Abstract: A system and method for data processing. A method includes sending, by a first system, a plurality of data processing requests to a buffer, wherein the buffer is stored in a second system, wherein the second system is remote from the first system; pulling a portion of the plurality of data processing requests from the buffer; processing the portion of the plurality of data processing requests pulled from the buffer in order to obtain at least one data processing result; and sending the at least one data processing result to the buffer.

Abstract: Systems and methods for visual content processing. A method includes obtaining a subset of media content selected based on outputs of a first machine learning model, wherein the first machine learning model is produced by training a student model using outputs of a teacher model, wherein the outputs of the first machine learning model include a plurality of first predictions for a plurality of portions of the media content; and applying a second machine learning model to the obtained subset of media content, wherein the second machine learning model outputs a plurality of second predictions for respective portions of the plurality of portions, wherein a domain used by the first machine learning model is a subset of a domain used by the second machine learning model.

Abstract: Systems and methods for visual content processing. A method includes applying teacher models to training candidates in order to output instances of a custom object label. The training candidates are selected using a student model based on search configuration parameters. A first set of media content is generated by labeling the training candidates based on the instances of the custom object label output by the teacher models. A custom model is created using the teacher models. The custom model is a machine learning model trained using the first set of media content. A subset of a second set of media content is obtained. The subset of the second set of media content is selected based on outputs of the custom model as applied to the second set of media content. An advanced machine learning model is applied to the obtained subset of the second set of media content.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a hybrid system including a pre-trained deep neural network and a subsequent iterative process using a suitable propagation model to generate diffraction pattern image data for a target holographic image such that the diffraction pattern image data is to generate a holographic image when implemented via a holographic display.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed for non-contact sensing of vital signs. An example electronic device to measure vital signs includes a camera to capture an image; a radar antenna to transmit and receive radar signals; and processing circuitry to: identify a subject in the image; identify a location of the subject in an environment; control the radar antenna to steer the radar signals toward the location; and determine a vital sign of the subject based on a reflected radar signal.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a hybrid system including a pre-trained deep neural network and a subsequent iterative process using a suitable propagation model to generate diffraction pattern image data for a target holographic image such that the diffraction pattern image data is to generate a holographic image when implemented via a holographic display.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a hybrid system including a pre-trained deep neural network and a subsequent iterative process using a sui table propagation model to generate diffraction pattern image data for a target holographic image such that the diffraction pattern image data is to generate a holographic image when implemented via a holographic display.

Abstract: A method and apparatus for auto range control are described. In one embodiment, the apparatus comprises a projector configured to project a sequence of light patterns on an object; a first camera configured to capture a sequence of images of the object illuminated with the projected light patterns; a controller coupled to the projector and first camera and operable to receive the sequence of images and perform range control by controlling power of the sequence of light patterns being projected on the object and exposure time of a camera based on information obtained from the sequence of images captured by the camera.

Abstract: A method and apparatus for auto range control are described. In one embodiment, the apparatus comprises a projector configured to project a sequence of light patterns on an object; a first camera configured to capture a sequence of images of the object illuminated with the projected light patterns; a controller coupled to the projector and first camera and operable to receive the sequence of images and perform range control by controlling power of the sequence of light patterns being projected on the object and exposure time of a camera based on information obtained from the sequence of images captured by the camera.

Abstract: A method and apparatus for auto range control are described. In one embodiment, the apparatus comprises a projector configured to project a sequence of light patterns on an object; a first camera configured to capture a sequence of images of the object illuminated with the projected light patterns; a controller coupled to the projector and first camera and operable to receive the sequence of images and perform range control by controlling power of the sequence of light patterns being projected on the object and exposure time of a camera based on information obtained from the sequence of images captured by the camera.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a hybrid system including a pre-trained deep neural network and a subsequent iterative process using a sui table propagation model to generate diffraction pattern image data for a target holographic image such that the diffraction pattern image data is to generate a holographic image when implemented via a holographic display.

Abstract: A method and apparatus for auto range control are described. In one embodiment, the apparatus comprises a projector configured to project a sequence of light patterns on an object; a first camera configured to capture a sequence of images of the object illuminated with the projected light patterns; a controller coupled to the projector and first camera and operable to receive the sequence of images and perform range control by controlling power of the sequence of light patterns being projected on the object and exposure time of a camera based on information obtained from the sequence of images captured by the camera.

Abstract: A method and apparatus for auto range control are described. In one embodiment, the apparatus comprises a projector configured to project a sequence of light patterns on an object; a first camera configured to capture a sequence of images of the object illuminated with the projected light patterns; a controller coupled to the projector and first camera and operable to receive the sequence of images and perform range control by controlling power of the sequence of light patterns being projected on the object and exposure time of a camera based on information obtained from the sequence of images captured by the camera.

Abstract: In one example an electronic device comprises a proximity detector, a camera, and a controller, comprising logic, at least partly including hardware logic, to receive, from the proximity sensor, an indication that an object approaching the electronic device is within a predetermined distance while the electronic device is in a first low-power state, and in response to the indication, to activate the camera on the electronic device while the electronic device remains in a low-power state and determine whether an image input to the camera is a human while the electronic device remains in a low-power state. Other examples may be described.

Abstract: In one example an electronic device comprises a proximity detector, a camera, and a controller, comprising logic, at least partly including hardware logic, to receive, from the proximity sensor, an indication that an object approaching the electronic device is within a predetermined distance while the electronic device is in a first low-power state, and in response to the indication, to activate the camera on the electronic device while the electronic device remains in a low-power state and determine whether an image input to the camera is a human while the electronic device remains in a low-power state. Other examples may be described.

Abstract: A method and apparatus for auto range control are described. In one embodiment, the apparatus comprises a projector configured to project a sequence of light patterns on an object; a first camera configured to capture a sequence of images of the object illuminated with the projected light patterns; a controller coupled to the projector and first camera and operable to receive the sequence of images and perform range control by controlling power of the sequence of light patterns being projected on the object and exposure time of a camera based on information obtained from the sequence of images captured by the camera.

Abstract: A method and apparatus to provide cryptographic integrity checks and replay protection to protect against hardware attacks on system memory is provided. A mode of operation for block ciphers enhances the standard XTS-AES mode of operation to perform memory encryption by extending a tweak to include a “time stamp” indicator. A tree-based replay protection scheme uses standard XTS-AES to encrypt contents of a cache line in the system memory. A Message-Authentication Code (MAC) for the cache line is encrypted using enhanced XTS-AES and a “time stamp” indicator associated with the cache line. The “time stamp indicator” is stored in a processor.

Abstract: A method and apparatus to provide cryptographic integrity checks and replay protection to protect against hardware attacks on system memory is provided. A mode of operation for block ciphers enhances the standard XTS-AES mode of operation to perform memory encryption by extending a tweak to include a “time stamp” indicator. A tree-based replay protection scheme uses standard XTS-AES to encrypt contents of a cache line in the system memory. A Message-Authentication Code (MAC) for the cache line is encrypted using enhanced XTS-AES and a “time stamp” indicator associated with the cache line. The “time stamp indicator” is stored in a processor.