QoS Ad Hoc Routing Protocol Analysis in Civil Safety Context

In this paper, we have conducted an investigation of quality of service (QoS) approaches supporting a ad hoc routing protocol in civil safety context. We have proposed different schemes of the QoS path selection among multiple paths in order to find the most suitable in our context. We analyze an influence of the route path information and per-hop information. This performance analysis shows us which method is adequate in civil safety environment

Vision-as-Inverse-Graphics: Obtaining a Rich 3D Explanation of a Scene from a Single Image

We develop an inverse graphics approach to the problem of scene understanding, obtaining a rich representation that includes descriptions of the objects in the scene and their spatial layout, as well as global latent variables like the camera parameters and lighting. The framework’s stages include object detection, the prediction of the camera and lighting variables, and prediction of object-specific variables (shape, appearance and pose). This acts like the encoder of an autoencoder, with graphics rendering as the decoder. Importantly the scene representation is interpretable and is of variable dimension to match the detected number of objects plus the global variables. For the prediction of the camera latent variables we introduce a novel architecture termed Probabilistic HoughNets (PHNs), which provides a principled approach to combining information from multiple detections. We demonstrate the quality of the reconstructions obtained quantitatively on synthetic data, and qualitatively on real scenes

Challenges in Representation Learning: A report on three machine learning contests

The ICML 2013 Workshop on Challenges in Representation Learning focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge. We describe the datasets created for these challenges and summarize the results of the competitions. We provide suggestions for organizers of future challenges and some comments on what kind of knowledge can be gained from machine learning competitions.Comment: 8 pages, 2 figure

Learning Direct Optimization for scene understanding

We develop a Learning Direct Optimization (LiDO) method for the refinement of a latent variable model that describes input image x. Our goal is to explain a single image x with an interpretable 3D computer graphics model having scene graph latent variables z (such as object appearance, camera position). Given a current estimate of z we can render a prediction of the image g(z), which can be compared to the image x. The standard way to proceed is then to measure the error E(x, g(z)) between the two, and use an optimizer to minimize the error. However, it is unknown which error measure E would be most effective for simultaneously addressing issues such as misaligned objects, occlusions, textures, etc. In contrast, the LiDO approach trains a Prediction Network to predict an update directly to correct z, rather than minimizing the error with respect to z. Experiments show that our LiDO method converges rapidly as it does not need to perform a search on the error landscape, produces better solutions than error-based competitors, and is able to handle the mismatch between the data and the fitted scene model. We apply LiDO to a realistic synthetic dataset, and show that the method also transfers to work well with real images

3D scene graph inference and refinement for vision-as-inverse-graphics

The goal of scene understanding is to interpret images, so as to infer the objects present in a scene, their poses and fine-grained details. This thesis focuses on methods that can provide a much more detailed explanation of the scene than standard bounding-boxes or pixel-level segmentation - we infer the underlying 3D scene given only its projection in the form of a single image. We employ the Vision-as-Inverse-Graphics (VIG) paradigm, which (a) infers the latent variables of a scene such as the objects present and their properties as well as the lighting and the camera, and (b) renders these latent variables to reconstruct the input image. One highly attractive aspect of the VIG approach is that it produces a compact and interpretable representation of the 3D scene in terms of an arbitrary number of objects, called a 'scene graph'. This representation is of a key importance, as it can be useful e.g. if we wish to edit, refine, interpret the scene or interact with it. First, we investigate how the recognition models can be used to infer the scene graph given only a single RGB image. These models are trained using realistic synthetic images and corresponding ground truth scene graphs, obtained from a rich stochastic scene generator. Once the objects have been detected, each object detection is further processed using neural networks to predict the object and global latent variables. This allows computing of object poses and sizes in 3D scene coordinates, given the camera parameters. This inference of the latent variables in the form of a 3D scene graph acts like the encoder of an autoencoder, with graphics rendering as the decoder. One of the major challenges is the problem of placing the detected objects in 3D at a reasonable size and distance with respect to the single camera, the parameters of which are unknown. Previous VIG approaches for multiple objects usually only considered a fixed camera, while we allow for variable camera pose. To infer the camera parameters given the votes cast by the detected objects, we introduce a Probabilistic HoughNets framework for combining probabilistic votes, robustified with an outlier model. Each detection provides one noisy low-dimensional manifold in the Hough space, and by intersecting them probabilistically we reduce the uncertainty on the camera parameters. Given an initialization of a scene graph, its refinement typically involves computationally expensive and inefficient search through the latent space. Since optimization of the 3D scene corresponding to an image is a challenging task even for a few LVs, previous work for multi-object scenes considered only refinement of the geometry, but not the appearance or illumination. To overcome this issue, we develop a framework called 'Learning Direct Optimization' (LiDO) for optimization of the latent variables of a multi-object scene. Instead of minimizing an error metric that compares observed image and the render, this optimization is driven by neural networks that make use of the auto-context in the form of a current scene graph and its render to predict the LV update. Our experiments show that the LiDO method converges rapidly as it does not need to perform a search on the error landscape, produces better solutions than error-based competitors, and is able to handle the mismatch between the data and the fitted scene model. We apply LiDO to a realistic synthetic dataset, and show that the method transfers to work well with real images. The advantages of LiDO mean that it could be a critical component in the development of future vision-as-inverse-graphics systems

