Abstract: A set of features including a first feature and a second feature is received at a server. A subset of the set of features is determined for use in generating a model usable by a device to locally make a malware classification decision. The device has reduced computing resources as compared to computing resources of the server. The subset of the set of features is used to generate the model. The generated model includes the first feature and does not include the second feature. A determination is made, at a time subsequent to the generation of the model, that an updated model should be deployed to the device. An updated model is generated.

Abstract: Pulverizers are very critical equipment in overall functioning of a plant. They need to be controlled and monitored properly for the optimized operation of the pulverizers. A system and method for performance and health monitoring to optimize operation of a pulverizer is provided. The system comprises a digital twin that can mimic the performance of the pulverizer in real-time and assist the operators in decision making related to operation, maintenance and scheduling. The digital twin is configured to receives real-time sensor data from a plurality of data sources and provides real-time soft sensing of key health and performance parameters of the pulverizer. One more key aspect of the solution is the advisory system that alerts and recommends corrective actions in terms of parameters controlled through other equipment or changes in operation or design or changes in cleaning schedule.

Abstract: A Radio Access Network (RAN) wirelessly serves a User Equipment (UE) over a frequency channel based on a radio signal metric. The RAN comprises baseband circuitry and a radio. The baseband circuitry determines when multiple Physical Cell Identifiers (PCIs) are available to serve the UE. In response, the baseband circuitry selects some resource blocks in the frequency channel and generates a UE instruction to measure the radio signal metric for the selected resource blocks. The radio receives the UE instruction for the baseband circuitry and wirelessly transfers the UE instruction to the UE. The radio wirelessly receives a measurement report from the UE that indicates the radio signal metric for the selected resource blocks. The radio transfers the measurement report to the baseband circuitry. The baseband circuitry receives the measurement report and responsively schedules the UE in the selected resource blocks based on the radio signal metric.

Abstract: In one aspect, there is provided a system including at least one data processor and at least one memory. The at least one memory may store instructions that cause operations when executed by the at least one data processor. The operations may include retrieving a schedule for a plurality of processes, the plurality of processes scheduled for multiple executions over a time period. The operations may further include generating a user interface configured to receive user input for scheduling a process of the plurality of processes, the user interface comprising one or more fields for scheduling the multiple executions. The operations may further restricting, by the processor, a quantity of scheduled executions for a process of the plurality of processes.

Abstract: The present application relates to executing a containerized application in a nested manner on two separate container orchestration services. For example, a user may submit a request to a container orchestration service to execute a containerized application, and in response, instead of identifying one of the existing compute instances belonging to the user and executing the containerized application on the identified compute instance, the container orchestration service may generate and submit a request to a serverless container management service that can not only acquire compute resources on behalf of the container orchestration service but also manage the compute resources such that the container orchestration service (or the original requesting user) does not need to manage scaling, monitoring, patching, and security of the compute resources.

Abstract: Preprocessing of data destined for storage in a data lake is accomplished upstream, such as at edge nodes. The preprocessing includes filtering data that is deemed to be unnecessary for subsequent analytical use purposes. An initial intelligent determination is performed on whether a data feed is to be preprocessed at (i) the data lake, or (ii) upstream of the data lake, such as at an edge node. Once upstream preprocessing has been determined, an intelligent determination of which edge node is to be chosen for preprocessing is performed. The determination on which edge node is to be chosen for preprocessing is based on response times between the application server and the edge nodes and network bandwidth usage encountered by the network transmitting the data feed.

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: A memory stores a first cache and a second cache. A processor copies a first portion of data from a first table stored in a database into a second table. The processor further determines that a second portion of data from the first table will be overwritten and copies the second portion into a third table. The processor further determines that a probability that a user will access a third portion of the first table is greater than a threshold and copies the third portion into the first cache. The processor further determines a fourth portion of the first table that the user accesses at a frequency greater than a set frequency and copies the fourth portion into the second cache.

Abstract: Systems and methods for distributed rendering using two-level binning include processing primitives of a frame to be rendered at a first graphics processing unit (GPU) chiplet in a set of GPU chiplets to generate visibility information of primitives for each coarse bin and providing the visibility information to the other GPU chiplets in the set of GPU chiplets. Each coarse bin is then assigned to one of the GPU chiplets of the set of GPU chiplets and rendered at the assigned GPU chiplet based on the corresponding visibility information.

