Abstract: Computer based methods, systems, and computer readable media are provided for intelligently sorting cells using machine learning. A biological cell analysis sorting machine, wherein the biological cell analysis sorting machine comprises a flow cytometry system and a cell analytics sorting system, may be configured to detect configuration issues by analyzing results of a sorting experiment performed by the biological cell analysis sorting machine. An analysis of a history of prior sorting experiments and associated configuration settings may be performed and a corpus of documents pertaining to the sorting experiment based on the detected configuration issues may be analyzed. Updated configuration settings for the biological cell analysis sorting machine based on the performed analysis may be determined, and the biological cell analysis sorting machine may be configured with the updated configuration settings to conduct a desired sorting experiment.

Abstract: Embodiments of the present disclosure provide a method and a device for music play. The method comprises receiving a first operation instruction in a target application for playing music; in response to the first operation instruction, presenting a first interface of the target application, the first interface including an operation control for enhancing a play effect of the music through at least one processing, the processing being used for representing music content in a way more than sound; receiving a second operation instruction for the operation control; processing the music based on the second operation instruction during a process of playing the music.

Abstract: Described are systems and methods to determine hair patterns presented in content items. The determined hair patterns may be associated with the content items to facilitate indexing, filtering, etc. of the content items based on the determined hair patterns. In exemplary implementations, a corpus of content items may be associated with an embedding vector that includes a binary representation of the content item. The embedding vectors associated with each content item can be provided as inputs to a trained machine learning model, which can process the embedding vectors to determine one or more hair patterns presented in each content item while eliminating the need for performing image pre-processing prior to determination of the hair pattern(s) presented in the content item.

Abstract: Described are systems and methods to determine hair patterns presented in content items. The determined hair patterns may be associated with the content items to facilitate indexing, filtering, etc. of the content items based on the determined hair patterns. In exemplary implementations, a corpus of content items may be associated with an embedding vector that includes a binary representation of the content item. The embedding vectors associated with each content item can be provided as inputs to a trained machine learning model, which can process the embedding vectors to determine one or more hair patterns presented in each content item while eliminating the need for performing image pre-processing prior to determination of the hair pattern(s) presented in the content item.

Abstract: Described are systems and methods to determine hair patterns presented in content items. The determined hair patterns may be associated with the content items to facilitate indexing, filtering, etc. of the content items based on the determined hair patterns. In exemplary implementations, a corpus of content items may be associated with an embedding vector that includes a binary representation of the content item. The embedding vectors associated with each content item can be provided as inputs to a trained machine learning model, which can process the embedding vectors to determine one or more hair patterns presented in each content item while eliminating the need for performing image pre-processing prior to determination of the hair pattern(s) presented in the content item.

Abstract: A learning model may provide a hierarchy of convolutional layers configured to perform convolutions upon image features, each layer other than a topmost layer convoluting the image features at a lower resolution to a higher layer, and each layer other than a bottommost layer returning the image features to a lower layer. Each layer fuses the lower resolution image features received from a higher layer with same resolution image features convoluted at the layer, so as to combine large-scale and small-scale features of images. Layers of the hierarchy may be substantially equal to a number of lateral convolutions at a bottommost convolutional layer. The bottommost convolutional layer ultimately passes the fused features to an attention mapping module, which utilizes two attention mapping pathways in combination to detect non-local dependencies and interactions between large-scale and small-scale features of images without de-emphasizing local interactions.

Abstract: Devices, methods, and non-transitory program storage devices are disclosed to provide enhanced images in multi-camera systems, e.g., by using information from images captured by cameras with different properties in terms of optics and/or sensors. In one embodiment, the techniques comprise: obtaining a first image from a first image capture device, wherein the first image has a first field of view (FOV) and a first set of quality characteristics; obtaining a second image from a second image capture device, wherein the second image has a second FOV and a second set of quality characteristics, and wherein the second FOV partially overlaps the first FOV; obtaining a neural network that produces a modified second image having a modified second set of quality characteristics determined by the neural network attempting to match the first set of quality characteristics; and generating an output image based, at least in part, on the modified second image.

