Abstract: Embodiments of this application provide a terminal device, including a middle frame, a heat source device, a first heat dissipation assembly, and a second heat dissipation assembly, where the first heat dissipation assembly is disposed on one side of the middle frame, the heat source device and the second heat dissipation assembly are disposed on the other side of the middle frame, and at least one of the first heat dissipation assembly and the second heat dissipation assembly is a graphene heat dissipation assembly.

Abstract: The present disclosure relates to a broadcast directing method, apparatus and system, and a non-transitory computer-storable medium, which relate to the technical field of computers. The broadcast directing method includes acquiring a reference video stream from a reference camera position; performing event recognition on the reference video stream to obtain at least one reference event frame interval; determining a local sequence of each reference event frame interval according to a correspondence relationship between the event and a camera position identification, the local sequence comprising a camera position identification of each frame image of a video to be played corresponding to the reference event frame interval and a frame identification corresponding to the camera position identification; generating a broadcast directing sequence according to the local sequence; and generating a broadcast directing video according to the broadcast directing sequence and a video stream of a camera position.

Abstract: A device is disclosed. The device may include an input buffer to receive a first low bit width message. A reconstruction circuit may implement a reconstruction function on the first low bit width message, producing a first high bit width message. A computation circuit may implementing a computation function on the first high bit width message, producing a second high bit width message. A quantization circuit may implementing a quantization function on the second high bit width message, producing a second low bit width message. A decision buffer may then store the second low bit width message. The reconstruction function and the quantization function may vary depending on an iteration and a layer of the device.

Abstract: A device is disclosed. The device may include an input buffer to receive a first low bit width message. A reconstruction circuit may implement a reconstruction function on the first low bit width message, producing a first high bit width message. A computation circuit may implementing a computation function on the first high bit width message, producing a second high bit width message. A quantization circuit may implementing a quantization function on the second high bit width message, producing a second low bit width message. A decision buffer may then store the second low bit width message. The reconstruction function and the quantization function may vary depending on an iteration and a layer of the device.

Abstract: Dynamic features are utilized with CRFs to handle long-distance dependencies of output labels. The dynamic features present a probability distribution involved in explicit distance from/to a special output label that is pre-defined according to each application scenario. Besides the number of units in the segment (from the previous special output label to the current unit), the dynamic features may also include the sum of any basic features of units in the segment. Since the added dynamic features are involved in the distance from the previous specific label, the searching lattice associated with Viterbi searching is expanded to distinguish the nodes with various distances. The dynamic features may be used in a variety of different applications, such as Natural Language Processing, Text-To-Speech and Automatic Speech Recognition. For example, the dynamic features may be used to assist in prosodic break and pause prediction.

Abstract: Instead of relying on humans to subjectively evaluate speech intelligibility of a subject, a system objectively evaluates the speech intelligibility. The system receives speech input and calculates confidence scores at multiple different levels using a Template Constrained Generalized Posterior Probability algorithm. One or multiple intelligibility classifiers are utilized to classify the desired entities on an intelligibility scale. A specific intelligibility classifier utilizes features such as the various confidence scores. The scale of the intelligibility classification can be adjusted to suit the application scenario. Based on the confidence score distributions and the intelligibility classification results at multiple levels an overall objective intelligibility score is calculated. The objective intelligibility scores can be used to rank different subjects or systems being assessed according to their intelligibility levels. The speech that is below a predetermined intelligibility (e.g.

Abstract: Dynamic features are utilized with CRFs to handle long-distance dependencies of output labels. The dynamic features present a probability distribution involved in explicit distance from/to a special output label that is pre-defined according to each application scenario. Besides the number of units in the segment (from the previous special output label to the current unit), the dynamic features may also include the sum of any basic features of units in the segment. Since the added dynamic features are involved in the distance from the previous specific label, the searching lattice associated with Viterbi searching is expanded to distinguish the nodes with various distances. The dynamic features may be used in a variety of different applications, such as Natural Language Processing, Text-To-Speech and Automatic Speech Recognition. For example, the dynamic features may be used to assist in prosodic break and pause prediction.