Abstract: Provided is a method for moderating abnormal users. The method includes: acquiring history behavior data of a plurality of to-be-moderated users, wherein the history behavior data includes behavior data of interactions of the plurality of to-be-moderated users; extracting a plurality of predetermined valid features from the history behavior data for each of the plurality of to-be-moderated users, wherein the predetermined valid features are features predetermined in sample data; calculating, based on predetermined probabilities of events associated with the plurality of valid features, a probability that each of the plurality of to-be-moderated users is the abnormal user; establishing a total probability function based on probabilities that the plurality of to-be-moderated users are the abnormal users; determining candidate users by solving, based on predetermined conditions, a maximum value of the total probability function; and determining the abnormal users by moderating the candidate users.

Abstract: An example method comprises receiving first historical data of a first time period and failure data, identifying at least some sensor data that was or potentially was generated during a first failure, removing the at least some sensor data to create filtered historical data, training a classification model using the filtered historical data, the classification model indicating at least one first classified state at a second period of time prior to the first failure indicated by the failure data, applying the classification model to second sensor data to identify a first potential failure state based on the at least one first classified state, the second sensor data being from a subsequent time period, generating an alert if the first potential failure state is identified based on at least a first subset of sensor signals generated during the subsequent time period, and providing the alert.

Abstract: Generating fault indications for an additive manufacturing machine based on a comparison of the outputs of multiple process models to measured sensor data. The method receiving sensor data from the additive manufacturing machine during manufacture of at least one part. Models are selected from a model database, each model generating expected sensor values for a defined condition. Difference values are computed between the received sensor data and an output of each of the models. A probability density function is computed, which defines, for each of the models, a likelihood that a given difference value corresponds to each respective model. A probabilistic rule is applied to determine, for each of the models, a probability that the corresponding model output matches the received sensor data. An indicator is output of a defined condition corresponding to a model having the highest match probability.

Abstract: In an approach, a processor receives a set of normal domains, a set of suspicious domains, and a set of malicious domains; labels each domain of the set of normal domains as normal producing a labelled set of normal domains and each domain of the set of suspicious domains and the set of malicious domains as malicious producing a labelled set of malicious domains; samples a preset percentage of the labelled set of normal domains producing a sampled set of normal domains; aggregates the sampled set of normal domains and the labelled set of malicious domains producing a set of aggregated domains; filters the set of aggregated domains using hit size, inter-arrival-time, and univariate volumetric filters producing a set of filtered domains; and determines a cluster of a set of clusters to which each of the set of filtered domains is to be assigned using a trained K-shape model.

Abstract: Automation is the key to build efficient workflows with minimum effort consumption. However, there is a large gap in workflow synthesis for automated AI application development. Computer vision workflow synthesis largely rely on domain expert due to lack of generalization over solution search space for given goal. This search space for creating suitable solution(s) using available algorithms is quite vast, which makes exploratory work of solution building a time-, effort- and intellect intensive endeavor. Embodiments of the present disclosure provide system and method for goal-driven algorithm selection approach for building computer vision workflows on the fly. The system generates one or more task workflows with associated success probability depending on initial conditions and input natural language goal query by combining various image processing algorithms. Symbolic AI planning is aided by Reinforcement Learning to recommend optimal workflows that are robust and adaptive to changes in the environment.

Abstract: A method, computer program product, and computing system for processing a plurality of input/output (IO) requests for a storage object of a storage system. A sampling interval may be determined for the plurality of IO requests for the storage object based upon, at least in part, a machine learning model processing the plurality of IO requests. The plurality of IO requests may be sampled using the determined sampling interval. The plurality of sampled IO requests may be processed using the machine learning model.

Abstract: Disclosed is generating an acquisition request of a component, using a probabilistic model based on current and planned life consumption, to maintain a usable component inventory, by: determining planned consumption of the component; retrieving historical lifetime data associated with the component; determining the associated normalized current life consumed for historical specimens at failure based on a power law equivalency model; determining a probability of survival distribution associated with the component; determine a probability of future failure of each component in inventory given planned consumption of the component; determine a projected number of component failures prior to completion of the job associated with the component; generate the at least one acquisition request to acquire a quantity of new components at least equal to the projected number of specific component failures prior to completion of the job; wherein the usable inventory of the component includes at least the quantity of new com

Abstract: The present invention transforms flight profile information, terrain, weather/atmospheric data and flight parameter data via system components into comprehensive hazard avoidance optimized flight plans. Comprehensive hazard avoidance includes synergistic comprehensive turbulence and airfoil-specific icing data. In one implementation, the system comprises a processor and a memory disposed in communication with the processor and storing processor-issuable instructions to receive anticipated flight plan parameter data, obtain weather data based on the flight plan parameter data, obtain atmospheric data based on the flight plan parameter data, and determine a plurality of four-dimensional grid points based on the flight plan parameter data. The system may then determine comprehensive hazards mappings.

