Abstract: A method is provided for performing dynamic inferencing at a node configured to communicate with other nodes of an IoT hierarchy. In the method, a schema for an asset object associated with one or more of at least one physical process and at least one physical device is received at the node. The schema is formatted according to an inferencing engine model format. An artificial intelligence model capable of being executed in an inferencing engine is received at the node. Data indicative of one or more of a current state of at least one physical process and a current state of at least one physical device is received at the node. The received data according to the schema and an inferencing engine are processed at the node. The inferencing engine generates a new predictive attribute based on the set of attributes, and the processing normalizes the received data according to the schema to generate normalized data, the normalized data includes the predictive attribute from the inferencing engine.

Abstract: Included are: a feature value extracting unit that extracts a feature value of input data; a similar data classifying unit that classifies, on the basis of a first dataset including a plurality of pieces of input data and the feature value extracted by the feature value extracting unit for each of the plurality of pieces of input data included in the first dataset, a plurality of pieces of input data having similar feature values as a second dataset among the plurality of pieces of input data included in the first dataset; and a model generating unit that generates a first trained model, which is a trained model for classifying input data, using the second dataset.

Abstract: A system for predicting performance of building equipment is configured to determine whether a machine learning model is capable of generating a predicted performance of the building equipment based on sensor data obtained while operating the building equipment. The machine learning model is used to generate the predicted performance if the machine learning model is determined to be capable, whereas additional data related to the predicted performance of the building equipment are obtained if the machine learning model is determined to be not capable. The system determines whether the building equipment is in need of maintenance based on the predicted performance generated using the machine learning model and/or the additional data and automatically initiates a maintenance activity for the building equipment in response to determining that the building equipment is in need of maintenance.

Abstract: The present disclosure relates to a dataset exploration system based on input data having a plurality of data samples having a plurality of features. In particular, the systems described herein generate preprocessed input data including one or more of performing data normalization, calculating covariance matrix, and assessing data quality of the preprocessed input data. The system further generates a domain structure from the preprocessed input data. The system further includes recovering a probabilistic graphical model (PGM) trained to discover the underlying joint distribution over the plurality of features based on the preprocessed input data and the domain structure. The learned PGM may be utilized to answer user queries by leveraging its probabilistic inference capabilities on the data and various different visual outputs may be presented via a display device.

Abstract: A method of performing a probability tree analysis. The method includes identifying a plurality of nodes in a probability tree with each of the plurality of nodes having a probability vector. At least one node structural value is calculated for each of the plurality of nodes. The at least one node structural value quantifies an entropy of a subtree extending from a corresponding one of the plurality of nodes. The at least one node structural value is assigned to a corresponding one of the plurality of nodes. An analyzed probability tree is output including the at least one node structural value assigned to the corresponding one of the plurality of nodes.

Abstract: A scalable Ising machine system comprises a plurality of chips, each chip comprising a plurality of N nodes, each node comprising a capacitor, a positive terminal, and a negative terminal, a plurality of N×M connection units, arranged in N rows and M columns, each connection unit comprising a set of reconfigurable resistive connections, each connection unit configurable to connect a pair of the N nodes via the reconfigurable resistive connections, and a plurality of interconnects, wherein each chip of the plurality of chips is communicatively connected all other chips of the plurality of chips via at least one interconnect. A method of calculating a Hamiltonian of a system of coupled spins is also disclosed.

Abstract: A method includes accessing, using a computing system, data including a plurality of variables, each variable having one or more elements. The method includes determining stochastic partial differences between elements of respective variables of the plurality of variables and combining respective stochastic partial differences into groups including one or more stochastic partial difference equations (SPDEs). The method includes evaluating, using a fitness measure criterion, the one or more SPDEs in relation to an objective function. The method includes determining, by the computing system, based on the evaluating, a prediction related to at least one data input to an application executable by one of the computing system or a second computing system communicatively coupled to the computing system. The at least one data input relies, at least in part, on one or more of the plurality of variables.

