Abstract: An identity document detector comprising a two-dimensional convolutional neural network is trained to detect categories of identity documents based on inputting unprocessed image data from documents. The documents comprise documents monitored by a data loss prevention (DLP) system across an organization, and each category of identity documents has associated risk levels for DLP. The DLP system performs corrective action to prevent data leakage based on detection of identity documents by the trained identity document detector and risk associated with the detected identity document categories as well as document context within the organization.

Abstract: Automatic partitioning of a machine learning model may be performed for training across multiple processing devices. A training job for a machine learning model may specify a number of partitions for a machine learning model. An optimization parameter may be determined for the machine learning model. Different partitions of the machine learning model to be trained across multiple processing devices may be determined based on the specified number of partitions and the optimization parameter. A schedule for executing the training job may be generated according to the respective partitions of the machine learning model. The training job may be executed according to the schedule.

Abstract: As discussed earlier, accretion happens to be the most critical problem faced by users of the rotary kiln as it leads to shutdown of the rotary kiln which ultimately leads to reduction in production. Currently available accretion monitoring systems require expensive equipment and sensors which increases the production cost. Present disclosure provides method and system for monitoring accretion happening inside the rotary kiln. The system first takes real-time data associated with a rotary kiln as input. The system then localizes accretion clusters present in the rotary kiln using an accretion localization model. Thereafter, the system estimates the accretion probability score based on the HTM statistics calculated based on the accretion cluster information and real-time data using an accretion scoring model. Further, the system compute accretion score representative of real-time accretion condition of the rotary kiln based on the accretion probability score and the inputs.

Abstract: Current approaches for identifying accretion in rotary kiln lack access to information regarding the internal condition of the rotary kiln such as temperatures of the wall, gas or solid bed and specific methodology that is required to calculate accretion and hence forecast. Present disclosure provides method and system for forecasting and diagnosing accretion in rotary kiln. The system first takes historical data associated with rotary kiln, real-time data, and a future time horizon information. Then, system predicts accretion scores for future time horizon based on received data using a pretrained accretion forecasting model which are further utilized to estimate a rate of accretion. Thereafter, the system identifies high accretion (HA) operating regime and low accretion (LA) operating regime over predefined time period. Further, system identifies one or more accretion variables responsible for causing each of the HA operating regime and the LA operating regime using an accretion diagnostic model.

Abstract: An information processing apparatus of the present invention acquires, from a communication device of a user, at least one of utterance information by the user and position information of the communication device, specifies a predetermined region according to a mark included in the utterance information; acquires a movement direction of the user from at least one of the utterance information and the position information acquired from the communication device of the user, and setting a probability distribution that the user exists to a divided region of the predetermined region based on the acquired movement direction of the user; and presumes the user based on the set probability distribution.

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 device receives a query from a user associated with a car dealership and applies the query to a first trained machine learning model configured to predict an intent, and to a second trained machine learning model to predict a set of entities. The device generates a normalized representation of the first query that is database language agnostic based on the predicted intent and the predicted set of entities, and translates the normalized representation into a second query having a format compatible with a language of a database of the car dealership. The device fetches data from the database of the car dealership using the second query, and provides the data for display to the user.

Abstract: Embodiments disclosed herein generally relate to identifying necrotic tissue in a multiplex immunofluorescence image of a slice of specimen. Particularly, aspects of the present disclosure are directed to accessing a multiplex immunofluorescence image of a slice of specimen comprising a first channel for a nuclei marker and a second channel for an epithelial tumor marker, wherein the slice of specimen comprises one or more necrotic tissue regions; providing the multiplex immunofluorescence image to a machine-learning model; receiving an output of the machine-learning model corresponding to a prediction that the multiplex immunofluorescence image includes one or more necrotic tissue regions at one or more particular portions of the multiplex immunofluorescence image; generating a mask for subsequent image processing of the multiplex immunofluorescence image based on the output of the machine-learning model; and outputting the mask for the subsequent image processing.

Abstract: Aspects of the subject technology include a method comprising obtaining an enrollment confidence score and a validation confidence score, and determining an identity assurance score associated with the user account that indicates a level of confidence in an identity of the user. The identity assurance score is determined based at least in part on one or more of the enrollment confidence score or the validation confidence score. The method also comprises receiving, from a service provider, a request for the identity assurance score for the user account, the request corresponding to at least one of an authorization or an authentication of the user account at the service provider, and providing, responsive to the request, the identity assurance score of the user account to the service provider for at least one of the authorization or the authentication of the user account at the service provider.

Abstract: System and methods for predicting future pest pressures are provided. A pest pressure prediction computing device includes a memory and a processor communicatively coupled to the memory. The processor is programmed to receive trap data for a plurality of pest traps in a geographic location, receive weather data for the geographic location, receive image data for the geographic location, identify at least one geospatial feature within or proximate to the geographic location, apply a machine learning algorithm to the trap data, the weather data, the image data, and the at least one identified geospatial feature to identify a correlation between pest pressure and the at least one geospatial feature, and generate predicted future pest pressures for the geographic location based at least on the identified correlation between pest pressure and the at least one geospatial feature.

