Abstract: A computer-implemented method for creating a new document space for a content collaboration system is disclosed. The method includes displaying a space-generation graphical user interface on a client device. The space-generation graphical user interface is displayed in response to a request for a new document space creation, and includes a first user input region, a second user input region, and a space-creation control. The method includes, in response to a user selection of the space-creation control, generating the new document space having a set of predefined space settings determined in accordance with a user selection of a particular selectable tab associated with a particular new-space type, generating space content having a space title determined in accordance with a proposed space title in the second user input region; and generating a new space path using a unique space key. The unique space key is generated based on the proposed space title.

Abstract: The present disclosure is directed to an improved method for distinguishing tissue from an embedding medium, such as paraffin in a formalin-fixed paraffin-embedded sample. The method involves the use of fluorescence of naturally-occurring species in tissue to determine the location of the tissue in the embedded sample. An embedded sample is generally excited by light of a selected wavelength, and the fluorescence emission at an emitted wavelength is used to locate the boundary or location of the tissue in the embedded sample.

Abstract: Aspects concern a method for predicting the destination location of a vehicle, comprising processing a local preference graph of a user of the vehicle having nodes corresponding to locations visited before by the user by a first graph neural network, processing one or more of a spatial graph representing information about geographical proximity of locations, a temporal graph representing information about locations which have been visited one after another by users and the time between the visits of the locations and a preference graph representing information about locations which have been visited one after another and the frequency of visits of the locations by a second graph neural network, combining the result of the processing by the first graph neural network and the result of the processing by the second graph neural network by at least one neural network layer and using the output of the at least one neural network layer as prediction of the destination location.

Abstract: Aspects concern a method for controlling vehicles to perform transport tasks comprising supplying information about vehicles and information about transport tasks to a graph neural network by associating each vehicle with a vehicle graph node and each transport task with a transport task graph node, processing the vehicle and the transport graph by the neural network, wherein the neural network determines a feature for each graph node, determining, for each pair of a transport graph node and vehicle graph node, a weight representing a similarity between the features determined for the transport graph node and the vehicle graph node, selecting an assignment between the transport graph nodes and the vehicle graph nodes from a set of possible assignments, wherein the selected assignment maximizes the sum of the weights of the pairs and controlling each vehicle according to the selected assignment.

Abstract: A system for predicting a destination location may include one or more processors and a memory having instructions stored therein. The one or more processors may use at least one recurrent neural network to: process spatial data which may include a first set of information about origin locations and destination locations; process temporal data which may include a second set of information about times at the origin locations and the destination locations; determine hidden state data based on the spatial data and the temporal data, wherein the hidden state data may include data on origin-destination relationships; receive a current input data from a user, wherein the current input data may include an identity of the user and the current origin location of the user; and predict the destination location based on the hidden state data and the current input data.

Abstract: Disclosed are systems and methods for predicting the destination location of a vehicle by processing a local preference graph of a user of the vehicle having nodes corresponding to locations visited by the user. In some embodiments, information about locations which have been visited one after another by users and the time between visits of the locations are also used in the prediction.

Abstract: Aspects concern a method for predicting the destination location of a vehicle, comprising processing a local preference graph of a user of the vehicle having nodes corresponding to locations visited before by the user by a first graph neural network, processing one or more of a spatial graph representing information about geographical proximity of locations, a temporal graph representing information about locations which have been visited one after another by users and the time between the visits of the locations and a preference graph representing information about locations which have been visited one after another and the frequency of visits of the locations by a second graph neural network, combining the result of the processing by the first graph neural network and the result of the processing by the second graph neural network by at least one neural network layer and using the output of the at least one neural network layer as prediction of the destination location.

Abstract: Network graphs are determined using data about the vertices. Vertices are clustered into community of vertices based on maximizing the density of linkages within each community. Vertex properties describing the extent to which each vertex's community has exhibited a particular behavior are determined. Vertex properties describing whether the most important vertex in each community has exhibited a particular behavior are determined. Functions describing the relationship between these two categories of vertex properties and other relevant vertex properties, and a particular behavior are determined. These functions are used to predict the likelihood of each vertex exhibiting the particular behavior.