The present disclosure advantageously provides a modulo operation unit that includes a first input configured to receive operand data, a second input configured to receive modulus data, an initial modulo stage, a sequence of intermediate modulo stages, and a final modulo stage.

The subject matter presently claimed relates to a data quality management system and method whereby a first data point comprising a first obtained data and a first assigned value from is received from a first data repository, a first quality score as well as a first storable data of the first data point is determined and/or stored. A second data point comprising a second obtained data, which is similar to the first obtained data according to a predefined similarity measure, and a second assigned value is received from the second data repository, a second quality score as well as a second storable data is determined from the second data point and/or stored and a second transmittable data, determined from the second data point and/or the second quality score is transmitted to the first data repository, causing the first data repository to re-evaluate the first assigned value.

The present disclosure provides computerized, automated real estate valuation systems and methods that take into account subjective factors of the property like curb appeal and property condition. In preferred embodiments, the valuation system uses image recognition of images (photos and/or videos) associated with the property to automatically identify which comparable properties to include and to exclude in determining the value of the property.

The present disclosure provides computerized, automated real estate valuation systems and methods that take into account subjective factors of the property like curb appeal and property condition. In preferred embodiments, the valuation system uses image recognition of images (photos and/or videos) associated with the property to automatically identify which comparable properties to include and to exclude in determining the value of the property.

Systems and methods described herein facilitate the search and presentation of historical data for wireless network usage and provide a mechanism for high-redundancy, low-latency record retrieval of data from large data sets. Network devices divide data for a historical data store into separate record type groups, store metadata for each record type in an application database, partition each record type group by date in a historical record database that is different from the application database, and form, within each date partition, buckets of common hash values of a key parameter from each record. When a user performs a query, the network devices generate a record-specific query form based on the record type metadata to obtain lookup parameters; generate a search hash value using a key parameter from the lookup parameters; and generate a query expression based on the record type, lookup parameters, and the search hash value.

A device includes a comparison circuit and a calculation circuit coupled to the comparison circuit. The comparison circuit is configured to receive a first digital input value (X) and a second digital input value (Y), and provide a first digital output value that indicates one of a first relationship, a second relationship, and a third relationship between X and Y. The calculation circuit is configured to receive X and Y, receive the first digital output value, and provide a second digital output value. The second digital output value is a first linear combination of X and Y responsive to the first digital output value indicating the first relationship, a second linear combination of X and Y responsive to the first digital output value indicating the second relationship, and a third linear combination of X and Y responsive to the first digital output value indicating the third relationship.

A memory-centric neural network system and operating method thereof includes: a processing unit; semiconductor memory devices coupled to the processing unit, the semiconductor memory devices containing instructions executed by the processing unit; a weight matrix constructed with rows and columns of memory cells, inputs of the memory cells of a same row being connected to one of axons, outputs of the memory cells of a same column being connected to one of neurons; timestamp registers registering timestamps of the axons and the neurons; and a lookup table containing adjusting values indexed in accordance with the timestamps, wherein the processing unit updates the weight matrix in accordance with the adjusting values.

A set of distance measurable encrypted feature vectors can be derived from any biometric data and/or physical or logical user behavioral data, and then using an associated deep neural network (“DNN”) on the output (i.e., biometric feature vector and/or behavioral feature vectors, etc.) an authentication system can determine matches or execute searches on encrypted data. Behavioral or biometric encrypted feature vectors can be stored and/or used in conjunction with respective classifications, or in subsequent comparisons without fear of compromising the original data. In various embodiments, the original behavioral and/or biometric data is discarded responsive to generating the encrypted vectors. In another embodiment, distance measurable or homomorphic encryption enables computations and comparisons on cypher-text without decryption of the encrypted feature vectors. Security of such privacy enabled embeddings can be increased by implementing an assurance factor (e.g., liveness) to establish a submitted credential has not been spoofed or faked.

Methods and systems are provided for generating training data for training a classifier to assign nodes of a taxonomy graph to items based on item descriptions. Each node has a label. For each item, the system identifies for that item one or more candidate paths within the taxonomy graph that are relevant to that item. The system identifies the candidate paths based on content of the item description of that item matching labels of nodes. A candidate path is a sequence of nodes starting a root node of the taxonomy graph. For each identified candidate path, the system labels the item description with the candidate path equivalently with leaf node or label of the leaf node. The labeled item descriptions compose the training data for training the classifier.

An electronic system includes a mapping circuit configured to receive input samples of a dataset within a defined range of values. The mapping circuit is configured to perform comparisons that compare each input sample to each of a plurality of comparison values selected from the defined range of values. For each comparison, the mapping circuit generates an indication value specifying whether the input sample used in the comparison is greater than or equal to the comparison value used in the comparison. The system includes an adder circuit configured to generate a sum of the indication values for each comparison value and a memory configured to maintain counts corresponding to the comparison values. The counts are updated by the respective sums. The system includes a threshold detection circuit configured to determine, for the dataset, a threshold value or threshold range based on the counts read from the memory.