Abstract: Disclosed are a display substrate and a method for manufacturing same, and a display apparatus.

Abstract: A method for video processing is described. The method includes determining, for a conversion between a video unit of a video and a bitstream representation of the video, whether to enable a mirrored padding process for padding an unavailable luma sample during an application of a loop filtering tool to the video unit; and performing the conversion based on the determining.

Abstract: Methods, devices, and systems related to video processing are disclosed. In one example aspect, a method of video processing includes performing a conversion between a block of a current picture of a video and a coded representation of the video using an intra-subblock partitioning (ISP) mode. A prediction is determined for each sub-partition using an intra-prediction process based on samples in the current picture using the ISP mode. The block is partitioned into multiple sub-partitions including a first sub-partitions having a same top-left corner position as a top-left corner position of the block.

Abstract: The application proposes a non-destructive testing method for the sealing degree of the small cigarette box packaging, which includes placing the small cigarette box to be tested in an airtight chamber, using a balance cabin to quickly form a stable negative pressure in the airtight chamber; The pressure change of the sealed chamber is continuously measured until equilibrium, and the data model is analyzed and established by using Darcy's law, Fick's diffusion law and the physical process of molecular kinetic theory. Through the non-destructive testing method for the sealing degree of the small cigarette box packaging of the present invention, the traditional destructive measurement (damaged small box packaging) for measuring the sealing degree of cigarette small box is changed into non-destructive testing; The test results have specific physical meaning; The test time of a single sample is reduced to ? of the time of destructive test.

Abstract: Methods, devices, and systems related to video processing are disclosed. In one example aspect, a method of video processing includes performing a conversion between a block of a current picture of a video and a coded representation of the video using an intra-subblock partitioning (ISP) mode. A prediction is determined for each sub-partition using an intra-prediction process based on samples in the current picture using the ISP mode. The block is partitioned into multiple sub-partitions including a first sub-partitions having a same top-left corner position as a top-left corner position of the block.

Abstract: An electronic device including a deep memory model includes at least one memory and at least one processor coupled to the at least one memory. The at least one processor is configured to receive input data to the deep memory model. The at least one processor is also configured to extract a history state of an external memory coupled to the deep memory model based on the input data. The at least one processor is further configured to update the history state of the external memory based on the input data. In addition, the at least one processor is configured to output a prediction based on the extracted history state of the external memory.

Abstract: A recognition method includes retrieving an input including data of a first window size. The method further includes classifying the input based on comparison of warping distance of the input with a pruning threshold.

Abstract: A recommendation method includes retrieving content consumption data including content consumed and content not consumed. Based on the content consumption data, identifying a first piece of content not consumed. A first feature of the first piece of content related to negative consumption of the first piece of content is determined. A first system is used to revise the first feature to a second feature. A second piece of content including the second feature is provided to an electronic device. The second piece of content is a revised instance of the first piece of content.

Abstract: Microelectronic assemblies, related devices and methods, are disclosed herein. In some embodiments, a microelectronic assembly may include a first die, having a first surface and an opposing second surface with conductive contacts, in a first layer; a first material surrounding the first die and extending along a thickness of the first die from the second surface, and wherein the first material includes first particles having an average diameter between 200 and 500 nanometers; a second material surrounding the first die and extending along the thickness of the first die from the first surface, and wherein the second material includes second particles having an average diameter between 0.5 and 12 microns; an interface portion, between the first and second materials, including the first and second particles; and a second die, in a second layer on the first layer, electrically coupled to the conductive contacts on the first die.

Abstract: A computer apparatus includes a hierarchy of solder joints in a multi-chip package, with solder joints at different levels of the packaging having different melting temperatures. Interconnections, such as pads or pins, on integrated circuit (IC) die can be electrically coupled to ends of contact pillars with solder joints having a higher melting temperature. The other ends of the contact pillars can electrically couple to another substrate or another device with solder joints having a lower melting temperature. The contact pillars can be, for example, a contact array or through-hole via in a substrate.

Abstract: Intent determination based on one or more multi-model structures can include generating an output from each of a plurality of domain-specific models in response to a received input. The domain-specific models can comprise simultaneously trained machine learning models that are trained using a corresponding local loss metric for each domain-specific model and a global loss metric for the plurality of domain-specific models. The presence or absence of an intent corresponding to one or more domain-specific models can be determined by classifying the output of each domain-specific model.

