Abstract: A method for evaluating a risk of a subject getting a specific disease includes steps of: storing a reference database that contains original parameter sets; selecting target alleles from an SNP profile derived from genome sequencing data of a subject; selecting target parameter sets from among the original parameter sets; calculating, for each of the target parameter sets, a race factor based on a global risk allele frequency and a group-specific risk allele frequency included in the target parameter set; calculating a genetic factor based on statistics, global reference allele frequencies, the race factors for the target parameter sets, and numbers of chromosomes in homologous chromosome pairs included in the target parameter sets; calculating a citation factor based on numbers of citation times included in the target parameter sets; and calculating a risk score based on the genetic factor and the citation factor.

Abstract: A system for providing a persona-driven and artificially-intelligent avatar is disclosed. The avatar may be utilized to represent an artificially-intelligent virtual assistant that may perform actions on behalf of a user, such as actions related to personal shopping for the user. Notably, the avatar may influence artificial intelligence algorithms supporting the functionality of the virtual assistant via quantifiable traits of a persona of the avatar. In particular, the quantifiable traits of the persona of the avatar may be utilized to range bound the controlling parameters of the algorithms such that the virtual assistant represented by the avatar performs actions that conform to the ranged-bounded controlling parameters. Metrics corresponding to the performance of the virtual assistant may be tracked, and the system may modify parameters of the virtual assistant based on the metrics so as to adjust future actions performed by the virtual assistant for the user.

Abstract: A system for providing an artificially-intelligent graph database is disclosed. The graph database has significant graph computing capabilities, which are facilitated through the integration of artificial intelligence into the graph database. The capabilities are enabled through the use of unique artificially-intelligent agents that are each responsible for a subgraph of a graph of the graph database. Each artificially-intelligent agent performs computations on the graph-structured data in each subgraph that each agent is responsible for managing. The artificially-intelligent agents may work cohesively together to provide artificial intelligence capabilities to the entire graph database. The graph database may optimize itself through intelligent local management by using the agents.

Abstract: A method includes steps of: based on protein structure data, selecting a residue pair that includes a specific residue and a paired residue respectively of two wild-type protein chains of a protein complex; determining a mutant residue to substitute for the specific residue; for a target interface between the mutant residue and the paired residue, calculating an atomic distance and an atomic interaction force based on the protein structure data and amino acid structure data; and estimating binding free energy of the target interface by feeding the atomic distance, the atomic interaction force, and physicochemical information related to the specific residue and the mutant residue into a deep neural network.

Abstract: A method includes: determining a mutation frequency for each residue in a wild-type spike protein of a specific virus; for each residue in the protein, counting a total number of contact residues related to said each residue and P antibodies based on P entries of protein structure data; for each residue in the protein, for a condition that said each residue mutates into one common amino acid residue, determining a normalized binding free energy value using a pre-established model based on the protein structure data, and determining a mutation effect score based on the mutation frequency, the total number of contact residues and the normalized binding free energy value; generating a mutation effect epitope map related to the mutation effect scores determined for all residues in the protein and all common amino acid residues; and determining, based on the map, a region in the protein as a target epitope.

Abstract: A system, method, and computer readable device that detects hidden correlation relationships among entities, such as companies and/or individuals is presented. A dataset that corresponds to a predefined set of correlation relationships of these companies and/or individuals may be collected. The dataset may be stored in a graph database and a machine learning system may be built using features computed from the graph database. At least a new pair of companies or a new pair of an individual and a company may be evaluated. The system, method, and/or computer readable device may determine whether a hidden correlation relationship exists between them.

Abstract: A system for providing a persona-driven and artificially-intelligent avatar is disclosed. The avatar may be utilized to represent an artificially-intelligent virtual assistant that may perform actions on behalf of a user, such as actions related to personal shopping for the user. Notably, the avatar may influence artificial intelligence algorithms supporting the functionality of the virtual assistant via quantifiable traits of a persona of the avatar. In particular, the quantifiable traits of the persona of the avatar may be utilized to range bound the controlling parameters of the algorithms such that the virtual assistant represented by the avatar performs actions that conform to the ranged-bounded controlling parameters. Metrics corresponding to the performance of the virtual assistant may be tracked, and the system may modify parameters of the virtual assistant based on the metrics so as to adjust future actions performed by the virtual assistant for the user.

Abstract: A novel distributed graph database is provided that is designed for efficient graph data storage and processing on modern computing architectures. In particular a single node graph database and a runtime & communication layer allows for composing a distributed graph database from multiple single node instances.

Abstract: A novel distributed graph database is provided that is designed for efficient graph data storage and processing on modern computing architectures. In particular a single node graph database and a runtime & communication layer allows for composing a distributed graph database from multiple single node instances.

Abstract: A novel distributed graph database is provided that is designed for efficient graph data storage and processing on modern computing architectures. In particular a single node graph database and a runtime & communication layer allows for composing a distributed graph database from multiple single node instances.

