Abstract: Disclosed are a non-fluorinated quinolone compound and use thereof, specifically relating to a compound represented by formula (II) and a pharmaceutically acceptable salt thereof.

Abstract: A method for training a task neural network for a task to be performed on an input including images. The method includes: providing 2D training images labelled with ground truth; expanding the training images into 3D representations; processing, by the task neural network, the 2D training images into task outputs; processing, by an auxiliary neural network, the 3D representations into auxiliary outputs; rating, by a task loss function, a deviation of the task output for each training image from the ground truth with which it is labelled; rating, by an auxiliary loss function, a plausibility of an outcome of the task neural network produced from at least one training image with the an outcome of the auxiliary neural network produced from the corresponding 3D representation; aggregating values of the task loss function and the auxiliary loss function; and optimizing parameters that characterize the behavior of the task neural network.

Abstract: The present invention relates to the technical field of drug synthesis. Disclosed are a crystal form of a macrocyclic compound, and a preparation method therefor and the use thereof. The macrocyclic compound is (6R,16R)-9-fluoro-16-methyl-13-oxa-2,17,21,25-tetraazapentane [16.6.2.02,6.07,12.022,26]dihexadecane-1(25),7,9,11,18(26),19,21,23-octane-19-carbonitrile. The X-ray diffraction pattern of the crystal form has characteristic peaks at 2? values of 9.49±0.2, 10.60±0.2, 11.54±0.2, 14.10±0.2, 17.09±0.2, 19.15±0.2, 20.30±0.2, 22.85±0.2, 23.89±0.2 and 27.74±0.2. The crystal form is hygroscopicity-free, has a good stability and pharmacokinetic properties.

Abstract: A device and a method for determining a class for at least a part of a digital image. The method includes: providing a classifier for a first class and a second class; determining a digital image including an object of the second class in at least the part of the digital image; determining the class for at least the part of the digital image with the classifier, wherein determining the digital image includes determining the object of the second class with a generative model depending on a label for the first class and/or depending on at least one pixel representing an object of the first class.

Abstract: A class of piperazine derivatives and application thereof, specifically compound represented by formula (III) or pharmaceutically acceptable salt thereof.