Abstract: The present disclosure provides methods for inhibiting entry of a virus into a host cell using picolinic acid or a salt or derivative thereof. The present disclosure also provides methods for treating or preventing a viral infection in a subject by administering picolinic acid or a salt or derivative thereof. In particular, the methods of the present disclosure are effective for enveloped viruses and syncytium-forming viruses. In some embodiments, the present disclosure provides methods of treating or preventing a SARS CoV-2, Influenza A virus, human parainfluenza virus, herpes simplex virus, Japanese encephalitis virus, Zika virus, or a flavivirus infection.

Abstract: Techniques are generally described for object detection in image data. First image data comprising a three-dimensional model representing an object may be received. First background image data comprising a first plurality of pixel values may be received. A first feature vector representing the three-dimensional model may be generated. A second feature vector representing the first plurality of pixel values of the first background image data may be generated. A first machine learning model may generate a transformed representation of the three-dimensional model using the first feature vector. First foreground image data comprising a two-dimensional representation of the transformed representation of the three-dimensional model may be generated. A frame of composite image data may be generated by combining the first foreground image data with the first background image data.

Abstract: Devices and techniques are generally described for estimating three-dimensional pose data. In some examples, a first machine learning network may generate first three-dimensional (3D) data representing input 2D data. In various examples, a first 2D projection of the first 3D data may be generated. A determination may be made that the first 2D projection conforms to a distribution of natural 2D data. A second machine learning network may generate parameters of a 3D model based at least in part on the input 2D data and based at least in part on the first 3D data. In some examples, second 3D data may be generated using the parameters of the 3D model.

Abstract: The present disclosure provides methods for inhibiting entry of a virus into a host cell using picolinic acid or a salt or derivative thereof. The present disclosure also provides methods for treating or preventing a viral infection in a subject by administering picolinic acid or a salt or derivative thereof. In particular, the methods of the present disclosure are effective for enveloped viruses and syncytium-forming viruses. In some embodiments, the present disclosure provides methods of treating or preventing a SARS CoV-2, Influenza A virus, human parainfluenza virus, herpes simplex virus, Japanese encephalitis virus, Zika virus, or a flavivirus infection.

Abstract: Techniques are generally described for object detection in image data. First image data comprising a three-dimensional model representing an object may be received. First background image data comprising a first plurality of pixel values may be received. A first feature vector representing the three-dimensional model may be generated. A second feature vector representing the first plurality of pixel values of the first background image data may be generated. A first machine learning model may generate a transformed representation of the three-dimensional model using the first feature vector. First foreground image data comprising a two-dimensional representation of the transformed representation of the three-dimensional model may be generated. A frame of composite image data may be generated by combining the first foreground image data with the first background image data.

Abstract: Techniques are generally described for object detection in image data. First image data comprising a first plurality of pixel values representing an object and a second plurality of pixel values representing a background may be received. First foreground image data and first background image data may be generated from the first image data. A first feature vector representing the first plurality of pixel values may be generated. A second feature vector representing a first plurality of pixel values of second background image data may be generated. A first machine learning model may determine a first operation to perform on the first foreground image data. A transformed representation of the first foreground image data may be generated by performing the first operation on the first foreground image data. Composite image data may be generated by compositing the transformed representation of the first foreground image data with the second background image data.

Abstract: A netlist of a multiple voltage circuit design having a plurality of power domains is established, then inter-power domain (IPD) paths traversing the circuit design are identified, according to whether they traverse multi-supply elements, or are clock paths capturing such a path. The netlist is then pruned to disable or remove cells or stages not traversed by an IPD path. A timing analyzer conducts a multi-domain timing analysis of the IPD timing paths in the pruned IPD netlist. Thereby, the circuit design is thoroughly tested according to the applicable ranges of voltage conditions without excessive runtime.

Abstract: A static timing analysis method for input/output modes of an integrated circuit design, that includes loading the integrated circuit design described in a hardware description language into a memory. An active zone for static timing analysis is defined, which comprises logic and interconnect between an input/output port and a selected level of sequential logic elements upstream from an input port and downstream from an output port. A description of the active zone is generated using the hardware description language. Then a static timing analysis is performed on the logic of the active zone.