Structured learning of assignment models for neuron reconstruction to minimize topological errors

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Structured learning provides a powerful framework for empirical risk minimization on the predictions of structured models. It allows end-to-end learning of model parameters to minimize an application specific loss function. This framework is particularly well suited for discrete optimization models that are used for neuron reconstruction from anisotropic electron microscopy (EM) volumes. However, current methods are still learning unary potentials by training a classifier that is agnostic about the model it is used in. We believe the reason for that lies in the difficulties of (1) finding a representative training sample, and (2) designing an application specific loss function that captures the quality of a proposed solution. In this paper, we show how to find a representative training sample from human generated ground truth, and propose a loss function that is suitable to minimize topological errors in the reconstruction. We compare different training methods on two challenging EM-datasets. Our structured learning approach shows consistently higher reconstruction accuracy than other current learning methods.Peer ReviewedPostprint (author's final draft

Climate change adaptation strategies in Sub-Saharan Africa: foundations for the future

Many institutions across sub-Saharan Africa (SSA) and many funding agencies that support them are currently engaged in initiatives that are targeted towards adapting rainfed agriculture to climate change. This does, however, present some very real and complex research and policy challenges. Given to date the generally low impact of agricultural research across SSA on improving the welfare of rainfed farmers under current climatic conditions, a comprehensive strategy is required if the considerably more complex challenge of adapting agriculture to future climate change is to bear fruit. In articulating such a strategy, it is useful to consider the criteria by which current successful initiatives should be judged

Biofuels supply chain characterization

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2007.Includes bibliographical references (leaves 84-89).Ethanol can be made from agricultural residues like wheat straw and from crops dedicated to energy use, like switchgrass. We study the logistics aspects of this transformation and determine the main characteristics of the supply chain making ethanol from cellulose. Important to the final acceptability of ethanol as a transportation fuel is both the economics as well as the environmental aspect of using ethanol. In this study we analyze the buildup of cost as biomass is transformed into fuel. We also look at all the steps involved and describe them from a supply chain perspective We have found that the main cost components in the cellulosic ethanol production are biomass production, harvesting and ethanol refining. We have also found that the main factor in reducing the overall production cost is the biomass to ethanol conversion factor. The development of new technologies to convert biomass into ethanol becomes a critical issue to achieve the cost targets imposed in order to make ethanol more competitive with other sources of energy such as fossil fuels. An increase in the current conversion factor of 42% could potentially yield to a decrease of nearly 15% in the: total production cost of cellulosic ethanol.(cont.) Other factors such as increasing the refining plant size and biomass yield can also help to reduce the production cost but we found its impact to be lower than that of the conversion factor. Finally, we also performed a strategic analysis of the entire supply chain to determine how is this industry likely to develop and who will have more bargaining power and therefore will realize most of the value and profits in the supply chain. Our analysis shows that in such a dynamic scenario as in the alternate energy industry, the best option is to build sustained advantage by strong alliances with different partners within the supply chain.by Anindya Banerjee [and] José Luis Noguer.M.Eng.in Logistic

Joint Coarse-And-Fine Reasoning for Deep Optical Flow

We propose a novel representation for dense pixel-wise estimation tasks using CNNs that boosts accuracy and reduces training time, by explicitly exploiting joint coarse-and-fine reasoning. The coarse reasoning is performed over a discrete classification space to obtain a general rough solution, while the fine details of the solution are obtained over a continuous regression space. In our approach both components are jointly estimated, which proved to be beneficial for improving estimation accuracy. Additionally, we propose a new network architecture, which combines coarse and fine components by treating the fine estimation as a refinement built on top of the coarse solution, and therefore adding details to the general prediction. We apply our approach to the challenging problem of optical flow estimation and empirically validate it against state-of-the-art CNN-based solutions trained from scratch and tested on large optical flow datasets.Comment: Accepted in IEEE ICIP 2017. IEEE Copyrights: Personal use of this material is permitted. Permission from IEEE must be obtained for all other use

