Abstract: Evaluating risk of sensitive data associated with a target data set includes a computer system receiving a pattern that defines sensitive data and a selection of a data set as the target data set for evaluating. The system determines portions of the target data set from which to select sample data sets and determines, responsive to a confidence limit and sizes of the respective portions of the target data, a size of a sample data set for each respective target data set portion. The system randomly samples the target data set portions to provide sample data sets of the determined sample data set sizes and determines whether there is an occurrence of the sensitive data in each sample data set by searching for the pattern in the sample data sets. The system determines a proportion of the sample data sets that have the occurrence of the sensitive data.

Abstract: The present invention provides a method, computer program product, and system of generating prioritized list. In an embodiment, the method, computer program product, and system include receiving, by a computer system, target user identification data identifying a target user, target action data, social network content for the one or more users, and social network activity data for the one or more users, analyzing, by a computer system, social network links between the source user and the target user and the social network activity data for the one or more users, determining, by a computer system, a prioritized list of probabilistic action paths that could result in the target user performing the target action on the content based on the analyzing, and outputting the prioritized list to the source user.

Abstract: Existing methods for analyzing person-specific ‘connectomes’ are not computationally equipped for scalable, flexible, and integrated processing across multiple network resolutions and drawing from disparate data modalities-a major roadblock to utilizing ensembles and hierarchies of connectomes to solve person-specific machine-learning problems. The processes implemented in software described herein consists of an end-to-end pipeline for deploying ensembles and hierarchies of network-generating workflows that can utilize multimodal, person-specific data to sample networks, extracted from that data, across a grid of network-defining hyperparameters. In essence, this pipeline enables users to perform ensemble sampling of connectomes for given individual(s) based on any input phenotypic datatype, constructed from any data modality or hierarchy of modalities at any scale, and based on any set of network-defining hyperparameters.

Abstract: A set of first results from a first query are received. Using a natural language processing technique, a set of second results using a second query having at least a portion of the first query and at least a portion of set of first results are generated, wherein the natural language processing technique comprises a machine learning model configured to analyze the set of first results. The set of second results are scored based upon a semantic comparison of the set of first results, the second query, and the set of second results. Using the scored set of the second results, a set of processed results are generated.

Abstract: Aspects of the present disclosure involve systems, methods, devices, and the like for presenting a recommendation. In one embodiment, a system is introduced that includes a plurality of models for obtaining a recommendation score. The recommendation score may be obtained using one or more recommendation models and a recommendation made based on the recommendation score determined. In another embodiment, the system is introduced that can re-train the recommendation model based on a feedback received in response to a recommendation made using on the recommendation score obtained.

Abstract: Embodiments described herein disclose methods and systems for using more than one Recurrent Neural Network (RNN) to analyze a user input and to predict a request being made by the user as the user is inputting the request. In an embodiment, a first RNN and a second RNN can simultaneously or near simultaneously process the user requested information by separating and analyzing the characters and words in the user's request. A third RNN can process the output vectors generated by the first and second RNNs to identify one or more solutions that predict the user's request.

Abstract: An audio playback device operation method is provided. First audio playback device information corresponding to a first transmission bus format is selected to respond to a host according to a status descriptor request thereof. Whether an audio class-specific request command is received from the host is determined. When the audio class-specific request command is received, the response to the status descriptor request from the host is maintained. When the audio control feature unit descriptor request command is not received, the host is determined to not support the first transmission bus format. Second audio playback device information corresponding to a second audio bus transmission format is selected to response to the host according to the status descriptor request of the host such that when the audio class-specific request command is received, the response to the status descriptor request from the host is maintained.

Abstract: The disclosed embodiments provide for identification of a remedial action based on analysis of a system log file. In some example embodiments, messages from the system log file are used as input to generate vectors within a vector space. Portions of the log messages may generate vectors that cluster into a region in the vector space. The region of vector space is associated with one or more remedial actions. The disclosed embodiments are configured, in some example embodiments, to perform the one or more remedial actions when activity in the log file maps to the region of vector space associated with the one or more remedial actions. In some example embodiments, a remedial action can include submitting a problem report to a problem tracking database.

