Abstract: The technology disclosed extracts intensities from sequencing images for base calling target clusters and attenuates spatial crosstalk from neighboring clusters. The technology disclosed accesses a particular section from a plurality of sections of an image output by a sensor, the particular section of the image including at least one pixel depicting intensity emission values from a target cluster and neighboring clusters located across the sensor, and convolves the particular section of the image with a corresponding convolution kernel in a plurality of convolution kernels, to generate a feature map comprising a plurality of feature values. The technology disclosed further assigns a corresponding feature value to the target cluster based on feature values in the plurality of feature values adjoining a center of the target cluster, and processes the corresponding feature value assigned to the target cluster, to base call the target cluster.

Abstract: Systems, methods, and apparatus are described herein for performing sequencing of one or more biological samples in at least two flow cells on a sequencing device. A sequencing system may comprise one or more of a scheduling engine, the sequencing device, and a display. The scheduling engine may maintain scheduling information of a state of compute resources and non-compute resources. The sequencing device may receive the scheduling information from the scheduling engine; determine the state of the compute resources and non-compute resources; determine a sequencing analysis priority associated with performing analysis of the at least two flow cells on the sequencing device; and perform the sequencing task related to the one or more biological samples in the at least two flow cells according to the sequencing analysis priority. The display may display real-time feedback associated with completion of the sequencing task for each flow cell.

Abstract: Disclosed are various examples for threat detection and security for edge devices in communication with Internet-of-Things (IoT) devices. In one example, a baseline behavior profile for a gateway virtual machine is transmitted from a management service to a gateway security process executed in a gateway device. The management service receives an anomaly notification including an indication of an anomaly from the baseline behavior profile. The managements service generates a user interface that shows a description of the anomaly.

Abstract: Disclosed is a system for converting a high-level runtime model to a low-level runtime model where the high-level runtime model runs on a client computer system, and the low-level runtime model runs on a server computer system. The server system has installed thereon a pool of hardware accelerators, and the low-level runtime model is targeted to the pool of accelerators. Outputs of the low-level runtime model are returned to the high-level runtime model as if the high-level runtime model computed the outputs.

Abstract: Disclosed are various examples for threat detection and security for edge devices in communication with Internet-of-Things (IoT) devices. In one example, a baseline behavior profile for a gateway virtual machine is transmitted from a management service to a gateway security process executed in a gateway device. The management service receives an anomaly notification including an indication of an anomaly from the baseline behavior profile. The managements service generates a user interface that shows a description of the anomaly.

Abstract: Disclosed are various examples for threat detection and security for edge devices in communication with Internet-of-Things (IoT) devices. In one example, a profile is associated with a virtual machine of a gateway device. The profile includes an expected behavior for the virtual machine. The virtual machine is executed by a hypervisor of the gateway device. An actual behavior for the virtual machine is determined. A remedial action is performed. The remedial action is based on an anomaly between the expected behavior and the actual behavior.

Abstract: Disclosed are various examples for threat detection and security for edge devices in communication with Internet-of-Things (IoT) devices. In one example, a profile is associated with a virtual machine of a gateway device. The profile includes an expected behavior for the virtual machine. The virtual machine is executed by a hypervisor of the gateway device. An actual behavior for the virtual machine is determined. A remedial action is performed. The remedial action is based on an anomaly between the expected behavior and the actual behavior.