A floor plan image generating methodology is provided. The methodology includes: acquiring a boundary of a target building and a layout constraint of the target building; outputting multiple first floor plan images according to the layout constraint of the target building; selecting multiple second floor plan images from the multiple first floor plan images; applying a layout constraint of each of the second floor plan images to the boundary of the target building, and obtaining a layout of the target building corresponding to each of the multiple second floor plan images; inputting the layout of the target building and the boundary of the target building into a floor plan image generating network; and obtaining a predicted floor plan image of the target building outputted by the floor plan image generating network.

A User Interface (UI) interface object detection system employs an initial dataset comprising a set of images, that may include synthesized images, to train a Machine Learning (ML) engine to generate an initial trained model. A data point generator is employed to generate an updated synthesized image set which is used to further train the ML engine. The data point generator may employ images generated by an application program as a reference by which to generate the updated synthesized image set. The images generated by the application program may be tagged in advance. Alternatively, or in addition, the images generated by the application program may be captured dynamically by a user using the application program.

A method and an intersection testing module in a ray tracing system for determining whether a ray intersects a three-dimensional axis-aligned box. It is determined whether a first condition is satisfied, wherein the first condition is, or is equivalent to,

[00001]$.Math.C_x-C_z\frac{D_x}{D_z}.Math.\le H_z\frac{D_x}{D_z}+H_x.$

It is determined whether a second condition is satisfied, wherein the second condition is, or is equivalent to,

[00002]$.Math.C_y-C_z\frac{D_y}{D_z}.Math.\le H_z\frac{D_y}{D_z}+H_y.$

It is determined whether a third condition is satisfied, wherein the third condition is, or is equivalent to,

[00003]

A computerized system, apparatus, and method of grading collectibles. The system comprises a grading apparatus that receives at least one image of the collectible. The grading apparatus applies at least one processing routine to said at least one image. The grading apparatus generates a grade report of the collectible based at least on results of the at least one processing routine. The system comprises an encasing apparatus configured to encase the graded collectible within a protective slab.

In an example, an apparatus comprises a plurality of execution units, and a first general register file (GRF) communicatively couple to the plurality of execution units, wherein the first GRF is shared by the plurality of execution units. Other embodiments are also disclosed and claimed.

There is described herein systems and methods for camera colliders and shot composition preservation within a 3D virtual environment that prevent a virtual procedural camera from getting stuck behind an object, or penetrating into an object, when filming a subject, while at the same time also maintaining the screen composition of the subject in the camera shot.

The present disclosure provides a system and method for magnetic resonance imaging. The method may include obtaining first k-space data collected from a subject in a non-Cartesian sampling manner. The method may also include generating second k-space data by regridding the first k-space data. The method may further include generating third k-space data by calibrating the second k-space data, wherein a calibrated field of view (FOV) corresponding to the third k-space data is constituted by a central portion of an intermediate FOV corresponding to the second k-space data. The method may still further include reconstructing, using at least one of a compressed sensing algorithm or a parallel imaging algorithm, a magnetic resonance (MR) image of the subject based at least in part on the third k-space data.

Embodiments of this application disclose a terminal device capability transmission method, apparatus, and system. A terminal device reports, to a network device, capability information used to indicate a channel state information CSI reporting capability of the terminal device. The capability information is associated with a quantity, supported by the terminal device in a time-domain unit, of ports of pilots used for CSI measurement, and is used to enable the network device receiving the capability information to learn the CSI reporting capability of the terminal device, thereby determining a CSI measurement configuration of the terminal device.

Disclosed is a method for predicting collisions and conflicts between multiple moving bodies. A method for predicting and avoiding collisions and conflicts between multiple moving bodies comprises the steps of: creating objects by modeling the shape of each of multiple moving bodies; creating two-dimensional circles by modeling the objects by using size information of the objects; modeling the two-dimensional circles into moving disks by using at least one of the moving speeds of the moving bodies, the monitoring time window for the moving bodies, and the size information of the two-dimensional circles; computing a Voronoi diagram between the moving disks and calculating edges of the Voronoi diagram; and during the monitoring time window for the moving bodies, calculating a flipping event in which at least one of the edges of the Voronoi diagram is converted into a vertex and then converted into another edge, and a collision event by which a collision between a pair of moving disks defining an edge of the Voronoi diagram is predicted, and calculating whether actual collisions occur between moving disks triggering the flipping event and between moving disks triggering the collision event, in chronological order of the occurrence of the flipping event and the collision event.

Semantic segmentation techniques and systems are described that overcome the challenges of limited availability of training data to describe the potentially millions of tags that may be used to describe semantic classes in digital images. In one example, the techniques are configured to train neural networks to leverage different types of training datasets using sequential neural networks and use of vector representations to represent the different semantic classes.