Abstract: An electroporation system including one or more of a pipette, a pipette tip, a pipette docking assembly, and a pulse generator. The pipette docking assembly includes a pipette station, a pipette station guard, and a reservoir (e.g., a buffer tube). A method for transfecting a cell with a payload including providing an electroporation system, providing the cell, providing the payload, introducing the cell and the payload into a pipette tip, and electroporating the cell within the pipette tip by operating the electroporation system.

Abstract: A system and method for table conversion including converting a table containing text in tabular form to an image, labeling each text area of the image with a bounding box, determining for each bounding box, a position information, a semantic information, and an image information, reconstructing the image into a graph form having a plurality of nodes, wherein each node represents the bounding box of the text areas of the image, inputting at least two nodes into a trained neural network to determine a relative relationship between the at least two nodes, building a knowledge graph using the relative relationship of the at least two nodes, and translating the knowledge graph into machine readable natural language.

Abstract: Disclosed is an electronic device including a device body and an antenna module. The antenna module includes a conductive element and at least one antenna element. The conductive element includes a main body portion and at least one assembly portion connected with each other. The at least one assembly portion is assembled on the device body. The at least one antenna element is disposed on the device body and coupled with the conductive element to excite a first resonance mode. The at least one assembly portion overlaps the at least one antenna element in the length direction of the main body portion.

Abstract: In various embodiments, a training application trains a convolutional neural network to downsample images in a video encoding pipeline. The convolution neural network includes at least two residual blocks and is associated with a downsampling factor. The training application executes the convolutional neural network on a source image to generate a downsampled image. The training application then executes an upsampling algorithm on the downsampled image to generate a reconstructed image having the same resolution as the source image. The training application computes a reconstruction error based on the reconstructed image and the source image. The training application updates at least one parameter of the convolutional neural network based on the reconstruction error to generate a trained convolutional neural network.

Abstract: An electronic device includes a device body and an antenna module disposed in the device body and including a conductive structure and a coaxial cable including a core wire, a shielding layer wrapping the core wire, and an outer jacket wrapping the shielding layer. The conductive structure includes a structure body and a slot formed on the structure body and penetrating the structure body in a thickness direction of the structure body. A section of the shielding layer extends from the outer jacket and is connected to the structure body. A physical portion of the structure body and the section of the shielding layer are respectively located on two opposite sides of the slot in a width direction of the slot. A section of the core wire extends from the section of the shielding layer and overlaps the slot and the physical portion in the thickness direction.

Abstract: In some embodiments, the present disclosure relates to a method for manufacturing an integrated chip. The method includes forming a transistor structure over a substrate. The transistor structure comprises a pair of source/drain regions and a gate electrode between the source/drain regions. A lower inter-level dielectric (ILD) layer is formed over the pair of source/drain regions and around the gate electrode. A gate capping layer is formed over the gate electrode. A selective etch and deposition process is performed to form a dielectric protection layer on the gate capping layer while forming a contact opening within the lower ILD layer. A lower source/drain contact is formed within the contact opening.

Abstract: A semiconductor device includes a first dielectric layer, a stack of semiconductor layers disposed over the first dielectric layer, a gate structure wrapping around each of the semiconductor layers and extending lengthwise along a direction, and a dielectric fin structure and an isolation structure disposed on opposite sides of the stack of semiconductor layers and embedded in the gate structure. The dielectric fin structure has a first width along the direction smaller than a second width of the isolation structure along the direction. The isolation structure includes a second dielectric layer extending through the gate structure and the first dielectric layer, and a third dielectric layer extending through the first dielectric layer and disposed on a bottom surface of the gate structure and a sidewall of the first dielectric layer.

Abstract: A method includes forming a gate stack on a first portion of a semiconductor substrate, removing a second portion of the semiconductor substrate on a side of the gate stack to form a recess, growing a semiconductor region starting from the recess, implanting the semiconductor region with an impurity, and performing a melt anneal on the semiconductor region. At least a portion of the semiconductor region is molten during the melt anneal.

Abstract: A method includes forming a gate stack on a first portion of a semiconductor substrate, removing a second portion of the semiconductor substrate on a side of the gate stack to form a recess, growing a semiconductor region starting from the recess, implanting the semiconductor region with an impurity, and performing a melt anneal on the semiconductor region. At least a portion of the semiconductor region is molten during the melt anneal.

Abstract: A communication equipment receives a plurality of network sessions and includes a memory and a processor. The memory stores a plurality of software modules. The processor is connected to the memory and configured to implement the following steps. A network session inferring step is performed to execute an inference software module, and the inference software module processes at least one network packet of each of the network sessions and a packet characteristic module according to a machine learning algorithm to infer a priority level. An adaptive priority list establishing step is performed to execute a classification software module, and the classification software module establishes an adaptive priority list according to the priority levels of the network sessions. The communication equipment transmits the network packets of each of the network sessions to a network according to the adaptive priority list so as to set QoS of the network sessions.

