Deep Shape-from-Template: Single-image quasi-isometric deformable registration and reconstruction

Shape-from-Template (SfT) solves 3D vision from a single image and a deformable 3D object model, called a template. Concretely, SfT computes registration (the correspondence between the template and the image) and reconstruction (the depth in camera frame). It constrains the object deformation to quasi-isometry. Real-time and automatic SfT represents an open problem for complex objects and imaging conditions. We present four contributions to address core unmet challenges to realise SfT with a Deep Neural Network (DNN). First, we propose a novel DNN called DeepSfT, which encodes the template in its weights and hence copes with highly complex templates. Second, we propose a semi-supervised training procedure to exploit real data. This is a practical solution to overcome the render gap that occurs when training only with simulated data. Third, we propose a geometry adaptation module to deal with different cameras at training and inference. Fourth, we combine statistical learning with physics-based reasoning. DeepSfT runs automatically and in real-time and we show with numerous experiments and an ablation study that it consistently achieves a lower 3D error than previous work. It outperforms in generalisation and achieves great performance in terms of reconstruction and registration error with wide-baseline, occlusions, illumination changes, weak texture and blur.Agencia Estatal de InvestigaciónMinisterio de Educació

Towards dense people detection with deep learning and depth images

This paper describes a novel DNN-based system, named PD3net, that detects multiple people from a single depth image, in real time. The proposed neural network processes a depth image and outputs a likelihood map in image coordinates, where each detection corresponds to a Gaussian-shaped local distribution, centered at each person?s head. This likelihood map encodes both the number of detected people as well as their position in the image, from which the 3D position can be computed. The proposed DNN includes spatially separated convolutions to increase performance, and runs in real-time with low budget GPUs. We use synthetic data for initially training the network, followed by fine tuning with a small amount of real data. This allows adapting the network to different scenarios without needing large and manually labeled image datasets. Due to that, the people detection system presented in this paper has numerous potential applications in different fields, such as capacity control, automatic video-surveillance, people or groups behavior analysis, healthcare or monitoring and assistance of elderly people in ambient assisted living environments. In addition, the use of depth information does not allow recognizing the identity of people in the scene, thus enabling their detection while preserving their privacy. The proposed DNN has been experimentally evaluated and compared with other state-of-the-art approaches, including both classical and DNN-based solutions, under a wide range of experimental conditions. The achieved results allows concluding that the proposed architecture and the training strategy are effective, and the network generalize to work with scenes different from those used during training. We also demonstrate that our proposal outperforms existing methods and can accurately detect people in scenes with significant occlusions.Ministerio de Economía y CompetitividadUniversidad de AlcaláAgencia Estatal de Investigació

3DFCNN: real-time action recognition using 3D deep neural networks with raw depth information

This work describes an end-to-end approach for real-time human action recognition from raw depth image-sequences. The proposal is based on a 3D fully convolutional neural network, named 3DFCNN, which automatically encodes spatio-temporal patterns from raw depth sequences. The described 3D-CNN allows actions classification from the spatial and temporal encoded information of depth sequences. The use of depth data ensures that action recognition is carried out protecting people"s privacy, since their identities can not be recognized from these data. The proposed 3DFCNN has been optimized to reach a good performance in terms of accuracy while working in real-time. Then, it has been evaluated and compared with other state-of-the-art systems in three widely used public datasets with different characteristics, demonstrating that 3DFCNN outperforms all the non-DNNbased state-of-the-art methods with a maximum accuracy of 83.6% and obtains results that are comparable to the DNN-based approaches, while maintaining a much lower computational cost of 1.09 seconds, what significantly increases its applicability in real-world environments.Agencia Estatal de InvestigaciónUniversidad de Alcal

Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review

Atmospheric Extreme Events (EEs) cause severe damages to human societies and ecosystems. The frequency and intensity of EEs and other associated events are increasing in the current climate change and global warming risk. The accurate prediction, characterization, and attribution of atmospheric EEs is therefore a key research field, in which many groups are currently working by applying different methodologies and computational tools. Machine Learning (ML) methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric EEs. This paper reviews the ML algorithms applied to the analysis, characterization, prediction, and attribution of the most important atmospheric EEs. A summary of the most used ML techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. A number of examples is discussed and perspectives and outlooks on the field are drawn.Comment: 93 pages, 18 figures, under revie

