Abstract: Disclosed are various examples for kernel level application data protection. In one example, a security label and a list of permitted applications are received. The security label is utilized to limit access to files that embed the security label. A security label map is written within a kernel layer of the client device. The security label map includes the security label and the list of permitted applications. A secured file is generated by embedding the security label within a file stored on the client device.

Abstract: A method of tiled rendering is provided which comprises dividing a frame to be rendered, into a plurality of tiles, receiving commands to execute a plurality of subpasses of the tiles and interleaving execution of same subpasses of multiple tiles of the frame. Interleaving execution of same subpasses of multiple tiles comprises executing a previously ordered first subpass of a second tile between execution of the previously ordered first subpass of a first tile and execution of a subsequently ordered second subpass of the first tile. The interleaving is performed, for example, by executing the plurality of subpasses in an order different from the order in which the commands to execute the plurality of subpasses are stored and issued. Alternatively, interleaving is performed by executing one or more subpasses as skip operations such that the plurality of subpasses are executed in the same order.

Abstract: Embodiments of the invention are directed to a system, method, or computer program product for multimodal and distributed database system structured for dynamic latency reduction. In this regard, the invention comprises a unified data layer structured to map a plurality of data storage mechanisms to a common abstraction and a query engine structured for heterogenous domain based data extraction without requiring input of schema-based queries. In some embodiments, the invention comprises determining (i) one or more data components and (ii) one or more associated data domains associated with the first domain-based query by parsing the user input based on derived metadata from data dictionaries associated with a unified data layer system component. Moreover, the invention is configured to extract stored data from each of a plurality of databases based on the associated one or more data domains.

Abstract: Multi-component modules (MCMs), including configurable electromagnetic interference (EMI) shield structures and related methods, are disclosed. An EMI shield enclosing an IC or another electrical component in an MCM can protect other components within the MCM from EMI generated by the enclosed component. The EMI shield also protects the enclosed component from the EMI generated by other electrical components. An EMI shield with sidewall structures, in which vertical conductors supported by a wall medium electrically couple a lid of the EMI shield to a ground layer in a substrate, provides configurable EMI protection in an MCM. The EMI shield may also be employed to increase heat dissipation. The sidewall structures of the EMI shield are disposed on one or more sides of an electrical component and are configurable to provide a desired level of EMI isolation.

Abstract: Systems and methods for authoring workflows for processing data from a large-scale dataset include defining a metadata schema for the large-scale dataset, and receiving user input defining a workflow as a plurality of operations to be performed on the data. Each of the operations includes input metadata formatted according to the metadata schema. The input metadata describes input data to be processed by the operation and identifying a location for the input data in the data storage system, programmed instructions for performing an atomic operation on the input data to generate output data; and output metadata formatted according to the metadata schema. The output metadata describes the output data and identifying a location for the output data in the data storage system.

Abstract: A system includes one or more processors and software, executable by the one or more processors that, when executed by the one or more processors, cause the system to retrieve Environmental, Social & Governance (ESG) related data from one or more first databases, retrieve financial-related data from one or more second databases, and generate a dynamically interactive graphical user interface (GUI), containing a combination of ESG and financial information, the GUI having multiple GUI Modules including a Screening GUI Module, a Ratings GUI Module, a Climate GUI Module, and a Sustainability GUI Module.

Abstract: Memory cell sensing by charge sharing between two sense nodes is disclosed. A first sense node and a second sense node are pre-charged and the second node is discharged initiating charge sharing between the first sense node and the second sense node that results in an improved sense margin. Sensing circuitry disclosed herein may include one or more pre-charge circuits, sense enable circuits, and charge-sharing circuits. The increased sense margin achieved by sensing circuitry disclosed herein provides better noise immunity and more accurate sensing results.

Abstract: A processor includes a task scheduling unit and a compute unit coupled to the task scheduling unit. The task scheduling unit performs a task dependency assessment of a task dependency graph and task data requirements that correspond to each task of the plurality of tasks. Based on the task dependency assessment, the task scheduling unit schedules a first task of the plurality of tasks and a second proxy object of a plurality of proxy objects specified by the task data requirements such that a memory transfer of the second proxy object of the plurality of proxy objects occurs while the first task is being executed.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize transformation and projection layers to implement neural networks in tabular data environments. For example, the disclosed systems identify tabular data sets and segregate measured tabular data values and placeholder tabular data values. In one or more embodiments, the disclosed systems transform the measured tabular data values to a neural network value range based on the distribution of the measured tabular data values. Moreover, the disclosed systems replace placeholder tabular data values with a constant value within the neural network value range. In addition, the disclosed systems identify numerical values and utilize a projection layer to generate feature vectors within a high-dimensionality feature space. In one or more embodiments, the disclosed systems utilize the resulting high-dimensionality tabular data set with a neural network to generate prediction results (e.g.

