Abstract: A method for validating a trained artificial intelligence (AI) model on a device is provided. The method includes deploying a validation model generated by applying a plurality of anticipated configurational changes associated with the trained AI model requiring validation. Further, the method includes providing input data to each of the validation model and the trained AI model for receiving an output from each of the validation model and the trained AI model, wherein the output of the validation model is further based on one or more actual configurational deviations that occurred during training of the trained AI model since deployment of the trained AI model on the device. Furthermore, the method includes combining the output of each of the validation model and the trained AI model to validate the trained AI model.

Abstract: In some examples, a method includes receiving, by an egress network device for a network, messages from each of a plurality of ingress network devices for the network, wherein each of the messages specifies a multicast source, a multicast group, and an upstream multicast hop weight value for multicast traffic for the multicast source and the multicast group; selecting, by the egress network device and based on the upstream multicast hop weight values specified by the received messages, one of the plurality of ingress network devices to which to send a multicast join message of a plurality of multicast join messages for the multicast source and multicast group; and sending, by the egress network device, the multicast join message to the selected one of the plurality of ingress network devices.

Abstract: A method for user credential authentication includes receiving real-time logs from a blockchain network that is configured to perform an authentication process of a user. The real-time logs are normalized and bucketized to generate processed real-time logs, which are stored in a block and are added to a blockchain. The processed real-time logs are analyzed to identify the plurality of real-time behavioral patterns of the user. A first authentication score is determined by comparing a first real-time behavioral pattern to a respective first historical behavioral pattern. The first authentication score is compared to a first authentication score threshold, which corresponds to the first real-time behavioral pattern and the respective first historical behavioral pattern. In response to the first authentication score being less than the first authentication score threshold, a first instruction is sent to the blockchain network to temporarily stop the authentication process of the user.

Abstract: A system is comprised of one or more processors coupled to memory. The one or more processors are configured to receive image data based on an image captured using a camera of a vehicle and to utilize the image data as a basis of an input to a trained machine learning model to at least in part identify a distance of an object from the vehicle. The trained machine learning model has been trained using a training image and a correlated output of an emitting distance sensor.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content providing, searching and/or hosting systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a novel framework for analyzing messages associated with an inbox of a user, and providing functionality to the inbox for alerting the user to specific forms of content included within each message. Such functionality can include displaying messages and/or the content contained therein in a novel, dedicated and modified and/or embedded portion of a message inbox. The messages can also be modified upon display within in the inbox to indicate that the message includes content of a specific type. Also, messages that include specific content types can also trigger the disclosed framework to generate and provide alerts to the user indicating the inbox has received or includes specific forms of such content.

Abstract: A method of resource selection by a user equipment (UE) is provided. The method includes detecting a need of an uplink transmission having a plurality of transmission parameters for at least one application from a plurality of applications having an associated transmission requirement. The method includes determining a set of configured grant resources available to the UE along with the associated plurality of grant resource parameters. The method includes determining transmission capability for the set of configured grant resources by analyzing the grant resource parameters. The method includes selecting at least one configured grant resource from the set of configured grant resources satisfying at least one transmission requirement of the at least one application.

Abstract: The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. Methods and systems for handling network slice admission control for User Equipment (UE) are provided. The disclosure relates to network slicing and more particularly, to a system and method of ensuring that a communication system is able to accurately enforce quota on the maximum number of UEs concurrently registering for a network slice defined by a Single Network Slice Selection Assistance Information (S-NSSAI). The disclosure describes how the limits of “Number of Terminals” configured for an S-NSSAI may be accurately maintained in a Network Slice Admission Control Function (NSACF), and thus the enforcement may be performed when the limit is really exhausted. The disclosure also describes that a deregistration notification from a Unified Data Management (UDM) may act as a trigger for an old Mobility Management Function (AMF) to decrement counts in the NSACF as the old AMF is no longer serving the UE.

Abstract: A method of analyzing images and generating a report is disclosed. The method may include inputting a test image to a trained prediction model. The trained prediction model may be a deep learning-based model. The method may further include obtaining at least one abnormality associated with the test image, using the trained prediction model. The method may further include identifying at least one relevant predetermined region from a plurality of predetermined regions associated with the test image, using the trained prediction model, based on the at least one abnormality. Each of the at least one relevant predetermined region is identified based on hierarchical classification. The method may further include determining within each of the at least one relevant predetermined region, a location associated with the at least one abnormality.

Abstract: Methods, apparatus, and computer programs are disclosed for managing processor power consumption based on message queue utilization. In one embodiment, a method comprising: distributing messages to a set of processor cores of a processor, wherein one message is distributed per distribution round to one queue within a set of queues, each queue corresponding to one processor core within the set of processor cores and including one or more queue entries to be processed by the one processor core, and where the distribution is based on utilization of the set of queues; based on utilization of a corresponding queue for a processor core of the set of processor cores, determining a power state for the processor core to be changed to; and distributing a message to the corresponding queue, the message to cause the processor core to be set to the power state.

