Abstract: According to some embodiments, an available algorithm data store may contain information about a pool of available algorithms. An algorithm selection platform coupled to the available algorithm data store may access the information about the pool of available algorithms and compare the information about each of the pool of available algorithms with at least one requirement associated with the current algorithm executing in the real environment. The algorithm selection platform may then automatically determine algorithm execution context information and, based on said comparison and the algorithm execution context information, select at least one of the pool of available algorithms as a potential replacement algorithm. An indication of the selected at least one potential replacement algorithm may then be transmitted (e.g., to be evaluated in a shadow environment by an algorithm evaluation platform).

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a disk controller of a plurality of disks of a data storage system, collecting usage telemetry from the plurality of disks and initiating determination of one or more candidate time slots for performing a battery learn cycle using a machine learning model. The machine learning model is trained using the usage telemetry from the plurality of disks. The method also includes, by the disk controller, presenting one or more of the one or more candidate time slots for performing a battery learn cycle.

Abstract: A system for retrieving patient information using large language models including a computing device configured to receive a natural language query as a function of a user input, input the natural language query into a large language model communicatively connected to the least a processor, receive a computer language query comprising a plurality of nodes from the large language model, map the plurality of nodes to one or more entries in a patient database, receive a database response from the patient database as a function of the mapping, generate a final database query as a function of the database response. query the patient database using the final database query, receive a user response as a function of the final database query, and transmit the user response to a graphical user interface as a function of the final database query.

Abstract: Systems and methods are provided to improve traditional chip processing. Using crossbar computations, the convolution layer can be flattened into vectors, and the vectors can be grouped into a matrix where each row or column is a flattened filter. Each submatrix of the input corresponding to a position of a convolution window is also flattened into a vector. The convolution is computed as the dot product of each input vector and the filter matrix. Using intra-crossbar computations, the unused space of the crossbars is used to store replicas of the filters matrices and the unused space in XIN is used to store more elements of the input. In inter-crossbar computations, the unused crossbars are used to store replicas of the filters matrices and the unused XINs are used to store more elements of the input. Then, the method performs multiple convolution iterations in a single step.

Abstract: According to an aspect of an embodiment, a method may include obtaining multiple sets of camera images and light detection and ranging (LIDAR) point clouds along a track within a geographic sector of a map. The method may include applying a learning model to the camera images to characterize objects within the camera images within classes of objects to generate segmented images. The method may additionally include mapping the sets of camera images and the LIDAR point clouds to three dimensional points of the geographic sector of the map. The method may also include projecting the three dimensional points onto the segmented images to obtain corresponding classes for the three dimensional points of the geographic sector of the map.

Abstract: Embodiments of the present invention analyze multiple factors—such as user input events, device motion data, other data from the endpoint, or data from an external system (such as a real-time location system)—to make a probabilistic determination whether a walkaway event has occurred.

Abstract: Systems and methods for exchanging synchronization information between processing units using a synchronization network are disclosed. The disclosed systems and methods include a device including a host and associated neural processing units. Each of the neural processing units can include a command communication module and a synchronization communication module. The command communication module can include circuitry for communicating with the host device over a host network. The synchronization communication module can include circuitry enabling communication between neural processing units over a synchronization network. The neural processing units can be configured to each obtain a synchronized update for a machine learning model. This synchronized update can be obtained at least in part by exchanging synchronization information using the synchronization network. The neural processing units can each maintain a version of the machine learning model and can synchronize it using the synchronized update.

Abstract: A medical image processing apparatus comprising processing circuitry configured to: receive medical image data including a region of interest; generate first rendering data by rendering the medical image data based on a first rendering condition; receive a characteristic value relating to detection certainty with respect to the region of interest by inputting the first rendering data to a machine learning model; determine a property of the first rendering condition based on the characteristic value.

Abstract: System and method for facilitating engineering of an industrial system (250) are disclosed.