Abstract: Described are systems and methods to extract image segments from an image and include those extracted image segments in a collage. The origin information, such as the source image, source image location, etc., from which the extracted image segment is generated is maintained as metadata so that interaction with the extracted image segment on the collage can be used to determine and/or return to the origin of the extracted image segment. Collages may be updated, shared, adjusted, etc., by the creator of the collage or other users.

Abstract: The disclosure provides methods and compositions, e.g., kits and microarray, for assessing the immune response in a murine tumor model based on the expression of a gene panel that characterizes tumor immune interactions.

Abstract: A method for training a model, the method including: defining a primary model for identifying a class of input data based on a first characteristic of the input data; defining a secondary model for detecting a change to a second characteristic between multiple input data captured at different times; defining a forward link from an output of an intermediate layer of the secondary model to an input of an intermediate layer of the primary model; and training the primary model and the secondary model in parallel based on a training set of input data.

Abstract: Methods and apparatus are disclosed to automatically generate a software test script from video and/or user input. Example methods disclosed herein include generating a first set of images from a video comprising recorded testing of software. The example method also includes processing the first set of images to remove unnecessary images. The example method also includes comparing each image to one or more adjacent images to identify one or more user actions performed in each image. The example method also includes translating each identified user action and/or user input into one or more test script parameters to generate an automated test script to execute on the software.

Abstract: Devices, methods, and computer readable media to provide enhanced images in multi-camera systems, e.g., by using images captured by cameras with different optical properties and/or sensors. In one embodiment, the techniques comprise reference-based image super-resolution techniques for producing, with a first neural network employing robust feature aggregation techniques (e.g., techniques able to blend between single-image enhancement and feature aggregation, when appropriate), an enhanced output image that attempts to match the quality characteristics of each of region in a lower quality (e.g., shorter focal length, larger field of view (FOV)) input image with the quality characteristics of the region's determined guidance region from at least a second, i.e., higher quality (e.g., longer focal length, smaller FOV image) input image.

Abstract: Described are systems and methods for extracting parameters associated with a look/beauty aesthetic presented in a content item such as an image or a video. The extracted parameters can be used to identify beauty products that can be used to create a similar look/beauty aesthetic and to render the beauty product on a streaming live-feed video of the user so that the user can assess how the product looks on the user. Aspects of the disclosure also relate to classifying content items presenting a look/beauty aesthetic based on a dominant skin tone present in the content item.

Abstract: A method for training a model, the method including: defining a primary model for identifying a class of input data based on a first characteristic of the input data; defining a secondary model for detecting a change to a second characteristic between multiple input data captured at different times; defining a forward link from an output of an intermediate layer of the secondary model to an input of an intermediate layer of the primary model; and training the primary model and the secondary model in parallel based on a training set of input data.

Abstract: Disclosed in the present invention is a method for preparing nilotinib intermediate 3-(4-methyl-1H-imidazol-1-yl)-5trifluoromethyl phenylamine (I). The method comprises the following steps: taking trifluorotoluene as an initial material, and preparing the nilotinib intermediate (I) by nitration, bromization, condensation and reduction successively Compared with the prior art, the preparation method has the following advantages: a relatively high yield, the raw materials are easily obtained, a concise process and few side reactions, and is adapted to industrial production, so the development of an economic technology of the bulk drug is promoted.

Abstract: Disclosed in the present invention is a method for preparing nilotinib intermediate 3-(4-methyl-1H-imidazol-1-yl)-5trifluoromethyl phenylamine (I). The method comprises the following steps: taking trifluorotoluene as an initial material, and preparing the nilotinib intermediate (I) by nitration, bromization, condensation and reduction successively Compared with the prior art, the preparation method has the following advantages: a relatively high yield, the raw materials are easily obtained, a concise process and few side reactions, and is adapted to industrial production, so the development of an economic technology of the bulk drug is pro moted.