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Funder: University of Cambridge, In this paper we study a class of fast geometric image inpainting methods based on the idea of filling the inpainting domain in successive shells from its boundary inwards. Image pixels are filled by assigning them a color equal to a weighted average of their already filled neighbors. However, there is flexibility in terms of the order in which pixels are filled, the weights used for averaging, and the neighborhood that is averaged over. Varying these degrees of freedom leads to different algorithms, and indeed the literature contains several methods falling into this general class. All of them are very fast, but at the same time all of them leave undesirable artifacts such as “kinking” (bending) or blurring of extrapolated isophotes. Our objective in this paper is to build a theoretical model in order to understand why these artifacts occur and what, if anything, can be done about them. Our model is based on two distinct limits: a continuum limit in which the pixel width h→0 and an asymptotic limit in which h>0 but h≪1. The former will allow us to explain “kinking” artifacts (and what to do about them) while the latter will allow us to understand blur. Both limits are derived based on a connection between the class of algorithms under consideration and stopped random walks. At the same time, we consider a semi-implicit extension in which pixels in a given shell are solved for simultaneously by solving a linear system. We prove (within the continuum limit) that this extension is able to completely eliminate kinking artifacts, which we also prove must always be present in the direct method. Finally, we show that although our results are derived in the context of inpainting, they are in fact abstract results that apply more generally. As an example, we show how our theory can also be applied to a problem in numerical linear algebra.

As the interest in genetic resequencing increases, so does the need for effective mathematical, computational, and statistical approaches. One of the difficult problems in genome annotation is determination of precise positions of transcription start sites. In this paper, we present TransPrise-an efficient deep learning tool for predicting positions of eukaryotic transcription start sites. TransPrise offers significant improvement over existing promoter-prediction methods. To illustrate this, we compared predictions of TransPrise with the TSSPlant approach for well-annotated genome of Oryza sativa. Using a computer with a graphics processing unit, the run time of TransPrise is 250 min on a genome of 374 Mb long.We provide the full basis for the comparison and encourage users to freely access a set of our computational tools to facilitate and streamline their own analyses. The ready-to-use Docker image with all the necessary packages, models, and code as well as the source code of the TransPrise algorithm are available at http://compubioverne.group/ . The source code is ready to use and to be customized to predict TSS in any eukaryotic organism.

Autoantibodies are antibodies against host self-proteins (autoantigens), which play significant roles in homeostasis maintenance and diseases with autoimmune disorders. Numerous papers were published in the past decade on the identification of human autoantigens in different human diseases. However, there is no consensus collection with all the reported autoantigens yet. To address this need, previously we developed a human autoantigen database, AAgAtlas 1.0, by text-mining and manual curation, which collects 1126 autoantigens associated with 1071 human diseases. AAgAtlas 1.0 provides a user-friendly interface to conveniently browse, retrieve, and download human autoantigen genes, their functional annotation, related diseases, and the evidence from the literature. AAgAtlas is freely available online http://biokb.ncpsb.org/aagatlas/ . In this chapter, we make an introduction and provide a guide to the users of AAgAtlas 1.0 database.

Conventional data envelopment analysis (DEA) requires the data to have crisp values, which can be measured precisely. However, there are cases where data is missing and has to be estimated, or the situation has not occurred yet and the data has to be predicted. There are also cases where the factors are qualitative, and thus the data cannot be measured precisely. In these cases, fuzzy numbers can be used to represent the imprecise values, and this paper discusses the corresponding measurement of efficiency. Based on the extension principle, two approaches are proposed; one views the membership function of the fuzzy data vertically, and the results are represented by membership grades. The other views it horizontally, and the results are represented by α-cuts. The former approach is easier to understand, yet is applicable only to very simple problems. The latter, in contrast, can be applied to all problems, and is easier to implement. An example explains the development and implementation of these two approaches.

Recommender systems are now popular both commercially and in the research community, where many approaches have been suggested for providing recommendations. In many cases a system designer that wishes to employ a recommendater system must choose between a set of candidate approaches. A first step towards selecting an appropriate algorithm is to decide which properties of the application to focus upon when making this choice. Indeed, recommender systems have a variety of properties that may affect user experience, such as accuracy, robustness, scalability, and so forth. In this paper we discuss how to compare recommenders based on a set of properties that are relevant for the application. We focus on comparative studies, where a few algorithms are compared using some evaluation metric, rather than absolute benchmarking of algorithms. We describe experimental settings appropriate for making choices between algorithms. We review three types of experiments, starting with an offline setting, where recommendation approaches are compared without user interaction, then reviewing user studies, where a small group of subjects experiment with the system and report on the experience, and finally describe large scale online experiments, where real user populations interact with the system. In each of these cases we describe types of questions that can be answered, and suggest protocols for experimentation. We also discuss how to draw trustworthy conclusions from the conducted experiments. We then review a large set of properties, and explain how to evaluate systems given relevant properties. We also survey a large set of evaluation metrics in the context of the property that they evaluate.

International audience; We describe a hierarchy of computational models of the human body operating at the geometrical, physical and physiological levels. Those models can be coupled with medical images which play a crucial role in the diagnosis, planning, control and follow-up of therapy. In this paper, we discuss the issue of building patient-specific physical and physiological models from macroscopic observations extracted from medical images. We illustrate the topic of model personalization with concrete examples in brain shift modeling, hepatic surgery simulation, cardiac and tumor growth modeling. We conclude this article with scientific perspectives.