Abstract: The present disclosure relates to a computer-implemented method for routing in an electronic design. Embodiments may include receiving, using at least one processor, global route data associated with an electronic design as an input and generating detail route data, based upon, at least in part, the global route data. Embodiments may further include transforming one or more of the detail route data and the global route data into at least one input feature and at least one output result of a deep neural network. Embodiments may also include training the deep neural network with the global route data and the detail route data and predicting an output associated with a detail route based upon, at least in part, a trained deep neural network model. Embodiments may also include generating routing information for each routing grid.

Abstract: A computer-implemented method and computing system are provided for failure prediction of a batch of manufactured objects. The method includes classifying, by a processor sing a simulation, a set of samples with uniformly distributed parameter values, to generate sample classifications for the batch of manufactured objects. The method further includes determining, by the processor, a centroid of failing ones of the samples in the set, based on the sample classifications. The method also includes generating, by the processor, a new set of samples with a distribution around the centroid of the failing ones of the sample in the set. The method additionally includes populating, by the processor, a nearest neighbor vector space using the new set of samples. The method further includes classifying, by the processor, the new set of samples by performing a nearest neighbor search on the nearest neighbor vector space using a distance metric.

Abstract: A computer-implemented method and computing system are provided for failure prediction of a batch of manufactured objects. The method includes classifying, by a processor using a simulation, a set of samples with uniformly distributed parameter values, to generate sample classifications for the batch of manufactured objects. The method further includes determining, by the processor, a centroid of failing ones of the samples in the set, based on the sample classifications. The method also includes generating, by the processor, a new set of samples with a distribution around the centroid of the failing ones of the sample in the set. The method additionally includes populating, by the processor, a nearest neighbor vector space using the new set of samples. The method further includes classifying, by the processor, the new set of samples by performing a nearest neighbor search on the nearest neighbor vector space using a distance metric.

Abstract: A method for generating an operational rule associated with a building management system includes identifying, with a processing device, a first pattern associated with a series of operational observations corresponding to a property of the building management system, correlating a first contextual attribute with the first pattern, and deriving the operational rule at least in part based on the first pattern and the first contextual attribute.

Abstract: A method and system for improving an automated hardware apparatus failure prediction system is provided. The method includes automatically retrieving operational data associated with operation of a hardware device being monitored for potential failure. Differing time frame software windows associated with observing operational data and hardware device are determined and the operational data is analyzed. In response, an apparatus malfunction prediction software application and a component prediction software application is generated. Features associated with execution of the software applications are generated and a first group of features are added to software code of the apparatus malfunction prediction software application. A second group of features are additionally added to software code of the component prediction software application.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for computing Q values for actions to be performed by an agent interacting with an environment from a continuous action space of actions. In one aspect, a system includes a value subnetwork configured to receive an observation characterizing a current state of the environment and process the observation to generate a value estimate; a policy subnetwork configured to receive the observation and process the observation to generate an ideal point in the continuous action space; and a subsystem configured to receive a particular point in the continuous action space representing a particular action; generate an advantage estimate for the particular action; and generate a Q value for the particular action that is an estimate of an expected return resulting from the agent performing the particular action when the environment is in the current state.

Abstract: There is provided is a method and an apparatus for training a neural network capable of improving the performance of the neural network by performing intelligent normalization according to a target task of the neural network. The method according to some embodiments of the present disclosure includes transforming the output data into first normalized data using a first normalization technique, transforming the output data into second normalized data using a second normalization technique and generating target normalized data by aggregating the first normalized data and the second normalized data based on a learnable parameter. At this time, a rate at which the first normalization data is applied in the target normalization data is adjusted by the learnable parameter so that the intelligent normalization according to the target task can be performed, and the performance of the neural network can be improved.

Abstract: Disclosed is a churn prediction system that predicts with a high level of accuracy which users will and which users will not stop opening the app over a 30-day time period. To this end a model is created using historical event data where the churn-related behavior of each user is known. New event data is then applied to the model to determine the likelihood of each user churning in the future. With these prediction scores a user is then qualified as falling into one of three classifications: low-risk, medium-risk, or high-risk of churn.