Abstract: An electronic device for active learning includes at least one memory and at least one processor coupled to the at least one memory. The at least one processor is configured to select one or more entries from a data set including unlabeled data based on a similarity between the one or more entries and labeled data. The at least one processor is further configured to cause the one or more entries to be labeled.

Abstract: Various embodiments disclosed relate to methods of making omni-directional semiconductor interconnect bridges. The present disclosure includes semiconductor assemblies including a mold layer having mold material, a first filler material dispersed in the mold material, and a second filler material dispersed in the mold material, wherein the second filler material is heterogeneously dispersed.

Abstract: Embodiments described herein may be related to apparatuses, processes, and techniques for a dam structure on a substrate that is proximate to a die coupled with the substrate, where the dam decreases the risk of die shift during encapsulation material flow over the die during the manufacturing process. The dam structure may fully encircle the die. During encapsulation material flow, the dam structure creates a cavity that moderates the different flow rates of material that otherwise would exert different pressures the sides of the die and cause to die to shift its position on the substrate. Other embodiments may be described and/or claimed.

Abstract: Embodiments disclosed herein include electronic packages with first level interconnects that comprise a first layer. In an embodiment, the electronic package comprises a package substrate and a pad on the package substrate. In an embodiment, the pad comprises copper. In an embodiment, a first layer is over the pad. In an embodiment, the first layer comprises iron. In an embodiment, a solder is over the first layer, and a die is coupled to the package substrate by the solder.

Abstract: The invention provides an automated batch sample preparation method for button battery, comprising the following steps: preparing an electrolyte and elements of different specifications, presetting an injection amount of a liquid injection component, scanning and recording the identification information of the elements by a scanning component, grabbing the elements onto a sealing component, injecting the electrolyte into the elements on the sealing component, sealing the elements as a button battery by the sealing component, removing the button battery, then repeat the above steps. The automated batch sample preparation method for button battery provided by the invention has the advantages of high automation degree, simple operation, high-precision assembly and high efficiency. The injection amount can be adjusted and controlled, and button batteries with different specifications can be produced in batch. The information recorded by the scanning component can facilitate the optimization of the process.

Abstract: The invention provides an automated batch sample preparation method for button battery, comprising the following steps: preparing an electrolyte and elements of different specifications, presetting an injection amount of a liquid injection component, scanning and recording the identification information of the elements by a scanning component, grabbing the elements onto a sealing component, injecting the electrolyte into the elements on the sealing component, sealing the elements as a button battery by the sealing component, removing the button battery, then repeat the above steps. The automated batch sample preparation method for button battery provided by the invention has the advantages of high automation degree, simple operation, high-precision assembly and high efficiency. The injection amount can be adjusted and controlled, and button batteries with different specifications can be produced in batch. The information recorded by the scanning component can facilitate the optimization of the process.

Abstract: Intent determination based on one or more multi-model structures can include generating an output from each of a plurality of domain-specific models in response to a received input. The domain-specific models can comprise simultaneously trained machine learning models that are trained using a corresponding local loss metric for each domain-specific model and a global loss metric for the plurality of domain-specific models. The presence or absence of an intent corresponding to one or more domain-specific models can be determined by classifying the output of each domain-specific model.

Abstract: Systems and methods are described for converting input content. A first model may convert input content to an output content that exhibits one or more desired properties. A second model may determine if the conversion meets a desired quality of conversion using a discriminating function. The discriminating function may determine a difference between properties of the output content and properties of desired content, where the difference corresponds to the success of the conversion applying the desired properties. Updated control data may be generated by a third model using information from the second model, where the updated control data may be used by the first model to reduce the determined difference. After updated control data has been generated, the foregoing steps may be repeated based upon the updated control data. One of a plurality of different actions may be determined in response to the difference.

Abstract: An electronic device including a deep memory model includes at least one memory and at least one processor coupled to the at least one memory. The at least one processor is configured to receive input data to the deep memory model. The at least one processor is also configured to extract a history state of an external memory coupled to the deep memory model based on the input data. The at least one processor is further configured to update the history state of the external memory based on the input data. In addition, the at least one processor is configured to output a prediction based on the extracted history state of the external memory.