Art & Performance Notes

2003 ◽  
Vol 25 (2) ◽  
pp. 65-65
2012 ◽  
Vol 57 (2) ◽  
pp. 150-161
Author(s):  
Leander Scholz

Als der Künstler Gregor Schneider im Frühjahr 2008 ein Kunstprojekt ankündigte, bei dem ein Mensch, der im Sterben liegt, im Rahmen einer künstlerischen Performance ausgestellt werden sollte, waren die Reaktionen überwiegend äußerst kritisch. Während Gregor Schneider sein Projekt explizit als einen humanistischen Beitrag verstand, der sich gegen die Tabuisierung des Sterbens richten sollte, sahen die meisten Kommentatoren darin eine pietätslose Preisgabe des Sterbenden an die voyeuristischen Blicke des Publikums. Vor dem Hintergrund dieser Diskussion geht der Aufsatz der Frage nach, was es bedeutet, den Tod eines Menschen wie ein künstlerisches Werk zu inszenieren, und ordnet den Anspruch einer nicht nur ethischen, sondern auch ästhetischen Selbstbestimmung angesichts des Todes in die humanistische Tradition des modernen Werkgedankens ein.<br><br>In the spring of 2008, the artist Georg Schneider announced an art performance with a mortally ill person. Most of the responses to this art project were very critical. While the artist argued that the exhibition of a dying person should be understood as a humanistic intervention against the social taboo of death, commentators often criticized the exhibition as voyeuristic. Based on this discussion, the article explores what it means to stage a dying person as a piece of art and investigates the historical conditions of this project by locating the longing for ethic and aesthetic self-determination within the humanistic tradition of the modern concept of the work of art.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Changyong Li ◽  
Yongxian Fan ◽  
Xiaodong Cai

Abstract Background With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. Results A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. Conclusions Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.


2019 ◽  
Vol 9 (13) ◽  
pp. 2684 ◽  
Author(s):  
Hongyang Li ◽  
Lizhuang Liu ◽  
Zhenqi Han ◽  
Dan Zhao

Peeling fibre is an indispensable process in the production of preserved Szechuan pickle, the accuracy of which can significantly influence the quality of the products, and thus the contour method of fibre detection, as a core algorithm of the automatic peeling device, is studied. The fibre contour is a kind of non-salient contour, characterized by big intra-class differences and small inter-class differences, meaning that the feature of the contour is not discriminative. The method called dilated-holistically-nested edge detection (Dilated-HED) is proposed to detect the fibre contour, which is built based on the HED network and dilated convolution. The experimental results for our dataset show that the Pixel Accuracy (PA) is 99.52% and the Mean Intersection over Union (MIoU) is 49.99%, achieving state-of-the-art performance.


2021 ◽  
pp. 1-21
Author(s):  
Andrei C. Apostol ◽  
Maarten C. Stol ◽  
Patrick Forré

We propose a novel pruning method which uses the oscillations around 0, i.e. sign flips, that a weight has undergone during training in order to determine its saliency. Our method can perform pruning before the network has converged, requires little tuning effort due to having good default values for its hyperparameters, and can directly target the level of sparsity desired by the user. Our experiments, performed on a variety of object classification architectures, show that it is competitive with existing methods and achieves state-of-the-art performance for levels of sparsity of 99.6 % and above for 2 out of 3 of the architectures tested. Moreover, we demonstrate that our method is compatible with quantization, another model compression technique. For reproducibility, we release our code at https://github.com/AndreiXYZ/flipout.


2020 ◽  
Author(s):  
Fei Qi ◽  
Zhaohui Xia ◽  
Gaoyang Tang ◽  
Hang Yang ◽  
Yu Song ◽  
...  

As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate the workflow of machine learning. On the PMLB dataset, the proposed approach shows the state-of-the-art performance compared with TPOT, Autostacker, and auto-sklearn. Some of the optimized models are with complex structures which are difficult to obtain in manual design.


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