Abstract: A computing system detects a defective object. An image is received of a manufacturing line that includes objects in a process of being manufactured. Each pixel included in the image is classified as a background pixel class, a non-defective object class, or a defective object class using a trained neural network model. The pixels included in the image that were classified as the non-defective object class or the defective object class are grouped into polygons. Each polygon is defined by a contiguous group of pixels classified as the non-defective object class or the defective object class. Each polygon is classified in the non-defective object class or in the defective object class based on a number of pixels included in a respective polygon that are classified in the non-defective object class relative to a number of pixels included in the respective polygon that are classified in the defective object class.

Abstract: A system, method, and computer readable medium is disclosed for accessing an object that is encrypted within at least a tertiary-level encryption key scheme. This includes receiving a request to access blocks making up an object by content management system and using a top-level encryption key from a key management service, a namespace encryption key from a namespace encryption key database, and a respective block encryption keys protecting the blocks making up the object. Using these keys, the system can decrypt the block encryption keys using the namespace encryption key and decrypt the blocks using the block encryption keys, thereby providing access to the object.

Abstract: In an aspect, first interaction data characterizing a first interaction of a user with a mobile device configured to operate an application client can be received. A plurality of segments of users of the application client can be determined, and each of the plurality of segments can be associated with one or more settings that characterize an operating parameter of the application client. The user can be assigned to a first segment of the determined plurality of segments based on a correspondence between a characteristic of the user and a characteristic associated with the first segment. The application client can be modified based on the one or more settings associated with the first segment. The modified application client can be provided for interaction by the user via the mobile device. Related systems, apparatus, techniques, and articles are also described.

Abstract: Aspects of the disclosure provide for enabling autonomous vehicles to pull over into driveways when picking up or dropping off passengers or goods. For instance, a request for a trip identifying a first location and a second location may be received. The first location may be a location of a client computing device, and the second location may be a starting location or a destination for the trip. A user preference for the trip indicating that a pickup for the trip be in a driveway may be identified. That the first location corresponds with the second location may be identified. Based on the determination that the first location corresponds with the second location, dispatch instructions may be to an autonomous vehicle. The dispatch instructions may identify a polygon for a driveway at the second location in order to cause the autonomous vehicle to pull over into the driveway.

Abstract: A system is disclosed herein. The system includes a splitter board. The splitter board includes a microprocessor, a converter, and a bypass relay. The converter includes analog-to-digital circuitry and digital-to-analog circuitry. The bypass relay is configurable between a first state and a second state. In the first state, the bypass relay is configured to direct an input signal to the converter. The converter converts the input signal to a converted input signal and splits the converted input signal into a first portion and a second portion. The first portion is directed to the microprocessor. The second portion is directed to an output port of the splitter board for downstream processes. In the second state, the bypass relay is configured to cause the input signal to bypass the converter. The bypass relay directs the input signal to the output port of the splitter board for the downstream processes.

Abstract: A simulated process is initiated. The simulated process includes generating, by an emulator, a control signal based on external inputs. The simulated process further includes processing, by a simulator, the control signal to generate simulated response data. The simulated process further includes generating, by a deep learning processor, expected behavioral pattern data based on the simulated response data. An actual process is initiated by initializing setpoints for a process station in a manufacturing system. The actual process includes generating, by the deep learning processor, actual behavioral pattern data based on actual process data from the at least one process station. The deep learning processor compares the expected behavioral pattern to the actual behavioral pattern. Based on the comparing, the deep learning processor determines that anomalous activity is present in the manufacturing system. Based on the anomalous activity being present, the deep learning processor initiates an alert protocol.

Abstract: Systems and methods for caching file system collections atomically. The systems and methods perform operations comprising: receiving a request to access a collection of data comprising a plurality of files stored on a storage device; in response to receiving the request, transferring the collection of data from the storage device to a first subfolder in a cache associated with the storage device; generating a lock file comprising a reference to the first subfolder in the cache; and atomically controlling access to the collection of data in the first subfolder and removal of the collection of data in the first subfolder from the cache via the lock file.

Abstract: A computing device including a processor configured to receive an exact objective function over a state space. The processor may receive an approximated objective function that approximates the exact objective function. The processor may compute an estimated optimal state of the exact objective function. Computing the estimated optimal state may include, starting at an initial state, computing a preliminary estimated optimal state by performing a plurality of fast-step iterations of a Monte Carlo algorithm with respective fast-step acceptance probabilities determined based at least in part on the approximated objective function. Computing the estimated optimal state may further include performing a correction iteration that has a correction-step acceptance probability determined based at least in part on respective values of the approximated objective function and the exact objective function computed at the preliminary estimated optimal state. The processor may output the estimated optimal state.

Abstract: A facility for valuing a distinguished home located in a distinguished geographic area is described. The facility receives home attributes for the distinguished home. The facility obtains valuation for the distinguished home by applying to the received home attributes evaluation model for homes in the distinguished geographic area that has been trained using selling price and home attribute data from homes recently sold in the distinguished geographic area. The facility reports the obtained valuation for the distinguished home.

