Abstract: Exemplary systems and methods for visualization of data analysis are provided. In various embodiments, a method comprises accessing a database, analyzing the database to identify clusters of data, generating an interactive visualization comprising a plurality of nodes and a plurality of edges wherein a first node of the plurality of nodes represents a cluster and an edge of the plurality of edges represents an intersection of nodes of the plurality of nodes, selecting and dragging the first node in response to a user action, and reorienting the interactive visualization in response to the user action of selecting and dragging the first node.

Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive an image frame from a camera, generate a feature map from the image frame, generate a depth map from the feature map, classify an object in the image frame based on the feature map, and estimate a distance to the object based on the depth map and based on an input to generating the feature map.

Abstract: In variants, a method for automated gamete selection can include: sampling a video of a scene having a plurality of gametes, tracking each gamete across successive images, and determining attribute values for a gamete, and selecting the gamete. The attribute values can be determined using a model trained to predict the attribute values for the gamete based on a video.

Abstract: In variants, a method for automated gamete selection can include: sampling a video of a scene having a plurality of gametes, tracking each gamete across successive images, and determining attribute values for a gamete, and selecting the gamete. The attribute values can be determined using a model trained to predict the attribute values for the gamete based on a video.

Abstract: In variants, a method for automated gamete selection can include: sampling a video of a scene having a plurality of gametes, tracking each gamete across successive images, and determining attribute values for a gamete, and selecting the gamete. The attribute values can be determined using a model trained to predict the attribute values for the gamete based on a video.

Abstract: In variants, a method for automated gamete selection can include: sampling a video of a scene having a plurality of gametes, tracking each gamete across successive images, and determining attribute values for a gamete, and selecting the gamete. The attribute values can be determined using a model trained to predict the attribute values for the gamete based on a video.

Abstract: A system and method for managing shared memory packet buffers is disclosed. In some embodiments, the system is configured to receive and classify a packet as one of: network-network, network-host, host-network, or host-host; select a minimum guarantee space for the packet according to the classification thereof; if the selected minimum guarantee space is available, store the packet therein; otherwise, if a dedicated shared space is available, store the packet therein; otherwise, if a global shared space is available, store the packet therein; and otherwise, drop the packet.

Abstract: Systems and methods for estimation of vehicle hitchball location are disclosed. A plurality of image frames may be received from a rear-facing camera of a vehicle. The rear-facing camera may be directed at a front of a trailer that is coupled to the vehicle at a hitchball. An approximate lateral location of the hitchball coupled to the vehicle may be determined by obtaining a plurality of cropped images along a hitch drawbar coupled to the vehicle by stepping along the vertical direction, and performing a stepwise lateral scan, centered at the hitch drawbar.

Abstract: A system for one-sided read remote memory access is disclosed. In some embodiments, the system is configured to receive, at a responder SFA, a first packet comprising a read request to read a remote memory of a second host from a first host, wherein a payload of the first packet is mapped to be a transmit header queue (TxHQ) entry (TxHQE), and the TxHQE includes a pointer to a memory map; separate, the received packet into portions including a upper level protocol (ULP) portion, the ULP portion being the TxHQE; create a ULP header queue for the TxHQE; generate a read response based on mapping the ULP header queue into hardware as the TxHQ, wherein the TxHQE includes a pointer to data from a valid memory region of the second host identified by the memory mapping; and transmit a read response packet with the data identified by the pointer using the TxHQ to the first host.

Abstract: The disclosure is generally directed to systems and methods for inference quality determination of a deep neural network (DNN) without requiring ground truth information for use in driver-assisted vehicles, including receiving an image frame from a source; applying a normal inference DNN model to the image frame to produce a first inference with a first bounding box using a normal inference DNN model; applying a deep inference DNN model to a plurality of filtered versions of the image frame to produce a plurality of deep inferences with a plurality of bounding boxes; comparing the plurality of bounding boxes to identify a cluster condition of the plurality of bounding boxes; and determining an inference quality of the image frame of the normal inference DNN model as a function of the cluster condition.