Abstract: A determination is made that a mobile device is associated with a reference activity of a user based on motion, orientation, rotational, magnetic field, and/or location data provided by sensors of the device. Activity data associated with the reference activity is obtained from the sensor-provided data. The activity data is recorded on the device for a configured period of time after which it is determined that the device is no longer performing the reference activity. The retained activity data for the reference activity is sent from the device to a network-based behavior analyzer when a network connection is available from the device. The network-based behavior analyzer derives user behaviors for the reference activity based on the activity data.

Abstract: Rather than randomly selecting neural networks to classify a media file, the conductor can determine which neural network engines (from the conductor ecosystem of neural networks) are the best candidates to classify a particular portion/segment of the media file (e.g., audio file, image file, video files). The best candidate neural network engine(s) can depend on the nature of the input media and the characteristics of the neural network engines. In object recognition and identification, certain neural networks can classify vehicles better than others, while another group of neural networks can classify structures better. The conductor can take out the guess work and construct in real-time an inter-classifier neural network using one or more layers selected from one or more pre-trained neural network, based on attribute(s) of the media file, to classify the media file.

Abstract: Computer systems and computer-implemented methods train a neural network iteratively training, through machine learning. The iterative training comprises imposing a first is-not-equal-to regularization link between first and second nodes, where imposing the first is-not-equal-to regularization link between the two nodes comprises adding, during back-propagation of partial derivatives through the neural network for a datum in a training data set, a first regularization cost to a network error loss function for the first node that is inversely proportional to a difference between an activation value for the first node for the datum and an activation value for the second node for the datum.

Abstract: Provided herein are systems, methods and computer readable media for managing a sales pipeline, and in some embodiments, calculating supply based on travel propensity. An example method comprises identifying a total demand for a promotion tuple at a geographic location, determining, using a processor, a promotion area for the promotion tuple, the promotion tuple comprising at least a category, price information and a geographic area, identifying one or more promotions offered by a promotion and marketing service that comprise at least the category and the price information of the promotion tuple, determining whether the total demand for the promotion tuple at the geographic location is satisfied, wherein the total demand is satisfied in an instance in which the geographic location is within the promotion area for the one or more promotions, and identifying a demand gap in an instance in which the total demand for the promotion tuple at the geographic location is not satisfied.

Abstract: A system and method include receiving a first set of variables associated with a real-time request, extracting a predetermined subset of the first set of variables for generating a second set of variables, identifying historical request data, computing a set of parameters based on the first set of variables and the historical request data, generating a plurality of numeric sequences and a plurality of string sequences for the real-time request, converting each of the plurality of string sequences into an encoded string sequence to obtain a plurality of encoded string sequences, inputting the plurality of numeric sequences and the plurality of encoded string sequences into a trained deep machine learning model, and computing a score from the trained deep machine learning model, the score indicative of a likelihood that the real-time request belongs to an unauthorized classification.

Abstract: Systems and methods for cooling a computer environment are disclosed. In at least one embodiment, one or more neural networks can be used to determine one or more temperature control settings associated with one or more servers.

Abstract: Techniques for improving system performance based on data characteristics are disclosed. A system may receive updates to a first data set at a first frequency. The system selects a first storage configuration, from a plurality of storage configurations, for storing the first data set based on the first frequency, and stores the first data set in accordance with the first storage configuration. The system may further receive updates to a second data set at a second frequency. The system selects a second storage configuration, from the plurality of storage configurations, for storing the second data set based on the second frequency, and stores the second data set in accordance with the second storage configuration. The second storage configuration is different than the first storage configuration.

Abstract: An approach is presented herein to use an in-situ algorithmic decision methodology during each stage of testing before 2C/4C to decide how long to test, how much margin should be used for each device under the test (DUT) to shorten or eliminate 2C/4C testing. Each DUT will be tested differently based on the risk level or the likelihood of failure at 2C/4C. To be able to achieve this, low-level hardware (HW) based sensors (on the printed circuit board assembly (PCBA), in power module, in silicon components, in silicon component complex, etc.) are used to collect telemetry data with a high frequency data acquisition rate during the testing. As testing is ongoing for each DUT, a margin distribution and algorithm modeling is performed in-situ.