Abstract: The disclosure relates to a method and system for adaptive antenna configuration in a user equipment comprising one or more antenna modules, each of the one or more antenna modules comprising a plurality of patch elements. The method comprises: determining whether a signal strength of the UE is above a signal threshold, determining whether a mobility of the UE is below a mobility threshold based on determining that the signal strength is above the signal threshold, determining at least one active antenna module based on determining that the mobility of the UE is below the mobility threshold, and deactivating at least one patch element in the at least one antenna module.

Abstract: The current document is directed to event-message collection, processing, and storage systems and, in particular, to event-message collection, processing, and storage computing systems that are configurable to facilitate scaling, load balancing, and selection of a centralizing/decentralizing level which, in turn, provide a variety of operational efficiencies and advantages. Decentralization combined with event-record filtering, in a described implementation, provides for a significant reduction in data-transmission, processing, and data-storage overheads. Dynamic reconfiguration of the components of the event-message collection, processing, and storage systems allows for increased precision in scaling and load balancing to adapt the event-message collection, processing, and storage systems to dynamically reconfigured distributed computer systems in which the event-message collection, processing, and storage systems run.

Abstract: This invention discloses composition and manufacturing processes of a toxicity free botanical drug formulation for relief from breathing difficulties. This botanical drug is manufactured from plants Syzygium aromaticum or component(s) thereof, plant Elettaria cardamomum or component(s) thereof and sweeteners or component(s) thereof. This botanical drug was administered to human volunteers by buccal, supralingual, sublingual and oral routes in effective amount for an effective time-period. Effective amount and effective time of administration varies from one human volunteer to another depending upon type and severity of breathing difficulties.

Abstract: Embodiments herein provide a method for channel state information (CSI) prediction by a user equipment (UE) in a wireless network. The method comprises determining a CSI; inputting the determined CSI to at least one machine learning (ML) based CSI prediction model to obtain at least one predicted precoder; encoding the at least one predicted precoder into at least one bit stream using at least one ML based CSI encoding model or at least one non-ML based CSI encoding model; and transmitting the at least one encoded bit stream to a network apparatus in the wireless network for the ML based CSI prediction at the network apparatus.

Abstract: The present disclosure advantageously provides a method and system for transferring data over a chip-to-chip interconnect (CCI). At a request node of a coherent interconnect (CHI) of a first chip, receiving at least one peripheral component interface express (PCIe) transaction from a PCIe master device, the PCIe transaction including a stream identifier; selecting a CCI port of the CHI of the first chip based on the stream identifier of the PCIe transaction; and sending the PCIe transaction to the selected CCI port.

Abstract: A method of image processing and classifying target entities with an image is disclosed that may include applying a contrast amplification procedure to a Lightness parameter associated with an input image, to amplify contrast of the input image and obtain an amplified-contrast image. The method may further include de-noising the amplified-contrast image by iteratively performing on the amplified-contrast image a blur correction, an erosion correction, and a dilation correction, to obtain a de-noised image corresponding to the amplified-contrast image. The method may further include determining edges of each of one or more target entities associated with the input image from the de-noised image and identifying the one or more target entities associated with the input image based on the identified edges, to generate a contoured image. The method may further include classifying the one or more target entities into one or more predefined classes, using a classification model.

Abstract: Methods, systems, and devices for wireless communications are described. In some systems, a user equipment (UE) may transmit a sounding reference signal (SRS) to a base station using an SRS antenna switch (SRS-AS) or an SRS carrier switch (SRS-CS) transmission technique and the UE may feature multiple antenna groups that have separate exposure budgets due to their spatial separation. The UE may employ one or more accounting procedures for tracking energy contributions associated with the SRS across the multiple antenna groups of the UE and may employ one or more power control procedures for setting a transmit power for an uplink signal in accordance with the energy contributions associated with the SRS.

Abstract: A memory cell comprises channel material, charge-passage material, programmable material, a charge-blocking region, and a control gate. The programmable material comprises at least two regions comprising SiNx having a region comprising SiOy therebetween, where “x” is 0.5 to 3.0 and “y” is 1.0 to 3.0. Methods are disclosed.

Abstract: Certain aspects of the present disclosure provide techniques for dynamic configuration of demodulation reference signals (DMRSs). A method that may be performed by a base station (BS) includes receiving one or more uplink signals from at least one user equipment (UE); estimating a Doppler shift associated with the one or more uplink signals; determining a density of reference signals (RSs) within a slot for the at least one UE based, at least in part, on the estimated Doppler shift associated with the one or more uplink signals; and transmitting information to the at least one UE indicating an allocation of RS resources for the UE, wherein the allocation of the RS resources is based on the density of the RSs for the at least one UE.

Abstract: A pharmaceutical or nutraceutical composition with raft-forming properties comprises (a) sodium alginate, (b) colloidal microcrystalline cellulose (c) a bicarbonate, and (d) a carbonate.

Abstract: Sensor data, including a group of time series elements, is received. A training data set is determined, including by determining for at least a selected time series element in the group of time series elements a corresponding ground truth. The corresponding ground truth is based on a plurality of time series elements in the group of time series elements. A processor is used to train a machine learning model using the training dataset.