Abstract: Methods and systems for collecting and analyzing data for managing and maintaining buildings are provided. An example method commences with collecting a plurality of data points from a plurality of equipment pieces associated with at least one building. The plurality of data points are associated with an operation of the plurality of equipment pieces. The method continues with analyzing the plurality of data points based on predetermined criteria. The analysis includes determining whether one or more rules of a plurality of rules are met by the plurality of equipment pieces. The method further includes determining, based on the analysis, that one or more equipment pieces of the plurality of equipment pieces do not meet the one or more rules. The method then continues with generating an alert associated with the one or more equipment pieces based on the determination that the one or more rules are not met.

Abstract: There is described a neural network system for generating a graph, the graph comprising a set of nodes and edges. The system comprises one or more neural networks configured to represent a probability distribution over sequences of node generating decisions and/or edge generating decisions, and one or more computers configured to sample the probability distribution represented by the one or more neural networks to generate a graph.

Abstract: Systems and methods for determining a remaining useful life of a system. The system and method include one or more processors; a memory coupled to the one or more processors; a data acquisition unit configured to receive run-to-failure time series data; a neural network training unit configured to train a neural network model to determine a point in time that a health index changes from a healthy stage to a degradation stage; a remaining useful life estimation unit configured to estimate a first remaining useful life of the system based on the point in time; estimate a second remaining useful life of the system by converting a feature representation output by the second neural network; minimize the difference between the first remaining useful life and the second remaining useful life; classify the health stage based on a probability; and an output unit configured to send a warning to a user.

Abstract: A vehicle computing system may implement techniques to determine relevance of objects detected in an environment to a vehicle operating in the environment. The techniques may include determining locations and trajectories associated with a detected object and generating simulations of movement (e.g., estimated states) of the detected object and the vehicle over a period of time. The vehicle computing system may compare locations, distances, and/or trajectories of the detected object and the vehicle in an estimated state to determine whether the detected object is relevant to the vehicle performing the action (e.g., impacts the vehicle's ability to safely perform the action). The relevance determination may be based in part on a semantic classification of the detected object.

Abstract: A system and method are disclosed for bottom-up modeling and prediction of asset anomalies. In one embodiment, the system receives first sensor data from a first sensor, the first sensor associated with a first asset; smooths the first sensor data; determines a first stage within sensor data, the sensor data including the first sensor data; determines a first set of anomalies within the first stage; generates a first asset state space associated with the first asset; trains a base model describing a first group of one or more assets, the first group of one or more assets including the first asset; and trains, using the base model, a final model particular to the first asset.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for controlling an agent. One of the methods includes sampling a behavior modulation in accordance with a current probability distribution; for each of one or more time steps: processing an input comprising an observation characterizing a current state of the environment at the time step using an action selection neural network to generate a respective action score for each action in a set of possible actions that can be performed by the agent; modifying the action scores using the sampled behavior modulation; and selecting the action to be performed by the agent at the time step based on the modified action scores; determining a fitness measure corresponding to the sampled behavior modulation; and updating the current probability distribution over the set of possible behavior modulations using the fitness measure corresponding to the behavior modulation.

Abstract: The various implementations described herein include methods and devices for recommending phrases for data visualizations. In one aspect, a method includes presenting a data visualization page to a user, the page including a first region for displaying a data visualization and a second region for phrase recommendations. The method further includes obtaining a dataset selected by the user, the dataset including a plurality of fields; and generating a first set of phrase recommendations based on the dataset, each phrase recommendation corresponding to a respective field. The method also includes displaying the first set of phrase recommendations in the second region; and receiving a user selection of a first phrase. The method further includes, in response to the user selection: presenting a data visualization in the first region using the first phrase; and displaying a second set of phrase recommendations generated based on the first phrase.

Abstract: Disclosed herein include systems, devices, computer readable media, and methods for subsampling flow cytometric event data. First and second flow cytometric event data can be transformed into a lower-dimensional space, associated with a plurality of bins, and assigned to a first bin and a second bin. Subsampled flow cytometric event data comprising the first flow cytometric event data can be generated. The subsampled flow cytometric event data can comprise the second flow cytometric event data if the first bin and the second bin are different. The subsampled flow cytometric event data may not comprise the second flow cytometric event data if the first bin and the second bin are identical.