Abstract: A need to retrain a machine learning model is determined based on identification of either a new scenario (i.e., new grouping/population of data variables) or a modification to an existing/trained scenario (i.e., addition and/or deletion of data variables in an already trained scenario). Change occurring across all of the data variables used in training the machine learning model are monitored as opposed to monitoring only the data variables on an individual basis. When concurrent change occurs across multiple inter-related data variables, the need to add or modify a scenario (i.e., retrain a machine learning model) is identified.

Abstract: A method for providing an optimal path through a computer network includes predicting a subsequent action of a target user through an optimal path prediction model that is trained in a form of a graph representing a user action trajectory of a session unit and recommending a path of the predicted action as an optimal path. The recommendation includes identifying a user intent based on a previous action trajectory of the target user in a current session, and guiding a path corresponding to the user intent as one of the optimal paths.

Abstract: A resource scheduling method and system, and a computer-readable storage medium are provided. The method comprises: a control instruction inputted by a user (S101) is acquired; on the basis of the control instruction, an intended device (S102) is determined; in the case where the intended device is not able to be started, with regard to each function of the intended device, a constructed knowledge graph is queried for alternative devices associated with the function, the knowledge graph having a plurality of functions stored therein as well as devices associated with the plurality of functions (S103, S105); and a device to be started is determined from all the alternative devices and starting the determined device (S106). In the resource scheduling method, in the case where an intended device specified by a user is not able to be started, alternative devices are appropriately determined.

Abstract: A machine learning model is sequentially fine-tuned with new training data as the training data becomes available. By using a suitable mix of old and new data, and weighting samples in the training data by age, the model can be efficiently updated to maintain accuracy against a changing malware landscape without manual modifications of the network layers or the computational expense of full retraining.

Abstract: In one example described herein a system can receive, by a gate analysis service, a quantum assembly language (QASM) file. The QASM file can define a quantum algorithm that can include logic gates that can be executed on a quantum computer system. The system can access, by the gate analysis service, a data repository that can include an estimated amount of quantum decoherence associated with each logic gate of a plurality of logic gates that includes the logic gates. The system can determine, by the gate analysis service, a prediction of an amount of quantum decoherence associated with executing at least one logic gate of the logic gates on the quantum computer system. Additionally, the system can adjust, by the gate analysis service, the QASM file to modify the prediction associated with executing the logic gates on the quantum computer system.

Abstract: Methods, systems and apparatus for estimating an expectation value of a quantum mechanical observable. In one aspect, a method includes identifying a first operator associated with the observable, wherein the first operator comprises a linear combination of terms. One or more constraints on expectation values of one or more of the terms in the linear combination are determined. A second operator is defined, wherein the second operator comprises a combination of the first operator and one or more of the determined constraints. The expectation value of the quantum mechanical observable is estimated using the second operator.

Abstract: A database node selects one of a number of database mirror hosts to perform a database read query. In theory, mirror hosts store redundant database entries. In practice, some mirror hosts fail to receive or retain some entries, e.g. if a mirror host was down for maintenance, a mirror host was overburdened, etc. The health of a mirror host, quantified as a health score, represents a likelihood that an insert statement was received and retained. Health scores are generated based on health metrics—data periodically retrieved from mirror hosts used to infer how well the database was operating, e.g. a count of entries that were inserted, a count of errors encountered while the entries were inserted, etc. If the database read query specifies entries inserted during a specific time period, the mirror host may be selected based on the health scores representing that time period.

Abstract: Various embodiments of the present disclosure provide computer interpretation techniques for implementing a query resolution process to improve upon traditional search resolutions within a search domain. The techniques may include generating a plurality of interaction embeddings for a plurality of textual descriptions corresponding to a plurality of interaction codes identified within an interaction dataset and a plurality of taxonomy embeddings for a plurality of taxonomy categories identified within a taxonomy dataset. The techniques may include generating similarity scores for a plurality of description-category pairs based on a comparison between the plurality of interaction embeddings and the plurality of taxonomy embeddings.