Abstract: An attachment to an e-mail message is replaced with a URL before that message is delivered to an end user, thus providing more time to perform a better scan at a cloud server computer. The attachment is removed from the e-mail message and sent to the cloud server computer for a dynamic scan and a static scan which will likely include updates better able to detect malicious software. The e-mail message with the URL is delivered to the end user and there is a delay before the end user reads the message or attempts to open the attachment. An artificial delay may be introduced at an e-mail gateway before the message is delivered to the end-user. If the attachment is benign then the end user is allowed to download it via the URL; if the attachment is malicious then the end user is only given a warning message.

Abstract: Crowdsourcing using active learning is described, for example, to select pairs of tasks and groups of workers so that information gained about answers to the tasks in the pool is optimized. In various examples a machine learning system learns variables describing characteristics of communities of workers, characteristics of workers, task variables and uncertainty of these variables. In various examples, the machine learning system predicts task variables and uncertainty of the predicted task variables for possible combinations of communities of workers and tasks. In examples the predicted variables and uncertainty are used to calculate expected information gain of the possible combinations and to rank the possible combinations. In examples, the crowdsourcing system uses the expected information gain to allocate tasks to worker communities and observe the results; the results may then be used to update the machine learning system.

Abstract: An input data set with a plurality of item descriptors comprising respective time series observations is identified. A routing directive indicating a predicate to be evaluated to determine whether a particular item descriptor is to be included in a training data set for a first learning algorithm is obtained. A plurality of learning algorithms are trained using training data sets derived from the input data set according to respective routing directives, and the trained algorithms are stored.

Abstract: Machine learning vision systems rely on very large numbers of training images to learn to recognize particular shapes and configurations of shapes. Traditionally, such datasets of training images needed to be selected and tagged (or labelled) manually. To recognize a particular object, such as a dog or vehicle, under realistic settings with an acceptable degree of reliability, may require data sets of thousands of images per object class. To improve this, a method is provided to generate datasets with a multiplicity of corresponding images are generated using a 3D rendering engine using a plurality of lighting arrangements and a plurality of views. Artefacts may also be introduced. In this way, very large data sets become feasible, with a variable degree of correspondence in each data set.

Abstract: A memory device includes an array of composite memory units. At least one of the composite memory units comprises a first memory cell of a first type, a second memory cell of a second type, a first intra-unit data path connecting the first memory cell to the second memory cell, and a first data path control switch. The first data path control switch is responsive to a data transfer enable signal which enables data transfer between the first memory cell and the second memory cell through the first intra-unit data path.

Abstract: A computer implemented method and system of presenting content on a display of a computing device is provided. Historical data including data of a plurality of customers and data of a plurality of products is received. A hybrid graph is created. The hybrid graph includes one or more customer nodes and product nodes. Between each two customers, a customer weight factor is applied. Between each two products, a product weight factor is applied. One or more products related to a seed product are identified. For each identified related product, a return affinity score towards the requestor customer is determined. A representation of the related products is sent to be displayed on the computing device of the requestor customer, based on the affinity score.

Abstract: The present disclosure relates to an information processing method and an information processing apparatus. The information processing method according to the present disclosure performs training on a classification model by using a plurality of training samples, and comprises the steps of: adjusting a distribution of feature vectors of the plurality of training samples in a feature space based on a typical sample in the plurality of training samples; and performing training on the classification model by using the adjusted feature vectors of the plurality of training samples. Through the technology according to the present disclosure, it is possible to perform pre-adjustment on training samples before training, such that it is possible to reduce discrimination between training samples belonging to a same class and increase discrimination between training samples belonging to different classes in the training process.