Abstract: A method for selectively processing a packet flow using a flow inspection engine is disclosed. The method includes receiving, by at least one hardware data plane processor component in a network packet broker, a plurality of packets associated with a packet flow, and forwarding, by the at least one hardware data plane processor component to at least one flow inspection engine, a copy of at least a portion of one or more of the initial packets of the packet flow.

Abstract: A system and method in accordance with example embodiments may include systems and methods for a cloud-based analytics platform. The cloud-based analytics platform may allow the manual and automatic uploading to and/or downloading from a cloud server. The platform may include single sign-on (SSO) capabilities such that a user may have one set of credentials to access data from the cloud-based analytics and/or data stored locally. The platform may include data validation and processing in order to provide real-time feedback on uploads based on file type, file size, access rights, extracted data, and transformed data.

Abstract: Techniques for coordinating movements of various objects in an electronic document are disclosed. A first-type object remains stationary so that it becomes outside a content boundary of the electronic document, as the content boundary is pulled inside an edge of a window displaying the electronic document. A second-type object remains stationary until the content boundary reaches the edge of the window and then moves along with the scrolled electronic document as the content boundary is pulled inside the edge of the window. A third-type object and a fourth-type object may show different movement behaviors.

Abstract: One variation of a system for implementing a key-value data store includes one or more processors, storage media and instructions stored in the storage media which, when executed by the system cause the system to: receive a request store a particular key-value item; request a first networked distributed data storage system to store the particular key-value item; based on a determination that a set of one or more offload criteria is satisfied: retrieve a first set of key-value items from the first networked distributed data storage system, and request a second networked distributed data storage system to store the first set of key-value items in a first set of one or more data objects. The first networked distributed data storage system can have a lower data write latency and a higher data storage cost than the second networked distributed data storage system.

Abstract: A deep learning process receives desired process values associated with the one or more process stations. The deep learning processor receives desired target values for one or more key performance indicators of the manufacturing process. The deep learning processor simulates the manufacturing process to generate expected process values and expected target values for the one or more key performance indicators to optimize the one or more key performance indicators. The simulating includes generating a proposed state change of at least one processing parameter of the initial set of processing parameters. The deep learning processor determines that expected process values and the expected target values are within an acceptable limit of the desired process values and the desired target values. Based on the determining, the deep learning processes causes a change to the initial set of processing parameters based on the proposed state change.

Abstract: The computing device obtains a training data set related to a plurality of historic user inputs associated with preferences of one or more services or items from an entity. For each of the one or more services or items, the computing device executes operations to train a plurality of models using the training data set to generate a plurality of recommended models, apply a validation data set to generate a plurality of predictions from the plurality of recommended models, obtain a weight of each metric of a plurality of metrics from the entity, obtain user inputs associated with user preferences, and determine a relevancy score for each metric. The computing device selects a recommended model based on the relevancy score of the selected metric or a combination of selected metrics, generates one or more recommendations for the users, and outputs the one or more generated recommendations to the users.

Abstract: Methods and systems for routing data payloads through a plurality of microservices are disclosed. The method may comprise: receiving a data payload and first data; predicting, by a trained machine learning model based on the first data, a path through a plurality of microservices associated with the data payload; generating a modified data payload by modifying, via the orchestrator service, the data payload to include: a first header, wherein the first header comprises a first microservice destination address associated with a first microservice of the plurality of microservices and a second header nested within the first header, wherein the second header comprises a second microservice destination address associated with a second microservice of the plurality of microservices; forwarding the modified data payload to the first microservice based on the first header for processing; and forwarding the modified data payload to the second microservice based on the second header for processing.

Abstract: A system and method for generating a training data set for training a machine learning model to detect defects in specimens is described herein. A computing system cause presentation of an image on a device of a user. The image includes at least one defect on an example specimen. The computing system receives an annotated image from the user. The user annotated the image using an input via the device. The input includes a first indication of a location of the defect and a second indication of a class corresponding to the defect. The computing system adjusts the annotated image to standardize the input based on an error profile of the user and the class corresponding to the defect. The computing system uploads the annotated image for training the machine learning model.

Abstract: Aspects of the disclosure relate to parking behaviors and maneuvering a vehicle in an autonomous driving mode accordingly. For instance, a pullover location for the vehicle to stop and wait for a passenger may be identified. The vehicle may be maneuvered in the autonomous driving mode in order to pull over by pulling forward into the pullover location. Whether to maneuver the vehicle in reverse in the pullover location before or after the passenger enters the vehicle may be determined based on context for the pull over with respect to the passenger. The vehicle may be maneuvered in the autonomous driving mode in reverse based on the determination of whether to maneuver the vehicle in reverse in the pullover location before or after the passenger enters the vehicle.