Abstract: An ambient pressure compensation structure for an ultraviolet difference spectrum gas sensor includes a closed box, an ultraviolet difference spectrum gas sensor, a digital pressure sensor, a thermostatic controller, an industrial control computer, a pressure regulating assembly and a limiting partition; the pressure regulating assembly and the limiting partition are arranged inside the closed box, the pressure regulating assembly comprises a piston, a regulating rod and a driving motor, the left side of the piston is connected with the regulating rod, the front end of the regulating rod penetrates out of the closed box and is connected with the driving motor, and the piston divides the interior of the closed box into a pressure regulating air chamber and a working air chamber. Also disclosed is an ambient pressure compensation method for an ultraviolet difference spectrum gas sensor.

Abstract: Compounds that modulate the oxidoreductase enzyme indoleamine 2,3-dioxygenase, and compositions containing the compounds, are described herein. The use of such compounds and compositions for the treatment and/or prevention of a diverse array of diseases, disorders and conditions, including cancer- and immune-related disorders, that are mediated by indoleamine 2,3-dioxygenase is also provided.

Abstract: A target cell marking method, including: determining an original image format of the original scanned image, and converting the original scanned image into a first image in a preset image format; segmenting the first image into a plurality of image blocks and recording arrangement positions of the image blocks in the first image; respectively inputting the image blocks into a preset deep learning detection model to obtain first position information of target cells in the image blocks; determining second position information of the target cells in the first image according to the first position information and the corresponding arrangement positions; integrating the image blocks according to the arrangement positions to obtain a second image, and marking the target cells in the second image; and converting the second image marked by the target cells into a third image in the original image format, and displaying the third image.

Abstract: Methods of processing a packet, APs, ACs and non-transitory machine-readable storage mediums are provided in examples of the present disclosure. In one aspect, an AP receives an Internet-of-Things data packet sent by an Internet-of-Things sensor, wherein a sensor identifier of the Internet-of-Things sensor is carried in the Internet-of-Things data packet; and sends the received Internet-of-Things data packet to an AC such that the AC processes the Internet-of-Things data packet based on the sensor identifier.

Abstract: Methods, systems, and computer programs are presented for determining parameters of neural networks and selecting embedding dimensions for the feature fields. One method includes an operation for initializing parameters of a neural network and weights for embedding sizes for each feature associated with the neural network. The parameters of the neural network and the weights are iteratively optimized. Each optimization iteration comprises training the neural network with current parameters of the neural network to optimize a value of the weights, and training the neural network with current values of the weights to optimize the parameters of the neural network. Further, the method includes operations for selecting embedding sizes for the features based on the optimized values of the weights, and for training the neural network based on the selected embedding sizes for the features to obtain an estimator model. A prediction is generated utilizing the estimator model.

Abstract: Methods of processing a packet, APs, ACs and non-transitory machine-readable storage mediums are provided in examples of the present disclosure. In one aspect, an AP receives an Internet-of-Things data packet sent by an Internet-of-Things sensor, wherein a sensor identifier of the Internet-of-Things sensor is carried in the Internet-of-Things data packet; and sends the received Internet-of-Things data packet to an AC such that the AC processes the Internet-of-Things data packet based on the sensor identifier.

Abstract: Machine learning based method for generating personalized query suggestions is described. Different users may have different search intent even when they are inputting the same search query. The technical problem of personalizing search query suggestions produced by a machine learning model is addressed by extending the sequence to sequence machine learning model framework to be able to take into consideration additional, personalized features of the user, such as, e.g., profile industry, language, geographic location, etc. This methodology includes an offline model training framework as well as an online serving framework.

Abstract: Methods of processing a packet, APs, ACs and non-transitory machine-readable storage mediums are provided in examples of the present disclosure. In one aspect, an AP receives an Internet-of-Things data packet sent by an Internet-of-Things sensor, wherein a sensor identifier of the Internet-of-Things sensor is carried in the Internet-of-Things data packet; and sends the received Internet-of-Things data packet to an AC such that the AC processes the Internet-of-Things data packet based on the sensor identifier.