Abstract: A device may receive contextual data, computational data, experiential data, and industry data associated with software code, and may receive detected vulnerabilities data identified by a scanning model based on the software code and software code metadata of the computational data. The device may process the contextual data, the computational data, and the experiential data, with a contextual identification model, to determine a set of rules and a set of actions, and may enrich, via an enrichment model, the industry data with the experiential data to generate enriched industry data. The device may process the software code metadata, the detected vulnerabilities data, and the enriched industry data, with a correlation model, to generate analysis data, and may process the analysis data and the set of rules, with a security model, to confirm security issues associated with the software code. The device may perform one of the set of actions based on the security issues.

Abstract: A method for detecting SQL injection interception is provided. The method includes: detecting a received SQL instruction according to a SQL syntax tree rule to determine that the received SQL instruction is a malicious instruction; and analyzing the received SQL instruction by using an analysis model to determine that the received SQL instruction is a potentially malicious instruction, in a case that the received SQL instruction is not determined to be a malicious instruction according to the SQL syntax tree rule. The method can combine the online detection and the offline analysis to intercept the invading malicious SQL commands, and can also find and supplement the loopholes of the rules through offline analysis.

Abstract: A computing server may receive master data, transaction data, and one or more existing process models of a domain. The computing server may aggregate, based on domain knowledge ontology of the domain, the master data and the transaction data to generate a fact table. For example, entries in the fact table may be identified as relevant to the target process model and include attributes and facts that are extracted from master data or transaction data. The computing server may convert the entries in the fact table into vectors. The computing server inputting vectors into one or more machine learning algorithms to generate one or more algorithm outputs. One or more algorithm outputs may correspond to one or more improved process models that are optimized compared to the existing process models. The computing server may provide the improved process model to the domain to replace one of the existing process models.

Abstract: A method and system of multi-modality classification using augmented data is disclosed. The method includes generating a pattern for each of a plurality of augmented data associated with each of a plurality of object classes, based on at least one modality associated with each of the plurality of objects classes using a Long Term Short Memory (LSTM) classifier and a Layer-wise Relevance Propagation (LRP). The method further includes classifying an input image into a first object class of the plurality of object classes based on one or more objects within the input image using a Convolution Neural Network (CNN). The method further includes re-classifying the input image into one of the first object class or a second object class of the plurality of object classes when the accuracy of classification by the CNN into the first object class is below a matching threshold.

Abstract: In one or more embodiments, one or more methods, processes, and/or systems may receive data associated with effective completion of tasks by agents and determine a positive correlation within the data between first particular feature values of feature vectors associated with the tasks and second particular feature values of feature vectors associated with the agents. A first agent associated with a feature vector that matches, within a first threshold, the second particular feature values may be selected, and a probability that the first agent will effectively complete a first task based on a feature vector associated with the first task matching, within a second threshold, the first particular feature values may be determined.

Abstract: Technologies for performing a hyper-dimensional operation in a memory of the compute device include a memory and a memory controller. The memory controller is configured to receive a query from a requestor and determine, in response to a receipt of the query, a key hyper-dimensional vector associated with the query, perform a hyper-dimensional operation to determine a reference hyper-dimensional vector associated with a value to the key. The memory controller is further configured to perform a nearest neighbor search by searching columns of a stochastic associative array of a hyper-dimensional vector table in the memory, identify a closest matching row in the stochastic associative array relative to the reference hyper-dimensional vector, wherein the closest matching row indicates a closest matching value hyper-dimensional vector, and output a value associated with the closest matching value hyper-dimensional vector.

Abstract: A model induction method for explainable artificial intelligence (XAI) may be shown and described. A model of a black-box AI may be an input to the model induction method, along with a set of sample input data. A linear or non-linear predictor function may be used to predict the output of the black-box model, producing a set of data points. The data points may be partitioned by a partitioning function, and each partition may represent one or more rules. The data may also be transformed using a number of transformation functions, such as a polynomial expansion. A local model may be fitted to the transformed function or functions. A set of rules may be interpreted from the local models and may form a white-box AI model. Linear or non-linear data may be modeled by the white-box model. Further, the white-box model may be implemented on a low-power device.

