Abstract: The present invention relates to a Camptothecin compound having anti-tumor activity, a preparation method therefor, and an application thereof. Specifically, the present invention relates to a compound as shown below or a pharmaceutically acceptable form thereof, a pharmaceutical composition thereof, a preparation method therefor, and a use thereof.

Abstract: Embodiments of the present disclosure relate to an audio system for artificial reality applications. One or more transducers of the audio system output, in accordance with audio instructions, one or more ultrasonic pressure waves simulating a virtual audio source near an ear of a user of the headset. A controller of the audio system generates the audio instructions such that the one or more ultrasonic pressure waves form at least a portion of audio content for presentation to the user. An array of microphones of the audio system detects audio signals in a local area. A deep neural network of the audio system processes the detected audio signals to generate enhanced audio content, and the one or more transducers present the enhanced audio content to a user.

Abstract: A system receives monaural sound which includes speech and background noises. The received sound is divided by frequency and time into time-frequency units (TFUs). Each TFU is classified as speech or non-speech by a processing unit. The processing unit for each frequency range includes at least one of a deep neural network (DNN) or a linear support vector machine (LSVM). The DNN extracts and classifies the features of the TFU and includes a pre-trained stack of Restricted Boltzmann Machines (RBM), and each RBM includes a visible and a hidden layer. The LSVM classifies each TFU based on extracted features from the DNN, including those from the visible layer of the first RBM, and those from the hidden layer of the last RBM in the stack. The LSVM and DNN include training with a plurality of training noises. Each TFU classified as speech is output.

Abstract: A system receives monaural sound which includes speech and background noises. The received sound is divided by frequency and time into time-frequency units (TFUs). Each TFU is classified as speech or non-speech by a processing unit. The processing unit for each frequency range includes at least one of a deep neural network (DNN) or a linear support vector machine (LSVM). The DNN extracts and classifies the features of the TFU and includes a pre-trained stack of Restricted Boltzmann Machines (RBM), and each RBM includes a visible and a hidden layer. The LSVM classifies each TFU based on extracted features from the DNN, including those from the visible layer of the first RBM, and those from the hidden layer of the last RBM in the stack. The LSVM and DNN include training with a plurality of training noises. Each TFU classified as speech is output.

Abstract: The present invention relates to a method of producing the sensors for monitoring corrosion of heat exchanger tubes in a thermal power plant. The sensor is made of the tubes taken out from actual heat-exchanger. As a result, the sensor not only has the same material as the actual heat-exchanger tubes, but also has the same surface state where it contacts the working medium. Therefore, a serious technical difficulties have been solved that the sensor made by the prior art can't measure different corrosion states of actual heat-exchanger tubes, that the corrosion rate measured is different from the corrosion rate of actual heat-exchanger tubes, and that the cost of anti-corrosive treatments is high . By use of the sensor made by the present invention, the different corrosion states of actual heat-exchanger tubes can be measured, not only the rate of uniform corrosion of actual heat-exchanger tubes can be measured but also the rate of localized corrosion of the heat-exchanger tubes can be measured.

Abstract: The present invention relates to a method of producing the sensors for monitoring corrosion of heat exchanger tubes in a thermal power plant. The sensor is made of the tubes taken out from actual heat-exchanger. As a result, the sensor not only has the same material as the actual heat-exchanger tubes, but also has the same surface state where it contacts the working medium. Therefore, a serious technical difficulties have been solved that the sensor made by the prior art can't measure different corrosion states of actual heat-exchanger tubes, that the corrosion rate measured is different from the corrosion rate of actual heat-exchanger tubes, and that the cost of anti-corrosive treatments is high . By use of the sensor made by the present invention, the different corrosion states of actual heat-exchanger tubes can be measured, not only the rate of uniform corrosion of actual heat-exchanger tubes can be measured but also the rate of localized corrosion of the heat-exchanger tubes can be measured.