Abstract: A computing system including a quantum computing device. The quantum computing device includes a Majorana island, a quantum dot (QD), an electrical ground, and a capacitance sensor. The computing system further includes a controller configured to, in each of a plurality of sampling iterations, control the quantum computing device to electrically couple the Majorana island to the electrical ground, disconnect the Majorana island from the electrical ground, electrically couple the Majorana island to the QD, scan over values of a first plunger gate voltage applied to a first plunger gate and a second plunger gate voltage applied to a second plunger gate, and output quantum capacitance measurements. The controller is further configured to receive the quantum capacitance measurements and determine a measured distribution of resonance regions associated with the sampling iterations.

Abstract: The present application discloses a quantum circuit optimization method, a device, equipment and a storage medium, which are applied to the technical field of quantum computing and include the steps of: obtaining a quantum circuit to be optimized; determining an optimization unit in the quantum circuit to be optimized, and replacing the optimization unit with a comprehensive expression to obtain a first optimized quantum circuit; the optimization unit corresponding to at least two-bit Pauli operators; replacing the comprehensive expression with an equivalent circuit according to a preset standard and circuits before and after the comprehensive expression to form a second optimized quantum circuit; the second optimized quantum circuit including an eliminable quantum gate; and performing gate elimination on the second optimized quantum circuit to obtain an optimized quantum circuit.

Abstract: One or more systems, devices and/or computer-implemented methods of use provided herein relate to single-device readout of the state of a coupler qubit and flux control of that coupler qubit, which processes can be performed simultaneously. In one or more embodiments, an electronic system comprises a coupler qubit, a pair of superconducting qubits coupled to the coupler qubit, a multiplexed input line, and a readout and flux control subsystem coupled between the multiplexed input line and the coupler qubit, wherein the readout and flux control subsystem is configured to allow for both readout of a state of the coupler qubit and flux control that adjusts a coupling between the pair of superconducting qubits. The diplexer couples together the readout resonator and a flux control portion of the electronic system, wherein the readout resonator and the flux control portion are separated from one another between the diplexer and the coupler qubit.

Abstract: Methods, systems and apparatus for dynamically decoupling and performing a target unitary operation to a qubit. In one aspect, a method includes generating a control signal that implements a dynamical decoupling control sequence and applying the control signal to the qubit to dynamically decouple the qubit and perform the target unitary operation on the qubit. The target unitary operation includes a product of multiple sub-unitary operations. The dynamical decoupling control sequence includes a plurality of single qubit gates, where one or more of the single qubit gates comprise a single qubit gate that implements one or more of sub-unitary operations of the multiple sub-unitary operations.

Abstract: The present invention relates to a quantum computing device based on manipulating Majorana Zero Mode excitations (MZMs) in topological superconducting regions formed in a tapered nanowire or similar structure. The tapering of the NW structure results in the formation of discrete regions of topological superconductivity having MZMs at their end boundaries, and thereby facilitates establishing the MZMs without delicate tuning procedures. A quantum swap gate is provided, utilizing two such structures in contact and disposed orthogonally to one another. Applying a magnetic field selectively lengthens the region of one structure while shortening the region of the other, allowing voltage and magnetic field changes to manipulate and interchange the MZM quantum states of the two structures to effect the swap operation.

Abstract: Aspects of the present disclosure are directed to fabrication methods for analogue quantum systems (AQSs). Further aspects of the present disclosure are directed to methods for solving computational problems using an AQS. Methods for fabricating an AQS include generating a Hamiltonian based on a computational problem, which may be an optimization problem or a simulation problem. Further, the method includes identifying AQS fabrication parameters based on one or more identified measurement methods and the Hamiltonian. Lastly, an AQS can be fabricated based on the identified fabrication parameters. An AQS may be used inter alia to simulate a battery or interfaces.

Abstract: A controller for a set of superconducting qubits includes a parametrically driven tunable coupler coupled to each superconducting qubit in the set of superconducting qubits comprising three or more superconducting qubits, a magnetic flux pump coupled to the parametrically driven tunable coupler, a first control line coupled to the magnetic flux pump, and a second control line coupled to the magnetic flux pump. The parametrically driven tunable coupler creates a parametric single superconducting qubit drive for a single superconducting qubit within the set of superconducting qubits or a parametric resonant interaction between a pair of superconducting qubits within the set of superconducting qubits when one or more first frequency signals on the first control line and one or more second frequency signals on the second control line satisfy a specified condition.