Abstract: A first input is received from at least one of a plurality of sensors. Based on the first input, a first probability associated with a first state is determined. A second input is received from at least one of the plurality of sensors. Based on the second input, a second probability associated with a second state is determined. The second state is based on a transition model and the second probability. The transition model associates the first state with the second state and indicates a likelihood of a transition from the first state to the second state. A rule is triggered to change a state of at least one network connected device based on the second state.

Abstract: An anomaly-based intrusion detection system is presented for use in vehicle networks. The intrusion detection system measures and exploits the intervals of periodic in-vehicle messages for fingerprinting electronic control units. Fingerprints are then used for constructing a baseline of clock behaviors, for example with a Recursive Least Squares algorithm. Based on the baseline, the intrusion detection system uses cumulative sum to detect any abnormal shifts in the identification errors—a clear sign of an intrusion. This approach allows quick identification of in-vehicle network intrusions with low false positive rates.

Abstract: A search term used in an online search is analyzed to determine a concept corresponding to the search term, the concept being present in an ontology related to a subject-matter domain of the search term. A set of concepts is selected from the ontology, including a related concept related to the concept in the organization of the ontology. A set of subscriber data sources is determined where a subscriber data source in the set subscribes to a concept in the set of concepts. From the set of subscriber data sources, a subset of those subscriber data sources is identified whose data is not participating in a result set corresponding to the search term. A non-participation message is sent to a subscriber data source in the subset of subscriber data sources.

Abstract: A method for modeling risk of a derogatory financial event. The method includes generating a risk profile of a small business (SMB) using a risk model based on accounting data and other third party business management application (BMA) data of the SMB. In particular, the risk model is generated using a training data set that includes accounting data and other third party BMA data of multiple SMBs over particular time periods related to historic derogatory events (e.g., to be delinquent and/or to default on a loan) of these SMBs. Generated using the risk model, the risk profile of the SMB represents the likelihood of a derogatory financial event occurring to the SMB.

Abstract: Methods, systems, and media for lot classification are disclosed. In one example, a classification system for identifying lot listings receives a description for a listing in a publication system, identifies a string in the listing, identifies a quantity word or digit in the string, and converts an identified quantity word into digit form. A normalized string is tokenized to produce tokens, the tokenizing of the normalized string including splitting the normalized string into a series of substrings using a sequence of delimiters. For each substring, an additional split is performed by separating any digit from any other adjacent character, unless that character is another digit, and maintaining an internal character order of each split substring to produce a flattened list of tokenized tokens.

Abstract: Methods for identifying visual portions of visual media files that are responsive to at least one visual media input file submitted as a search query are provided. In one aspect, a method includes generating an index of visual portions of a plurality of visual media files from a collection of media files, and receiving from a user a search query, for the collection, including at least one visual media input file. The method also includes analyzing the index to identify at least one responsive visual media file from the collection that includes a visual portion associated with a visual similarity score, to the at least one visual media input file, which exceeds a similarity threshold value, and providing, in response to the search query, an identifier of the at least one responsive visual media file for display as responsive to the search query. Systems and machine-readable media are also provided.

Abstract: An asynchronous convolutional neural network (CNN) can interpret a sequence of input data. An input value representing a sample of the sequence of input data is received by a computational unit (CU) in a layer of the asynchronous CNN. The CU calculates a dot product of the input value and a weight assigned to the CU to produce an activation value. A change detector (CD) associated with the CU detects a difference between the activation value and previous activation values. The CD determines whether the detected difference is significant, indicating that the sample of the sequence of input data includes a significant change. If the detected difference is significant, the activation value is supplied to at least one subsequent CU included in a subsequent layer of the asynchronous CNN.