GANimation: one-shot anatomically consistent facial animation

The final publication is available at link.springer.comRecent advances in generative adversarial networks (GANs) have shown impressive results for the task of facial expression synthesis. The most successful architecture is StarGAN (Choi et al. in CVPR, 2018), that conditions GANs’ generation process with images of a specific domain, namely a set of images of people sharing the same expression. While effective, this approach can only generate a discrete number of expressions, determined by the content and granularity of the dataset. To address this limitation, in this paper, we introduce a novel GAN conditioning scheme based on action units (AU) annotations, which describes in a continuous manifold the anatomical facial movements defining a human expression. Our approach allows controlling the magnitude of activation of each AU and combining several of them. Additionally, we propose a weakly supervised strategy to train the model, that only requires images annotated with their activated AUs, and exploit a novel self-learned attention mechanism that makes our network robust to changing backgrounds, lighting conditions and occlusions. Extensive evaluation shows that our approach goes beyond competing conditional generators both in the capability to synthesize a much wider range of expressions ruled by anatomically feasible muscle movements, as in the capacity of dealing with images in the wild. The code of this work is publicly available at https://github.com/albertpumarola/GANimation.Peer ReviewedPostprint (author's final draft

Fitting a 3D Morphable Model to Edges: A Comparison Between Hard and Soft Correspondences

We propose a fully automatic method for fitting a 3D morphable model to single face images in arbitrary pose and lighting. Our approach relies on geometric features (edges and landmarks) and, inspired by the iterated closest point algorithm, is based on computing hard correspondences between model vertices and edge pixels. We demonstrate that this is superior to previous work that uses soft correspondences to form an edge-derived cost surface that is minimised by nonlinear optimisation.Comment: To appear in ACCV 2016 Workshop on Facial Informatic

Histiocytoid cardiomyopathy and microphthalmia with linear skin defects syndrome: phenotypes linked by truncating variants in NDUFB11

Variants in NDUFB11, which encodes a structural component of complex I of the mitochondrial respiratory chain (MRC), were recently independently reported to cause histiocytoid cardiomyopathy (histiocytoid CM) and microphthalmia with linear skin defects syndrome (MLS syndrome). Here we report an additional case of histiocytoid CM, which carries a de novo nonsense variant in NDUFB11 (ENST00000276062.8: c.262C > T; p.[Arg88*]) identified using whole-exome sequencing (WES) of a family trio. An identical variant has been previously reported in association with MLS syndrome. The case we describe here lacked the diagnostic features of MLS syndrome, but a detailed clinical comparison of the two cases revealed significant phenotypic overlap. Heterozygous variants in HCCS (which encodes an important mitochondrially targeted protein) and COX7B, which, like NDUFB11, encodes a protein of the MRC, have also previously been identified in MLS syndrome including a case with features of both MLS syndrome and histiocytoid CM. However, a systematic review of WES data from previously published histiocytoid CM cases, alongside four additional cases presented here for the first time, did not identify any variants in these genes. We conclude that NDUFB11 variants play a role in the pathogenesis of both histiocytoid CM and MLS and that these disorders are allelic (genetically related)

Análisis de las empresas de turismo rural en Cataluña y Galicia: rentabilidad económica y solvencia 2014 – 2018

We study rural tourism based on the accounting data obtained from the SABI database (Iberian Balance Sheet Analysis System). The study has been limited to two Spanish communities, specifically Catalonia and Galicia, during 2014 and 2018. We analyse the economic viability of the farms at an aggregate level, through financial ratios applying the CoDa methodology (Compositional Data), which solves the problems of asymmetry, nonlinearity and outliers present in traditional sectoral analysis through ratios. The data have been classified into three groups (clusters), which differ with respect to return on equity, decomposed into turnover, margin and leverage. The compositional biplot has also been used, which makes it possible to diagnose individual companies and trace their trajectories over time. We conclude that in all clusters, the industry presents mostly negative margins and returns, although the situation improves somewhat in 2018. We identify a cluster with major indebtedness problems located mostly in Galicia.El objeto de estudio es el turismo rural a partir de los datos contables que se obtienen de la base de datos SABI (Iberian Balance Sheet Analysis System). Se ha delimitado el estudio a dos comunidades españolas, en concreto Catalunya y Galicia, durante los años 2014 y 2018. Se ha analizado la viabilidad económica de las explotaciones a nivel agregado, a través de las ratios financieras aplicando la metodología CoDa (Datos Composicionales), que soluciona los problemas de asimetría, no linealidad y valores atípicos que presenta el análisis sectorial tradicional, a través de las ratios. Los datos se han clasificado en tres grupos (clústeres), analizando las divergencias que presentan respecto a la rentabilidad financiera, descomponiendo esta variable en rotación, margen y apalancamiento. También se ha utilizado el biplot composicional que permite hacer diagnósticos de empresas individuales y trazar sus trayectorias en el tiempo. Se concluye que, en todos los clústeres, el sector presenta márgenes y rentabilidades mayoritariamente negativos, aunque la situación mejora algo en 2018. Se identifica un grupo con grandes problemas de endeudamiento situado mayoritariamente en Galicia