Abstract: Provided is an electronic apparatus and an operation method thereof for, based on location information on a vehicle on a first occupancy grid map, identifying a second occupancy grid map having a first location as an origin and identifying an occupancy state of a grid included in the second occupancy grid map, updating the occupancy state of the grid included in the second occupancy grid map based on information acquired from a sensor, identifying an inflation range corresponding to a first grid among grids included in the second occupancy grid map based on appearance information on the vehicle, and identifying an inflated occupancy state of a second grid included in the inflation range based on an occupancy state of the first grid.

Abstract: Provided is a method for simulating electricity of a wafer chip. The method includes: a database is constructed, the database including spectroscopic data of a semiconductor structure of the wafer chip obtained from a target key process, actual electrical data of the wafer chip, and a correspondence between the spectroscopic data and the actual electrical data; the target key process is performed on a target wafer chip to obtain the spectroscopic data of the semiconductor structure of the target wafer chip obtained from the target key process, the spectroscopic data being target spectroscopic data; the electrical data of the target wafer chip is simulated based on the obtained target spectroscopic data and the database, the electrical data being target electrical data.

Abstract: A method can include accessing data generated during field operations; generating a graph that includes vertices and edges using at least a portion of the data, where the edges represent relationships between vertices; and generating a query result using the graph responsive to receipt of a query.

Abstract: An illustrative optical measurement system includes a light source configured to emit light directed at a target, an array of photodetectors configured to detect photons of the light after the light is scattered by the target, and a processing unit. The processing unit is configured to measure a noise level of a photodetector included in the array of photodetectors and determine that the noise level meets a predetermined threshold. The processing unit is further configured to prevent, based on the determining that the noise level meets the predetermined threshold, an output of the photodetector from being used in generating a histogram based on a temporal distribution of photons detected by the array of photodetectors.

Abstract: A system for performing automated inspection of a sewer line using machine learning. The system comprises a inspection data database storing sewer inspection data including images and metadata associated therewith; an inspection upload module receiving and uploading the sewer inspection data to the inspection data database; and an inspection module receiving the sewer inspection data from the inspection data database and generating therefrom identification data including characteristics of the sewer line identified and categorized.

Abstract: Some embodiments of the invention provide methods for performing root cause analysis for non-deterministic anomalies in a datacenter. For instance, the method of some embodiments identifies a root cause for degradation in performance of one or more components in a network of the datacenter. This method collects and generates resource consumption data regarding resources consumed by a set of components in this network. The method performs a first analysis on the collected and/or generated data to identify an instance in time when one or more components, while still operational, are possibly suffering from performance degradation. The method then performs a second analysis on the collected and/or generated data associated with the identified time instance to identify a root cause of a performance degradation of at least one component in the network.

Abstract: A test arrangement and a method for controlling a measurement apparatus are provided. The measurement apparatus, in particular the triggering of the measurement apparatus is controlled by a state machine. If a desired state for triggering the measurement apparatus is not entered within a predetermined time period, the conditions for triggering the measurement apparatus can be adapted. For example, a required state for triggering can be changed or an automated triggering can be initiated.

Abstract: A building security system of a building including one or more memory devices configured to store instructions causing one or more processors to receive door lock data collected from one or more sensors for one or more doors within the building indicating operation of the one or more doors, select a period for which the door lock data was collected, select a first door from the one or more doors of the building for which data is collected, and identify segments of the data indicating activity of the door. The instructions also apply a model to segments of the data indicating activity of the first door, compute a probability metric for each segment of the data, compute a combined probability score for the first door based on the computed probability metrics, and classify the first door as normally functioning or malfunctioning by comparing the combined probability score to a threshold.

Abstract: An embodiment provides a method, including: obtaining, from a multi-sensor pipe inspection robot that traverses through the interior of a pipe, two or more sets of condition assessment data for the interior of the pipe collected during a single pass through the interior of the pipe; the two or more sets of condition assessment data comprising a first data type obtained using a first sensor type and a second data type obtained using a second sensor type; combining, using a processor, two or more image processing techniques to adjust imaging of a pipe feature; and forming, using the processor, an image of the interior of the pipe using the two or more image processing techniques. Other embodiments are described and claimed.

