Abstract: A chemical mechanical polishing system includes a platen to support a polishing pad having a polishing surface, a conduit having an inlet to be coupled to a gas source, and a dispenser coupled to the conduit and having a convergent-divergent nozzle suspended over the platen to direct gas from the gas source onto the polishing surface of the polishing pad.

Abstract: A chemical mechanical polishing system includes a platen to support a polishing pad having a polishing surface, a source of coolant, a dispenser having one or more apertures suspended over the platen to direct coolant from the source of coolant onto the polishing surface of the polishing pad; and a controller coupled to the source of coolant and configured to cause the source of coolant to deliver the coolant through the nozzles onto the polishing surface during a selected step of a polishing operation.

Abstract: Systems and methods are disclosed configured to train an autoencoder using images that include faces, wherein the autoencoder comprises an input layer, an encoder configured to output a latent image from a corresponding input image, and a decoder configured to attempt to reconstruct the input image from the latent image. An image sequence of a face exhibiting a plurality of facial expressions and transitions between facial expressions is generated and accessed. Images of the plurality of facial expressions and transitions between facial expressions are captured from a plurality of different angles and using different lighting. An autoencoder is trained using source images that include the face with different facial expressions captured at different angles with different lighting, and using destination images that include a destination face.

Abstract: Systems and methods are disclosed configured to train an autoencoder. A data training set is generated comprising images of different faces. A first autoencoder configuration is generated, comprising a first encoder, and a first decoder. The first autoencoder configuration is trained using dataset images, wherein weights associated with the first encoder and weights associated with the first decoder are modified. A second autoencoder configuration is generated comprising the first encoder and a second decoder. The second decoder is trained using images of a first target face. First encoder weights are substantially maintained, and weights associated with the second decoder are modified. An autoencoder comprising the trained first encoder and the trained second decoder generates an output using a source image of a first face having a facial expression, where the facial expression of the first face from the source image is applied to the first specific target face.

Abstract: The present document discloses a camera device comprising a camera sensor, a wireless communication unit configured to receive a wireless communication signal from an external device, a rotating body configured to be rotated together with the camera sensor and the wireless communication unit, a rotation driving unit configured to rotate the rotating body, a wireless power transmitter configured to transfer wireless powers to the external device, and a control unit configured to rotate the rotating body in a panning direction by the rotation driving unit, estimate direction information of the external device at least partially based on a change of a received signal strength of a wireless communication signal caused by the rotation of the rotating body, determine a transmission direction of a wireless power signal for the external device based on the direction information of the external device, and transmit the wireless power signal in the transmission direction.

Abstract: A method for generating an event, which is performed by a doorbell camera system disclosed in the present disclosure may include acquiring video of a visitor, outputting the acquired video of the visitor to a user, learning the user's feedback on the visitor, storing information of the visitor and a learning result of the user's feedback on the visitor, and generating an event based on the information of the visitor and the learning result of the user's feedback.

Abstract: A disposable dental articulator is proposed. The disposable dental articulator includes a body, a socket coupled to the body and a fixing portion one side of which is slidably coupled to the first socket and the other side of which is configured to be coupled to one of a maxillary dental model and a mandibular dental model.

Abstract: Systems and methods are disclosed configured to train an autoencoder. A data training set is generated comprising images of different faces. A first autoencoder configuration is generated, comprising a first encoder, and a first decoder. The first autoencoder configuration is trained using dataset images, wherein weights associated with the first encoder and weights associated with the first decoder are modified. A second autoencoder configuration is generated comprising the first encoder and a second decoder. The second decoder is trained using a plurality of images of a first target face. First encoder weights are substantially maintained, and weights associated with the second decoder are modified. An autoencoder comprising the trained first encoder and the trained second decoder is used to generate an output using a source image of a first face having a facial expression, where the facial expression of the first face from the source image is applied to the first specific target face.

Abstract: Systems and methods are disclosed configured to train an autoencoder using images that include faces, wherein the autoencoder comprises an input layer, an encoder configured to output a latent image from a corresponding input image, and a decoder configured to attempt to reconstruct the input image from the latent image. An image sequence of a face exhibiting a plurality of facial expressions and transitions between facial expressions is generated and accessed. Images of the plurality of facial expressions and transitions between facial expressions are captured from a plurality of different angles and using different lighting. An autoencoder is trained using source images that include the face with different facial expressions captured at different angles with different lighting, and using destination images that include a destination face.

Abstract: A method for generating an event, which is performed by a doorbell camera system disclosed in the present disclosure may include acquiring video of a visitor, outputting the acquired video of the visitor to a user, learning the user's feedback on the visitor, storing information of the visitor and a learning result of the user's feedback on the visitor, and generating an event based on the information of the visitor and the learning result of the user's feedback.