Identification of clinical risk factors of atrial fibrillation in congestive heart failure

Background: Factors associated with the development of atrial fibrillation (AF) in generalpopulation have been described, but it is still unknown whether the same risk factors applyto heart failure (HF) patients. The aim of this study was to identify clinical factors related tovarious forms of AF in HF patients.Methods: The clinical and echocardiographic characteristics were assessed in 155 HF patients:50 with sinus rhythm, 52 with non-permanent AF, and 53 with permanent AF.Results: Multivariate logistic regression analysis showed that the increase in the NYHAclass was an independent risk factor for both forms of AF. The occurrence of permanent AF incomparison to sinus rhythm group was independently associated with hs-C-reactive protein(CRP) elevation above 1 mg/dL (OR 1.87, 95% CI 1.05–3.35), left atrial dimension above4 cm (OR 3.78, 95% CI 1.29–11.06) and tricuspid maximal pressure gradient elevation above35 mm Hg (OR 5.01, 95% CI 1.38–18.27). The presence of coronary disease was independentlyassociated with less frequent occurrence of permanent AF in comparison to sinus rhythm group(OR 0.21, 95% CI 0.06–0.67).Conclusions: More advanced congestive HF was associated with presence of both types of AF.Non-ischemic etiology of HF and elevated CRP are independently associated with permanentAF compared to sinus rhythm. Left ventricular diastolic dysfunction indicators (increasedtricuspid maximal pressure gradient and left artial dimension) are independently associatedwith permanent AF

Níveis de Atividade Física de Intensidade Ligeira em pessoas com 75 ou mais anos: associação com a dor, o número de quedas e a capacidade funcional Dissertação