Abstract: An electronic device, for each media content item of a plurality of media content items, receives a respective score for each a first set of objectives and one or more other objectives and generates a respective score between a user and the media content item. The generating includes applying a first ordered weighted average to the respective scores for the first set of objectives, to produce a first combined score for the first set of objectives, applying a second ordered weighted average to the respective scores for a second set of objectives, wherein the second set of objectives includes (i) a resulting objective corresponding to the first set of objectives and having the first combined score and (ii) the one or more other objectives. The electronic device streams media content to the user selected based on the respective scores between the user and the media content items.

Abstract: A method and system for training a fall detection classifier using subject-specific movement data. A subject sets a preferred non-fall detection rate. Movement data responsive to a subject's movements during everyday activities are obtained over a predetermined data collection period. For each detected event in the movement data, values for one or more parameters that may (together or individually) indicate a fall are obtained. The obtained values are used to generate a subject-specific non-fall detection rate function. This non-fall detection rate function is used to derive a threshold value, in reference to the subject-set preferred non-fall detection rate, to distinguish fall events from non-fall events.

Abstract: The present disclosure provides a method and apparatus for processing astronomical survey data. The method comprises: a data distribution step: delivering a spectral data file to be processed to a distributed file system, dividing the spectral data file into a plurality of data blocks, and distributing the data blocks to computing nodes; a data computing step: performing a spectral classification of spectra to be measured in the data blocks, with a template matching method; the data computing step comprises: reading template spectra, and extracting template universal operators; reading the spectra to be measured; performing spectral matching, and computing Chi-square error values for flow rate values of the template spectra multiplied by the polynomial and flow rate values of the spectra to be measured; and finding corresponding minimum Chi-square error and corresponding redshift for each of the template spectra, so as to determine classifications of the spectra to be measured.

Abstract: To perform a prognostic health analysis for an asset, a plurality of independent stochastic simulations are performed using transition probabilities of a discrete Markov Chain model. A prognostic asset health state evolution is computed over a time horizon from the plurality of independent stochastic simulations. An output is generated based on the computed prognostic asset health state evolution.

Abstract: Disclosed herein are computer-implemented methods and systems for forecasting security ratings for an entity. The methods and systems can include generating a plurality of simulated instantiations of a security scenario for the entity, in which the security scenario characterized by a plurality of security events associated with at least one event type. The methods and systems can further include determining a security rating for each instantiation of the plurality of instantiations; and generating a forecast cone based on the determined security ratings for the plurality of instantiations. In some examples, for each event type of the at least one event type, the methods and systems can include determining a rate, duration, and/or temporal placement of the security events associated with the event type over a forecasting period.

Abstract: A computer-implemented method for simulation of an integrated circuit for yield analysis includes: a) for plurality of variables, generating initial sampling sets by sampling from provided distributions related to physical properties of circuits; b) selecting at least one sample from each initial set randomly and combining into initial simulation set; c) running initial simulation of operation of circuit, applying initial simulation set, the operation having passing/failing criterion; d) if fails: storing samples of initial set into initial sampling distributions for each variable; e) repeating steps b)-d) until sufficient failures obtained; f) building importance sampling distribution based on each initial sampling distribution, the importance distribution having lower, center, upper portions; g) generating secondary simulation set by drawing samples from importance sampling distribution for each variable; h) simulating circuit by applying the secondary set; i) repeating steps g)-h); j) mapping resulting yie

Abstract: Systems and methods for building predictive and prescriptive analytics of wind turbines generate a historical operational dataset by loading historical operational SCADA data of one or more wind turbines. Each sensor measurement is associated with an engineering tag and at least one component of a wind turbine. The system creates one or more performance indicators corresponding to one or more sensor measurements, and applies at least one data clustering algorithm onto the dataset to identify and label normal operation data clusters. The system builds a normal operation model using normal operational data clusters with Efficiency of Wind-To-Power (EWTP) and defines a statistical confidence range around the normal operation model as criterion for monitoring wind turbine performance. As real-time SCADA data is received by the system, the system can detect an anomalous event, and issue an alert notification and prescriptive early-action recommendations to a user, such as a turbine operator, technician or manager.

Abstract: A case data set storage unit includes a plurality of case data sets including difference data representing, a variation from measurement data taken at an initial state to measurement data taken at a plurality of points of time for the same sample under the same measurement condition. An aging degradation estimation unit uses these case data sets to output difference data representing an estimate value of a variation from the measurement data taken at the initial state to measurement data taken, by a measurement device, at or after a first elapsed time, based on difference data representing a variation from the measurement data taken at the initial state to measurement data taken at a plurality of points of time that precede the first elapsed time. An aging degradation correction unit corrects the measurement data taken at or after the first elapsed time, based on the output difference data.

