Abstract: Disclosed in the present application are a compression and training method and apparatus for a defect detection model. The method comprises: obtaining, by means of segmentation labeling, a segmentation labeling factor matrix of each sample image; inputting each sample image into both a first defect detection model and a second defect detection model, and extracting first feature maps outputted by target convolutional layers in the first defect detection model and second feature maps outputted by corresponding target convolutional layers in the second defect detection model; and calculating, by using the segmentation labeling factor matrix, corrected distances between corresponding feature vectors of the first feature maps and the second feature maps, and calculating, as a first loss function, the sum of the corrected distances between all the feature vectors of the first feature maps and the second feature maps.

Abstract: This document relates to relational databases and corresponding data tables. Non-conforming data tables can be automatically transformed into conforming relational data tables. One example can obtain conforming relational data tables and can generate training data without human labelling by identifying a transformational operator that will transform an individual conforming relational data table to a non-conforming data table and an inverse transformational operator that will transform the non-conforming data table back to the individual conforming relational data table. The example can train a model with the training data. The trained model can synthesize programs to transform other non-conforming data tables to conforming relational data tables.

Abstract: This document relates to relational databases and corresponding data tables. Non-conforming data tables can be automatically transformed into conforming relational data tables. One example can obtain conforming relational data tables and can generate training data without human labelling by identifying a transformational operator that will transform an individual conforming relational data table to a non-conforming data table and an inverse transformational operator that will transform the non-conforming data table back to the individual conforming relational data table. The example can train a model with the training data. The trained model can synthesize programs to transform other non-conforming data tables to conforming relational data tables.

Abstract: This document relates to relational databases and corresponding data tables. Non-conforming data tables can be automatically transformed into conforming relational data tables. One example can obtain conforming relational data tables and can generate training data without human labelling by identifying a transformational operator that will transform an individual conforming relational data table to a non-conforming data table and an inverse transformational operator that will transform the non-conforming data table back to the individual conforming relational data table. The example can train a model with the training data. The trained model can synthesize programs to transform other non-conforming data tables to conforming relational data tables.

Abstract: The present disclosure relates to methods and compositions that inhibit a tumor microenvironment and methods of co-administering such compositions to inhibit the tumor microenvironment and treat cancer in a subject.

Abstract: Systems are provided for facilitating the building and use of models used to make data preparation recommendations. The systems identify ground truth from a plurality of notebooks and utilizes the ground truth to generate the corresponding data preparation recommendation models. The data preparation recommendation models are used to predict accurate (e.g., useful and relevant) data preparations steps based on user input and user notebook data. The data preparation computing system generates a recommendation prompt based on output from the data preparation recommendation model that can be viewed and/or selected by the user to be applied to the user's notebook data.

Abstract: Systems are provided for facilitating the building and use of models used to make data preparation recommendations. The systems identify ground truth from a plurality of notebooks and utilizes the ground truth to generate the corresponding data preparation recommendation models. The data preparation recommendation models are used to predict accurate (e.g., useful and relevant) data preparations steps based on user input and user notebook data. The data preparation computing system generates a recommendation prompt based on output from the data preparation recommendation model that can be viewed and/or selected by the user to be applied to the user's notebook data.

Abstract: Systems are provided for facilitating the building and use of models used to make data preparation recommendations. The systems identify ground truth from a plurality of notebooks and utilizes the ground truth to generate the corresponding data preparation recommendation models. The data preparation recommendation models are used to predict accurate (e.g., useful and relevant) data preparations steps based on user input and user notebook data. The data preparation computing system generates a recommendation prompt based on output from the data preparation recommendation model that can be viewed and/or selected by the user to be applied to the user's notebook data.

Abstract: Systems are provided for facilitating the building and use of models used to make data preparation recommendations. The systems identify ground truth from a plurality of notebooks and utilizes the ground truth to generate the corresponding data preparation recommendation models. The data preparation recommendation models are used to predict accurate (e.g., useful and relevant) data preparations steps based on user input and user notebook data. The data preparation computing system generates a recommendation prompt based on output from the data preparation recommendation model that can be viewed and/or selected by the user to be applied to the user's notebook data.

