Abstract: An information management method, a device, a system and a medium. The information management method applied to a first terminal, including: sending an information acquisition request to a server, so that the server responds to the information acquisition request and sends visitor information to the first terminal; receiving the visitor information sent by the server; generating card information according to the visitor information; and sending the card information to an electronic card, so that the electronic card displays the card information.

Abstract: Provided is a system for analyzing features associated with entities using an embedding tree, the system including at least one processor programmed or configured to receive a dataset associated with a plurality of entities, wherein the dataset comprises a plurality of data instances for a plurality of entities. The processor may be programmed or configured to generate at least two embeddings based on the dataset and determine split criteria for partitioning an embedding space of at least one embedding tree associated with the dataset based on feature data associated with an entity and embedding data associated with the at least two embeddings. The processor may be programmed or configured to generate at least one embedding tree having a plurality of nodes based on the split criteria. Methods and computer program products are also provided.

Abstract: Provided is a method for normalizing embeddings for cross-embedding alignment. The method may include applying mean centering to the at least one embedding set, applying spectral normalization to the at least one embedding set, and/or applying length normalization to the at least one embedding set. Spectral normalization may include decomposing the at least one embedding set, determining an average singular value of the at least one embedding set, determining a respective substitute singular value for each respective singular value of a diagonal matrix, and/or replacing the at least one embedding set with a product of the at least one embedding set, a right singular vector, and an inverse of the substitute diagonal matrix. The mean centering, spectral normalization, and/or length normalization may be iteratively repeated for a configurable number of iterations. A system and computer program product are also disclosed.

Abstract: A method performed by a computer is disclosed. The method comprises receiving interaction data between electronic devices of a plurality of entities. The interaction data is used to form an entity interaction vector containing a number of interactions between the electronic devices of a chosen entity and an entity time series containing a plurality of metrics per unit time of the interactions. An interaction encoder of the computer can generate an interaction hidden representation of the entity interaction vector using embeddings of the plurality of entities. A temporal encoder of the computer can generate a temporal hidden representation of the entity time series. The interaction hidden representation and the temporal hidden representation can be used to generate a predicted scale and a shape estimation of a target interaction metric. The computer can then generate an estimated interaction metric of a time period using the predicted scale and the shape estimation.

Abstract: Examples of performing selective Fine Timing Measurement (FTM) are described. For an FTM scan cycle, a first AP (i.e., an initiator) may determine scanning parameter values of a plurality of second APs (i.e., potential responders) based on previously performed FTM scans. The first AP may determine weights of the plurality of second APs based on the scanning parameter values and select a set of target APs based on the weights. The first AP may then scan the set of target APs for the FTM scan so that the rest of the plurality of second APs are relieved from participating in the FTM scan thereby reducing the performance impact on the rest of the plurality of second APs. After the FTM scan cycle is completed, the first AP may update the weights and select another set of target APs based on the updated weights to perform another FTM scan cycle.

Abstract: Provided is a method for normalizing embeddings for cross-embedding alignment. The method may include applying mean centering to the at least one embedding set, applying spectral normalization to the at least one embedding set, and/or applying length normalization to the at least one embedding set. Spectral normalization may include decomposing the at least one embedding set, determining an average singular value of the at least one embedding set, determining a respective substitute singular value for each respective singular value of a diagonal matrix, and/or replacing the at least one embedding set with a product of the at least one embedding set, a right singular vector, and an inverse of the substitute diagonal matrix. The mean centering, spectral normalization, and/or length normalization may be iteratively repeated for a configurable number of iterations. A system and computer program product are also disclosed.

Abstract: A monomer capable forming a polymer through ring-opening metathesis polymerization and capable of depolymerization thereafter through ring-closing metathesis, wherein the monomer comprises a cycloalkene having a fused ring attached thereto which decreases the ring strain energy to 5.3 kcal/mol or lower as compared to the same cycloalkene without a fused ring having a ring strain energy above 5.3 kcal/mol.

Abstract: A method includes extracting, by an analysis computer, a plurality of first datasets from a plurality of graph snapshots using a graph structural learning module. The analysis computer can then extract a plurality of second datasets from the plurality of first datasets using a temporal convolution module across the plurality of graph snapshots.

Abstract: A macromonomer capable of forming a graft polymer through graft-through polymerization, the macromonomer including a plurality of monomer units including a cycloalkene having a fused ring attached thereto to form a cycloalkene-fused ring monomer where the fused ring decreases the ring strain energy of the cycloalkene to a lower ring strain energy state of 5.3 kcal/mol or lower and where the cycloalkene of the cycloalkene-fused ring monomer is capable of isomerization into a higher ring strain energy state before graft-through polymerization, and at least one polymer sidechain is bonded to the fused ring. Graft polymers formed by the macromonomer include graft polymers and graft copolymers including statistical and block copolymers. The graft (co)polymers are capable of depolymerization under mild conditions into reusable monomer units.

Abstract: A method, system, and computer program product is provided for embedding compression and reconstruction. The method includes receiving embedding vector data comprising a plurality of embedding vectors. A beta-variational autoencoder is trained based on the embedding vector data and a loss equation. The method includes determining a respective entropy of a respective mean and a respective variance of each respective dimension of a plurality of dimensions. A first subset of the plurality of dimensions is determined based on the respective entropy of the respective mean and the respective variance for each respective dimension of the plurality of dimensions. A second subset of the plurality of dimensions is discarded based on the respective entropy of the respective mean and the respective variance for each respective dimension of the plurality of dimensions. The method includes generating a compressed representation of the embedding vector data based on the first subset of dimensions.