Abstract: Disclosed are a method and apparatus for detecting a fault, and a method and apparatus for training a model. The method includes: acquiring characteristic data and actual temperature of a first wind turbine among n wind turbines, wherein the characteristic data of the first wind turbine is intended to characterize a working state of the first wind turbine, and n is an integer greater than 1; acquiring a prediction temperature set by inputting the characteristic data of the first wind turbine into a temperature prediction model corresponding to each of the n wind turbines; and detecting, based on the predicted temperature set and the actual temperature of the first wind turbine, whether the first wind turbine encounters a fault.

Abstract: The present disclosure provides a method and apparatus for processing astronomical survey data. The method comprises: a data distribution step: delivering a spectral data file to be processed to a distributed file system, dividing the spectral data file into a plurality of data blocks, and distributing the data blocks to computing nodes; a data computing step: performing a spectral classification of spectra to be measured in the data blocks, with a template matching method; the data computing step comprises: reading template spectra, and extracting template universal operators; reading the spectra to be measured; performing spectral matching, and computing Chi-square error values for flow rate values of the template spectra multiplied by the polynomial and flow rate values of the spectra to be measured; and finding corresponding minimum Chi-square error and corresponding redshift for each of the template spectra, so as to determine classifications of the spectra to be measured.

Abstract: Described herein are software and systems for analyzing videos and/or images. Software and systems described herein are configured in different embodiments to carry out different types of analyses. For example, in some embodiments, software and systems described herein are configured to locate an object of interest within a video and/or image.

Abstract: A target trajectory generation device generates and outputs target trajectories each including a target position and a target speed of a vehicle. A first target trajectory is intended to perform at least one of steering, acceleration, and deceleration of the vehicle. A second target trajectory is intended to decelerate and stop the vehicle. When a malfunctioning device does not exist, a vehicle traveling control device executes vehicle traveling control based on the first target trajectory. When the malfunctioning device exists, the vehicle traveling control device stops the vehicle by executing the vehicle traveling control based on the second target trajectory output before the malfunction occurs, or based on the second target trajectory output from the target trajectory generation device other than the malfunctioning device.

Abstract: Methods of detecting and/or identifying an artificial target within an image are provided. These methods comprise: applying to a region of the image a primary classification algorithm for performing a feature extraction of the image region, the primary classification algorithm being based on a spectral profile defined by one or more spectral signatures with one or more features in at least part of the infrared spectrum; obtaining a relation between the extracted features of the image region and the spectral profile; verifying whether a level of confidence of the obtained relation between the extracted features and the spectral profile is higher than a first predetermined confirmation level; and, in case of positive (or true) result of said verification, determining that the image region corresponds to artificial target to be detected, thereby obtaining a confirmed artificial target. Systems and computer programs are also provided that are suitable for performing said methods.

Abstract: A system and method for utilizing automatically generating related subject matter areas to create footer links for a subject matter page of a tutor matching website is disclosed and claimed. In particular, a document embedding algorithm such as word2vec, doc2vec or GloVe can be trained using on a particular subject using pertinent material such as, for example, textbooks, learned papers, and transcripts of lectures. Once trained, the document embedding algorithm can be used to generate a list of related subjects that can be used to automatically build footer links for a particular subject matter page. The related footer links can improve the positioning of the tutor matching website with common Internet search engines. The same document embedding algorithm can be used to identify previously answered questions that may assist a user posing a question to the tutor matching website.

Abstract: Example methods and systems are provided for network diagnosis. One example method may comprise: detecting an egress packet and determining whether each of multiple network issues is detected for the egress packet or a datapath between a first virtualized computing instance and a second virtualized computing instance. The method may also comprise: generating network diagnosis code information specifying whether each of the multiple network issues is detected or not detected; generating an encapsulated packet by encapsulating the egress packet with an outer header that specifies the network diagnosis code information; and sending the encapsulated packet towards the second virtualized computing instance to cause a second computer system to perform one or more remediation actions based on the network diagnosis code information.

Abstract: A method is used in issuing alerts for storage volumes using machine learning. A machine learning system analyzes Input/Output (I/O) data of a storage volume in a data storage system. The machine learning system is trained with sample I/O data patterns associated with the storage volume. Based on the I/O data, the machine learning system identifies atypical behavior associated with I/O data patterns of the I/O data. The method then issues an alert.