Abstract: A method for solving continuous-variable quantum quadratic optimization problems is disclosed. Entangled qubits encoded with an Ising model problem are coupled to a heat reservoir. The Ising model problem expresses a continuous-variable quantum quadratic optimization problem. The entangled qubits are evolved by thermal annealing, resulting in optimized solutions to the continuous-variable quantum quadratic optimization problem. A temperature of the heat reservoir is varied on a schedule such that each evolution of the entangled qubits occurs while the heat reservoir is at a desired temperature.

Abstract: Techniques are described for implementing a class of multimode bosonic codes that protect against errors within an ancilla qubit coupled to a bosonic system, that can be realized experimentally. A logical qubit state is represented by the states of multiple different modes of one or more bosonic systems, which may include multiple modes of a single bosonic system and/or single modes from multiple bosonic systems. Techniques for correcting errors are also described. In particular, a series of operations are described that autonomously detect and correct errors by repeatedly performing a sequence of operations that are each applied to the multiple bosonic modes and/or to the ancilla qubit that is coupled to each of the bosonic modes. The codes allow ancilla errors to propagate to the modes of the bosonic system as correctable errors, where they can be corrected, instead of presenting as logical errors in the ancilla qubit.

Abstract: A method includes receiving data characterizing a first output of one or more of a first set of models associated with a first organization, the first set of models trained on a first dataset using a first set of resourcing levels; training one or more of a second set of models associated with a second organization based on a second dataset using a second set of resourcing levels, global constraints and the first output, wherein the second set of resourcing levels specifying a second condition on outputs of the one or more of the second set of models; assessing, based on a second output of the one or more of the second set of models, performance of the one or more of second set of models; and retraining the first set of models or a subset thereof. Related apparatus, systems, articles, and techniques are also described.

Abstract: Multi-factor metric drift evaluation and attribution techniques are described. A drift attribution model is trained to compute, for a segment of input data that defines an observed value for a metric and observed values for each of a plurality of factors that influence the value of the metric, a contribution by each of the plurality of factors to the observed metric value. Drift observations output by the trained drift attribution model are further processed using a Shapely explainer to represent contributions of each of the metric factors, and their associated values, relative to one or more observed values of a metric during the time segment. The respective magnitude by which each metric factor affects an observed value of the metric is described in a metric drift report, which objectively quantifies respective impacts of a factor, relative to other factors that affect a metric.

Abstract: An example operation may include one or more of querying data of a merchant read from a point of sale (POS) system of the merchant and converting the data into an encoding, executing a machine learning model on the input encoding to generate a vector that comprises vectorized values corresponding to latent features of the merchant embedded within slots of the vector, respectively, generating an entry comprising an identifier of the merchant, context of the merchant, and the generated vector, and storing the entry in the feature store.

Abstract: An example operation may include one or more of receiving a query parameter input via an interface of a software application, querying the feature store based on the query parameter, wherein the querying comprises identifying one or more vectors stored in the features store that match the query parameter via execution of a query on the feature store, executing a machine learning model on the one or more vectors identified in the feature store to generate a predicted output, and displaying the predicted output via the interface of the software application.

Abstract: Aspects of the subject disclosure may include, for example, obtaining one or more information items or documents, checking the one or more information items or documents for determined sensitivity and determining role-based access for the one or more information items or documents, based on the checking and the determining, performing a preliminary analysis of the one or more information items or documents, wherein the preliminary analysis involves one or more pre-processing procedures, one or more chunking procedures, one or more embedding procedures, one or more customization procedures for particular use cases, or a combination thereof, and storing results of the preliminary analysis in a vector knowledge base for training one or more generative artificial intelligence (AI) large language models (LLMs). Other embodiments are disclosed.