Abstract: Various embodiments of a system and associated method for audio source separation based on generative priors trained on individual sources. Through the use of projected gradient descent optimization, the present approach simultaneously searches in the source-specific latent spaces to effectively recover the constituent sources. Though the generative priors can be defined in the time domain directly, it was found that using spectral domain loss functions leads to good-quality source estimates.

Abstract: The present application relates to a method of providing a therapeutic regimen for the treatment of pain, wherein said method comprises administering an oral liquid pharmaceutical composition comprising celecoxib or a pharmaceutically acceptable salt thereof to a subject in need thereof.

Abstract: A computer-implemented method for relaxing queries on a queried connection graph can include obtaining, by a computing system including one or more computing devices, a connected graph. The computer-implemented method can include evaluating, by the computing system, a first set of connected components of the connected graph with respect to a first query set. The first query set can include one or more query criteria. The query criteria can include one or more attributes associated with operating an autonomous vehicle. The computer-implemented method can include determining, by the computing system, one or more relaxed query sets based at least in part on the first query set. The computer-implemented method can include evaluating, by the computing system, a second set of connected components of the connected graph with respect to the one or more relaxed query sets, the connected graph being associated with autonomous vehicle operation within a geographic area.

Abstract: Examples of a record generation system are provided. The system may receive a record generation requirement from a user. The system may obtain record data, a plurality of user documents, and identify a record corpus from the record data. The system may sort the record data into a plurality of data domains. The system may determine at least one record mapping context including a record value from the plurality of user documents. The system may determine a selection rule from the plurality of data domains for each of the record mapping context. The system may create a record index corresponding to the plurality of user documents. The system may create a record generation model corresponding to the record generation requirement based on the record index. The system may generate a record generation result corresponding to the record generation requirement comprising the relevant record generation model.

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: The ability to comprehend the context of a given programming artifact and extracting the underlying functionality is a complex task extending beyond just syntactic and semantic analysis of code. All existing automation capabilities, hence heavily depend on manual involvement of domain experts. Even recent approaches leveraging Machine Learning Capabilities are supervised techniques, whereby the dependency on domain experts still remains—in preparing suitable training sets. A method and system for automated classification of variables using unsupervised distribution agnostic clustering has been provided. The present disclosure focuses to tap the flexibility of the code and presents a domain agnostic approach using unsupervised machine learning which automatically extracts the context from source code, by classifying the underlying elements of the code. The method and system do not require any manual intervention and opens a wide range of opportunities in reverse engineering and variable level analysis space.

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: A system for classifying a target image with segments having attributes is provided. The system generates a graph for the target image that includes vertices representing segments of the image and edges representing relationships between the connected vertices. For each vertex, the system generates a subgraph that includes the vertex as a home vertex and neighboring vertices representing segments of the target image within a neighborhood of the segment represented by the home vertex. The system applies an autoencoder to each subgraph to generate latent variables to represent the subgraph. The system applies a machine learning algorithm to a feature vector comprising a universal image representation of the target image that is derived from the generated latent variables of the subgraphs to generate a classification for the target image.

Abstract: A battery health monitoring system that utilizes adaptive cathode and adaptive anode observers to estimate the ion concentrations at the respective cathode and anode of a battery. Subsequently, the estimated ion concentrations can be used in a battery model to estimate the state of heath and state of charge of the battery. Additionally, the model and ion concentrations can be updated real time as aging components of the battery are evaluated in the output data from the battery.

Abstract: A machine learning model, such as a Gradient Boosting Machine model, is trained using historical data associated with different types of operations. The machine learning model extracts features from the historical data and learns which features result in a predefined outcome, such as approval by a predefined entity. After the machine learning model is trained and validated as being accurate, it is used to predict the likelihood of a prospective operation achieving the predefined outcome when submitted right away versus a future date. If the likelihood of achieving the predefined outcome is low if submitted right away, the submission of the prospective operation is temporarily suspended. The machine learning model calculates a future time during which to submit the prospective operation for approval, where the future time has a greater likelihood of achieving the predefined outcome. This reduces waste of electronic resources including computer processing power and network communication bandwidth.

Abstract: Disclosed are various examples for kernel level application data protection. In one example, a security label map is written to a kernel layer. The security label map includes a security label and a list of permitted applications for files originated by a protected application. A file access system call is intercepted by kernel-level management instructions. An application identity is identified for an application requesting to access a secure file. Access to the secured file is permitted or denied based on a comparison of the application identity with the list of permitted applications.