Anomaly detection for imbalanced datasets with deep generative models

Many important data analysis applications present with severely imbalanced datasets with respect to the target variable. A typical example is medical image analysis, where positive samples are scarce, while performance is commonly estimated against the correct detection of these positive examples. We approach this challenge by formulating the problem as anomaly detection with generative models. We train a generative model without supervision on the ‘negative’ (common) datapoints and use this model to estimate the likelihood of unseen data. A successful model allows us to detect the ‘positive’ case as low likelihooddatapoints.In this position paper, we present the use of state-of-the-art deep generative models (GAN and VAE) for the estimation of a likelihood of the data. Our results show that on the one hand both GANs and VAEs are able to separate the ‘positive’ and ‘negative’ samples in the MNIST case. On the other hand, for the NLST case, neither GANs nor VAEs were able to capture the complexity of the data and discriminate anomalies at the level that this task requires. These results show that even though there are a number of successes presented in the literature for using generative models in similar applications, there remain further challenges for broad successful implementation

Bose-Einstein condensate collapse: a comparison between theory and experiment

We solve the Gross-Pitaevskii equation numerically for the collapse induced by a switch from positive to negative scattering lengths. We compare our results with experiments performed at JILA with Bose-Einstein condensates of Rb-85, in which the scattering length was controlled using a Feshbach resonance. Building on previous theoretical work we identify quantitative differences between the predictions of mean-field theory and the results of the experiments. Besides the previously reported difference between the predicted and observed critical atom number for collapse, we also find that the predicted collapse times systematically exceed those observed experimentally. Quantum field effects, such as fragmentation, that might account for these discrepancies are discussed.Comment: 4 pages, 2 figure

Miscellaneous skin lesions of unknown aetiology in cetaceans from South America. Scientific Committee document SC/60/DW4, International Whaling Commission, June 2008, Santiago, Chile

We report on miscellaneous skin diseases or syndromes of unknown aetiology including whitish, velvety lesions (WVL, often associated with unrelated skin injuries, scars and tooth rakes), large, rounded lesions (LRL, large to very large lesions with an orange or dark outline and a light inner colour) and vesicular skin disease (VSD, small to medium vesicles) in Megaptera novaeangliae, Cephalorhynchus commersonii, C. eutropia, Pseudorca crassidens, Sotalia guianensis and Tursiops truncates from marine waters of Argentina, Brazil, Chile, Ecuador, Peru and the Antarctic. No biopsy samples have been available yet for histopathology. WVL are now commonly recorded opportunistically through photo-identification studies in several coastal species and populations from South America. Mortality rates, if any, associated with these skin diseases is unknown. Though sometimes extensive and ulcerated WVL do not seem life-threatening and, at least in some individuals, may eventually heal. A calf C. eutropia with LRL died some weeks after being first sighted. While unknown bacteria or fungi superinfecting miscellaneous skin traumata and poxvirus tattoos are thought to cause WVL and LRL, vesiviruses are suspected as the aetiological agents of VSD. Importantly, all lesions were primarily seen in coastal cetaceans living in biologically or chemically contaminated waters. These various skin conditions may be indicative of a deteriorating coastal water environment and should be systematically monitored. Collection of biopsies or fresh samples for histopathology and microbiological analysis is urgently needed

A pervasive approach to a real-time intelligent decision support system in intensive medicine

The decision on the most appropriate procedure to provide to the patients the best healthcare possible is a critical and complex task in Intensive Care Units (ICU). Clinical Decision Support Systems (CDSS) should deal with huge amounts of data and online monitoring, analyzing numerous parameters and providing outputs in a short real-time. Although the advances attained in this area of knowledge new challenges should be taken into account in future CDSS developments, principally in ICUs environments. The next generation of CDSS will be pervasive and ubiquitous providing the doctors with the appropriate services and information in order to support decisions regardless the time or the local where they are. Consequently new requirements arise namely the privacy of data and the security in data access. This paper will present a pervasive perspective of the decision making process in the context of INTCare system, an intelligent decision support system for intensive medicine. Three scenarios are explored using data mining models continuously assessed and optimized. Some preliminary results are depicted and discussed.Fundação para a Ciência e a Tecnologia (FCT

Role of carbonate burial in Blue Carbon budgets

Calcium carbonates (CaCO 3 ) often accumulate in mangrove and seagrass sediments. As CaCO 3 production emits CO 2 , there is concern that this may partially offset the role of Blue Carbon ecosystems as CO 2 sinks through the burial of organic carbon (C org ). A global collection of data on inorganic carbon burial rates (C inorg , 12% of CaCO 3 mass) revealed global rates of 0.8 TgC inorg yr −1 and 15–62 TgC inorg yr −1 in mangrove and seagrass ecosystems, respectively. In seagrass, CaCO 3 burial may correspond to an offset of 30% of the net CO 2 sequestration. However, a mass balance assessment highlights that the C inorg burial is mainly supported by inputs from adjacent ecosystems rather than by local calcification, and that Blue Carbon ecosystems are sites of net CaCO 3 dissolution. Hence, CaCO 3 burial in Blue Carbon ecosystems contribute to seabed elevation and therefore buffers sea-level rise, without undermining their role as CO 2 sinks. © 2019, The Author(s)