Abstract: Described are systems and methods for generating a debiased training dataset and training one or more stages of a multi-stage recommendation system and/or service using the debiased training dataset. Rather than generating training datasets from the output data generated by the multi-stage recommendation system and/or data served by later stages of the multi-stage recommendation system, debiased training datasets may be generated from data that was served by the particular stage of the multi-stage recommendation system that is to be trained using the generated debiased training dataset. The debiased training dataset may be generated by generating pseudo-labels for each data record and comparing the generated pseudo-labels against two thresholds to generate a binary classification of the data records.

Abstract: Disclosed are systems and methods for determining a beta value of at least one candidate relative to a class. The method comprises receiving historical data for the at least one candidate and historical data for the class; determining a first set of regression coefficients for the historical data for the at least one candidate, and a second set of regression coefficients for the historical data for the class; and calculating the beta value for the at least one candidate relative to the class based on the first set of regression coefficients and the second sets of regression coefficients. The method may further include generating an indication that the beta value for the candidate is one of positive, negative, or zero; and transmitting to at least one display window of a graphical user interface (GUI) a graphical symbol corresponding to the indication of the beta value for the candidate.

Abstract: Provided are a method, system, and device for evaluating a machine learning (ML) model. The method may include: receiving, by a requirements management layer, at least one requirement obtained from a storage layer; interpreting, by the requirements management layer, the at least one requirement; and transmitting, by the requirements management layer, instructions to perform an ML evaluation process to an execution layer based on the interpreted requirements, wherein the execution layer transmits an output signal with the results of the ML evaluation process upon completing the ML evaluation process.

Abstract: Systems and methods of generating synthetic training data for training an AI model usable to operate a train are disclosed. A method of generating the synthetic training data includes obtaining run data corresponding to a real-world run of the train. The method includes generating a physics-based simulation of the real-world run of the train. The method includes receiving a user input modifying at least one operation command of the train in the physics-based simulation. The method includes updating the physics-based simulation based on the received user input. The method includes generating the synthetic training data for training the AI model, based on the updated physics-based simulation.

Abstract: Improved methods for semi-supervised training are provided. These methods include using a model that has been trained used ground-truth labeled training examples to predict the class of a set of unlabeled training examples. A subset of the unlabeled training examples are then added to the labeled training dataset, labeled with the model-generated labels. The added subset are balanced across the predicted classes (e.g., an equal number added from each predicted class) in order to reduce bias in the augmented training dataset toward representation of ‘easy’ classes. Confidence scores used to select which training examples to add from each predicted class could be based on a confidence output of the model. Additionally or alternatively, the confidence scores could be determined based on distance, in an embedding space of the model, between the unlabeled training examples and labeled training examples whose class label match the predicted label of the unlabeled training examples.

Abstract: In some embodiments, a computing system may generate a prediction related to a new category (not included in a set of categories) using a machine learning model trained on a set of embeddings corresponding to the category set. As an example, the computing system may generate a set of hashes such that each hash of the hash set is mapped to an embedding of the embedding set. When a new category added to the category set, the computing system may generate a given hash for the new category and identify a first hash of the hash set that matches the given hash. Based on identifying the first hash as a matching hash, the computing system may use an existing embedding (e.g., mapped to the first hash) with the machine learning model in connection with the new category, thereby avoiding a need to add a new embedding to the embedding set.

Abstract: A computerized method validates trained models in stage environments prior to deployment to production environments. A validation dataset is generated using a trained model of a first version and sampled input data as input to the trained model. The trained model of the first version is then deployed to a stage environment with the generated validation dataset. The deployed model of the first version is validated in the stage environment using the generated validation dataset and it is determined that results of the validation indicate that the trained model of the first version is invalid. Based on the invalid results, an invalidity action associated with the trained model of the first version is performed. The described method enables computationally efficient validation of accuracy and performance of trained models in stage environments, thereby reducing the likelihood that an inaccurate or underperforming model is deployed to associated production environments.

Abstract: Computer implemented methods, systems, and computer program products include program code executing on a processor(s) that obtains a planning problem and one or more bounding conditions for the solutions. The program code transforms the planning problem into a single-goal form. The program code computes stubborn sets over the single-goal form of the planning problem. The program code defines a pruned search space utilizing the single-goal form of the planning problem and the stubborn sets. The program code performs a K* search over the pruned search space. The program code obtains the set of solutions.

