Abstract: The disclosure provides a method for synthesizing paraphthaloyl chloride through terephthalic acid chlorination in a reaction mode of a microchannel continuous flow. Compared with an existing technology, this method has characteristics of accurate control of reaction conditions, high phosgene/triphosgene utilization ratio, low catalyst dosage, high TPA conversion ratio within few tens of seconds of reaction time, high TPC yield and the like.

Abstract: Techniques related to implementing convolutional neural networks for object recognition are discussed. Such techniques may include generating a set of binary neural features via convolutional neural network layers based on input image data and applying a strong classifier to the set of binary neural features to generate an object label for the input image data.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: a gateway stack that includes a master, a backup, and at least one slave amongst nodes of the GS based on an election pursuant to a gateway stack protocol. The gateway stack provides gateway services for a Network Virtualization over Layer 3 (NVO3) network in a fail-safe manner by utilizing all of the nodes in the gateway stack. A data interface between the gateway stack and a switch is aggregated to evenly distribute inbound packets amongst the nodes of the gateway stack.

Abstract: An organic electroluminescent display panel is provided, comprising a planarization layer; a pixel electrode formed on the planarization layer; and an auxiliary electrode coated by the planarization layer. The planarization layer defines a contact hole extending to a top surface of the planarization layer to have the auxiliary electrode contacted with the corresponding pixel electrode. A display device comprising the organic electroluminescent display panel and a method for manufacturing the organic electroluminescent display panel are further provided.

Abstract: The present application provides a display panel includes a substrate and a thin film transistor layer. The substrate included a surface defining a plurality of first grooves. The thin film transistor layer is disposed on the surface of the substrate defining the first grooves. The thin film transistor layer includes a plurality of pixel circuits. The pixel circuits are located in the first grooves.

Abstract: The present disclosure provides an organic light-emitting display panel, a method for making the same, and a display device including the same. The organic light-emitting display panel comprises an active region and a pixel spacer located within the active region, and the pixel spacer is provided with a buffer chamber.

Abstract: A method for information processing includes displaying a display object at a first display location in a first space area of a physical scene; detecting a first user-related event; and in response to the first user-related event satisfying a preset condition, performing a display operation in connection to the display object with respect to a second space area of the physical scene.

Abstract: Exemplary etching methods may include flowing a fluorine-containing precursor into a substrate processing region of a semiconductor processing chamber. The methods may include flowing a hydrogen-containing precursor into the substrate processing region. The methods may include contacting a substrate housed in the substrate processing region with the fluorine-containing precursor and the hydrogen-containing precursor. The substrate may include a trench or recessed feature, and a spacer may be formed along a sidewall of the trench or feature. The spacer may include a plurality of layers including a first layer of a carbon-containing or nitrogen-containing material, a second layer of an oxygen-containing material, and a third layer of a carbon-containing or nitrogen-containing material. The second layer of the spacer may be disposed between the first layer and third layer of the spacer. The methods may also include removing the oxygen-containing material.

Abstract: A flexible display module and a manufacturing method thereof are disclosed. The flexible display module includes a first flexible material layer and a second flexible material layer stacked on the first flexible layer, a sealing layer disposed between the first flexible material layer and the second flexible material layer, and a module layer disposed in the hollow of the sealing layer. The sealing layer has a hollow defined in a middle of the sealing layer. The area of the bottom area of the hollow is larger than the area of the module layer.

Abstract: Techniques for categorizing mobile uniform resource locators (URLs) that are used by mobile applications are disclosed. A URL is extracted from a mobile application. A category for the URL is determined based on a categorization of the mobile application. The URL and its determined category are then generated as output.

Abstract: Dosage regimens, methods, compositions and kits are described herein useful for detecting and/or treating a CNS demyelinating disease.

Abstract: Described herein are systems and methods for providing deeply stacked automated program synthesis. In one embodiment, an apparatus to perform automated program synthesis includes a memory to store instructions for automated program synthesis and a compute cluster coupled to the memory. The compute cluster supports the instructions for performing the automated program synthesis including partitioning sketched data into partitions, training diverse sets of individual program synthesis units each having different capabilities with partitioned sketched data and for each partition applying respective transformations, and generating sketched baseline data for each individual program synthesis unit.

Abstract: Described herein are hardware acceleration of random number generation for machine learning and deep learning applications. An apparatus (700) includes a uniform random number generator (URNG) circuit (710) to generate uniform random numbers and an adder circuit (750) that is coupled to the URNG circuit (710). The adder circuit hardware (750) accelerates generation of Gaussian random numbers for machine learning.

Abstract: Methods and systems for budgeted and simplified training of deep neural networks (DNNs) are disclosed. In one example, a trainer is to train a DNN using a plurality of training sub-images derived from a down-sampled training image. A tester is to test the trained DNN using a plurality of testing sub-images derived from a down-sampled testing image. In another example, in a recurrent deep Q-network (RDQN) having a local attention mechanism located between a convolutional neural network (CNN) and a long-short time memory (LSTM), a plurality of feature maps are generated by the CNN from an input image. Hard-attention is applied by the local attention mechanism to the generated plurality of feature maps by selecting a subset of the generated feature maps. Soft attention is applied by the local attention mechanism to the selected subset of generated feature maps by providing weights to the selected subset of generated feature maps in obtaining weighted feature maps.

Abstract: Methods and systems are disclosed using improved training and learning for deep neural networks. In one example, a deep neural network includes a plurality of layers, and each layer has a plurality of nodes. For each L layer in the plurality of layers, the nodes of each L layer are randomly connected to nodes in a L+1 layer. For each L+1 layer in the plurality of layers, the nodes of each L+1 layer are connected to nodes in a subsequent L layer in a one-to-one manner. Parameters related to the nodes of each L layer are fixed. Parameters related to the nodes of each L+1 layers are updated, and L is an integer starting with 1. In another example, a deep neural network includes an input layer, output layer, and a plurality of hidden layers. Inputs for the input layer and labels for the output layer are determined related to a first sample. Similarity between different pairs of inputs and labels between a second sample with the first sample is estimated using Gaussian regression process.

Abstract: Methods and systems are disclosed for boosting deep neural networks for deep learning. In one example, in a deep neural network including a first shallow network and a second shallow network, a first training sample is processed by the first shallow network using equal weights. A loss for the first shallow network is determined based on the processed training sample using equal weights. Weights for the second shallow network are adjusted based on the determined loss for the first shallow network. A second training sample is processed by the second shallow network using the adjusted weights. In another example, in a deep neural network including a first weak network and a second weak network, a first subset of training samples is processed by the first weak network using initialized weights. A classification error for the first weak network on the first subset of training samples is determined.

Abstract: Exemplary cleaning or etching methods may include flowing a fluorine-containing precursor into a remote plasma region of a semiconductor processing chamber. Methods may include forming a plasma within the remote plasma region to generate plasma effluents of the fluorine-containing precursor. The methods may also include flowing the plasma effluents into a processing region of the semiconductor processing chamber. A substrate may be positioned within the processing region, and the substrate may include a region of exposed oxide and a region of exposed metal. Methods may also include providing a hydrogen-containing precursor to the processing region. The methods may further include removing at least a portion of the exposed oxide.