Abstract: Distributed storage systems, devices, and associated methods of data replication are disclosed herein. In one embodiment, a server in a distributed storage system is configured to write, with an RDMA enabled NIC, a block of data from a memory of the server to a memory at another server via an RDMA network. Upon completion of writing the block of data to the another server, the server can also send metadata representing a memory location and a data size of the written block of data in the memory of the another server via the RDMA network. The sent metadata is to be written into a memory location containing data representing a memory descriptor that is a part of a data structure representing a pre-posted work request comgured to write a copy of the block of data from the another server to an additional server via the RDMA network.

Abstract: Systems for provisioning virtual network interfaces (VNIs) for tasks running on a virtual machine instance in a distributed computing environment are provided. The systems receive a request to launch a task corresponding to a plurality of containers in an instance in association with an instruction to provide a VNI for the task with a set of network security rules. The system may select an instance with sufficient resources to launch the task and enable communication using the VNI. The system may inhibit processes running on the instance other than containers associated with the task from communicating via the VNI.

Abstract: The present application relates to a method of treating an inflammatory skin condition by administering a pharmaceutical composition comprising a reduced dose of minocycline to a subject in need thereof, wherein said administration provides an effective plasma or interstitial fluid concentration of minocycline for treating the inflammatory skin condition.

Abstract: A method and apparatus for automated reconstruction of a 3D computed tomography (CT) volume from a small number of X-ray images is disclosed. A sparse 3D volume is generated from a small number of x-ray images using a tomographic reconstruction algorithm. A final reconstructed 3D CT volume is generated from the sparse 3D volume using a trained deep neural network. A 3D segmentation mask can also be generated from the sparse 3D volume using the trained deep neural network.

Abstract: A memory stores a first and second cache. A processor stores a first query in a transaction log. The processor selects information from the transaction log, uses it to determine that a user will likely access a first portion of the database at a future date, and copies that portion into the first cache prior to the date. The processor further copies a second, frequently accessed portion of the database into the second cache. The processor additionally determines that a portion of data is in both caches and deletes it from the second cache. The processor receives a second query from the user, determines that it is directed at data in the first cache and accesses that cache. The processor also determines that the second query is not directed at data in the first cache but is directed at data in the second cache, and accesses that cache.

Abstract: A resource offloading system comprises a relational database configured to store relational queries including searching features, a server configured to offload the relational queries to non-relational queries, and a non-relational database configured to store the non-relational queries. The server is configured to generate access patterns for each relational query based on searching features included in the relational query, classify the access pattern based on a terminology defined by a searching feature into a classification, rank the classified access patterns, generate candidate access patterns from the ranked access patterns, create non-relational queries based on the candidate access patterns which are ranked beyond a threshold, and store the non-relational queries in the non-relational database.

Abstract: Techniques for performing automatic interactive animation by automatically matching objects between multiple artboards, allowing an animator to link multiple artboards in a temporal sequence using time as a trigger and allowing an animator to preview an animation using intuitive drag controls via an input device such as a mouse or touch screen. An automatic animation process is performed by matching objects/nodes between artboards by determining a ranking of similarity between objects based upon a distance metric computed for a set of one or more attributes associated with each object in the artboards. If sufficient match is found, the matched objects can be treated as a single entity to be animated. In another embodiment, dominant direction of movement with respect to the matched objects is determined, and receipt of a drag event (mouse input or touch screen gesture input) in said dominant direction causes a preview of animation of that entity.

Abstract: Methods described herein relate to reducing the computational intensity of vision-based localization. Methods may include: receiving sensor data from a vehicle traveling along a road; identifying one or more features of the environment from the sensor data; classifying the one or more identified features into one or more of a plurality of semantic classifications for the features; identifying map image data based on an identified location of the vehicle; identifying one or more features in the map image data; comparing one or more identified features of a first semantic classification with one or more features of the map image data of the first semantic classification; and registering a localized location of the vehicle within the environment based, at least in part, on the one or more identified features of the first semantic classification corresponding to the one or more features of the map image data of the first semantic classification.

Abstract: Various methods are provided for facilitating map update to an environment map using gradient thresholding. One example method may include detecting an observed feature associated with a first feature decay and generating an interpolated feature that approximates the observed feature associated with a second decay. The method also includes determining a gradient difference between the interpolated feature and a stored map feature. The stored map feature represents an encoding of the observed feature associated with a third decay associated with an environment map. The method also includes determining a relationship between the gradient difference and a feature gradient update threshold, and, based upon the relationship, updating the environment map by at least replacing the map feature representation associated with the environment map with the approximated feature representation.

Abstract: A method, apparatus and computer program product are provided for warping a perspective image into the ground plane using a homography transformation to estimate a bird's eye view in real time. Methods may include: receiving first sensor data from a first vehicle traveling along a road segment in an environment, where the first sensor data includes perspective image data of the environment, and where the first sensor data includes a location and a heading; retrieving a satellite image associated with the location and heading; applying a deep neural network to regress a bird's eye view image from the perspective image data; applying a Generative Adversarial Network (GAN) to the regressed bird's eye view image using the satellite image as a target of the GAN to obtain a stabilized bird's eye view image; and deriving values of a homography matrix between the sensor data and the established bird's eye view image.