Abstract: A distribution of a plurality of predictions generated by a deep neural network using sensor data is calculated, and the deep neural network includes a plurality of neurons. At least one of a measurement or a classification corresponding to an object is determined based on the distribution. The deep neural network generates each prediction of the plurality of predictions with a different number of neurons.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to receive image data from a camera, generate a depth map from the image data, detect an object in the image data, apply a bounding box circumscribing the object to the depth map, mask the depth map by setting depth values for pixels in the bounding box in the depth map to a depth value of a closest pixel in the bounding box, and determine a distance to the object based on the masked depth map. The closest pixel is closest to the camera of the pixels in the bounding box.

Abstract: A system for congestion control using a flow level transmit mechanism is disclosed. In some embodiments, the system comprises a source SFA and a receive SFA. The source SFA is configured to detect and classify a congestion notification packet (CNP) generated based on congestion in a network; select a receive block from a plurality of receive blocks based on the CNP; forward the CNP to a dedicated congestion notification queue of the receive block; identify a transmit queue from a plurality of transmit blocks based on processing the congestion notification queue, wherein the transmit queue originated a particular transmit flow causing the congestion; and stop the transmit queue.

Abstract: A multi-plane, multi-protocol memory switch system is disclosed. In some embodiments, a memory switch includes a plurality of switch ports, the memory switch connectable to one or more root complex (RC) devices through one or more respective switch ports of the plurality of switch ports, and the memory switch connectable to a set of endpoints through a set of other switch ports of the plurality of switch ports, wherein the set includes zero or multiple endpoints; a cacheline exchange engine configured to provide a data-exchange path between two endpoints and to map an address space of one endpoint to an address space of another endpoint; and a bulk data transfer engine configured to facilitate data-exchange between two endpoints as a source-destination data stream, one endpoint being designated a source address and another endpoint being designated a destination address.

Abstract: An example method comprises receiving data points, determining at least one size of a plurality of subsets based on a constraint of at least one computation device or an analysis server, transferring each of the subsets to different computation devices, each computation device selecting a group of data points to generate a first sub-subset of landmarks, add non-landmark data points that have the farthest distance to the closest landmark to create an expanded sub-subset of landmarks, create an analysis landmark set based on a combination of expanded sub-subsets of expanded landmarks from different computation devices, perform a similarity function on the analysis landmark set, generate a cover of the mathematical reference space to create overlapping subsets, cluster the mapped landmark points based on the overlapping subsets, create a plurality of nodes, each node being based on the clustering, each landmark point being a member of at least one node.

Abstract: A first image can be acquired from a first sensor included in a vehicle and input to a deep neural network to determine a first bounding box for a first object. A second image can be acquired from the first sensor. Input latitudinal and longitudinal motion data from second sensors included in the vehicle corresponding to the time between inputting the first image and inputting the second image. A second bounding box can be determined by translating the first bounding box based on the latitudinal and longitudinal motion data. The second image can be cropped based on the second bounding box. The cropped second image can be input to the deep neural network to detect a second object. The first image, the first bounding box, the second image, and the second bounding box can be output.

Abstract: In variants, a method for automated gamete selection can include: sampling a video of a scene having a plurality of gametes, tracking each gamete across successive images, and determining attribute values for a gamete, and selecting the gamete. The attribute values can be determined using a model trained to predict the attribute values for the gamete based on a video.

Abstract: In variants, a method for automated gamete selection can include: sampling a video of a scene having a plurality of gametes, tracking each gamete across successive images, and determining attribute values for a gamete, and selecting the gamete. The attribute values can be determined using a model trained to predict the attribute values for the gamete based on a video.

Abstract: In variants, a method for DFI-based gamete selection can include: sampling a video of a scene having a plurality of gametes, tracking each gamete across successive images, and determining attribute values for a gamete, and selecting the gamete. The attribute values can be determined using a model trained to predict the attribute values for the gamete based on a video.

Abstract: In variants, a method for automated gamete selection can include: sampling a video of a scene having a plurality of gametes, tracking each gamete across successive images, and determining attribute values for a gamete, and selecting the gamete. The attribute values can be determined using a model trained to predict the attribute values for the gamete based on a video.