Palo Alto Research Center Inc.

Alverix, Inc.

Xerox Corporation

Other

Palo Alto Research Company Incorporated

Palo Alto Research Center Incorporate

Rong Zhou

Minh Binh Do

Daniel Bernardo Roitman

Michael John Brosnan

John Francis Petrilla

Patrick T Petruno

Ryan A Rossi

Robert Roy Price

Johan De Kleer

Lukas Daniel Kuhn

Tim C Schmidt

Sudhendu Rai

Bo U Curry

Wheeler Ruml

Robert Michael Lofthus

Daniel Davies

Florent C Perronnin

Kristine A German

Serdar Uckun

Michael David Shepherd

Dale Ellen Gaucas

Kirk John Ocke

Eric Huang

Claudio Cifarelli

Rui Zhang

Eric Saund

Oliver Brdiczka

Juan Liu

Haitham Hindi

Lawrence Lee

Craig Lambrecht

Johan Dekleer

Frederick Lidington

Muzammil Hussain

Ethan Burns

Hector Luis Palacios Verdes

Lukas Kuhn

A diagnostic test system includes a housing, a reader, and a data analyzer. The housing includes a port constructed and arranged to receive a test strip that includes a flow path for a fluid sample, a sample receiving zone couple to the flow path, a label that specifically binds a target analyte, a detection zone coupled to the flow path and comprising a test region exposed for optical inspection and having an immobilized test reagent that specifically binds the target analyte, and at least one reference feature. The reader is operable to obtain light intensity measurements from exposed regions of the test strip when the test strip is loaded in the port. The data analyzer is operable to perform operations including at least one of (a) identifying ones of the light intensity measurements obtained from the test region based on at least one measurement obtained from the at least one reference feature, and (b) generating a control signal modifying at least one operational parameter of the reader based on at least one measurement obtained from the at least one reference feature.

Diagnostic test system using measurement obtained from reference feature to modify operational parameter of reader

Embodiments of the present invention provide a system for querying a graph based on applying filters to a visual representation of the graph. The system allows complicated graph query operations to be performed with ease visually. During operation, the system obtains data indicating vertices and edges of a graph. The system displays a visual representation of the graph for a user. The system receives, from the user, a command defining a local graph filter comprising a region in the visual representation. The system then filters a representation of the graph, and stores the filtered representation.

Localized visual graph filters for complex graph queries

System and methods for relational time-series learning are provided. Unlike traditional time series forecasting techniques, which assume either complete time series independence or complete dependence, the disclosed system and method allow time series forecasting that can be performed on multivariate time series represented as vertices in graphs with arbitrary structures and predicting a future classification for data items represented by one of nodes in the graph. The system and methods also utilize non-relational, relational, temporal data for classification, and allow using fast and parallel classification techniques with linear speedups. The system and methods are well-suited for processing data in a streaming or online setting and naturally handle training data with skewed or unbalanced class labels.

System and method for relational time series learning with the aid of a digital computer

One embodiment of the present invention provides a system for generating a product recommendation. During operation, the system obtains data indicating vertices and edges of a graph. The vertices represent consumers and products and an edge represents an access relationship. The system may receive a query indicating an ego for determining a product recommendation. The system may then traverse the graph from a vertex representing the ego through a plurality of edges to a plurality of vertices representing products. The system may traverse the graph from the plurality of vertices representing products to a plurality of vertices representing other consumers. The system may then traverse the graph from the plurality of vertices representing other consumers to a plurality of vertices representing other products. The system may generate a recommendation that based on the plurality of vertices representing other products.

System and method for a real-time egocentric collaborative filter on large datasets

A method of deep graph representation learning includes: deriving a set of base features; and automatically developing, by a processing device, a multi-layered hierarchical graph representation based on the set of base features, wherein each successive layer of the multi-layered hierarchical graph representation leverages an output from a previous layer to learn features of a higher-order.

Deep graph representation learning

One embodiment provides a system for facilitating a graph search engine. During operation, the system receives, by a server from a client computing device, a search request which includes a user-inputted graph. The system performs a search based on a structure of the user-inputted graph for a plurality of relevant graphs. The system orders the plurality of relevant graphs from a most relevant ranking to a least relevant ranking. The system returns, to the client computing device, the ordered plurality of relevant graphs for display on a user interface of the client computing device, thereby enhancing the search for relevant graphs by allowing the graph search engine to take as an input the user-inputted graph and return as an output the relevant graphs.