Abstract: A system for providing a persona-driven and artificially-intelligent avatar is disclosed. The avatar may be utilized to represent an artificially-intelligent virtual assistant that may perform actions on behalf of a user, such as actions related to an investment strategy for the user. Notably, the avatar may influence artificial intelligence algorithms supporting the functionality of the virtual assistant via quantifiable traits of a persona of the avatar. In particular, the quantifiable traits of the persona of the avatar may be utilized to range bound the controlling parameters of the algorithms such that the virtual assistant represented by the avatar performs actions that conform to the ranged-bounded controlling parameters. Metrics corresponding to the performance of the virtual assistant may be tracked, and the system may modify parameters of the virtual assistant based on the metrics so as to adjust future actions performed by the virtual assistant for the user.

Abstract: A system for providing a graph protocol for forming a decentralized and distributed graph database is disclosed. The system involves receiving, from a device, a bid request to generate a graph on a graph protocol network via the graph protocol. A plurality of masternodes in the system may transmit ask requests associated with providing storage and computation power to service the bid request. If the system determines that the requirements of the bid request match or correlate with the requirements of the ask request, the system may select any masternodes having matching or correlating ask requests to service the ask request. The system may then arrange the masternodes into a unique artificially-intelligent router of the graph protocol network. The system may then proceed to generate the graph on the graph protocol network by utilizing the masternodes of the router and may process any queries associated with the generated graph.

Abstract: A query image is obtained. In a database including a plurality of reference image graphs, at least one of the reference image graphs, with feature vectors similar to the query image, is identified. Image querying is carried out by graph traversal on the at least one of the reference image graphs with the feature vectors similar to the query image. An image from the at least one of the reference image graphs having a highest matching score in the graph traversal is returned as a response to the query image. techniques for building the database are also disclosed.

Abstract: Method and apparatus for optimizing a convolutional neural network (CNN). A respective measure of importance is calculated for each of a plurality of elements within a CNN. A first one of the measures of importance is calculated by back propagating a second one of the measures of importance through the CNN. One or more of the plurality of elements is pruned from the CNN, based on the calculated measures of importance.

Abstract: A data storage and retrieval system for a computer memory includes a stream graph engine extracting graph data from streaming data, the graph data occupying a sliding window and comprising a plurality of slices representing a set of contiguous graphs, and where each slice of the plurality of slices corresponds to a given graph structure and its properties at a particular time, the stream graph engine causing the computer memory to store an on-disk portion of the graph data including a key table, a timestamp table and a plurality of property files, wherein the key table comprises a plurality of pointers to corresponding entries of the timestamp table, wherein each of the entries of the timestamp table comprise a corresponding timestamp and a pointer to the properties files, wherein the properties files comprise properties of a corresponding graph of the set of contiguous graphs, an in-memory portion of the graph data having a cache data structure storing a subset of the key table, and a versioning control modu

Abstract: Techniques for multimodal data fusion having a multimodal hierarchical dictionary learning framework that learns latent subspaces with hierarchical overlaps are provided. In one aspect, a method for multi-view data fusion with hierarchical multi-view dictionary learning is provided which includes the steps of: extracting multi-view features from input data; defining feature groups that group together the multi-view features that are related; defining a hierarchical structure of the feature groups; and learning a dictionary using the feature groups and the hierarchy of the feature groups. A system for multi-view data fusion with hierarchical multi-view dictionary learning is also provided.

Abstract: Detecting propensity profile for a person may comprise receiving artifacts associated with the person; detecting profile characteristics for the person based on the artifacts; receiving a plurality of predefined profiles comprising a plurality of characteristics and relationships between the characteristics over time, each of the plurality of predefined profiles specifying an indication of propensity; matching the profile characteristics for the person with one or more of the plurality of predefined profiles; and outputting one or more propensity indicators based on the matching, the propensity indicators comprising at least an expressed strength of a given propensity in the person at a given time.

Abstract: A method includes analyzing a preview image made by an image sensor when operating in a preview image mode to determine if the preview image contains some portion that corresponds to information that is considered not subject to being imaged. The method further includes, if it is determined that the preview image does comprise some image portion that corresponds to information that is considered not subject to being imaged, disabling the image sensor from capturing an image that would correspond to the preview image. Analyzing can include accessing a data storage that contains representations of information that is considered not subject to being imaged for comparison with image features extracted from the preview image, where the data storage is at least one of contained in a device that also contains the image sensor, or is located remotely from the device and can be accessed via a wireless connection.

Abstract: A system for providing a graph protocol for forming a decentralized and distributed graph database is disclosed. The system involves receiving, from a device, a bid request to generate a graph on a graph protocol network via the graph protocol. A plurality of masternodes in the system may transmit ask requests associated with providing storage and computation power to service the bid request. If the system determines that the requirements of the bid request match or correlate with the requirements of the ask request, the system may select any masternodes having matching or correlating ask requests to service the ask request. The system may then arrange the masternodes into a unique artificially-intelligent router of the graph protocol network. The system may then proceed to generate the graph on the graph protocol network by utilizing the masternodes of the router and may process any queries associated with the generated graph.

Abstract: A system for providing an artificially-intelligent graph database is disclosed. The graph database has significant graph computing capabilities, which are facilitated through the integration of artificial intelligence into the graph database. The capabilities are enabled through the use of unique artificially-intelligent agents that are each responsible for a subgraph of a graph of the graph database. Each artificially-intelligent agent performs computations on the graph-structured data in each subgraph that each agent is responsible for managing. The artificially-intelligent agents may work cohesively together to provide artificial intelligence capabilities to the entire graph database. The graph database may optimize itself through intelligent local management by using the agents.