Abstract: A non-transitory computer readable medium includes a set of instructions that in operation cause a processor to determine at least one modelled parameter of a feature of interest in petroleum exploration. The instructions also cause a processor to assign a likelihood value to each modelled parameter of the at least one modelled parameter and to generate an initial chance map for each modelled parameter of the at least one modelled parameter. Further, the instructions cause a processor to assign a weighting factor for each modelled parameter of the at least one modelled parameter, and to combine the initial chance maps using the weighting factor for each modelled parameter of the at least one modelled parameter to generate a first simulation chance map.

Abstract: A method and an apparatus for a messaging service are disclosed. A method of operating a terminal on which an application for a messaging service is installed includes detecting a keyword from a text input through a message input window, cumulatively displaying the detected keyword on a suggest area, setting information about a link to a selected keyword in the text, and separately displaying the selected keyword from a non-selected keyword on the suggest area.

Abstract: A controller is provided for building equipment including a plurality of devices that operate in parallel to affect an environmental condition of a building. The controller includes one or more processing circuits including one or more processors and memory. The memory store instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations include lowering an upper bound or raising a lower bound of one or more constraints based on a minimum off schedule or a minimum on schedule for the building equipment, performing an optimization of an objective function subject to the one or more constraints to generate control decisions for the building equipment, and operating the building equipment in accordance with the control decisions to affect the environmental condition of the building.

Abstract: Methods and systems for fault identification and mitigation in an engine system. A state observer obtains current state information from the engine system, and a feature calculator uses data obtained from the state observer to calculate one or more feature indicators, which are monitored by a health estimator for the occurrence of a change using one or more change probability models. When the health estimator identifies a change, a fault isolator determines a component of the engine system that is subject to fault or health deterioration.

Abstract: Methods, systems, and devices for wind turbine control based on reinforcement learning are disclosed. The method comprises receiving data indicative of a current environmental state of the wind turbine, determining one or more controlling actions of the wind turbine based on the current environmental state of the wind turbine and a reinforcement learning algorithm, and applying the determined one or more controlling actions to the wind turbine.

Abstract: Mechanisms are provided for performing an automated machine learning (AutoML) operation to configure parameters of a machine learning model. AutoML logic is configured based on an initial parameter sampling configuration for sampling values of parameter(s) of the machine learning (ML) model. An initial AutoML process is executed on the ML model based on a dataset utilizing the initially configured AutoML logic, to generate at least one learned value for the parameter(s) of the ML model. The dataset is analyzed to extract a set of dataset characteristics that define properties of a format and/or a content of the dataset which are stored in association with the at least one learned value as part of a training dataset. A ML prediction model is trained based on the training dataset to predict, for new datasets, corresponding new sampling configuration information based on characteristics of the new datasets.

Abstract: A method for predicting the intention of a user through an image acquired by capturing the user includes: receiving an image acquired by capturing a user; and predicting the intention of the user for the next motion by using spatial information and temporal information about the user and a target object included in the image.

Abstract: A method for mining an entity relationship, apparatus, electronic device, and a storage medium are provided. An implementation of the method may include: searching, in a query statement set, for a query statement satisfying a preset condition corresponding to a target entity word; in response to at least one query statement satisfying the preset condition corresponding to the target entity word being found in the query statement set, mining an associated entity word corresponding to the target entity word based on the found at least one query statement satisfying the preset condition corresponding to the target entity word.

Abstract: A method and system architecture for designing a compressive sensing matrix for machine learning includes receiving an image associated with a classification task and; generating a sensing matrix. The sensing matrix includes an array of nonzero elements of the image. A prism array of prism elements is in communication with the sensing matrix. A row of values corresponding with an input angle of the prism array is mapped to a respective column corresponding with a detector. Then the detector detects light refracted at an output angle dictated by the physical shape of the prism element. A physical model of the detector is fabricated and generates a compressed representation of the image. A machine learning classification algorithm is applied to the compressed representation of the image and generates an optimized non-invertible final determination of the image.