Abstract: According to an embodiment of the disclosure, an IR-drop prediction system includes a data frame generator configured to generate a raw data frame including IR-drop data for each of a plurality of instances in a designed circuit, a training performer configured to select input instances among the plurality of instances included in the raw data frame and perform training for an IR-drop prediction based on the IR-drop data for the input instances, and an IR-drop predictor configured to predict an IR-drop based on an IR-drop prediction model obtained according to a result of the training. The IR-drop data includes an IR-drop value of a corresponding instance and a plurality of IR-drop factors related to the IR-drop value, and the input instances have the IR-drop values greater than or equal to a preset value.

Abstract: A system and method for predicting a set of probable classes for test data is described. The method comprises retrieving, from a memory a training dataset, a plurality of classes, and a plurality of corresponding training feature vectors for each of the plurality of classes. The method comprises receiving an input indicative of a target probability required for the test data. The method comprises determining a set of membership probabilities for the test data that include a corresponding membership probability associated with each of the plurality of classes, the corresponding membership probability being indicative of a probability of the test data belonging to a corresponding class of the plurality of classes. The method comprises determining, based on the input and the set of membership probabilities, the set of probable classes, from the plurality of classes, for the test data.

Abstract: The present disclosure provides a method, an apparatus, an electronic device and a storage medium of data labeling. In some embodiments, the method comprises: receiving a data labeling request, the data labeling request including one or more labeling tasks, one or more task types, labeling data, a constraint, one or more labeling index values; determining a target labeling task based on the one or more labeling index values and the one or more labeling tasks; determining a labeling procedure based on the data labeling request and at least one selected from a group consisting of the task type, a labeling quality, and a labeling metric; and labeling the labeling data using the labeling procedure. In certain embodiments, multiple target values, including the data labeling quality, the labeling efficiency and the labeling costs, can be dynamically balanced to achieve better resource allocation and provide an individualized labeling configuration for labeling tasks.

Abstract: A method for creating a classification model includes: obtaining at least one group of initial time-series signals associated with at least one initial acquisition parameter, creating at least one group of simulated time-series signals from the at least one group of initial time-series signals, creating various test classification models, from groups of initial or simulated time-series signals, assessing the performances of each test classification model, obtaining at least one group of final time-series signals associated with at least one final acquisition parameter, and creating the classification model.

Abstract: Machine learning models can be trained to predict imaging characteristics with respect to variation in a pattern on a wafer resulting from a patterning process. However, due to low pattern coverage provided by limited wafer data used for training, machine learning models tend to overfit, and predictions from the machine learning models deviate from physical trends that characterize the pattern on the wafer and/or the patterning process with respect to the pattern variation. To enhance pattern coverage, training data is augmented with pattern data that conforms to a certain expected physical trend, and applies to new patterns not covered by previously measured wafer data.

Abstract: Systems and methods are disclosed for implementing advanced statistical models and machine-learning algorithms to generate predictions. The method includes receiving a plurality of data from one or more sources; processing the plurality of data to select one or more relevant variables; training one or more prediction models based on the one or more relevant variables, and a combination of an advanced statistical model and a machine-learning model; evaluating performance of the one or more trained prediction models based on one or more validation techniques; and deploying at least one prediction model based on the performance for generating predictions.

Abstract: Examples are disclosed that relate to methods and systems for classifying a surface type. One example provides a system comprising a wearable device comprising at least one force sensor, and a computing device having a processor and associated memory storing instructions executable by the processor. The instructions are executable by the processor to, during a training phase, receive training data including a plurality of training data pairs. Each training data pair includes force sensor training data received from the at least one force sensor, or from a simulation or observation, and a label indicating at least one of a plurality of defined surface types. An AI model is trained to predict a classified surface type based on run-time force sensor data. The run-time force sensor data is input into the trained AI model to thereby cause the AI model to output a predicted classification of a run-time surface type.