Abstract: Aspects include a system, computer program production and computer-implemented method for tagging an image. An image classification engine stored in a memory of a computer device generates a plurality of tags for the image and uses the plurality of tags to generate a relevance subgraph for the image. An embedding engine embeds nodes and edges of the relevance subgraph into fixed dimension vectors of a matrix. A neural network stored in the memory determines a feature vector from the image. A processor applies the feature vector to the matrix to generate a context vector for the image. The context vector is used to tag the image.

Abstract: A system for controlling an operating condition of an operating system includes a data collector, controller and processing logic comprising a model of the operating system including sets of operation parameters and correlated operating conditions. Values of a portion of the operation parameters are retrieved and form an initial parameter set which is compared with correlated operating conditions. The processing logic determines a correlated operating condition with a corresponding probability of coincidence and for a matching operating condition with the highest probability of coincidence checks whether other matching operating conditions with a similar probability of coincidence exist.

Abstract: A system, method and program product for a query-based interface in a cognitive computing platform. A system is provided that includes a query processor that inputs a query for an identified user and determines a set of results for inclusion in a cognitive response based on a set of information sources; a modality selection system that selects an interface modality for the cognitive response based on user profile data and at least one external input; and a response formatting system that determines a response format for the set of results in the cognitive response, wherein the response format is determined based on confidence levels determined from social networking information associated with the identified user.

Abstract: A computer-implemented method includes determining first passages (FP) that provide evidence for one or more first relations (OOMFR) between first entities in a knowledge graph. The method includes generating an evidence index (EI) that associates the FP with the OOMFR and the first entities, receiving a query subsequent to generating the EI, and identifying, using the EI, the FP responsive to receiving the query. The method includes determining presentation aspects of the FP based on similarity information determined for the FP, and determining that second passages of the FP are substantially similar to at least one other passage of the FP. The method includes pruning the FP to generate a pruned set of passages (PSOP) that includes one or more third passages of the second passages and that does not include any other passages of the second passages. The method includes outputting the PSOP according to the presentation aspects.

Abstract: A method and system for autonomously operating an aircraft. The method comprises a pre-flight training step comprising: retrieving recorded surveillance data of a plurality of flights corresponding to at least one aircraft type and at least one route; inferring aircraft intent from the recorded surveillance data; computing reconstructed trajectories using the inferred aircraft intent; selecting a training dataset comprising aircraft intent and reconstructed trajectories of flights corresponding to a particular aircraft type and route; and applying a machine learning algorithm on the training dataset to obtain a mapping function between aircraft states and actions.

Abstract: The present disclosure provides a composite machine-learning system for a transaction labeling service. A transaction labeling service receives at least one descriptive string describing a transaction associated with a user. The service identifies a preliminary grouping from a generalized scheme. The service extracts a set of N-grams from the descriptive string and converts the N-grams and the preliminary grouping into a set of features. A machine-learning model determines a label from a labeling scheme for the transaction based on the features. User input related to the label includes an accuracy indicator and a reliability indicator. If the reliability indicator satisfies a reliability condition, a set of training data for the machine-learning model is updated based on the descriptive string and the label. The machine-learning model is then trained against the updated set of training data.

Abstract: In one embodiment, a method includes receiving a user input from a client system associated with a user, generating a plurality of candidate responses by a natural-language generation module in response to the user input, determining a quality-indication by a filtering module for each candidate response of the plurality of candidate responses based on one or more classification models and one or more classification rules, wherein the classification models and classification rules are based on one or more acceptance-criteria, selecting one or more candidate responses from the plurality of candidate responses based on their respective quality-indications, ranking the selected candidate responses based on one or more ranking-criteria, and sending instructions for presenting a top-ranked candidate response to the user to the client system associated with the user responsive to receiving the user input.

