Abstract: Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. A transformed version of image pixels is accessed as input to an attention layer of a machine learning model. A number of local attention operations to apply, in one transformer, to the transformed version of image pixels is selected based at least in part on a size of the transformed version of image pixels. A transformer output for the attention layer of the machine learning model is generated based on applying the number of local attention operations and at least one global attention operation to the transformed version of image pixels.

Abstract: In implementations of systems for generating images for human reposing, a computing device implements a reposing system to receive input data describing an input digital image depicting a person in a first pose, a first plurality of keypoints representing the first pose, and a second plurality of keypoints representing a second pose. The reposing system generates a mapping by processing the input data using a first machine learning model. The mapping indicates a plurality of first portions of the person in the second pose that are visible in the input digital image and a plurality of second portions of the person in the second pose that are invisible in the input digital image. The reposing system generates an output digital image depicting the person in the second pose by processing the mapping, the first plurality of keypoints, and the second plurality of keypoints using a second machine learning model.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for a priority-aware transmit power control in compliance with a radio frequency (RF) exposure limit. An example method of wireless communication includes obtaining a transmit power budget for a time interval. The method further includes obtaining at least a first packet having a first priority and a second packet having a second priority lower than the first priority for transmission in the time interval. The method further includes determining a first transmit power for a first signal representing at least the first packet based at least in part on the transmit power budget, the first priority, and a time elapsed in the time interval. The method further includes transmitting the first signal representing at least the first packet at the determined first transmit power.

Abstract: The system described herein introduces a file system operation that enables a file system to delete a directory tree without the overhead associated with opening each file in the directory tree, marking the file for deletion, and closing the file. Accordingly, the file system is able to delete directories and files in the directory tree independent of communicating with, and being transparent to, filter drivers in the operating system. The file system operation is implemented via a directory tree delete attribute included in a request for deletion of a directory tree. The file system can perform the file system operation in a foreground manner or in a deferred manner based on the directory tree delete attribute respectively specifying foreground deletion or deferred deletion. By operating on files and directories in a single invocation, the file system can optimize overheads related to security checks and data transfer across a user-to-kernel boundary.

Abstract: Monitoring of animals in a herd has various challenges. A method and a system for monitoring and measuring the behavior of a plurality of animals in a herd chamber is provided. The disclosure provides a method to develop a model to identify the animal interactions between a given set of herds of two or more animals. The system uses image, RFID sensors, animal phenotyping information such as species, breed, age and reproduction cycle information to generate an animal profile. This profile is used to quantify the interaction into an aggression score to the animals in the given herd. The aggression score over a given duration can help herd manager to put the animals with similar aggression score together. This will also help analyze the temporal aspects of the aggression score for a given animal and take preventive measures.

Abstract: The disclosure addresses problems associated with a systematic integration of multi-modal data for effective training, and handling of large volume of data because of high resolution of the multiple modalities. Embodiments herein provide a method and a system for a distributed training of a multi-modal data fusion transformer. Herein, a distributed training approach called a Distributed Architecture for Fusion-Transformer Training Acceleration (DAFTA) is proposed for processing large multimodal remote sensing data. DAFTA is enabled to handle any combination of remote sensing modalities. Additionally, similarity of feature space is leveraged to optimize the training process and to achieve the training with reduced data set which is equivalent to a complete data set. The proposed approach provides a systematic and efficient method for managing large sensing data and enables accurate and timely insights for various applications.

Abstract: An method for load balancing session initiation protocol (SIP) registration requests using Cx health status includes monitoring, by an S-CSCF, at least one health parameter of a Cx interface associated with the S-CSCF. The method further includes determining, by the S-CSCF, a health category of the Cx interface associated with the S-CSCF based on the at least one health parameter. The method further includes sending, by the S-CSCF to an Interrogating Call Session Control Function (I-CSCF), an indication of the health category of the Cx interface associated with the S-CSCF. The method further includes load balancing, by the I-CSCF, SIP registration requests between the S-CSCF and at least one additional S-CSCF based on the received indication of the health category of the Cx interface associated with the S-CSCF.

Abstract: Synthetic endornaviral satellite RNA molecules and satellite particles containing the same are disclosed. The synthetic endornaviral satellite RNA molecules can include coding and/or non-coding cargo sequences, and are heritable through generations of plants. Also disclosed are methods of using the endornaviral satellite RNA molecules and satellite particles containing the same to change plant phenotypes, improve plant stress resistance, and improve plant pest and pathogen resistance.

Abstract: Embodiments herein disclose systems and methods for relay selection in mmWave networks, wherein the relay is selected using a Q-learning based method and the selected relay can be used by a source for communication with a destination in each time slot. Embodiments herein disclose systems and methods for relay selection in mmWave networks, wherein there are multiple source-destination pairs, and a relay is assigned to each source-destination pair in each time slot using the Q-learning based method.