Abstract: Input image data having a plurality of pixel values represented in a two-dimensional matrix form of columns and rows is received. The input image data is transformed into a plurality of input rows. The pixel values in each input row correspond to the pixel values in a predetermined subset of the columns of the input image data and all of the rows of each column of the subset of columns. A plurality of subsets of pixel values in the plurality of input rows is determined. The number of pixel values in each row of a subset of pixel values equal in number to a number of filter values in a filter. Each input row of each subset of pixel values is convolved with the filter values of the filter to determine a corresponding output value and stored in a memory.

Abstract: In one embodiment, a system, apparatus and a method is described, the system, apparatus and a method including, a storage device and a memory operative to store target content items, a comparator operative to compare one content item of the target content items with the other target content items, and, at least on the basis of comparing the one content item of the target content items with the other content items of the target content items, to develop a correlation graph between each one content item of the target content items and the other content items of the target content items, and a machine learning system operative to receive the correlation graph and to output a decision, on the basis of in the correlation graph, indicating if the content items represented in the correlation graph are pirated content items or not. Related system, apparatuses and methods are also described.

Abstract: Methods and systems for aiding users in generating object pattern designs with increased speed. In particular, one or more embodiments train a sequence-based machine-learning model using training objects, each training object including a plurality of regions with a plurality of design elements. One or more embodiments identify a plurality regions of an object with a first region adjacent a second region. One or more embodiments receive a user selection of a design element for populating the first region with a first design element from a plurality of design elements. One or more embodiments identify a second design element from the plurality of design elements based on the first design element using the trained sequence-based machine-learning model. One or more embodiments also populate the second region with one or more instances of the second design element.

Abstract: Technology is directed to text message based concierge services (“the technology”). A user interacts with a concierge service (CS) via text messages to obtain a specific concierge service. For example, the user can send a text message to the CS, e.g., to a contact number provided by the CS, requesting for a recommendation of a restaurant, and the CS can respond by sending the recommendation as a text message. The CS determines a context of the request and generates recommendations that are personalized to the user and is relevant to the context. The CS can use various techniques, e.g., artificial intelligence, machine learning, natural language processing, to determine a context of the request and generate the recommendations accordingly. The CS can also receive additional information from a person associated with the CS, such as a concierge, to further customize or personalize the recommendations to the user.

Abstract: The technology disclosed introduces a concept of training a neural network to create an embedding space. The neural network is trained by providing a set of K+2 training documents, each training document being represented by a training vector x, the set including a target document represented by a vector xt, a favored document represented by a vector xs, and K>1 unfavored documents represented by vectors xiu, each of the vectors including input vector elements, passing the vector representing each document set through the neural network to derive an output vectors yt, ys and yiu, each output vector including output vector elements, the neural network including adjustable parameters which dictate an amount of influence imposed on each input vector element to derive each output vector element, adjusting the parameters of the neural network to reduce a loss, which is an average over all of the output vectors yiu of [D(yt,ys)?D(yt, yiu)].

Abstract: A computational method via a hybrid processor comprising an analog processor and a digital processor includes determining a first classical spin configuration via the digital processor, determining preparatory biases toward the first classical spin configuration, programming an Ising problem and the preparatory biases in the analog processor via the digital processor, evolving the analog processor in a first direction, latching the state of the analog processor for a first dwell time, programming the analog processor to remove the preparatory biases via the digital processor, determining a tunneling energy via the digital processor, determining a second dwell time via the digital processor, evolving the analog processor in a second direction until the analog processor reaches the tunneling energy, and evolving the analog processor in the first direction until the analog processor reaches a second classical spin configuration.

Abstract: A method includes maintaining respective episodic memory data for each of multiple actions; receiving a current observation characterizing a current state of an environment being interacted with by an agent; processing the current observation using an embedding neural network in accordance with current values of parameters of the embedding neural network to generate a current key embedding for the current observation; for each action of the plurality of actions: determining the p nearest key embeddings in the episodic memory data for the action to the current key embedding according to a distance measure, and determining a Q value for the action from the return estimates mapped to by the p nearest key embeddings in the episodic memory data for the action; and selecting, using the Q values for the actions, an action from the multiple actions as the action to be performed by the agent.