Abstract: Systems and methods for deep neural network (DNN)-based cross component prediction are provided. A method includes inputting a reconstructed luma block of an image or video into a DNN; and predicting, by the DNN, a reconstructed chroma block of the image or video based on the reconstructed luma block that is input. Luma and chroma reference information and side information may also be input into the DNN to predict the reconstructed chroma block. The various inputs may also be generated using processes such as downsampling and transformation.

Abstract: Aspects of the disclosure provide a method, an apparatus, and a non-transitory computer-readable storage medium for video decoding. The apparatus can include processing circuitry. The processing circuitry is configured to receive an image or video comprising one or more blocks. The processing circuitry can decode a first post-filtering parameter in the image or video corresponding to the one or more blocks to be reconstructed. The first post-filtering parameter applies to at least one of the one or more blocks and has been updated by a post-filtering module in a post-filtering neural network (NN) that is trained based on a training dataset. The processing circuitry can determine the post-filtering NN in a video decoder corresponding to the one or more blocks based on the first post-filtering parameter. The processing circuitry can decode the one or more blocks based on the determined post-filtering NN corresponding to the one or more blocks.

Abstract: The present disclosure relates to a complex probiotic composition and a method for improving exercise performance of a subject with low intrinsic aerobic exercise capacity. The complex probiotic composition, which includes Lactobacillus rhamnosus GKLC1, Bifidobacterium lactis GKK24 and Clostridium butyricum GKB7, administered to the subject with the low intrinsic aerobic exercise capacity in a continuation period, can effectively reduce serum lactic acid and serum urea nitrogen after aerobic exercise, reduce proportion of offal fat and/or increase liver and muscle glycogen contents, thereby being as an effective ingredient for preparation of various compositions.

Abstract: A torsion balance is provided which includes a twisting wire and a reflector. The twisting wire is a suspended carbon nanotube. The reflector is hung on the twisting wire. The reflector further includes a film, a first reflecting layer, and a second reflecting layer; and the film includes a first surface and a second surface opposite to the first surface, and the first reflecting layer is located on the first surface and the second reflecting layer is located on the second surface.

Abstract: Provided herein are methods of determining that a subject has or is at risk of having a disease (e.g., cancer) using analysis of gene expression of loci identified in nucleic acid molecules derived from a cell-free biological sample of the subject.

Abstract: A method and apparatus for neural network based cross component prediction with scaling factors during encoding or decoding of an image frame or a video sequence, which may include training a deep neural network (DNN) cross component prediction (CCP) model with at least one or more scaling factors, wherein the at least one or more scaling factors are learned by optimizing a rate-distortion loss based on an input video sequence comprising a luma component, and reconstructing a chroma component based on the luma component using the trained DNN CCP model with the at least one or more scaling factors for chroma prediction. The trained DNN CCP may be updated for chroma prediction of the input video sequence using the one or more scaling factors, and performing chroma prediction of the input video sequence using the updated DNN CCP model with the one or more scaling factors.

Abstract: In the present disclosure, a method for compressing a feature map is provided, where the feature map is generated by passing a first input through a deep neural network (DNN). A respective optimal index order and a respective optimal unifying method are determined for each of super-blocks that are partitioned from the feature map. A selective structured unification (SSU) layer is subsequently determined based on the respective optimal index order and the respective optimal unifying method for each of the super-blocks. The SSU layer is added to the DNN to form an updated DNN, and is configured to perform unification operations on the feature map. Further, a first estimated output is determined, where the first estimated output is generated by passing the first input through the updated DNN.

Abstract: A method and apparatus for neural network based cross component prediction with low-bit precision during encoding or decoding of an image frame or a video sequence, which may include reconstructing a chroma component based on a received luma component using a pre-trained deep neural network (DNN) cross component prediction (CCP) model for chroma prediction, and updating a set of parameters of the pre-trained DNN CCP model with low-bit precision. The method may also include generating an updated DNN CCP model for chroma prediction with low-bit precision based on at least one video sequence, and using the updated DNN CCP model for cross component prediction of the at least one video sequence at reduced processing time.

Abstract: A method of removing a metal-containing layer (e.g., tungsten) from a substrate is provided. The method includes generating a first plasma in a process volume of a plasma chamber when a patterned device is disposed on a substrate support in the process volume. The patterned device includes a patterned region and an unpatterned region; a substrate; a tungsten-containing layer formed over the substrate; a supporting layer disposed between the tungsten-containing layer and the substrate. The patterned region includes exposed surfaces of the supporting layer and the unpatterned region does not include any exposed surfaces of the supporting layer. The method further includes depositing a first film over the patterned region of the tungsten-containing layer with the first plasma; and removing portions of the unpatterned region of the tungsten-containing layer with the first plasma without depositing the first film over the unpatterned region.

Abstract: An indication to remove one or more data in a time series database is received. A metadata index associated with the time series database is updated to indicate a soft removal of each data of the one or more data. A data hole index associated with the time series database is updated to indicate a data hole at a location of each data of the one or more data in the time series database. Responsive to an input/output rate for the time series database being below a threshold, the data hole of each data of the one or more data is optimized.

