Abstract: Provided are a porous silicone resin and a light-weight flexible flame-retardant composite material, belonging to the technical field of nano-porous materials. The porous silicon resin is prepared from a porous silicon resin precursor solution by means of curing and drying processes. The porous silicone resin precursor solution includes a flame retardant, a surfactant, a siloxane monomer, a catalyst, an auxiliary agent and a solvent. The porous silicone resin precursor solution is obtained by firstly dissolving the flame retardant and the surfactant in the solvent, and then adding the siloxane monomer, the catalyst and the auxiliary agent and mixing them uniformly. The porous silicone resin and the light-weight flexible flame-retardant composite material prepared therefrom have the excellent properties of high porosity, high flame retardancy, high elasticity and high strain.

Abstract: Provided is a projection screen. The projection screen includes a first transparent cover plate, a transparent touch panel, and a first nanoparticle layer, wherein the first nanoparticle layer and the first transparent cover plate are sequentially laminated on one side of the transparent touch panel, and the first nanoparticle layer comprises a plurality of dispersed nanoparticles of different particle sizes. A method for manufacturing a projection screen, a projection display system, and a projection display method are also provided.

Abstract: A sense amplifier, a memory and a method for controlling the sense amplifier are provided. The sense amplifier includes: an amplification module, arranged to read data in a memory cell; and a control module, electrically connected to the amplification module. In a first offset compensation stage of the sense amplifier, the control module is arranged to configure the amplification module to include a first inverter and a second inverter, and each of the first inverter and the second inverter is an inverter an input terminal and an output terminal connected to each other; and in a second offset compensation stage of the sense amplifier, the control module is arranged to configure the amplification module to include a current mirror structure.

Abstract: The sense amplifier includes: an amplification module configured to amplify a voltage transmitted by a bit line or a reference bit line, when the sense amplifier is at an amplification stage; a first switch module configured to control the amplification module to be disconnected from the reference bit line, when the sense amplifier performs a read operation for the bit line and is at the amplification stage. In the disclosure, the power consumption of the sense amplifier may be reduced.

Abstract: A tiled display apparatus includes a main display driving board and display modules. Each display module includes a system board. The main display driving board is coupled to a first stage system board. The main display driving board is configured to receive a display signal, acquire a main screen region display signal, and transmit the main screen region display signal to the first stage system board. The system board is configured to receive the main screen region display signal, and extract a group of main screen region display data corresponding thereto from the main screen region display signal to control a display module to which the system board belongs to display an image according to the main screen region display data. Except for a last stage system board, remaining system boards are each further configured to transmit the received main screen region display signal to a next stage system board.

Abstract: A method for image segmentation comprises receiving volumetric image data for an anatomical region and generating a first volumetric patch from the volumetric image data. The method also comprises generating a second volumetric patch from the first volumetric patch by weighting a plurality of volumetric units in the first volumetric patch and receiving the second volumetric patch as an input to a convolutional neural network. The method also comprises conducting a down-sampling filter process and conducting an up-sampling filter process within the convolutional neural network.

Abstract: A method for image segmentation comprises receiving volumetric image data for an anatomical region and generating a first volumetric patch from the volumetric image data. The method also comprises generating a second volumetric patch from the first volumetric patch by weighting a plurality of volumetric units in the first volumetric patch and receiving the second volumetric patch as an input to a convolutional neural network. The method also comprises conducting a down-sampling filter process and conducting an up-sampling filter process within the convolutional neural network.

Abstract: An exemplary processing system accesses a three-dimensional (3D) model of an anatomical structure of a patient and applies a detection process to the 3D model to detect a single-layer anatomical feature in the anatomical structure. The detection process includes generating, from the 3D model, a probability map of candidate points for the single-layer anatomical feature, and generating, based on the probability map of candidate points, a single-layer mesh representing the single-layer anatomical feature.

Abstract: A method for image segmentation comprises receiving volumetric image data for an anatomical region and generating a first volumetric patch from the volumetric image data. The method also comprises generating a second volumetric patch from the first volumetric patch by weighting a plurality of volumetric units in the first volumetric patch and receiving the second volumetric patch as an input to a convolutional neural network. The method also comprises conducting a down-sampling filter process and conducting an up-sampling filter process within the convolutional neural network.

Abstract: A method for image segmentation comprises receiving volumetric image data for an anatomical region and generating a first volumetric patch from the volumetric image data. The method also comprises generating a second volumetric patch from the first volumetric patch by weighting a plurality of volumetric units in the first volumetric patch and receiving the second volumetric patch as an input to a convolutional neural network. The method also comprises conducting a down-sampling filter process and conducting an up-sampling filter process within the convolutional neural network.

Abstract: A method for image segmentation comprises receiving volumetric image data for an anatomical region and generating a first volumetric patch from the volumetric image data. The method also comprises generating a second volumetric patch from the first volumetric patch by weighting a plurality of volumetric units in the first volumetric patch and receiving the second volumetric patch as an input to a convolutional neural network. The method also comprises conducting a down-sampling filter process and conducting an up-sampling filter process within the convolutional neural network.