Abstract: An automated model development tool can be used for automatically developing a model (e.g., an analytical model). The automated model development tool can perform various automated operations for automatically developing the model including, for example, performing automated operations on variables in a data set that can be used to develop the model. The automated operations can include automatically analyzing the predictor variables. The automated operations can also include automatically binning (e.g., combining) data associated with the predictor variables to provide monotonicity between the predictor variables and one or more output variables. The automated operations can further include automatically reducing the number of predictor variables in the data set and using the reduced number of predictor variables to develop the analytical model.

Abstract: A web server based system for 24/7 managed care services to clients with Alzheimer, Autism, and assisted living is provided in the invention. The system uses intelligent digital camera with built in AI algorithms to monitor client's activities, uses voice recognition, and two way voice communication and thus requires no body wearing devices. The camera detects client's movements—such as sitting, lying, and falls—and generates appropriate alarms to a central server for a client assistance operator to act. The smarts in the camera can analyze client's vicinity to pre stored scenes, client's emotion and generate alerts to the client assistant operator. The system uses an intelligent digital client assistant device with built in smarts for speaker independent voice recognition to provide client access to entertainment, e-commerce, and other voice activated services. The system in the invention uses a drone to monitor remotely movement of the client in case it leaves premises without informing a caregiver.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, of training a global policy neural network. One of the methods includes initializing a plurality of instances of the robotic task. For each instance of the robotic task, the method includes generating a trajectory of state-action pairs by selecting actions to be performed by the robotic agent while performing the instance of the robotic task in accordance with current values of the parameters of the global policy neural network, and optimizing a local policy controller that is specific to the instance on the trajectory of state-action pairs for the instance. The method further includes generating training data for the global policy neural network using the local policy controllers, and training the global policy neural network on the training data to adjust the current values of the parameters of the global policy neural network.

Abstract: A method using machine learned, scenario based control heuristics including: providing a simulation model for predicting a system state vector of the dynamical system in time based on a current scenario parameter vector and a control vector; using a Model Predictive Control, MPC, algorithm to provide the control vector during a simulation of the dynamical system using the simulation model for different scenario parameter vectors and initial system state vectors; calculating a scenario parameter vector and initial system state vector a resulting optimal control value by the MPC algorithm; generating machine learned control heuristics approximating the relationship between the corresponding scenario parameter vector and the initial system state vector for the resulting optimal control value using a machine learning algorithm; and using the generated machine learned control heuristics to control the complex dynamical system modelled by the simulation model.

Abstract: Technologies for performing hyper-dimensional operations in memory includes a device with a memory media and a memory controller. The memory controller is configured to receive a query from a requestor and determine, in response to receiving the query, a reference hyper-dimensional vector associated with the query. The memory controller is further configured to perform a nearest neighbor search by searching columns of a stochastic associative array in the memory media to determine a number of matching bit values for each row relative to the reference hyper-dimensional vector, wherein each bit in a column of the stochastic associative array represents a bit value of a corresponding row, identify a closest matching row that has a highest number of matching bit values, and output data of the closest matching row.

Abstract: An approach is provided that trains neural network by receiving data inputs to a cloud-based system from users, gathering a sets of responsive system data from the cloud-based system, and training the neural network by passing the received data inputs and corresponding sets of responsive data through the neural network. After the training is performed, the approach receives user actions from a user of the cloud-based system, inputs the user actions to the trained neural network and receives one or more predicted next user actions from the trained neural network. Based on the predicted next user actions, the approach then performs a proactive response at the cloud-based system.

Abstract: To this end, the invention relates to a method for controlling a switched electronic circuit connecting an electrical voltage source u to a load R and forming a system having an output y and a plurality of operating modes i, at least some of which thus can be activated by following a switching rule o. According to the invention, the following steps are provided: —measuring state variables x of the system having equilibrium values xr; —introducing at least one parameter p into the system, representing a measuring error of an electrical unknown of the operation of the circuit; —estimating the parameter from a state monitor and applying a Lyapunov function thereto; —deducing therefrom the equilibrium values of the system in order to obtain the control rule.