Abstract: Techniques for adapting previously-annotated training examples into updated training examples for training a machine learning model are disclosed. One example includes a computer program that recognizes a find expression, a replacement expression, and a filtering constraint in which the filtering constraint distinguishes a subset of previously-annotated training examples from others of the previously-annotated training examples. An instance of the find expression is identified by the computer program within the subset of the previously-annotated training examples that were identified among the previously-annotated training examples based on the filtering constraint. The instance of the find expression identified within the subset of the previously-annotated training examples is replaced by the computer program with an instance of the replacement expression to obtain an updated subset of training examples.

Abstract: Embodiments of the invention are directed to a system, method, and computer program product for processing data analytics within memory. The system first receives a request for data associated with a database. The system utilizes a feature catalog and a deep learning neural network to determine the data that comprises the one or more desired features. The system can also provide the requested data to a computing device.

Abstract: An apparatus includes a memory and a processor. The memory stores first and second machine learning algorithms. The processor receives, from a user, voice signals associated with an information request and converts them into text. The processor uses the first machine learning algorithm to determine, based on the text, to automatically generate a reply to the request, rather than transmitting the request to an agent. The processor uses the second machine learning algorithm to generate, based on the set of text, the reply, which it transmits to the user. The processor receives feedback associated with the reply, indicating that the reply does or does not include the requested information. The processor uses the feedback to update either or both machine learning algorithms.

Abstract: A device, such as an autonomous mobile device (AMD), includes sensors such as a camera. Multiple applications executing on the device may concurrently use sensor data from a single shared sensor. For example, a first application may use image data to localize the AMD while a second application uses the image data to recognize users. The first application and the second application may have different parameters for operation of the sensor or resulting sensor data, such as different image resolutions and frame rates. An arbitrator system manages these different parameters, and may also provide sensor data to the respective application that is consistent with the application's parameters. For example, different resolutions of image data may be provided to different applications based on their parameters.

Abstract: A software update manager is configured to collect logs, container statistics, product configuration information, and system state from an application that is being modified when an issue is encountered during a software modification operation. The software update manager includes an issue analyzer that is configured to analyze the reported issue and search knowledge base article database(s) to determine if the reported issue is a known issue or a previously unknown issue. When the reported issue is a known issue, the software update manager retrieves a known workaround and applies the known workaround on the system to resolve the reported issue and continue the planned software modification operations. When the reported issue is a previously unknown issue, the software update manager identifies the correct engineering team responsible for responding to the reported issue and generates a bug fix request that is submitted to a software bug tracking system.

Abstract: By subjecting a neural network to machine learning using teacher data consisting of learning data and correct answer data, a trained neural network that outputs at least one of a composition of a target subject or a body thickness of the target subject in a case in which at least one target radiation image of the target subject is input is constructed. The learning data includes a standard image acquired by irradiating a standard object of which a thickness and a material are known with radiation in a state in which an object is interposed between the standard object and a radiation detector, an energy characteristic of the radiation, a thickness and a material of the object, and an imaging condition in a case in which the standard image is acquired.

Abstract: A computing device learns a best topological order vector of a plurality of variables. A target variable and zero or more input variables are defined. (A) A machine learning model is trained with observation vectors using the target variable and the zero or more input variables. (B) The machine learning model is executed to compute an equation loss value. (C) The equation loss value is stored with the identifier. (D) The identifier is incremented. (E) (A) through (D) are repeated a plurality of times. (F) A topological order vector is defined. (G) A loss value is computed from a subset of the stored equation loss values based on the topological order vector. (F) through (G) are repeated for each unique permutation of the topological order vector. A best topological order vector is determined based on a comparison between the loss value computed for each topological order vector in (G).

Abstract: A word completion method and apparatus are described. The method is applied to a search scenario and is used to complete an incomplete word entered by a user. The method is based on an improved trie. Hot words are stored in some nodes of the trie. In the word completion method, a target node that matches a character string is searched in the trie, and at least one completed word is output to a user based on a hot word stored in the target node. Word completion efficiency is improved, and a case in which a word is recommended to a user when the user enters an excessively short character string is avoided.

