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Als kulturgeschichtlich bedeutsame Phänomene entstammen Masken der Ritual- und Theaterpraxis und traten schon in der Antike als Metapher in den Sprachgebrauch über. Kaum ein Philosoph hat den Masken so viel Raum gegeben wie Friedrich Nietzsche: Sie sind ihm Hilfsmittel der Erkenntnis und conditio humana, sie ermöglichen Höflichkeit und Selbstschutz, fungieren aber auch als Darstellungsform. Corinna Schubert führt zentrale Themen seines Denkens unter einem neuen Gesichtspunkt zusammen und erschließt sie als Philosophie der Masken. Dabei geht es nicht nur darum, was Nietzsche über Masken denkt, sondern auch, wie er mit und in Masken denkt. Besprochen in: https://signaturen-magazin.de, 2 (2021), Jan Kuhlbrodt

Friedrich Nietzsche; Maske; Schauspieler; Theater; Denken; Rolle; Figur; Sprache; Kultur; Kulturphilosophie; Politische Philosophie; Philosophie; Mask; Actor; Theatre; Thought; Part; Figure; Language; Culture; Philosophy of Culture; Political Philosophy; Philosophy --- Actor. --- Culture. --- Figure. --- Language. --- Mask. --- Part. --- Philosophy of Culture. --- Philosophy. --- Political Philosophy. --- Theatre. --- Thought. --- Nietzsche, Friedrich Wilhelm, 1844-1900

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Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification.

convolutional neural network --- image segmentation --- multi-scale feature fusion --- semantic features --- Gaofen 6 --- aerial images --- land-use --- Tai’an --- convolutional neural networks (CNNs) --- feature fusion --- ship detection --- optical remote sensing images --- end-to-end detection --- transfer learning --- remote sensing --- single shot multi-box detector (SSD) --- You Look Only Once-v3 (YOLO-v3) --- Faster RCNN --- statistical features --- Gaofen-2 imagery --- winter wheat --- post-processing --- spatial distribution --- Feicheng --- China --- light detection and ranging --- LiDAR --- deep learning --- convolutional neural networks --- CNNs --- mask regional-convolutional neural networks --- mask R-CNN --- digital terrain analysis --- resource extraction --- hyperspectral image classification --- few-shot learning --- quadruplet loss --- dense network --- dilated convolutional network --- artificial neural networks --- classification --- superstructure optimization --- mixed-inter nonlinear programming --- hyperspectral images --- super-resolution --- SRGAN --- model generalization --- image downscaling --- mixed forest --- multi-label segmentation --- semantic segmentation --- unmanned aerial vehicles --- classification ensemble --- machine learning --- Sentinel-2 --- geographic information system (GIS) --- earth observation --- on-board --- microsat --- mission --- nanosat --- AI on the edge --- CNN

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This book presents the latest advances in applications of continuous, objective, and automated sensing technologies and computer tools for sustainable and efficient poultry production, and it offers solutions to the poultry industry to address challenges in terms of poultry management, the environment, nutrition, automation and robotics, health, welfare assessment, behavior monitoring, waste management, etc. The reader will find original research papers that address, on a global scale, the sustainability and efficiency of the poultry industry and explore the above-mentioned areas through applications of PPF solutions in poultry meat and egg production

broiler --- activity index --- time interval --- age --- image processing --- poultry --- cage-free --- preening behavior --- mask R-CNN --- residual network --- broiler chicken --- machine vision --- image restoring --- precision poultry farming --- feeding system --- pecking force --- precision livestock farming --- poultry farming --- information management --- cloud database --- disease detection --- acoustic --- audio --- frequency --- behavior --- image analysis --- animal welfare --- movement analysis --- LED --- comfort index --- manure area --- manure coverage proportion --- environment control --- ammonia emission --- layer house --- laying hen --- daily behavior --- machine learning --- inertia sensor --- walking ability --- animal behavior --- precision livestock --- PLF --- precise feeding --- ideal protein --- animal health --- immune system --- productive parameters --- management

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The Advanced Thermal Emission and Reflection Radiometer (ASTER) is a research facility instrument on NASA’s Terra spacecraft. We celebrated the 20th anniversary of ASTER’s launch in December 1999. ASTER has been providing high spatial resolution multispectral data in the VNIR, SWIR, and TIR regions, and along-track stereo data. Starting April 2016, ASTER data have been distributed to the public at no cost. Another important and the most popular data set is the ASTER Global DEM, which covers almost the entire land surface at a 30 m grid size. ASTER data have been widely used in a variety of application areas such as land surface mapping and change detection, volcano and other natural hazard monitoring, mineral exploration, and urban heat island monitoring. This Special Issue consists of 12 papers (2 reviews, 9 articles, and 1 technical note) and covers topics including development of new techniques to process ASTER data, calibration activities to ensure long-term consistency of ASTER data, validation of the ASTER data products, and scientific achievements using ASTER data.

emissivity --- n/a --- vicarious calibration --- digital elevation model --- optical sensor --- loess --- Terra ASTER --- land-surface temperature --- lithological mapping --- sediment transport pathways --- Mosaic --- validation --- thermal infrared --- Terra MODIS --- True Color imagery --- ASTER multispectral data --- fluvial --- water body detection --- morphology --- MOD35 --- lunar calibration --- cloud coverage --- volcanic processes --- Wudalianchi volcano --- Ming Dynasty --- Artificial Neural Network --- permafrost --- ASTER --- Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) --- stereo --- erosion --- Mako --- geologic mapping --- Algodones --- future concepts --- 3D perspective view --- observation scheduler --- earthen Great Wall --- thermal infrared data --- lithology --- mineral exploration --- VNIR --- cloud mask --- cloud avoidance --- aeolian --- phase change --- mission operations --- temperature --- global database --- zero curtain effect --- image archive --- mineral system --- deposition --- DEM --- MODIS --- success rate --- uncertain flag --- TES algorithm --- ASTER instrument --- radiometric calibration --- mineral mapping --- atmospheric correction --- TIR

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[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.]

melanoma detection --- deep learning --- transfer learning --- ensemble classification --- 3D-CNN --- immunotherapy --- radiomics --- self-attention --- breast imaging --- microwave imaging --- image reconstruction --- segmentation --- unsupervised machine learning --- k-means clustering --- Kolmogorov-Smirnov hypothesis test --- statistical inference --- performance metrics --- contrast source inversion --- brain tumor segmentation --- magnetic resonance imaging --- survey --- brain MRI image --- tumor region --- skull stripping --- region growing --- U-Net --- BRATS dataset --- incoherent imaging --- clutter rejection --- breast cancer detection --- MRgFUS --- proton resonance frequency shift --- temperature variations --- referenceless thermometry --- RBF neural networks --- interferometric optical fibers --- breast cancer --- risk assessment --- machine learning --- texture --- mammography --- medical imaging --- imaging biomarkers --- bone scintigraphy --- prostate cancer --- semisupervised classification --- false positives reduction --- computer-aided detection --- breast mass --- mass detection --- mass segmentation --- Mask R-CNN --- dataset partition --- brain tumor --- classification --- shallow machine learning --- breast cancer diagnosis --- Wisconsin Breast Cancer Dataset --- feature selection --- dimensionality reduction --- principal component analysis --- ensemble method --- n/a