Abstract: Systems, methods, and non-transitory computer-readable media can determine a set of features associated with a road segment based at least in part on data captured by one or more sensors of a vehicle. At least one scenario that is associated with the set of features can be determined. The at least one scenario can be associated with the road segment. The associated at least one scenario and the road segment can be maintained in a scenario information database.

Abstract: Methods and systems are provided for sequencing of input prompts for data structure completion. The method includes: providing a data structure including object classes with typed attributes and rules defining constraints to be met by a data instance of a data structure to meet a regulation body and building a data instance of the data structure by prompting user input via a user interface. The method includes: selecting a working object instance from the top of a stack of object instances; gathering applicable rules for the working object instance with missing required facts in the form of object attributes, including any related rules where a new object class is required; and sequencing input prompts in a user interface of a complex form to obtain the required facts in the form of missing object attributes.

Abstract: Systems and methods for building a response for a machine learning-based dialogue agent includes implementing machine learning classifiers that predict slot segments of the utterance data based on an input of the utterance data; predict a slot classification label for each of the slot segments of the utterance data; computing a semantic vector value for each of the slot segments of the utterance data; assessing the semantic vector value of the slot segments of the utterance data against a multi-dimensional vector space of structured categories of dialogue, wherein the assessment includes: for each of a distinct structured categories of dialogue computing a similarity metric value; selecting one structured category of dialogue from the distinct structured categories of dialogue based on the computed similarity metric value for each of distinct structured categories; and producing a response to the utterance data.

Abstract: An optimization device is applied to a production line in which multiple production devices respectively execute a production process. The optimization device includes a cycle time setting section configured to set a cycle time for each production device, a target time setting section configured to set a target time based on a cycle time and a time required for periodic operation in each of multiple production devices except for the component mounter, and a line design section configured to calculate the unit number of the component mounters constituting the production line based on the target time.

Abstract: A non-transitory computer-readable medium stores a program designed to be used for a patient attempting to quit smoking. The program causes a computer to execute: receiving report information input by the patient and indicative of the presence or absence of smoking by the patient; receiving a biomarker concentration measurement value measured from a biological sample of the patient by a biomarker concentration meter for measuring a biomarker concentration indicative of a smoking state; based on the received biomarker concentration measurement value and a biomarker concentration reference value, determining consistency between the biomarker concentration measurement value and the report information; based on a result of the consistency determination, generating smoking cessation therapy information for smoking cessation therapy to be performed for the patient; and transmitting the generated smoking cessation therapy information to a patient-side device.

Abstract: Computer systems and methods are provided for increasing a rate of simulation for quantum computing devices. A quantum computing device includes a plurality of gates, each of which is coupled to one or more modes. In the provided computer systems and methods, a device definition and state information for the quantum computing device are received. The state information includes a plurality of input patterns, each of which indicates a number of input bosons that correspond to a respective mode of the quantum computing device, and an amplitude that corresponds to each input pattern. The device definition includes a plurality of sets of gate values that indicate modification by a respective gate of an input pattern probability. A first group of input patterns is generated for a first gate. The first group of input patterns includes a subset of the plurality of input patterns that meet grouping criteria.

Abstract: Embodiments include a system for reconciling input datasets with a model ontology, the system comprising a processor coupled to a memory, to execute: a model ontology storage process, storing a model ontology graph defining a concept hierarchy, the concept hierarchy comprising nodes at various levels. The processor and memory further to execute: a user interface process, receiving, via a user interface, user input assignments of nodes from upper levels of the concept hierarchy to input datasets with particular properties from among the lower level nodes. The processor and memory further to execute an input dataset reconciliation process using the concept hierarchy and a record of historical user input assignments to determine which higher level nodes to assign to a new input dataset, based on the lower level nodes represented in the new input dataset.