Abstract: Methods of cancer treatment are disclosed. Particularly, disclosed herein are methods of administering LAL and PPAR? ligands for treating various cancer patients to promote anti-cancer immunity (suppress MDSC), use for inhibiting tumor progression, and use for inhibiting tumor metastasis.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating data type detection, according to embodiments of the present invention. In one embodiment, existing code is searched to identify a set of functions related to a target data type. Such functions can be executed using positive example values and negative example values. For each executed function, a logical explanation is generated that represents a distinction in execution of the positive example values from the negative example values. The executed functions can then be ranked based on the extent to which the corresponding logical explanations distinguish execution of the positive example values from the negative example values. A function suggestion corresponding with at least a highest ranked function can then be provided, for example to a user, to indicate a function for use in detecting the target data type.

Abstract: The present invention provides diagnostics for identifying and distinguishing various types of lung cancers using serum and/or bronchioalveolar lavage fluid. Signatures of secretory proteins are used to identify and distinguish lung cancers. The biomarker signatures may also be used to separate lung cancers from other inflammatory diseases, monitor progression, or assess treatment efficacy.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating data type detection, according to embodiments of the present invention. In one embodiment, existing code is searched to identify a set of functions related to a target data type. Such functions can be executed using positive example values and negative example values. For each executed function, a logical explanation is generated that represents a distinction in execution of the positive example values from the negative example values. The executed functions can then be ranked based on the extent to which the corresponding logical explanations distinguish execution of the positive example values from the negative example values. A function suggestion corresponding with at least a highest ranked function can then be provided, for example to a user, to indicate a function for use in detecting the target data type.

Abstract: Methods of cancer treatment are disclosed. Particularly, disclosed herein are methods of administering LAL and PPAR? ligands for treating various cancer patients to promote anti-cancer immunity immunity (suppress MDSCs), use for inhibiting tumor progression, and use for inhibiting tumor metastasis.

Abstract: The present invention provides diagnostics for identifying and distinguishing various types of lung cancers using serum and/or bronchioalveolar lavage fluid. Signatures of secretory proteins are used to identify and distinguish lung cancers. The biomarker signatures may also be used to separate lung cancers from other inflammatory diseases, monitor progression, or assess treatment efficacy.

Abstract: A myeloid-specific c-fms-rtTA/(TetO)7-CMV-MMP12 bitransgenic mouse model was created. Induction of MMP12 abnormally elevated frequencies and numbers of common myeloid progenitor (CMP) and granulocyte/macrophage progenitor (GMP) populations, and decreased the frequency and number of the megakaryocyte/erythrocyte progenitor (MEP) population in bone marrow. CD11b+/Gr-1+ immature cell population increased in multiple organs. An immunosuppressive function on T cell proliferation and function by CD11b+/Gr-1+ immature cells was seen in vitro and in vivo from MMP12 over-expression. MMP12 stimulated (Lin?) progenitor cells to differentiate into CD11b+/Gr-1+ immature cells showing immunosuppression on T cell proliferation and function in vitro. Regulatory T cells were increased. In the lung, concentration of interleukin (IL)-6 was increased, which activated oncogenic signal transducer and increased expression of Stat3 downstream genes in epithelial tumor progenitor cells.

Abstract: A myeloid-specific c-fms-rtTA/(TetO)7-CMV-MMP12 bitransgenic mouse model was created. Induction of MMP12 abnormally elevated frequencies and numbers of common myeloid progenitor (CMP) and granulocyte/macrophage progenitor (GMP) populations, and decreased the frequency and number of the megakaryocyte/erythrocyte progenitor (MEP) population in bone marrow. CD11b+/Gr-1+ immature cell population increased in multiple organs. An immunosuppressive function on T cell proliferation and function by CD11b+/Gr-1+ immature cells was seen in vitro and in vivo from MMP12 over-expression. MMP12 stimulated (Lin?) progenitor cells to differentiate into CD11b+/Gr-1+ immature cells showing immunosuppression on T cell proliferation and function in vitro. Regulatory T cells were increased. In the lung, concentration of interleukin (IL)-6 was increased, which activated oncogenic signal transducer and increased expression of Stat3 downstream genes in epithelial tumor progenitor cells.