Competence in metered-dose inhaler technique among healthcare workers of three general hospitals in Mexico: it is not good after all these years

Introduction: Inhaled medication is the cornerstone of pharmacological treatment for chronic respiratory diseases. Therefore, it is important to use a metered-dose inhaler (MDI) correctly to get the appropriate dosage and benefit from the drug. Health-care workers (HCW) are responsible for teaching the correct MDI technique. Unfortunately, numerous studies consistently show that HCW have poor MDI technique. This study aimed to evaluate the current knowledge of MDI technique in HCW working in three general hospitals. Material and methods: A hospital-based, cross-sectional descriptive study was conducted in three general hospitals in Aguascalientes, México. Three surveyors simultaneously scored through a 14 dichotomic questions list as bad, regular, good, and very good MDI technique. Data were analyzed with SPSS version 16. Statistical analyses were performed using chi-square test or unpaired t-tests. An analysis of one-way ANOVA was used for comparison of three independent general hospitals. Values of p < 0.05 were considered to indicate statistical significance. Results: A total of 244 HCWs were surveyed: 78.3% were nurses whereas 21.3% were physicians. The inter-observer concor-dance analysis among observers was 0.97. We observed that 32.4% (79) performed a bad technique, 51.6% (126) a regular technique, 13.5% (33) a good one, and 2.5% HCW (6) a very good technique. No difference between gender, labor category, schedule, service, age, seniority, and education degree between the three hospitals was observed. The most common mistakes were “insufficient expiration prior to activation of the device”, and “the distance the inhaler was placed for inhalation” (83 and 84% respectively). Conclusion: We observed that a high percentage of HCW do not follow the MDI technique correctly, being this percentage even higher than the reported in other studies. These observations suggest the urgent need to establish frequent training programs for the correct use of MDI

Stacked LSTM sequence-to-sequence autoencoder with feature selection for daily solar radiation prediction: a review and new modeling results

We review the latest modeling techniques and propose new hybrid SAELSTM framework based on Deep Learning (DL) to construct prediction intervals for daily Global Solar Radiation (GSR) using the Manta Ray Foraging Optimization (MRFO) feature selection to select model parameters. Features are employed as potential inputs for Long Short-Term Memory and a seq2seq SAELSTM autoencoder Deep Learning (DL) system in the final GSR prediction. Six solar energy farms in Queensland, Australia are considered to evaluate the method with predictors from Global Climate Models and ground-based observation. Comparisons are carried out among DL models (i.e., Deep Neural Network) and conventional Machine Learning algorithms (i.e., Gradient Boosting Regression, Random Forest Regression, Extremely Randomized Trees, and Adaptive Boosting Regression). The hyperparameters are deduced with grid search, and simulations demonstrate that the DL hybrid SAELSTM model is accurate compared with the other models as well as the persistence methods. The SAELSTM model obtains quality solar energy prediction intervals with high coverage probability and low interval errors. The review and new modelling results utilising an autoencoder deep learning method show that our approach is acceptable to predict solar radiation, and therefore is useful in solar energy monitoring systems to capture the stochastic variations in solar power generation due to cloud cover, aerosols, ozone changes, and other atmospheric attenuation factors

Analysis of end-to-end multi-domain management and orchestration frameworks for software defined infrastructures: an architectural survey

Over the last couple of years, industry operators' associations issued requirements towards an end-to-end management and orchestration plane for 5G networks. Consequently, standard organisations started their activities in this domain. This article provides an analysis and an architectural survey of these initiatives and of the main requirements, proposes descriptions for the key concepts of domain, resource and service slicing, end-to-end orchestration and a reference architecture for the end-to-end orchestration plane. Then, a set of currently available or under development domain orchestration frameworks are mapped to this reference architecture. These frameworks, meant to provide coordination and automated management of cloud and networking resources, network functions and services, fulfil multi-domain (i.e. multi-technology and multi-operator) orchestration requirements, thus enabling the realisation of an end-to-end orchestration plane. Finally, based on the analysis of existing single-domain and multi-domain orchestration components and requirements, this paper presents a functional architecture for the end-to-end management and orchestration plane, paving the way to its full realisation.This work was partially supported by the ICT14 5GExchange (5GEx) innovation project (grant agreement no.671636) co-funded by the European Union under the Horizon 2020 EU Framework Programme.Publicad