Abstract: System and method for end-to-end differentiable joint image refinement and perception are provided. A learning machine employs an image acquisition device for acquiring a set of training raw images. A processor determines a representation of a raw image, initializes a set of image representation parameters, defines a set of analysis parameters of an image analysis network configured to process the image's representation, and jointly trains the set of representation parameters and the set of analysis parameters to optimize a combined objective function. A module for transforming pixel-values of the raw image to produce a transformed image comprising pixels of variance-stabilized values, a module for successively performing processes of soft camera projection and image projection, and a module for inverse transforming the transformed pixels are disclosed. The image projection performs multi-level spatial convolution, pooling, subsampling, and interpolation.

Abstract: System and method for end-to-end differentiable joint image refinement and perception are provided. A learning machine employs an image acquisition device for acquiring a set of training raw images. A processor determines a representation of a raw image, initializes a set of image representation parameters, defines a set of analysis parameters of an image analysis network configured to process the image's representation, and jointly trains the set of representation parameters and the set of analysis parameters to optimize a combined objective function. A module for transforming pixel-values of the raw image to produce a transformed image comprising pixels of variance-stabilized values, a module for successively performing processes of soft camera projection and image projection, and a module for inverse transforming the transformed pixels are disclosed. The image projection performs multi-level spatial convolution, pooling, subsampling, and interpolation.

Abstract: System and method for end-to-end differentiable joint image refinement and perception are provided. A learning machine employs an image acquisition device for acquiring a set of training raw images. A processor determines a representation of a raw image, initializes a set of image representation parameters, defines a set of analysis parameters of an image analysis network configured to process the image's representation, and jointly trains the set of representation parameters and the set of analysis parameters to optimize a combined objective function. A module for transforming pixel-values of the raw image to produce a transformed image comprising pixels of variance-stabilized values, a module for successively performing processes of soft camera projection and image projection, and a module for inverse transforming the transformed pixels are disclosed. The image projection performs multi-level spatial convolution, pooling, subsampling, and interpolation.

Abstract: System and method for end-to-end differentiable joint image refinement and perception are provided. A learning machine employs an image acquisition device for acquiring a set of training raw images. A processor determines a representation of a raw image, initializes a set of image representation parameters, defines a set of analysis parameters of an image analysis network configured to process the image's representation, and jointly trains the set of representation parameters and the set of analysis parameters to optimize a combined objective function. A module for transforming pixel-values of the raw image to produce a transformed image comprising pixels of variance-stabilized values, a module for successively performing processes of soft camera projection and image projection, and a module for inverse transforming the transformed pixels are disclosed. The image projection performs multi-level spatial convolution, pooling, subsampling, and interpolation.

Abstract: Aspects relate to a machine learning system implementing an evolutionary boosting machine. The system may initially select randomized feature sets for an initial generation of candidate models. Evolutionary algorithms may be applied to the system to create later generations of the cycle, combining and mutating the feature selections of the candidate models. The system may determine optimal number of boosting iterations for each candidate model in a generation by building boosting iterations from an initial value up to a predetermined maximum number of boosting iterations. When a final generation is achieved, the system may evaluate the optimal model of the generation. If the optimal boosting iterations of the optimal model does not meet solution constraints on the optimal boosting iterations, the system may adjust a learning rate parameter and then proceed to the next cycle. Based on termination criteria, the system may determine a resulting/final optimal mode.

Abstract: The present disclosure is directed to systems and methods for predicting and correcting data anomalies. In one example aspect, data is received by the system. The system may analyze the data by profiling the data for certain profiling statistics (e.g., min, max, mean, cardinality, etc.). At least one machine-learning algorithm (e.g., a Random-Forest algorithm) may be applied to the profiled data to identify potential relationships among certain data columns in the data. Once certain relationships are identified, the data that is related may be extracted to form an itemset. A second machine-learning algorithm (e.g., Frequent Pattern Growth algorithm) may be applied to the itemset to identify certain frequencies of related values in the itemset. Low frequency values may indicate anomalies in the dataset. If an anomaly is detected, the system may be configured to provide an intelligent remedial action, such as substituting certain values and/or filling in a missing value.