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: The present invention discloses a system and method for fine-grained sentiment analysis using a hybrid model which is extensible to multiple languages, wherein the system (100) comprises a polarity detection module (101) for continuously detecting the positive sentiments, negative sentiments, and neutral sentiments of one or more sentences in a document. Further, a sentiment classification module (102) predicts the intensity and classifies positive sentiments and negative sentiments into one or more pre-defined sentiment classes, wherein the sentiment classification module (102) provides a reference sentiment interval for the classified sentences in the document. Furthermore, a length-based sentiment scoring module (103) continuously assigns a score to the classified sentences in the document ranging between ?s to +s, wherein ?s indicates extremely negative sentiment of the sentence and +s indicates extremely positive sentiment of the sentence.

Abstract: An anomaly detection system using machine learning to generate predicted survey scores for a given duration and a given metric based on historic survey score data. The system compares a predicted survey score to the actual survey score and identifies anomalous actual survey scores. The anomaly detection system trains a plurality of survey score prediction models using historic survey score data. Each survey score prediction model is based on a specific survey score metric and a specific duration. The survey score prediction models generate expected survey score results for the given duration and the given metric. Based on the user-determined filtering and tolerances, the system determines if the actual survey score result is anomalous. The system generates reports for the detected anomalies and continually updates the survey score prediction models with newly obtained actual survey results, thereby improving the anomaly detection accuracy over time.

Abstract: A request for one or more files is received. It is determined that the requested one or more files have been archived at an archival storage tier. Access to a first portion of the one or more requested files stored at the archival storage tier is requested. Access is provided to the first portion in response to the request. Based at least in part on a metric associated with a utilization of the first portion, it is determined when to request access to a second portion of the one or more requested files stored at the archival storage tier.

Abstract: Disclosed herein are system, method, and computer program product embodiments for synchronizing data in a source system to an application without relying on middleware. An embodiment operates by performing an initial load of records for an object for an application from a source system, thereby retrieving a first set of records. The embodiment first maps the first set of records to the object using a transform template. The embodiment creates the object in the application using the first set of records based on the first mapping. The embodiment performs a delta load of records for the object from the source system, thereby retrieving a second set of records. The embodiment second maps the second set of records to the object using the transform template. The embodiment then updates the object using the second set of records based on the second mapping.

Abstract: A deep learning system is used to predict crop characteristics from inputs that include crop variety features, environmental features, and field management features. The deep learning system includes domain-specific modules for each category of features. Some of the domain-specific modules are implemented as convolutional neural networks (CNN) while others are implemented as fully-connected neural networks. Interactions between different domains are captured with cross attention between respective embeddings. Embeddings from the multiple domain-specific modules are concatenated to create a deep neural network (DNN). The prediction generated by the DNN is a characteristic of the crop such as yield, height, or disease resistance. The DNN can be used to select a crop variety for planting in a field. For a crop that is planted, the DNN may be used to select a field management technique.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for classifying user behavior as anomalous. One of the methods includes obtaining user behavior data representing behavior of a user in a subject system. An initial model is generated from training data, the initial model having first characteristic features of the training data. A resampling model is generated from the training data and from multiple instances of the first representation for a test time period. A difference between the initial model and the resampling model is computed. The user behavior in the test time period is classified as anomalous based on the difference between the initial model and the resampling model.

Abstract: A processor supports secure memory access in a virtualized computing environment by employing requestor identifiers at bus devices (such as a graphics processing unit) to identify the virtual machine associated with each memory access request. The virtualized computing environment uses the requestor identifiers to control access to different regions of system memory, ensuring that each VM accesses only those regions of memory that the VM is allowed to access. The virtualized computing environment thereby supports efficient memory access by the bus devices while ensuring that the different regions of memory are protected from unauthorized access.

Abstract: The present disclosure generally relates to depth estimation. For example, aspects of the present disclosure include systems and techniques for performing depth estimation using filtered image data. Certain aspects provide an apparatus for processing frame data. The apparatus generally includes at least one memory; and at least one processor coupled to the at least one memory. The at least one processor may obtain a plurality of images for image processing, extract one or more features associated with each of the plurality of images, and analyze whether each of the plurality of images is a candidate for the image processing based on the one or more features. The at least one processor may also select a subset of the plurality of images based on analyzing the plurality of images and perform the image processing based on the selected subset of the plurality of images.

Abstract: A computing system measures terrain coverage by: obtaining sample image data representing a multispectral image of a geographic region at a sample resolution; generating, based on the sample image data, an index array of pixels for a subject terrain in which each pixel has an index value that represents a predefined relationship between a first wavelength reflectance and a second wavelength reflectance; providing the index array to a trained calibration model to generate an estimated value based on the index array, the estimated value representing an estimated amount of terrain coverage within the geographic region for the subject terrain; and outputting the estimated value for the subject terrain. The trained calibration model may be trained based on training data representing one or more reference images of one or more training geographic regions containing the subject terrain at a higher resolution than the sample resolution.

Abstract: Systems and methods for units of work based on business objectives are disclosed. Exemplary implementations may: manage environment state information maintaining a collaboration environment configured to facilitate interaction by users, the environment state information defining units of work and business objectives associated with the units of work; obtain progress information, the progress information for individual business objectives conveying progress toward fulfillment of the individual business objectives; update objective records for the business objectives based on the progress information so that the progress of the business objectives is maintained by the objective records; and/or perform other operations.