them using an annotated dataset having labels for activity \u201cupstream/downstream navigation\u201d. Obtained results are encouraging in terms of clustering purity and silhouette which reach values greater than 0.94 and 0.20, respectively, in the best models

Marine alien species in Italy: A contribution to the implementation of descriptor D2 of the Marine Strategy Framework Directive

A re-examination of marine alien species or Non Indigenous Species (NIS) reported in Italian Seas, until December 2018, is provided, focusing on establishment success, year of first record, origin, potential invasiveness, and likely pathways, in particular. Furthermore, their distribution is assessed according to the marine subregions outlined by the European Union (EU) Marine Strategy Framework Directive: Adriatic Sea (ADRIA), Ionian Sea and Central Mediterranean Sea (CMED), and Western Mediterranean Sea (WMED). In Italy, 265 NIS have been detected with the highest number of species being recorded in the CMED (154 species) and the WMED (151 species) subregions, followed by the ADRIA (143) subregion. Most of these species were recorded in more than one subregion. One hundred and eighty (180 or 68%) NIS have established stable populations in Italian Seas among which 26 have exhibited invasive traits. As regards the taxa involved, Macrophyta rank first with 65 taxa. Fifty-five of them are established in at least one subregion, mostly in the ADRIA and the CMED. Crustacea rank second with 48 taxa, followed by Polychaeta with 43 taxa, Mollusca with 29 taxa, and Fishes with 28 taxa, which were mainly reported from the CMED. In the period 2012-2017, 44 new alien species were recorded, resulting in approximately one new entry every two months. Approximately half of the NIS (~52%) recorded in Italy have most likely arrived through the transport-stowaway pathway related to shipping traffic (~28% as biofoulers, ~22% in ballast waters, and ~2% as hitchhikers). The second most common pathway is the unaided movement with currents (~19%), followed by the transport-contaminant on farmed shellfishes pathway (~18%). "Unaided" is the most common pathway for alien Fishes, especially in the CMED; escapes from confinement account for ~3% and release in nature for ~2%. The present NIS distribution hotspots for new introductions were defined at the first recipient area/location in Italy. In the ADRIA, the hotspot, Venice, accounts for the highest number of alien taxa introduced in Italy, with 50 newly recorded taxa. In the CMED subregion, the hotspots of introduction are the Taranto and Catania Gulfs, hosting 21 first records each. The Strait of Sicily represents a crossroad between alien taxa from the Atlantic Ocean and the Indo-Pacific area. In the WMED, bioinvasion hotspots include the Gulfs of Naples, Genoa and Livorno. This review can serve as an updated baseline for future coordination and harmonization of monitoring initiatives under international, EU and regional policies, for the compilation of new data from established monitoring programs, and for rapid assessment surveys

Multiple structure recovery via probabilistic biclustering

Multiple Structure Recovery (MSR) represents an important and challenging problem in the field of Computer Vision and Pattern Recognition. Recent approaches to MSR advocate the use of clustering techniques. In this paper we propose an alternative method which investigates the usage of biclustering in MSR scenario. The main idea behind the use of biclustering approaches to MSR is to isolate subsets of points that behave “coherently” in a subset of models/structures. Specifically, we adopt a recent generative biclustering algorithm and we test the approach on a widely accepted MSR benchmark. The results show that biclustering techniques favorably compares with state-of-the-art clustering methods

Recruitment of Serpuloidea (Annelida: Polychaeta) in a marine cave of the Ionian Sea (Italy, central Mediterranean).

Artificial panels have been displaced at different distances from the entrance in a submarine cave. the presence of Serpuloidea has been registered after 1, 3, 6, 12, and 24 months of panel exposition. A total of 18 species were listed, with the highest species richness at intermediate distance from the entrance. Serpuloidea confirmed to be the most important colonizers of harsh inner portion of the marine caves

Occurrence of the Guinean species Herbstia nitida Manning and Holthuis, 1981 (Crustacea: Decapoda: Brachiura) in a Mediterranean submarine cave and a comparison with the co-generic H. condyliata (Fabricius, 1787)

A NEW SPECIES FOR THE MEDITERRANEAN HAS BEEN FOUND IN A SUBMERGED CAVE. IT IS A SPECIES DESCRIBED FROM THE GUINEA GULF. IT IS NOT SURE IT IS OF RECENT INTRODUCTION DUE THE CRIPTIC BEHAVIOUR IT HAS

Dominant Set Biclustering

Biclustering, which can be defined as the simultaneous clustering of rows and columns in a data matrix, has received increasing attention in recent years, being applied in many scientific scenarios (e.g. bioinformatics, text analysis, computer vision). This paper proposes a novel biclustering approach, which extends the dominant-set clustering algorithm to the biclustering case. In particular, we propose a new way of representing the problem, encoded as a graph, which allows to exploit dominant set to analyse both rows and columns simultaneously. The proposed approach has been tested by using a well known synthetic microarray benchmark, with encouraging result

Biclustering with Dominant Sets

Biclustering can be defined as the simultaneous clustering of rows and columns in a data matrix and it has been recently applied to many scientific scenarios such as bioinformatics, text analysis and computer vision to name a few. In this paper we propose a novel biclustering approach, that is based on the concept of dominant-set clustering and extends such algorithm to the biclustering problem. In more detail, we propose a novel encoding of the biclustering problem as a graph so to use the dominant set concept to analyse rows and columns simultaneously. Moreover, we extend the Dominant Set Biclustering approach to facilitate the insertion of prior knowledge that may be available on the domain. We evaluated the proposed approach on a synthetic benchmark and on two computer vision tasks: multiple structure recovery and region-based correspondence. The empirical evaluation shows that the method achieves promising results that are comparable to the state-of-the-art and that outperforms competitors in various cases

Spike and slab biclustering

Biclustering refers to the problem of simultaneously clustering the rows and columns of a given data matrix, with the goal of obtaining submatrices where the selected rows present a coherent behaviour in the selected columns, and vice-versa. To face this intrinsically difficult problem, we propose a novel generative model, where biclustering is approached from a sparse low-rank matrix factorization perspective. The main idea is to design a probabilistic model describing the factorization of a given data matrix in two other matrices, from which information about rows and columns belonging to the sought for biclusters can be obtained. One crucial ingredient in the proposed model is the use of a spike and slab sparsity inducing prior, thus we term the approach spike and slab biclustering (SSBi). To estimate the parameters of the SSBi model, we propose an expectation-maximization (EM) algorithm, termed SSBiEM, which solves a low-rank factorization problem at each iteration, using a recently proposed augmented Lagrangian algorithm. Experiments with both synthetic and real data show that the SSBi approach compares favorably with the state-of-the-art. (C) 2017 Elsevier Ltd. All rights reserved