Abstract: A zone assignment system includes a schedule obtainer that obtains usage schedules of users each indicating the intended use of a space by a user; an assigner that assigns each user a zone to use, based on the usage schedules obtained; and an outputter that outputs an assignment result indicating the zone assigned. The assigner assigns a first zone out of the zones to the user, among the users, who has a usage schedule indicating a first intended use as the intended use, and the assigner assigns a second zone out of the zones to the user, among the users, who has a usage schedule indicating a second intended use as the intended use, the second zone being a zone not adjacent to the first zone.

Abstract: An electronic ticket processing system that includes the management of a venue with auction-based, during event, peer-to-peer electronic ticket exchanges. The peer-to-peer during event ticket exchange may be limited by the location of the electronic device of a user and may allow the event attendees enhancement of their experience (e.g. by upgrading their seat location or rights to access). In some embodiments, the during event peer-to-peer ticket exchange may be encouraged or controlled by the venue based on certain factors, such as, broadcasting considerations, crowd control, analytics, to encourage consumer spending, memberships, and/or other real time updates (e.g., weather, game scores, security concerns).

Abstract: A computer-implemented system and method for determining optimal placement strategy for rental accommodations. The system is executed in a computer-implemented environment, and comprises a server and a visitor device in communication. The server is configured to receive a request data for placement strategy from the visitor and determine one or more placement strategies based on the request data. The request data includes check-in date, check-out date, location of interest and one or more amenities specified either as mandatory or as useful. A visitor assigns criterial monetary value of useful but not necessary amenities, for commute from the lodging unit to the location of interest, for moving from one to another lodging unit. The placement strategy includes information about moving from one premise to another within the same trip, the amount of the customer's payment, and a criterial value that allows the visitor to choose the base placement strategy.

Abstract: Embodiments relate to determining an availability of a service option for delivery of an order placed with an online system. The online system receives an order placed with the online system. The online system accesses a computer model trained to predict a value of metric for an order placed with the online system. The online system applies the computer model to predict the value of the metric for the order. The online system determines which service option of a plurality of service options of the online system is available for delivery of the order, based at least in part on the predicted value of the metric and a threshold. The online system causes the device of the user to display an availability of the determined service option for delivery of the order.

Abstract: Computer implemented methods, systems, and computer program products include program code executing on a processor(s) that obtains a planning problem. The program code obtains a bound on a number of plans (to address the planning problem). The program code identifies symmetries of the planning problem. The program code utilizes the symmetries to identify an orbit search space of the planning problem. The program code executes a two-phase search iteratively over the orbit space to identify surrogate plans in the orbit space. The program code generates new plans by utilizing the surrogate plans and the symmetries of the planning problem to map the surrogate plans to new plans. The program code extends the new plans. The extended new plans comprise the set of solutions for the planning problem.

Abstract: A medical error reduction system may include a medical error reduction software for use in creating and revising at least one drug library. The software configured to provide one of a plurality of sets of privileges to each of a plurality of sets of users. Each of the plurality of sets of privileges arranged to allocate a degree of software functionality to one of the plurality of sets of users. The degree of software functionality configured to define the ability of a user to alter the at least one drug library. The medical error reduction system may include at least one server. The medical error reduction system may include at least one editor computer each of the at least one editor computer comprising a processor in communication with a display. The at least one editor computer and at least one server may be configured to communicate via a network in a client-server based model. Each of the at least one drug library may be for use in at least one medical device.

Abstract: An apparatus, method and computer program product for determining at least one target location of a mobile mining machine, receiving data relating to operation of the mobile mining machine in a mining environment, determining at least one operation characteristic of the mobile mining machine based on the data relating to operation of the mobile mining machine in the mining environment, determining, based on the at least one target location and the at least one operation characteristic, an estimated demand of the mobile mining machine for at least one flowing resource in the at least one target location at a particular time instance, and providing, based on the estimated demand, control information for controlling provision of the at least one flowing resource.

Abstract: An example method, executed by a processing system, includes receiving data from a data source, routing the data to a plurality of different data labelers to label the data, determining that an inflection point associated with an accuracy of the plurality of different data labelers is reached, re-routing additional data from the data source to a machine learning model, and activating the machine learning model to label the additional data.