Abstract: The present disclosure provides a curvature determination method, a curved display panel, and a display device. The curvature determination method includes: establishing a first association relationship between deflection and a shear stress of the curved display panel in a first direction, the first direction being a bending direction of the curved display panel; determining a constraint condition for the shear stress in accordance with an influence of the shear stress on a light leakage of the curved display panel; and generating a curvature change relationship of the curved display panel in the first direction in accordance with the first association relationship when the constraint condition has been satisfied.

Abstract: A front light source and a display device are disclosed, and the front light source includes a housing extending along a first direction, and a side wall of the housing having an opening extending along the first direction; a supporting frame, located inside the housing and extending along the first direction, where the supporting frame includes a supporting surface; a light bar, where the light bar extends along the first direction and is attached to the supporting surface; and a side of the light bar far away from the supporting surface has a plurality of light-emitting units arranged along the first direction; a polarizing lens located between the light bar and the opening and extending along the first direction; where the polarizing lens includes: a light incident surface, a light exiting surface, a first reflective curved surface and a second reflective curved surface, extending along the first direction.

Abstract: Use a computerized trained graph neural network model to classify an input instance with a predicted label. With a computerized graph neural network interpretation module, compute a gradient-based saliency matrix based on the input instance and the predicted label, by taking a partial derivative of class prediction with respect to an adjacency matrix of the model. With a computerized user interface, obtain user input responsive to the gradient-based saliency matrix. Optionally, modify the trained graph neural network model based on the user input; and re-classify the input instance with a new predicted label based on the modified trained graph neural network model.

Abstract: A benzo[b]selenophene STING regulator, a preparation method therefore and use thereof are disclosed. The structure of the compound is shown in Formula I. A derivative, salt, stereoisomer, prodrug molecule and pharmaceutical composition of the present invention can serve as an immune regulator to effectively activate an innate immune regulation passageway to kill tumor cells.

Abstract: Materials and methods are provided herein for determining if a subject has, or is at risk of developing, a clinical condition (e.g., cervical cancer). For example, this document provides a cost-effective self-test for determining whether a biological fluid from a subject contains a high-risk HPV strain, and a method for use of the self-test.

Abstract: The present invention relates to an anti-fouling, semi-permeable membrane comprising a porous support layer, a thin film composite (TFC) layer formed on a surface of the support layer, and a cross-linked polyvinyl alcohol (PVA) layer formed on top of the TFC layer, wherein the cross-linked PVA layer is the reaction product of PVA and a cross-linking agent, said cross-linking agent being a polybasic acid comprising three or more acid groups or precursors thereof. The obtained membrane shows a high water flux and a low roughness suitable for an effective membrane notable for feed solution having a tendency of fouling the membrane.

Abstract: Use a computerized trained graph neural network model to classify an input instance with a predicted label. With a computerized graph neural network interpretation module, compute a gradient-based saliency matrix based on the input instance and the predicted label, by taking a partial derivative of class prediction with respect to an adjacency matrix of the model. With a computerized user interface, obtain user input responsive to the gradient-based saliency matrix. Optionally, modify the trained graph neural network model based on the user input; and re-classify the input instance with a new predicted label based on the modified trained graph neural network model.

Abstract: A method for extracting a pattern from spatio-temporal (ST) data includes receiving ST data, storing the ST data as s multi-dimensional array in a memory, and extracting at least one pattern from the ST data. The extracting includes generating a model approximating at least a portion of the array, and generating a visualization of a loading vector of the approximation. The ST data includes records with multiple categories of information, one of which is spatial, and one of which is temporal. Each dimension corresponds to a respective one of the categories of information. Generating the model includes applying tensor decomposition to the array, and extracting the at least one loading vector of the approximation. The extracted loading vector is indicative of a pattern in the ST data.

Abstract: A method for extracting a pattern from spatio-temporal (ST) data includes receiving ST data, storing the ST data as s multi-dimensional array in a memory, and extracting at least one pattern from the ST data. The extracting includes generating a model approximating at least a portion of the array, and generating a visualization of a loading vector of the approximation. The ST data includes records with multiple categories of information, one of which is spatial, and one of which is temporal. Each dimension corresponds to a respective one of the categories of information. Generating the model includes applying tensor decomposition to the array, and extracting the at least one loading vector of the approximation. The extracted loading vector is indicative of a pattern in the ST data.

Abstract: Distinct gene expression programs activated in response to different pathogens in dendritic cells are disclosed. Methods of diagnosis and treatment are also disclosed.