Abstract: Provided is a method for normalizing embeddings for cross-embedding alignment. The method may include applying mean centering to the at least one embedding set, applying spectral normalization to the at least one embedding set, and/or applying length normalization to the at least one embedding set. Spectral normalization may include decomposing the at least one embedding set, determining an average singular value of the at least one embedding set, determining a respective substitute singular value for each respective singular value of a diagonal matrix, and/or replacing the at least one embedding set with a product of the at least one embedding set, a right singular vector, and an inverse of the substitute diagonal matrix. The mean centering, spectral normalization, and/or length normalization may be iteratively repeated for a configurable number of iterations. A system and computer program product are also disclosed.

Abstract: A method for local approximation of a predictive model may include receiving unclassified data associated with a plurality of unclassified data items. The unclassified data may be classified based on a first predictive model to generate classified data. A first data item may be selected from the classified data. A plurality of generated data items associated with the first data item may be generated using a generative model. The plurality of generated data items may be classified based on the first predictive model to generate classified generated data. A second predictive model may be trained with the classified generated data. A system and computer program product are also disclosed.

Abstract: A method for evaluating a RNN-based deep learning model includes: receiving model data generated by the RNN-based model, the model data including a plurality of events associated with a plurality of states; generating a first GUI based on the events and states including a chart visually representing a timeline for the events in relation to a parameter value; generating a second GUI including a point chart visually representing a two-dimensional projection of the multi-dimensional intermediate data, each point of the point chart representing a time step and an event from the time step, based on multi-dimensional intermediate data between transformations in the model that connect a state to an event; and perturbing the environment at a time step based on user interaction with at least one of the first and second GUIs.

Abstract: A monomer capable forming a polymer through ring-opening metathesis polymerization and capable of depolymerization thereafter through ring-closing metathesis, wherein the monomer comprises a cycloalkene having a fused ring attached thereto to form a cycloalkene-fused ring monomer, wherein the fused ring decreases the ring strain energy of the cycloalkene to a lower ring strain energy state of 5.3 kcal/mol or lower as compared to the same cycloalkene without the fused ring having a ring strain energy above 5.3 kcal/mol and wherein the cycloalkene-fused ring monomer is capable of isomerization into a higher ring strain energy state before polymerization and method of synthesizing the monomer. The monomer is further capable of forming chemically recyclable to monomers block copolymers.

Abstract: A method for detecting collusive transaction fraud includes: generating a merchant baseline including a transaction data baseline and a time series baseline; extracting time series data of the first merchant system; generating a first score and second score with a deep learning model; generating a first merchant risk score of the first merchant system based on the first and second scores; in response to determining that the first merchant risk score satisfies the threshold, determining a plurality of related entities related to the first merchant system; and classifying the first merchant system and at least one related entity of the plurality of related entities in a first group risk class based on at least one risk score of the at least one related entity.

Abstract: A method for local approximation of a predictive model may include receiving unclassified data associated with a plurality of unclassified data items. The unclassified data may be classified based on a first predictive model to generate classified data. A first data item may be selected from the classified data. A plurality of generated data items associated with the first data item may be generated using a generative model. The plurality of generated data items may be classified based on the first predictive model to generate classified generated data. A second predictive model may be trained with the classified generated data. A system and computer program product are also disclosed.

Abstract: A method for optimizing mobile network congestion includes detecting that the UE is in a congestion mode; determining whether the UE enters the RRC connection mode in a first priority cell; determining whether shortening at least one timer of the UE is able to improve the mobile network congestion if the UE does not enter the RRC connection mode; determining whether reporting the larger data size of the BSR to the mobile network is able to improve the mobile network congestion if the UE enters the RRC connection mode; reselecting a second priority cell if shortening the at least one timer of the UE is not able to improve the mobile network congestion; and handing over to the second priority cell if reporting the larger data size of the BSR to the mobile network is not able to improve the mobile network congestion.

Abstract: Described are a system, method, and computer program product for multivariate event prediction using multi-stream recurrent neural networks. The method includes receiving event data from a sample time period and generating feature vectors for each subperiod of each day. The method also includes providing the feature vectors as inputs to a set of first recurrent neural network (RNN) models and generating first outputs for each RNN node. The method further includes merging the first outputs for each same subperiod to form aggregated time-series layers. The method further includes providing the aggregated time-series layers as an input to a second RNN model and generating final outputs for each RNN node of the second RNN model.

Abstract: Provided is a method for detecting group activities in a network. The method may include receiving interaction data associated with a plurality of interactions. For each account identifier associated with at least one interaction, a value may be determined for each of a first set of categories, and a vector may be generated based on the value for each category. The length of each vector may be determined. At least one relational graph may be generated based on the interaction data. Each relational graph may be associated with a respective category of a second set of categories. At least one cluster of nodes may be determined based on the relational graph(s). A score for each cluster may be determined based on the length of the vector associated with the account identifier of each node of the cluster of nodes. A system and computer program product are also disclosed.

Abstract: A microwave circulator/isolator includes PINs, a shell, and a locating dielectric slice, wherein a laminated assembly is arranged in the shell, notches are formed in the peripheral wall of the shell, and the laminated assembly includes a central conductor electrically connected to the PINs which are fixed to the locating dielectric slice arranged in the shell. The microwave circulator/isolator simplifies the production process, lowers the machining difficulty of the shell, reduces the production cost, and improves the production efficiency.