Abstract: A method, computer program, or computer system is provided for compressing a neural network model. One or more blocks are identified from among a superblock corresponding to a multi-dimensional tensor associated with a neural network. A set of weight coefficients associated with the superblock is unified. A model of the neural network is compressed based on the unified set of weight coefficients.

Abstract: An additive for a water-based wood stain includes a polymeric particle including a polymer that includes a repeating unit formed from a compound having the formula X-L1-A, wherein X is a substituted or unsubstituted (C2-C8)alkenyl containing one or more carbon-carbon double bonds, L1 is a bond, —C(O)—O—, —O—C(O)—, or a substituted or unsubstituted (C1-C10)alkylene interrupted by 0, 1, 2, or 3 heteroatoms chosen from O and NR1 wherein R1 is —H or (C1-C5)alkyl, and A is —H, substituted or unsubstituted (C1-C20)hydrocarbyl, or substituted or unsubstituted phenyl. The polymeric particle has a particle size in solution of 10 nm to 2000 nm.

Abstract: Aspects of the disclosure provide a method and an apparatus for video encoding. The apparatus includes processing circuitry configured to generate an initial feature representation from an input image to be encoded and perform an iterative update of values of a plurality of elements in the initial feature representation. The iterative update includes generate a coded representation corresponding to a final feature representation based on the final feature representation that has been updated from the initial feature representation by a number of iterations of the iterative update. A reconstructed image corresponding to the final feature representation is generated based on the coded representation. An encoded image corresponding to the final feature representation having updated values of the plurality of elements is generated. One of (i) a rate-distortion loss corresponding to the final feature representation or (ii) the number of iterations of the iterative update satisfies a pre-determined condition.

Abstract: End-to-end neural image compression using deep reinforcement learning (DRL) is performed by at least one processor and includes encoding an input, generating encoded representations of the input, generating a set of quantization keys using a first neural network, based on a set of previous quantization states, wherein each quantization key in the set of quantization keys and each previous quantization state in the set of previous quantization states correspond to the encoded representations of the input, generating a set of dequantized numbers representing dequantized representations of the encoded representations of the input, based on the set of quantization keys, using a second neural network, and generating a reconstructed output, based on the set of dequantized numbers.

Abstract: A method of adaptive neural image compression with rate control by meta-learning includes receiving an input image and a hyperparameter; and encoding the received input image, based on the received hyperparameter, using an encoding neural network, to generate a compressed representation. The encoding includes performing a first shared encoding on the received input image, using a first shared encoding layer having first shared encoding parameters, performing a first adaptive encoding on the received input image, using a first adaptive encoding layer having first adaptive encoding parameters, combining the first shared encoded input image and the first adaptive encoded input image, to generate a first combined output, and performing a second shared encoding on the first combined output, using a second shared encoding layer having second shared encoding parameters.

Abstract: Spatially expandable probes and scaffolds for spatially expandable probes are provided that allow for interfacing across distant regions of the brain. The scaffolds include a plurality of helical channels extending along a length of the scaffold. Each of the plurality of helical channels are configured to receive a flexible probe, slidable within the helical channel such that they can be extended from the first end in different directions to access the distant regions of the brain. The scaffolds can be used with various flexible probes. Multi-functional fiber probes are provided capable of being used within the scaffolds. The multi-functional fiber probes include one or more sites on an exterior surface of the elongated fiber along the length of the fiber probe to allow for the interfacing to occur along the length of the fiber probe. Methods of making and using the multi-functional fiber probes and scaffolds are also provided.

Abstract: A testing circuit for multiple SMs of a cascaded converter and a control method thereof are provided. A current generator generates a testing current flowing into a testing module group. The testing module group includes two series-connected testing arms that each contains multiple SMs, or in an alternative way, the testing module group is composed of one or multiple testing units connected in series, and each testing unit includes two testing SMs connected in series reversely. The testing circuit the control method thereof realizes a mission profile emulation of the multiple SMs of the cascaded converter in both inverting and rectifying modes simultaneously to improve a test efficiency. A reverse series connection structure of the two testing SMs offsets a DC component in a capacitor voltage to reduce a power supply voltage required for a test. Dynamic and static control methods under different modulations for the test circuit are provided.

Abstract: The present invention relates to an inertial electrostatic confinement fusion apparatus for electron injection neutralization, which includes a cathode spherical net, an anode, a cathode high-voltage introduction supporting rod, an electron gun for high-energy electron injection, a vacuum system and a high-voltage power supply system. Neutralizing electrons are injected by the electron gun for the high-energy electron injection and an inner electron gun for electron injection in the spherical net into the spherical net and between the spherical net and the anode of the inertial electrostatic confinement fusion apparatus, thereby reducing or eliminating a space charge force generated by deuterium ions, and increasing the deuterium ion density in the spherical net, so that neutron yield and a profit-loss ratio can be increased.

Abstract: An ink includes metal nanoparticles stabilized with cellulose or a cellulose derivative. A method of forming an electrically conductive pathway includes printing the ink on a substrate, and sintering the printed ink, to form the electrically conductive pathway on the substrate.

Abstract: Improved biolistic bombardment is observable when using a modification to a double-barrel device. One such modification is a custom attachment that can be retrofit to a standard helium-based gene gun. The custom attachment facilitates DNA and protein delivery. The attachment confines the flow of particles in a barrel similar to the choke on a shotgun barrel.