Introdução: Estima-se que 49% dos idosos de Portugal tenham 75 ou mais anos de idade. Estes idosos são a classe mais sedentária e com maior declínio funcional e incapacidade. As guidelines de Atividade Física (AF) recomendam que esta seja no mínimo de intensidade moderada para que se obtenham benefícios em saúde. No entanto começa a ser sugerida a associação da Atividade Física de Intensidade Ligeira (AFIL) com algumas variáveis em saúde. Contudo, poucos estudos abordam a associação da AFIL com indicadores de saúde nos idosos. Objetivo: Identificação dos níveis auto-reportados de AFIL e sua associação com a dor, número de quedas e capacidade funcional. Metodologia: Realizou-se um estudo analítico observacional transversal, com uma amostra constituída por 65 participantes entre os 75 anos e os 96 anos de idade e uma média de idade de 79,48 ± 4,98. As variáveis em estudo foram a AFIL, avaliada pelo Diário de AF, a dor e número de quedas, avaliadas no questionário de caracterização da amostra e a capacidade funcional avaliada pelo “Short Physical Perfomance Battery” (SPPB). Resultados: A média diária do tempo passado em AFIL foi de 268±107,80 minutos. Existiu uma associação positiva estatisticamente significativa (rs=0,45; p≤0,01) entre a AFIL e o SPPB. Não se obtiveram associações estatisticamente significativas entre a AFIL e a dor ou o número de quedas. Conclusão: Os resultados demonstram uma associação positiva significativa entre a AFIL e a capacidade funcional. Sugere-se a realização de mais estudos da associação da AFIL com indicadores relevantes para a população idosa, por esta poder ser uma alternativa viável e mais realista à AF de intensidade moderada ou vigorosa nos idosos, essencialmente os inativos, pouco ativos ou os com maior fragilidade, aparentando menos riscos de lesão e maior adesão à sua execução e similarmente poder contribuir para a diminuição dos comportamentos sedentários.Background: It is estimated that 49% of the elderly in Portugal are 75 years old or older. These old adults are the most sedentary class of people and with more functional decline and incapacity. Current Physical Activity (PA) guidelines advise on PA of at least moderate intensity for health benefits. Accumulating evidence suggests that PA of light intensity might be beneficial in some health variables. However, studies explore the association of Lightintensity Physical Activity (LIPA) with health benefits in elderly. Aim: The purpose of this study is to identify the self-reported levels of LIPA of people aged 75 years and over and their association with pain, number of falls and physical performance. Methods: It’s a crosssectional observational study with a sample of 65 participants aged 75 to 96 with a mean age of 79,48 ± 4,98. The LIPA levels was assessed by a diary of PA. Pain and number of falls data were given by the sociodemographic and clinic survey. The Physical performance was assessed by the Short Physical Perfomance Battery (SPPB). Results: Participants accumulated 268 ±107,80 (mean ± SD) minutes of LIPA per day. A statistically significant positive correlation was found between LIPA and SPPB (rs=0,45; p=0,01). LIPA was not correlated significantly with pain or number of falls. Conclusion: In the present study, the LIPA was positively associated with physical performance. There should be more studies in the future about this intensity of PA, perhaps a feasible and more realistic alternative to the moderate-intensity PA or exercise on the elderly, especially the inactive, insufficiently active, or the frailty ones, presenting lower risk of injuries and better compliance and due the possible contribution to decrease the time spent on sedentary behaviours

Improving combined intra‐carrier transport with the application of modernized wheel scrapers

В статье предлагается комплекс горно- транспортного оборудования для работы совместно с горными фрезами. На участках расположенных вблизи перегрузочного комплекса выемку и транспортировку горной массы предполагается вести посредством модернизированных колесных скреперов, а с удаленных участков выемку и транспортировку горной массы производить одноковшовыми погрузчиками и авто-самосвалами. Выемка различными комплектами горного оборудования в зависимости от дальности транспортировки разрыхленной горными фрезами породы позволяет оптимизировать затраты на функционирование сборочного карьерного транспорта и повысить рентабельность горного производства

Massive Dimensionality Reduction for the Left Ventricular Mesh

Statistical emulation is a promising approach for the translation of cardio-mechanical modelling into the clinical practice. However, a key challenge is to find a low-dimensional representation of the heart, or, for the specific purpose of diagnosing the risk of heart attacks, the left-ventricle of the heart. We consider the problem of dimensionality reduction of the left ventricular mesh, in which we investigate three classes of techniques: principal component analysis (PCA), deep learning (DL) methods based on auto-encoders, and a parametric model from the cardio-mechanical literature. Our finding is that PCA performs as well as the computationally more expensive DL methods, and both outperform the state-of-the-art parametric model

Massive Dimensionality Reduction for the Left Ventricular Mesh

Statistical emulation is a promising approach for the translation of cardio-mechanical modelling into the clinical practice. However, a key challenge is to find a low-dimensional representation of the heart, or, for the specific purpose of diagnosing the risk of heart attacks, the left-ventricle of the heart. We consider the problem of dimensionality reduction of the left ventricular mesh, in which we investigate three classes of techniques: principal component analysis (PCA), deep learning (DL) methods based on auto-encoders, and a parametric model from the cardio-mechanical literature. Our finding is that PCA performs as well as the computationally more expensive DL methods, and both outperform the state-of-the-art parametric model