Abstract: In various embodiments, an image preprocessing application preprocesses images. To preprocess an image, the image preprocessing application executes a trained machine learning model on first data corresponding to both the image and a first set of components of a luma-chroma color space to generate first preprocessed data. The image preprocessing application executes at least a different trained machine learning model or a non-machine learning algorithm on second data corresponding to both the image and a second set of components of the luma-chroma color space to generate second preprocessed data. Subsequently, the image preprocessing application aggregates at least the first preprocessed data and the second preprocessed data to generate a preprocessed image.

Abstract: A quality of service adjusting method based on application categories is configured to adjust a Quality of Service (QoS) of a communication device. A network session processing step is performed to drive a hardware accelerator to process a plurality of network sessions. A packet receiving step is performed to drive a processing unit to receive a first packet group of each of the network sessions. A packet analyzing step is performed to execute a packet inspecting module to analyze a plurality of packet data of the first packet group of each of the network sessions, and to classify an application corresponding to each of the network sessions. A list establishing step is performed to establish a priority list. A bandwidth distributing step is performed to distribute a network bandwidth to a second packet group of each of the network sessions according to the priority list.

Abstract: In various embodiments, a training application trains a machine learning model to preprocess images. In operation, the training application computes a chroma sampling factor based on a downscaling factor and a chroma subsampling ratio. The training application executes a machine learning model that is associated with the chroma sampling factor on data that corresponds to both an image and a first chroma component to generate preprocessed data corresponding to the first chroma component. Based on the preprocessed data, the training application updates at least one parameter of the machine learning model to generate a trained machine learning model that is associated with the first chroma component.

Abstract: In various embodiments, a training application generates a perceptual video model. The training application computes a first feature value for a first feature included in a feature vector based on a first color component associated with a first reconstructed training video. The training application also computes a second feature value for a second feature included in the feature vector based on a first brightness component associated with the first reconstructed training video. Subsequently, the training application performs one or more machine learning operations based on the first feature value, the second feature value, and a first subjective quality score for the first reconstructed training video to generate a trained perceptual quality model. The trained perceptual quality model maps a feature value vector for the feature vector to a perceptual quality score.

Abstract: In various embodiments, a quality inference application estimates the perceived quality of reconstructed videos. The quality inference application computes a first feature value for a first feature included in a feature vector based on a color component associated with a reconstructed video. The quality inference application also computes a second feature value for a second feature included in the feature vector based on a brightness component associated with the reconstructed video. Subsequently, the quality inference application computes a perceptual quality score based on the first feature value and the second feature value. The perceptual quality score indicates a level of visual quality associated with at least one frame included in the reconstructed video.

Abstract: In various embodiments, a training application trains a convolutional neural network to downsample images in a video encoding pipeline. The convolution neural network includes at least two residual blocks and is associated with a downsampling factor. The training application executes the convolutional neural network on a source image to generate a downsampled image. The training application then executes an upsampling algorithm on the downsampled image to generate a reconstructed image having the same resolution as the source image. The training application computes a reconstruction error based on the reconstructed image and the source image. The training application updates at least one parameter of the convolutional neural network based on the reconstruction error to generate a trained convolutional neural network.

Abstract: A quality of service adjusting method based on application categories is configured to adjust a Quality of Service (QoS) of a communication device. A network session processing step is performed to drive a hardware accelerator to process a plurality of network sessions. A packet receiving step is performed to drive a processing unit to receive a first packet group of each of the network sessions. A packet analyzing step is performed to execute a packet inspecting module to analyze a plurality of packet data of the first packet group of each of the network sessions, and to classify an application corresponding to each of the network sessions. A list establishing step is performed to establish a priority list. A bandwidth distributing step is performed to distribute a network bandwidth to a second packet group of each of the network sessions according to the priority list.

Abstract: A compound capable of being used as a tumor inhibitor, a preparation method therefor, and application thereof. The compound has a structure represented by general formula I; a stereoisomer, an enantiomer, a raceme, a cis/trans isomer, a tautomer, and an isotopic variant of the compound are comprised; the compound can be used separately or in combination with other drugs for treating tumors or inflammatory diseases, or for treating other disorders or diseases mediated by the activity of MDM2 and/or MDM4, and shows prominent curative activity.

Abstract: A method includes forming a gate stack on a first portion of a semiconductor substrate, removing a second portion of the semiconductor substrate on a side of the gate stack to form a recess, growing a semiconductor region starting from the recess, implanting the semiconductor region with an impurity, and performing a melt anneal on the semiconductor region. At least a portion of the semiconductor region is molten during the melt anneal.

Abstract: In various embodiments, a quality inference application estimates the perceived quality of reconstructed videos. The quality inference application computes a first feature value for a first feature included in a feature vector based on a color component associated with a reconstructed video. The quality inference application also computes a second feature value for a second feature included in the feature vector based on a brightness component associated with the reconstructed video. Subsequently, the quality inference application computes a perceptual quality score based on the first feature value and the second feature value. The perceptual quality score indicates a level of visual quality associated with at least one frame included in the reconstructed video.