Abstract: Systems and methods are disclosed configured to train an autoencoder using images that include faces, wherein the autoencoder comprises an input layer, an encoder configured to output a latent image from a corresponding input image, and a decoder configured to attempt to reconstruct the input image from the latent image. An image sequence of a face exhibiting a plurality of facial expressions and transitions between facial expressions is generated and accessed. Images of the plurality of facial expressions and transitions between facial expressions are captured from a plurality of different angles and using different lighting. An autoencoder is trained using source images that include the face with different facial expressions captured at different angles with different lighting, and using destination images that include a destination face.

Abstract: Systems and methods are disclosed configured to train an autoencoder using images that include faces, wherein the autoencoder comprises an input layer, an encoder configured to output a latent image from a corresponding input image, and a decoder configured to attempt to reconstruct the input image from the latent image. An image sequence of a face exhibiting a plurality of facial expressions and transitions between facial expressions is generated and accessed. Images of the plurality of facial expressions and transitions between facial expressions are captured from a plurality of different angles and using different lighting. An autoencoder is trained using source images that include the face with different facial expressions captured at different angles with different lighting, and using destination images that include a destination face.

Abstract: The present document discloses a camera device comprising a camera sensor, a wireless communication unit configured to receive a wireless communication signal from an external device, a rotating body configured to be rotated together with the camera sensor and the wireless communication unit, a rotation driving unit configured to rotate the rotating body, a wireless power transmitter configured to transfer wireless powers to the external device, and a control unit configured to rotate the rotating body in a panning direction by the rotation driving unit, estimate direction information of the external device at least partially based on a change of a received signal strength of a wireless communication signal caused by the rotation of the rotating body, determine a transmission direction of a wireless power signal for the external device based on the direction information of the external device, and transmit the wireless power signal in the transmission direction.

Abstract: Systems and methods are disclosed configured to train an autoencoder using images that include faces, wherein the autoencoder comprises an input layer, an encoder configured to output a latent image from a corresponding input image, and a decoder configured to attempt to reconstruct the input image from the latent image. An image sequence of a face exhibiting a plurality of facial expressions and transitions between facial expressions is generated and accessed. Images of the plurality of facial expressions and transitions between facial expressions are captured from a plurality of different angles and using different lighting. An autoencoder is trained using source images that include the face with different facial expressions captured at different angles with different lighting, and using destination images that include a destination face.

Abstract: Systems and methods are disclosed configured to train an autoencoder using images that include faces, wherein the autoencoder comprises an input layer, an encoder configured to output a latent image from a corresponding input image, and a decoder configured to attempt to reconstruct the input image from the latent image. An image sequence of a face exhibiting a plurality of facial expressions and transitions between facial expressions is generated and accessed. Images of the plurality of facial expressions and transitions between facial expressions are captured from a plurality of different angles and using different lighting. An autoencoder is trained using source images that include the face with different facial expressions captured at different angles with different lighting, and using destination images that include a destination face.

Abstract: Systems and methods are disclosed configured to train an autoencoder using images that include faces, wherein the autoencoder comprises an input layer, an encoder configured to output a latent image from a corresponding input image, and a decoder configured to attempt to reconstruct the input image from the latent image. An image sequence of a face exhibiting a plurality of facial expressions and transitions between facial expressions is generated and accessed. Images of the plurality of facial expressions and transitions between facial expressions are captured from a plurality of different angles and using different lighting. An autoencoder is trained using source images that include the face with different facial expressions captured at different angles with different lighting, and using destination images that include a destination face.

Abstract: Systems and methods are disclosed configured to pre-train an autoencoder using images that include faces, wherein the autoencoder comprises an input layer, an encoder configured to output a latent image from a corresponding input image, and a decoder configured to attempt to reconstruct the input image from the latent image. An image sequence of a CGI sculpted and textured face exhibiting a plurality of facial expressions and transitions between facial expressions is accessed. Images of the plurality of facial expressions and transitions between facial expressions are captured from a plurality of different angles. The pre-trained autoencoder is trained using source images that include a CGI face with different facial expressions captured at different angles, and using destination images that include a real face. The trained autoencoder is used to generate an output where the real face in the destination images is swapped with the CGI face, while preserving expressions of the real face.

Abstract: A vehicle modular side view mirror assembly is disclosed. The mirror base has a first section capable of attachment to a vehicle and a second section capable of attachment to a mirror head, wherein the second section is movable relative to the first section. The mirror assembly can include a mirror head having a reflective surface and a motor for moving the mirror head relative to a mirror base, the mirror head detachably connectable to the mirror base. Lidar sensors can be mounted in addition to, or in place of, the side view mirrors. Interchangeable modular heads can allow a user to customize the vehicle to have mirror heads, sensors, both, or neither. Mirror bases can move, allowing the mirrors and/or sensors to be positioned at different angles. In some configurations, the position of the sensors can be dynamically adjusted according to the speed and/or direction of the vehicle.