Abstract: Various embodiments of the present disclosure provide machine learning techniques for transforming disparate, third-party datasets to canonical representations. The techniques include generating, using a machine learning prediction model, a canonical representation for an input dataset. The machine learning prediction model is previously trained using permutative input embeddings for a training dataset based on canonical data entity features, such that each permutative input embedding corresponds to a different sequence of the canonical data entity features. The permutative input embeddings are leveraged to generate a latent representation for the training dataset. The latent representation is combined with a canonical data map to generate an alignment vector, which is refined to generate an output vector for the input dataset. The machine learning prediction model is trained using a model loss generated based on a comparison of the output vector with a corresponding labeled vector.

Abstract: Systems and techniques are provided for processing image data. According to some aspects, a computing device can generate a gradient (e.g., a classifier gradient using a trained classifier) associated with a current sample. The computing device can combine the gradient with an iterative model estimated score function or data associated with the current sample to generate a score function estimate. The computing device can predict, using the diffusion machine learning model and based on the score function estimate, a new sample.

Abstract: Techniques for combining the functionality of fabric interconnects and switches (e.g., Top-of-Rack (ToR) switches) into one network entity, thereby reducing the number of devices in a fabric and complexity of communications in the fabric. By collapsing FI and ToR switch functionality into one network entity, server traffic may be directly forwarded by the ToR switch and an entire tier is now eliminated from the topology hierarchy which may improve the control, data, and management plane. Further, this disclosure describes techniques for dynamically managing the number of gateway proxies running on one or more computer clusters based on a number of managed switch domains.

Abstract: Described herein are systems and methods in which a surgeon can use computer-implemented deformity assessment and correction tools to create 3D models of a bone. To ensure adequate healing, a surgeon may choose to use a prefabricated bone plate, a semi-customized bone plate, or a fully-customized bone plate to hold first and second bone portions in a corrected position. To select the appropriate prefabricated bone plate, the surgeon may identify three landmark locations on the first and second bone portions corresponding to desired fixation hole locations on the bone plate. The surgeon can then use a software application to evaluate multiple bone plate designs in a library and compare the average proximity of the landmark locations to fixation hole locations on each of the bone plate designs. Then, the surgeon can determine which bone plate design best fits the patient anatomy based on his comparison.

Abstract: Systems, apparatuses and methods may provide for technology to store hardware details associated with a plurality of hardware components of a computing device. The technology further extracts a first hardware identifier from first authentication data received from the computing device, where the first hardware identifier is associated with a first hardware component of the computing device, and determines whether to validate the first hardware component based on a comparison of the first hardware identifier to a first hardware detail of the hardware details.

Abstract: Embodiments include systems and methods for generative image filling based on text and a reference image. In one aspect, the system obtains an input image, a reference image, and a text prompt. Then, the system encodes the reference image to obtain an image embedding and encodes the text prompt to obtain a text embedding. Subsequently, a composite image is generated based on the input image, the image embedding, and the text embedding.

Abstract: An apparatus includes a processor and a memory having instructions stored thereon that, when executed by the processor, cause the apparatus to process a first notification received from a first network node to determine a first status of the first network node. The apparatus is also caused to process a second notification received from a second network node to determine a second status of the second network node. The apparatus is further caused to, response to determining the first status and the second status indicate an alarm state, cause a workload assigned to a first data center associated with the first network node and the second network node to be reassigned to a second data center different from the first data center.

Abstract: Methods, compositions and kits for regulating complement activity or treating a complement activity disorder in a subject using soluble, membrane-independent CD59 protein, methods of assaying human macular degeneration (MD), and methods and kits for assaying potential therapeutic agents for treatment of human MD are provided herein.

Abstract: A method comprises receiving one or more indications corresponding to one or more derivative video files. Each derivative video file of the one or more derivative video files has a different frame rate. The method further comprises selecting an indication of a derivative video file with a corresponding derivative video frame rate. The method further comprises receiving the derivative video file and an original video frame rate. The method further comprises determining a playback frame rate to slow a derivative video of the derivative video file using a desired frame rate, the derivative video frame rate, and the original video frame rate. The method further comprises displaying the derivative video at the determined playback frame rate.

Abstract: Embodiment herein provides a method for classifier based constraint management. The method comprises creating a constraint graph database comprising a plurality of constraint classes associated with a plurality of Application program interfaces (APIs); receiving a request to perform at least one operation associated with at least one entity; determining at least one constraint class from the plurality of constraint classes based on the information about the at least one entity to perform the at least one operation; invoking the at least one API from the plurality of APIs based on the at least one constraint class to access the plurality of constraints associated with the at least one constraint class; and performing the at least one operation associated with the at least one entity based on the plurality of constraints associated with the at least one constraint class and the information about the at least one entity.

Abstract: In implementations of systems for image inversion using multiple latent spaces, a computing device implements an inversion system to generate a segment map that segments an input digital image into a first image region and a second image region and assigns the first image region to a first latent space and the second image region to a second latent space that corresponds to a layer of a convolutional neural network. An inverted latent representation of the input digital image is computed using a binary mask for the second image region. The inversion system modifies the inverted latent representation of the input digital image using an edit direction vector that corresponds to a visual feature. An output digital image is generated that depicts a reconstruction of the input digital image having the visual feature based on the modified inverted latent representation of the input digital image.