Abstract: A trained one-dimensional convolutional neural network (1D CNN) efficiently detects credentials that allow access to sensitive data across an organization. The 1D CNN has a lightweight architecture with one or more one-dimensional convolutional layers that capture semantic context of text data and a one-hot encoding embedding layer that takes unprocessed characters from documents as input. Lightweight architecture of the 1D CNN allows for high volume, fast detection of credentials for data loss prevention. The 1D CNN is trained on documents augmented with natural language processing techniques including token replacement, machine translation, token rearrangement, and text summarization.

Abstract: A network device or a system can be used to implement a private virtual local area network (VLAN). Such network device or system can receive a packet via an ingress port, perform a VLAN mapping lookup to identify a private VLAN domain based on the ingress port and an ingress subdomain associated with a primary VLAN or a secondary VLAN in the private VLAN domain, set a forwarding domain of the packet to the private VLAN domain, store the ingress subdomain and optionally the private VLAN domain as metadata, perform learning and forwarding lookups using the private VLAN domain to identify the ingress port and an egress port for the packet, reset the forwarding domain of the packet back to the ingress subdomain by the end of the forwarding lookup, and perform VLAN filtering based on the ingress subdomain.

Abstract: State of the art model fairness approaches do not address the degree of local fairness of a ML model. A method and system for determining local fairness of a classification Machine Learning (ML) model with degree of fairness is disclosed. The method creates multiple perturb instances using multilevel GMM clustering approach and a constrained perturbation technique to ensure feature distribution of perturbed data, generated from a tabular base data is within the feature distribution of the tabular base data of the ML model. Further, the class of a protected attribute is flipped, black box model prediction probabilities and the cosine similarity constraint and multiplication factor on the probabilities is used to provide a degree of fairness for the local instance. Thus, provides magnitude of fairness or unfairness to the local instance.

Abstract: Systems, apparatuses, and methods for abstracting tasks in virtual memory identifier (VMID) containers are disclosed. A processor coupled to a memory executes a plurality of concurrent tasks including a first task. Responsive to detecting one or more instructions of the first task which correspond to a first operation, the processor retrieves a first identifier (ID) which is used to uniquely identify the first task, wherein the first ID is transparent to the first task. Then, the processor maps the first ID to a second ID and/or a third ID. The processor completes the first operation by using the second ID and/or the third ID to identify the first task to at least a first data structure. In one implementation, the first operation is a memory access operation and the first data structure is a set of page tables. Also, in one implementation, the second ID identifies a first application of the first task and the third ID identifies a first operating system (OS) of the first task.

Abstract: A system and a method for managing workload of an application in a cloud infrastructure is disclosed. The cloud infrastructure may include an existing cloud infrastructure (ECI) and an Elastic Machine Pool Infrastructure (EMPI). The method may include connecting the EMPI to the ECI by configuring cloud control manager of the ECI. Further, the method may include receiving the workload from the application running on the cloud infrastructure. The workload may be allocated to an Elastic Virtual Machine (EVM) hosted by the EMPI or a VM hosted by the ECI based on at least one of an EMP profile of the application, status of the EVM, and workload characteristics of the EVM. Further, the one or more bare metal servers and the one or more EVMs may be managed based on at least one of the workload characteristics and the status of the EVM.

Abstract: Some implementations provide systems, devices, and methods for rendering a plurality of primitives of a frame, the plurality of primitives being divided into a plurality of batches of primitives and the frame being divided into a plurality of bins. For at least one batch of the plurality of batches the rendering includes, for each of the plurality of bins, rendering primitives of a first sub-batch rasterizing to that bin, and for each of the plurality of bins, rendering primitives of a second sub-batch rasterizing to that bin.

Abstract: A method includes predicting a first future timeseries for a first variable using a first model, predicting future values for a plurality of second variables using one or more second models, wherein the first variable is a function of the second variables, generating a second future timeseries for the first variable as based on the future values for the plurality of second variables, and providing a composite timeseries forecast for the first variable by combining the first future timeseries and the second future timeseries.

Abstract: A method is disclosed. The method includes processing a group interaction request for an interaction involving a group. Better assurance for the interaction is provided by providing a one-time password that has a number of portions that are sent to a plurality of user devices. The portions are received and one user device may concatenate the portions to form the one-time password. It may then be entered to authenticate the interaction. Other examples include the use of an authorization request message that is authorized for an initial value. Later, separate authorization request messages with different credentials may be transmitted for different users in the group.

Abstract: The present invention relates to a pharmaceutical combination comprising a KRAS G12C inhibitor and one or more therapeutic agents which is selected from an agent targeting the MARK pathway or an agent targeting parallel pathways; and pharmaceutical compositions comprising the same. The invention also relates to KRAS G12C inhibitors alone or said combinations for use in methods of treating a cancer or a tumor, in particular wherein the cancer or tumor is KRAS G12C mutant.

Abstract: A method and system for optimizing performance of Genetic Algorithm (GA) in solving scheduling problem is disclosed. The method includes receiving input constraints associated with supply and demand sides, for scheduling problem. The method include initializing set of schedules using initializer that sets initial set of solutions for GA to start optimization. The method may include generating parent population for GA. The method may include creating child population via evolution using current probabilistic parameters including crossover and mutation operators. The method may include utilizing a Multi-Level Hierarchical Grouping (MLHG) to de-duplicate child population. The method includes determining a new population from a total population including the parent population and the child population, using custom multi-objective sorting technique. The method may further include updating probabilistic parameters of the GA during runtime using runtime adapter, when pre-determined iterations unattained.