An device is disclosed. A first buffer to store a query data point, and a second buffer to store a matrix of candidate data points. A processing element may process the query data point and the matrix of candidate data points to identify candidate data points in the matrix of candidate data points that are nearest to the query data point.

Systems, methods, and computer-readable media are disclosed for high impedance detection in electric distribution systems. An example method may include calculating, by a processor, a relative randomness of a signal, wherein the relative randomness is a derivative of a first scale wavelet transform divided by an energy of the signal. The example method may also include calculating, by the processor, one or more scales of a wavelet transform of the signal. The example method may also include calculating, by the processor, one or more energy ratios between energy of the wavelet transform in the one or more scales. The example method may also include calculating, by the processor, a zero-crossing phase difference between a third harmonic and a fundamental component of the signal. The example method may also include determining, by the processor, that a high impedance fault occurs based on at least one of: the relative randomness, a comparison between the one or more scales of the wavelet transform, and the zero-crossing phase difference.

Systems, methods, and computer-readable media are disclosed for high impedance detection in electric distribution systems. An example method may include calculating, by a processor, a relative randomness of a signal, wherein the relative randomness is a derivative of a first scale wavelet transform divided by an energy of the signal. The example method may also include calculating, by the processor, one or more scales of a wavelet transform of the signal. The example method may also include calculating, by the processor, one or more energy ratios between energy of the wavelet transform in the one or more scales. The example method may also include calculating, by the processor, a zero-crossing phase difference between a third harmonic and a fundamental component of the signal. The example method may also include determining, by the processor, that a high impedance fault occurs based on at least one of: the relative randomness, a comparison between the one or more scales of the wavelet transform, and the zero-crossing phase difference.

Non-limiting examples of the present disclosure relate to application of artificial intelligence (AI) processing to generate classifications of user activity for a group of users. For example, a classification prediction is generated indicating whether students in an educational class are predicted, over a predetermined time period, to have a high or low activity level based on contextual analysis of multiple types of user-driven events. As user activity data is typically quite robust, the present disclosure applies dimensionality reduction processing to efficiently manage user activity data and further improve accuracy in generating downstream binary classifications. A dimensionality reduction transformation of user activity data results in a low-dimensional representation of input feature data that is contextually relevant for generating a binary classification. Derived classifications are then utilized to generate data insights pertaining to user activity levels of one or more users. Data insights are can then be rendered for presentation.

Non-limiting examples of the present disclosure relate to application of artificial intelligence (AI) processing to generate classifications of user activity for a group of users. For example, a classification prediction is generated indicating whether students in an educational class are predicted, over a predetermined time period, to have a high or low activity level based on contextual analysis of multiple types of user-driven events. As user activity data is typically quite robust, the present disclosure applies dimensionality reduction processing to efficiently manage user activity data and further improve accuracy in generating downstream binary classifications. A dimensionality reduction transformation of user activity data results in a low-dimensional representation of input feature data that is contextually relevant for generating a binary classification. Derived classifications are then utilized to generate data insights pertaining to user activity levels of one or more users. Data insights are can then be rendered for presentation.

Systems and methods for automatically analyzing spectral power measurements to identify abnormalities. The systems and methods may receive measurements comprising RF power measured over a contiguous range of frequencies, where at least a first portion of the contiguous range is used to transmit signals and at least a second portion of the contiguous range is unused. Respective boundaries of the unused portions may be identified and infilled to provide modified measurements. The modified measurements may be automatically analyzed to identify the abnormalities.

Systems and methods for determining how topics evolve in a messaging system extract at least one N-gram from data content (e.g., caption of messages) in the messaging system and detect anomalous behavior in N-gram frequencies over time. The anomalous behavior is used to select candidate N-grams for a determination of whether a topic of a candidate N-gram is evolving or fading. The candidate N-grams are clustered into cluster groups that are used to train at least one time series forecasting model to predict N-gram frequencies in a future time window. A time series of the N-gram frequency is divided into old and recent partitions and pattern recognition is applied to the predicted N-gram frequencies to identify an evolving or fading topic when the difference between a frequency of each anomaly and an average rolling median for each partition is greater for the most recent partition.

Systems and methods for determining how topics evolve in a messaging system extract at least one N-gram from data content (e.g., caption of messages) in the messaging system and detect anomalous behavior in N-gram frequencies over time. The anomalous behavior is used to select candidate N-grams for a determination of whether a topic of a candidate N-gram is evolving or fading. The candidate N-grams are clustered into cluster groups that are used to train at least one time series forecasting model to predict N-gram frequencies in a future time window. A time series of the N-gram frequency is divided into old and recent partitions and pattern recognition is applied to the predicted N-gram frequencies to identify an evolving or fading topic when the difference between a frequency of each anomaly and an average rolling median for each partition is greater for the most recent partition.

An information handling system includes a memory subsystem and a basic/input out system (BIOS). The BIOS performs multiple trainings of the memory subsystem, and each of the trainings is performed at a different temperature. The BIOS stores multiple derating values in a derating table of the BIOS, and each of the derating values corresponds to a respective tap value at a respective temperature. During a subsequent power on self test of the information handling system, the BIOS performs a first training of the memory subsystem, and stores a first set of tap values. During a runtime of the information handling system, a memory controller determines whether a temperature of the information handling system has changed by a predetermined amount. In response to the temperature changing by the predetermined amount, the memory controller utilizes the derating values in the derating table to automatically update the tap values.

A system and method for conducting a parameter update event including one or more processors for transmitting first parameter settings to a program used by multiple users, such as a mobile device application at a plurality of mobile devices, receiving performance information indicating performance of the program after the first parameter setting, the performance information for each user being separately identifiable, and for each individual user of the plurality of users, determining a parameter setting update based at least in part on the performance information of the individual user and transmitting the parameter setting update to the program.