High-resolution hepatitis C virus subtyping using NS5B deep sequencing and phylogeny, an alternative to current methods

HepatitisCvirus(HCV)is classified into seven major genotypesand67 subtypes. Recent studies haveshownthat inHCVgenotype 1-infected patients, response rates to regimens containingdirect-acting antivirals(DAAs)are subtype dependent. Currently available genotypingmethods have limited subtyping accuracy.Wehave evaluated theperformanceof adeep-sequencing-basedHCVsubtyping assay, developed for the 454/GS-Junior platform, in comparisonwith thoseof two commercial assays (VersantHCVgenotype 2.0andAbbott Real-timeHCVGenotype II)andusingdirectNS5Bsequencing as a gold standard (direct sequencing), in 114 clinical specimenspreviously tested by first-generation hybridization assay (82 genotype 1and32 with uninterpretable results). Phylogenetic analysis of deep-sequencing reads matched subtype 1 callingbypopulation Sanger sequencing(69%1b,31%1a) in 81 specimensandidentified amixed-subtype infection (1b/3a/1a) in one sample. Similarly,amongthe 32previously indeterminate specimens, identical genotypeandsubtype results were obtained by directanddeep sequencing in all but four samples with dual infection. In contrast, both VersantHCVGenotype 2.0andAbbott Real-timeHCVGenotype II failed subtype 1 calling in 13 (16%) samples eachandwere unable to identify theHCVgenotype and/or subtype inmore than half of the nongenotype 1 samples.Weconcluded that deep sequencing ismore efficient forHCVsubtyping than currently available methodsandallows qualitative identificationofmixed infectionsandmay bemorehelpfulwith respect to informing treatment strategies withnewDAA-containing regimens across allHCVsubtypesThis study has been supported by CDTI (Centro para el Desarrollo Tecnológico Industrial), Spanish Ministry of Economics and Competitiveness (MINECO), IDI-20110115; MINECO projects SAF 2009-10403; and also by the Spanish Ministry of Health, Instituto de Salud Carlos III (FIS) projects PI10/01505, PI12/01893, and PI13/00456. CIBERehd is funded by the Instituto de Salud Carlos III, Madrid, Spain. Work at CBMSO was supported by grant MINECO-BFU2011-23604, FIPSE, and Fundación Ramón Areces. X. Forns received unrestricted grant support from Roche and has acted as advisor for MSD, Gilead, and Abbvie. M. Alvarez-Tejado, J. Gregori, and J. M. Muñoz work in Roche Diagnostic

On strategic choices faced by large pharmaceutical laboratories and their effect on innovation risk under fuzzy conditions

ObjectivesWe develop a fuzzy evaluation model that provides managers at different responsibility levels in pharmaceutical laboratories with a rich picture of their innovation risk as well as that of competitors. This would help them take better strategic decisions around the management of their present and future portfolio of clinical trials in an uncertain environment. Through three structured fuzzy inference systems (FISs), the model evaluates the overall innovation risk of the laboratories by capturing the financial and pipeline sides of the risk.Methods and materialsThree FISs, based on the Mamdani model, determine the level of innovation risk of large pharmaceutical laboratories according to their strategic choices. Two subsystems measure different aspects of innovation risk while the third one builds on the results of the previous two. In all of them, both the partitions of the variables and the rules of the knowledge base are agreed through an innovative 2-tuple-based method. With the aid of experts, we have embedded knowledge into the FIS and later validated the model.ResultsIn an empirical application of the proposed methodology, we evaluate a sample of 31 large pharmaceutical laboratories in the period 2008–2013. Depending on the relative weight of the two subsystems in the first layer (capturing the financial and the pipeline sides of innovation risk), we estimate the overall risk. Comparisons across laboratories are made and graphical surfaces are analyzed in order to interpret our results. We have also run regressions to better understand the implications of our results.ConclusionsThe main contribution of this work is the development of an innovative fuzzy evaluation model that is useful for analyzing the innovation risk characteristics of large pharmaceutical laboratories given their strategic choices. The methodology is valid for carrying out a systematic analysis of the potential for developing new drugs over time and in a stable manner while managing the risks involved. We provide all the necessary tools and datasets to facilitate the replication of our system, which also may be easily applied to other settings