System and method for a graph search engine

Embodiments of the present invention provide a system for fast, accurate, and scalable unbiased graphlet estimation. The system utilizes neighborhood sampling and combinatorial relations to estimate graphlet counts, statistics, and frequency distributions in a small fraction of the computing time of existing systems. The obtained unbiased estimates are highly accurate, and have applications in the analysis, mining, and predictive modeling of massive real-world networks. During operation, the system obtains data indicating vertices and edges of a graph. The system samples a portion of the graph and counts a number of graph features in the sampled portion of the graph. The system then computes an occurrence frequency of a graphlet pattern and a total number of graphlets associated with the graphlet pattern in the graph.

Fast and accurate graphlet estimation

In one aspect, an assay test strip includes a test label that specifically binds a target analyte and a control label that is free of any specific binding affinity for the target analyte and has a different optical characteristic than the test label. In another aspect, an assay test strip includes a test label that specifically binds a target analyte and at least one non-specific-binding label that is free of any specific binding affinity for the target analyte. Systems and methods of reading assay test strips also are described.

Assay test strips with multiple labels and reading same

A system and method is used to manage scheduling of a plurality of print jobs in a multi-site print shop environment. The multi-site environment includes a plurality of print shops each having resources and equipment to complete at least one type of print job. Also included is a multi-site scheduler configuration arranged to assign and schedule print jobs to one of a home shop and a non-home shop. The assigning and scheduling is based on a fastest completion time, wherein a completion time of a print job in a home shop is defined as the actual time taken to complete the print job and a completion time of a print job in a non-home shop is defined as the actual time taken to complete the print job and a transportation delay.

System and method for multi-site cellular manufacturing with transportation delays

A method of deep graph representation learning includes: calculating a plurality of base features from a graph and adding the plurality of base features to a feature matrix. The method further includes generating, by a processing device, a current feature layer from the feature matrix and a set of relational feature operators, wherein the current feature layer corresponds to a set of current features, evaluating feature pairs associated with the current feature layer, and selecting a subset of features from the set of current features based on the evaluated feature pairs. The method further includes adding the subset of features to the feature matrix to generate an updated feature matrix.

Computer-implemented system and method for relational time series learning

A method and system for searching a graph in parallel which constructs an abstract representation of an AND/OR graph using state-space abstraction. The abstract representation of the graph includes one or more abstract nodes having duplicate detection scopes and one or more abstract edges having operator groups adjusted for AND node outcomes. The duplicate detection scopes of the abstract nodes are partitioned into smaller duplicate detection scopes using edge partitioning, wherein the abstract edges are used to define the smaller duplicate detection scopes. Nodes in the current search layer are expanded by a processing unit using the adjusted operator groups of outgoing abstract edges of the abstract nodes mapped into by the nodes, wherein the nodes expanded in parallel use adjusted operator groups associated with abstract edges having disjoint duplicate detection scopes. The method progresses to the next search layer once all the adjusted operator groups in the current search layer have been used for node expansions.

System and method for parallel edge partitioning in and/or graph search

Embodiments of the present invention provide a time- and space-efficient system for representing and searching a set of intervals to find all the intervals that overlap with a given query interval or point. A new structure called an interval hash table is introduced to significantly reduce the average search time, thereby improving computing and search technology. During operation, the system obtains data indicating a set of intervals to be hashed. The system divides a respective interval into a set of sub-intervals based on a locality-preserving hashing. The system then obtains a hash code associated with a respective sub-interval, and inserts the respective sub-interval into an interval hash table at a location corresponding to the hash code. The system may further search the interval hash table.

System and method for efficient interval search using locality-preserving hashing

Provided is a high-performance implementation of the k-means clustering algorithm on a graphics processing unit (GPU), which leverages a set of GPU kernels with complimentary strengths for datasets of various dimensions and for different numbers of clusters. The concepts of non-dominated GPU kernels and efficient strategies to select high-throughput kernels that match the arguments of the clustering problem with the underlying GPU hardware for maximum speedup are provided.

[object Object]

A system and method for efficient sparse matrix processing are provided in one embodiment. A compressed representation of a sparse matrix, the sparse matrix including one or more non-zero entries in one or more of a plurality of portions of the matrix, is obtained by at least one server including one or more streaming multiprocessors, each of the streaming multiprocessors including one or more graphics processing unit (GPU) processor cores. Each of the portions are assigned into one of a plurality of partitions based on a number of the non-zero entries in that portion. For each of the partitions, a predefined number of the GPU processor cores are assigned for processing each of the portions assigned to that partition based on the numbers of the non-zero entries in the portions assigned to that partition. For each of the partitions, each of the portions associated with that partition are processed.

System and method for efficient sparse matrix processing

Growing community of inventors

San Jose, CA, United States of America

Rong Zhou