Abstract: An artificial neural network-based method for selecting a surface type of an object is suitable for selecting a plurality of objects. The artificial neural network-based method for selecting a surface type of an object includes performing surface type identification on a plurality of object images by using a plurality of predictive models to obtain a prediction defect rate of each of the predictive models, wherein the object images correspond to surface types of a part of the objects, and cascading the predictive models according to the respective prediction defect rates of the predictive models into an artificial neural network so as to select the remaining objects.

Abstract: The value for equipment to be replaced can be maximized by determining a threshold cutoff value for a failure prediction indicator and a window size for obtaining the threshold cutoff value for a piece of oil or gas drilling or recovery equipment; applying the threshold cutoff value and the window size to an equipment failure prediction model; and deriving a recall value and an average hour-loss value from the equipment failure prediction model. Predictive maintenance for the piece of oil or gas drilling or recovery equipment may be performed based on the recall value and the average hour-loss value to perform predictive maintenance for a piece of equipment in an oil or gas recovery operation.

Abstract: A system and method are disclosed for bottom-up modeling and prediction of asset anomalies. In one embodiment, the system receives first sensor data from a first sensor, the first sensor associated with a first asset; smooths the first sensor data; determines a first stage within sensor data, the sensor data including the first sensor data; determines a first set of anomalies within the first stage; generates a first asset state space associated with the first asset; trains a base model describing a first group of one or more assets, the first group of one or more assets including the first asset; and trains, using the base model, a final model particular to the first asset.

Abstract: A demand prediction method using a computer includes calculating an error between a prediction value of an amount of a past demand and a measurement value of the amount of the past demand, the prediction value of the amount of the past demand being calculated by inputting a measurement value of a past explanatory variable to a prediction model that is constructed in accordance with a measurement value of an amount of a demand at a predetermined location and an explanatory variable that serves as an external factor that affects an increase or a decrease in the amount of the demand at the predetermined location, determining whether the calculated error is an abnormal value, acquiring a new explanatory variable if the error is determined to be the abnormal value, updating the prediction model in accordance with the acquired new explanatory variable, and newly predicting an amount of a future demand using the updated prediction model.

Abstract: Techniques for tuning a machine learning algorithm using automatically determined optimal hyperparameters are described. An exemplary method includes receiving a request to determine a search space for at least one hyperparameter of a machine learning algorithm; determining, according to the request, optimal hyperparameter values from the search space for at least the one hyperparameter of the machine learning algorithm based on an evaluation of hyperparameters from the same machine learning algorithm on different datasets; and tuning the machine learning algorithm using the determined optimal hyperparameter values for the at least one hyperparameter of the machine learning algorithm to generate a machine learning model.

Abstract: The serving of advertisements with (e.g., on) video documents may be improved in a number of ways. For example, a system may (a) accept information defining at least one ad spot associated with at least one instance of an video document, (b) accept offers to have advertisements served in the ad spot(s), and (c) arbitrate among competing advertisements, using at least the offers, to determine at least one advertisement to be served in that ad spot(s). As another example, a system may (a) accept relevance information for an advertisement, (b) determine at least one video document using the accepted relevance information, (c) present information about the video document(s) to an advertiser associated with the advertisement, and (d) accept, from the advertiser, an offer to have its advertisement served with at least one of the video document(s) accepted.

Abstract: A flash memory system may include a flash memory and a circuit for decoding a result of a read operation on the flash memory using a first codeword. The circuit may be configured to generate an estimated codeword based on a result of hard decoding the first codeword and a result of hard decoding a second codeword. The circuit may be further configured to generate soft information based on the hard decoding result of the first codeword and the estimated codeword. The circuit may be further configured to decode the result of the read operation on the flash memory using the soft information.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to estimate an audience population.

Abstract: Systems and methods for unsupervised training and evaluation of anomaly detection models are described. In some embodiments, an unsupervised process comprises generating an approximation of a data distribution for a training dataset including varying values for a metric of a computing resource. The process further determines, based on the size of the training dataset, a first quantile probability and a second quantile probability that represent an interval for covering a prescribed proportion of values for the metric within a prescribed confidence level. The process further trains a lower limit of the anomaly detection model using a first quantile that represents the first quantile probability in the approximation of the data distribution and an upper limit using a second quantile that represents the second quantile probability in the approximation. The trained upper and lower limits may be used to monitor input data for anomalous behavior and, if detected, trigger responsive action(s).