Multi-task deep learning with incomplete training samples for the image-based prediction of variables describing silk fabrics

This paper presents a method for the classification of images of silk fabrics with the aim to predict properties such as the placeand time of origin and the production technique. The proposed method was developed in the context of the EU project SILKNOW(http://silknow.eu/). In the context of classification, we address the problem of limited as well as not fully labelled data andinvestigate the connection between the distinct variables. A pre-trained Convolutional Neural Network (CNN) is used for thefeature extraction and a classification network realizing Multi-task learning (MTL) is trained based on these features. The trainingprocedure is adapted to enable the consideration of images that do not have a label for all tasks. Additionally, MTL with fullylabeled training data is investigated for the classification of silk fabrics. The impact of both MTL approaches is compared to singletask learning based on two different class structures. We achieve overall accuracies of 92-95% and average F1-scores of 88-90% inour best experiments. © 2019 Authors

Improving on the Contingent Fee

Two basic fees--contingent and hourly--dominate the variety of fees that lawyers charge clients for pursuing damage claims. Each of these two types has its advantages; each is plagued with substantial disadvantages. This Article proposes a new type of fee, one that preserves the respective advantages of the two present fees while minimizing their distinct disadvantages. In essence, the proposed fee calls for the payment, on a contingent basis, of an amount computed by adding one component tied to hours worked and another component linked to amount recovered. The preferability and feasibility of this proposed fee argue for the abolishment, or at least for the severe restriction, of the contingent fee as it is now known; the hourly fee should continue as a client\u27s option

Improving on the Contingent Fee

Two basic fees--contingent and hourly--dominate the variety of fees that lawyers charge clients for pursuing damage claims. Each of these two types has its advantages; each is plagued with substantial disadvantages. This Article proposes a new type of fee, one that preserves the respective advantages of the two present fees while minimizing their distinct disadvantages. In essence, the proposed fee calls for the payment, on a contingent basis, of an amount computed by adding one component tied to hours worked and another component linked to amount recovered. The preferability and feasibility of this proposed fee argue for the abolishment, or at least for the severe restriction, of the contingent fee as it is now known; the hourly fee should continue as a client\u27s option

Supervised detection of bomb craters in historical aerial images using convolutional neural networks

The aftermath of the air strikes during World War II is still present today. Numerous bombs dropped by planes did not explode, still exist in the ground and pose a considerable explosion hazard. Tracking down these duds can be tackled by detecting bomb craters. The existence of a dud can be inferred from the existence of a crater. This work proposes a method for the automatic detection of bomb craters in aerial wartime images. First of all, crater candidates are extracted from an image using a blob detector. Based on given crater references, for every candidate it is checked whether it, in fact, represents a crater or not. Candidates from various aerial images are used to train, validate and test Convolutional Neural Networks (CNNs) in the context of a two-class classification problem. A loss function (controlling what the CNNs are learning) is adapted to the given task. The trained CNNs are then used for the classification of crater candidates. Our work focuses on the classification of crater candidates and we investigate if combining data from related domains is beneficial for the classification. We achieve a F1-score of up to 65.4% when classifying crater candidates with a realistic class distribution. © Authors 2019. CC BY 4.0 License

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Celebrating the 70th Birthday of V.V. Rubtso