Abstract: Compounds that modulate the oxidoreductase enzyme indoleamine 2,3-dioxygenase, and compositions containing the compounds, are described herein. The use of such compounds and compositions for the treatment and/or prevention of a diverse array of diseases, disorders and conditions, including cancer- and immune-related disorders, that are mediated by indoleamine 2,3-dioxygenase is also provided.

Abstract: Techniques for training a machine-learned model based on feedback are provided. In one technique, reformulation data that comprises a plurality of sequence pairs is stored. Also, feedback data that comprises a plurality of sequence triples is stored. Based on the reformulation data and the feedback data, one or more machine learning techniques are used to train a sequence-to-sequence model. Training the sequence-to-sequence model involves using a loss function that comprises (1) a first term that takes, as input, sequence pairs from the reformulation data and (2) a second term that takes, as input, sequence triples from the feedback data. After training the sequence-to-sequence, a search query is received from a computing device. In response to receiving the search query, a set of embeddings is retrieved, each corresponding to a token in the search query. The set of embeddings is input into the sequence-to-sequence model, which generates one or more query suggestions.

Abstract: Techniques for using machine learning to predict document types for incomplete queries are provided. In one technique, one or more characters from input are identified. For each character, an embedding that corresponds to that character is retrieved. The embedding was machine-learned while training a neural network that outputs multiple classifications, each corresponding to a different document type. One or more embeddings, each corresponding to one of the characters, are input into the neural network. Based on the inputting, the neural network generates an output that comprises multiple values that includes (1) a first value that reflects a first probability that the input is associated with a first document type and (2) a second value that reflects a second probability that the input is associated with a second document type. Based on the first and second probabilities, a set of query completions is identified and presented on the computing device.

Abstract: Compounds that modulate the oxidoreductase enzyme indoleamine 2,3-dioxygenase, and compositions containing the compounds, are described herein. The use of such compounds and compositions for the treatment and/or prevention of a diverse array of diseases, disorders and conditions, including cancer- and immune-related disorders, that are mediated by indoleamine 2,3-dioxygenase is also provided.

Abstract: A neural related query generation approach in a search system uses a neural encoder that reads through a source query to build a query intent vector. The approach then processes the query intent vector through a neural decoder to emit a related query. By doing so, the approach gathers information from the entire source query before generating the related query. As a result, the neural encoder-decoder approach captures long-range dependencies in the source query such as, for example, structural ordering of query keywords. The approach can be used to generate related queries for long-tail source queries, including long-tail source queries never before or not recently submitted to the search system.

Abstract: In an embodiment, the disclosed technologies include extracting, from at least one search log, session data including at least three semantically related queries and corresponding timestamp data; using the session data, creating a training sequence that includes source query data, context query data, and target query data, the source query data having both a temporal relationship and a lexical relationship to the target query data and the context query data having a temporal relationship to the source query data; creating a learned model by, using a machine learning-based modeling process, learning a mapping of a semantic representation of the context query data and the source query data to a semantic representation of the target query data; in response to a new query, using the learned model to generate at least one recommended query that is semantically related to the new query.

Abstract: Machine learning based method for generating personalized query suggestions is described. Different users may have different search intent even when they are inputting the same search query. The technical problem of personalizing search query suggestions produced by a machine learning model is addressed by extending the sequence to sequence machine learning model framework to be able to take into consideration additional, personalized features of the user, such as, e.g., profile industry, language, geographic location, etc. This methodology includes an offline model training framework as well as an online serving framework.

Abstract: There are disclosed compounds that modulate or inhibit the enzymatic activity of indoleamine 2,3-dioxygenase (IDO), pharmaceutical compositions containing said compounds and methods of treating proliferative disorders, such as cancer, viral infections and/or inflammatory disorders utilizing the compounds of the invention.