Abstract: A method is provided that includes receiving content data captured by a sensor and receiving a context signal representing a user context. The received content data is scaled using a trained model, wherein the context signal is an input to the trained model, and the scaled content data is provided for presentation to a user.

Abstract: An apparatus, computer program product, and method are provided for predictive recommendations of device rendered objects using one or more objective models. In the context of a method, the method generates, for each device rendered object of a plurality of device rendered objects, a multiple objective score. The method also determines a real-time adjustment factor based in part on a received objective adjustment signal and generates, for each device rendered object of the plurality of device rendered objects, an adjusted multiple objective score based on the real-time adjustment factor. The method also generate a ranked device rendered object set, selects a subset of the ranked rendered object set, and transmits the ranked device rendered object subset to a client device associated with the user object of the user object objective, the subset configured for rendering within the user interface area of the client device.

Abstract: The invention provides systems and method for generating device-specific artificial neural network (ANN) models for distribution across user devices. Sample datasets are collected from devices in a particular environment or use case and include predictions by device-specific ANN models executing the user devices. The received datasets are used with existing datasets and stored ANN models to generate updated device-specific ANN models from each of the stored instances of the device ANN models based on the training data.

Abstract: A method of generating a controller for a continuous process. The method includes receiving from a storage memory off-line stored values of one or more controlled variables and one or more manipulated variables of the continuous process over a plurality of time points. The off-line stored values are used to train a first neural network to operate as a predictor of the controlled variables. Then, the method includes training a second neural network to operate as a controller of the continuous process using the first neural network after it was trained to operate as the predictor for the continuous process and employing the second neural network as a controller of the continuous process.

Abstract: A server system may receive two or more Quality of Service (QoS) dimensions for the multi-objective optimization model, wherein the two or more QoS dimensions include at least a first QoS dimension and a second QoS dimension. The server system may maximize the multi-objective optimization model along the first QoS dimension, wherein the maximizing includes selecting one or more pipelines for the multi-objective optimization model in the software architecture that meet QoS expectations specified for the first QoS dimension and the second QoS dimension, wherein an ordering of the pipelines is dependent on which QoS dimensions were optimized and de-optimized and to what extent, wherein the multi-objective optimization model is partially de-optimized along the second QoS dimension in order to comply with the QoS expectations for the first QoS dimension, and whereby there is a tradeoff between the first QoS dimension and the second QoS dimension.

Abstract: Provided are a method of controlling a mobile robot, apparatus for supporting the method, and delivery system using the mobile robot. The method, which is performed by a control apparatus, comprises acquiring a first control value for the mobile robot, which is input through a remote control apparatus, acquiring a second control value for the mobile robot, which is generated by an autonomous driving module, determining a weight for each control value based on a delay between the mobile robot and the remote control apparatus and generating a target control value of the mobile robot in combination of the first control value and the second control value based on the determined weights, wherein a first weight for the first control value and a second weight for the second control value are inversely proportional to each other.

Abstract: A method and a system for maintaining network integrity for incrementally training machine learning (ML) models at edge devices is provided. The method includes registering, by a certifying node, one or more edge devices with a peer to peer network. Upon registration, an incrementally updated ML model is received from a first registered device at the certifying node. The certifying node accepts the incrementally updated ML model if a contribution of the first edge device is within a predetermined threshold, and else rejects the updated ML model if the contribution is beyond the predetermined threshold. Limiting the contribution by each edge device enables prevention of skew by any of the edge devices at the certifying node. Upon accepting the updated ML model, the certifying node certifies the updated ML model and transfers the certified ML model to one or more other edge devices in the peer to peer network.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, modifying queries based on non-textual content. In one aspect, a method includes receiving, from a user device, a query including a plurality of terms; determining active non-textual data displayed in an application environment on the user device; determining, from the non-textual textual data, modification data for the query; generating a set of modified queries based on the query and the modification parameters; scoring the modified queries according to one or more scoring criteria; selecting one of the modified queries based on the scoring; and providing, to the user device, search results responsive to the selected modified query.