Abstract: Methods, systems, apparatuses, and computer program products are provided that are configured to perform localization of position data, specifically using a trained localization neural network. In the context of an apparatus, the apparatus is caused to receive observed feature representation data. The apparatus is further configured to transform the observed feature representation data into standardized feature representation data utilizing a trained localization neural network. The apparatus is further configured to compare the standardized feature representation data and the map feature representation data and identify local position data based on the comparison.

Abstract: Methods described herein relate to iteratively refining parameters of a localization framework for different data sources. Methods may include: receiving a map data set from a data source; applying an offset to the map data to generate an offset map data set; applying a localization framework between the map data set and the offset map data set to generate a localized data set; recovering offset data from the localized data set; iteratively tuning parameters of the localization framework in response to the offset data from the localized data set failing to correspond with the applied offset until offset data recovered from the localized data set corresponds to the applied offset; receiving sensor data from at least one sensor of a vehicle; applying the localization framework to the sensor data to locate the vehicle within a mapped region.

Abstract: Methods described herein relate to identifying critical parameters of a localization framework that may require tuning for accuracy of the localization framework based on the data source. Methods may include: receiving a data set from a data source; comparing the data set against data stored in a database; identifying, based on the comparison, a subset of parameters of a localization framework for the data set from among a plurality of parameters of the localization framework; providing for tuning of the subset of parameters of the localization framework for the data set to generate a tuned subset of parameters; processing the data set from the data source using the localization framework including the tuned subset of parameters; and receiving an indication of a location from the localization framework of the data set within a mapped region.

Abstract: Described herein are methods of detecting and labeling features within an image of an environment. Methods may include: receiving sensor data from an image sensor, where the sensor data is representative of a first image including an aerial view of a geographic region; detecting, using a perception module, at least one vehicle within the image of the geographic region; identifying an area around the at least one vehicle as a road segment in response to detecting the at least one vehicle; based on the identification of the area around the vehicle as a road segment, identifying features within the area as road features based on a context of the area; generating a map update for the road features of the road segment; and causing a map database to be updated with the road features of the road segment.

Abstract: A method, apparatus and computer program product are provided for predicting the likelihood that road infrastructure has changed. Methods may include: receiving sensor data from a sensor network of a first environment; identifying first map data of an environment, where the first map data is identified based on visual similarity to the first environment, where the first map data includes a history of map updates; identifying second map data of an environment, where the second map data is identified based on visual similarity to the first environment; analyzing the sensor data against the first map data and the second map data to establish correspondence between the sensor data and the first map data and between the sensor data and the second map data; and identifying the first environment as a location of at-risk infrastructure based on the sensor data corresponding to the first map data.

Abstract: Methods, systems, apparatuses, and computer program products are provided that are configured to processor sensor data using a single component specialized sensor data processing neural network. The single component data processing system is configured to receive input data from a sensor, analyze the input data using a neural network embodied by a single trained sensor data processing component, wherein the single trained sensor data processing component is trained to product output data that approximates a plurality of task-specific transformations performed in a pipeline manner, and produce the output data following transformation of the input data. Additionally, methods, systems, apparatuses, and computer program products are provided for training a single trained sensor data processing component to approximate a plurality of task-specific transformations performed in a pipeline manner.

Abstract: A method, apparatus and computer program product are provided for constructing a high definition map from crowd sourced data using semantic attributes to bootstrap map construction. Methods may include: receiving first sensor data from a first vehicle having traversed a first path along a first lane of a first road segment; identifying features from the first sensor data of the first road segment; receiving second sensor data from a second vehicle having traversed a second path along a second lane of the first road segment; identifying features from the second sensor data of the first road segment; aligning the identified features from the second sensor data with the identified features from the first sensor data of the first road segment; and combining the identified features from the first sensor data and the second sensor data based, at least in part, on the confidence of the respective sensor data.

Abstract: A method is provided for dynamically throttling processing and memory consumption of sensors based on context. Methods may include: receiving first sensor data from a first sensor, where the first sensor data includes data associated with an environment of the sensor; applying an auto-encoder framework to the first sensor data to establish a data difference score between frames of the first sensor data, where a relatively high data difference score corresponds to substantial differences between frames of the first sensor data, and wherein a relatively low data difference score corresponds to insubstantial differences between frames of the first sensor data; reducing a frame rate of data capture of the first sensor in response to the data difference score between frames of the first sensor data being relatively low; capturing first sensor data from the first sensor at the reduced frame rate; and providing for storage of the first sensor data.

Abstract: Techniques are described for populating visual designs with web content. In implementations, a document design is generated via a digital media application. The document design includes a layout of repeating design elements, such as a grid or table of repeating areas each having various design elements. Web content having a structured arrangement of data elements is accessed through a data panel exposed via the application. The web content data may be linked to the document design to populate elements of the document design with “real-world” data. To do so, the web content is to detect semantically similar repeating data elements by based on element positions, node types, style types, and node hierarchies reflected by structured data defining the web content. Design elements in the layout of the document design are then auto-populated with content of the semantically similar repeating data elements to produce a preview linked to “real-world” data.

Abstract: A wireless Josephson-junction-based parametric converter is described. The converter may be formed on a substrate with antennas that pump are configured to wirelessly receive pump, signal and idler frequencies and couple the received frequencies to the converter's circuitry. Capacitors may also be fabricated on the same substrate and sized to tune operation of the converter to desired frequencies. The converter may be coupled directly to microwave waveguides, and may be tuned to different signal frequencies by applying magnetic flux to the converter circuitry.