Abstract: A display substrate and a preparation method therefor, and a display device. The display substrate comprises a hole region, a transition region surrounding the hole region, and a display region surrounding the transition region, wherein the transition region is provided with an isolation structure surrounding the hole region, and the isolation structure comprises at least one isolation groove; the display substrate comprises a base, and the isolation groove comprises a groove and a blocking edge arranged on the side of the groove away from the base; and in a direction perpendicular to the base, the transition region comprises a first structure layer and a second structure layer which are sequentially stacked in the direction away from the base, the first structure layer comprises a blocking layer, the groove extends through the second structure layer and exposes at least part of the surface of the blocking layer.

Abstract: A display substrate, a manufacturing method therefor, and a display apparatus are provided. The display substrate includes a first semiconductor layer, a first conductive layer, a second conductive layer, a second semiconductor layer, a third conductive layer, a fourth conductive layer, and a fifth conductive layer disposed on an substrate. The first semiconductor layer includes active layers of a plurality of poly silicon transistors, the first conductive layer includes gates of a plurality of poly silicon transistors, a first electrode plate of a storage capacitor and a first scan signal line. The second conductive layer includes a second electrode plate of the storage capacitor. The second semiconductor layer includes active layers of a plurality of oxide transistors. The third conductive layer includes gates of the plurality of oxide transistors.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network to perform any one or more of a variety of machine learning tasks. For example, the neural network can be configured as a generative neural network, e.g., an autoregressive generative neural network.

Abstract: Provided are a display substrate and a preparation method thereof, and a display apparatus. The display substrate includes a display region and at least one hole region located in the display region, the hole region includes a function hole and a partition region surrounding the function hole, the partition region is provided with at least one partition dam surrounding the function hole; the partition dam includes a first partition layer disposed on a base substrate and a second partition layer disposed on a side of the first partition layer away from the base substrate, at least one partition layer includes a second sub-layer and a third sub-layer disposed on a side of the second sub-layer away from the base substrate, the third sub-layer has a protrusion with respect to a sidewall of the second sub-layer, and the protrusion and the sidewall of the second sub-layer form an inwardly recessed structure.

Abstract: An apparatus for generating a binary numerical sequence is provided. The apparatus is configured to apply a first write voltage or a second write voltage, different from the first write voltage, as a write voltage to each of two or more switchable elements, and/or to apply a first read voltage or a second read voltage, different from the first read voltage, as a read voltage to each of the two or more switchable elements. Each switchable element of the two or more switchable elements is configured to output, in dependence on the write voltage applied to the switchable element and/or in dependence on the read voltage applied to the switchable element, an output voltage with a first random or pseudo-random voltage value from a first voltage value range or with a second random or pseudo-random voltage value from a second voltage value range.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network to perform any one or more of a variety of machine learning tasks. For example, the neural network can be configured as a generative neural network, e.g., an autoregressive generative neural network.

Abstract: According to various aspects, a memristive crossbar array is provided including: first control lines and second control lines in a crossbar configuration defining a plurality of cross-point regions, a memristive material portion disposed in each of the plurality of cross-point regions between a corresponding pair of one of the first control lines and one of the second control lines to form a corresponding memristive structure. Each memristive material portion may have a thickness in a predefined range such that each corresponding memristive structure has a symmetric read characteristic and/or at least one curvature change in the read characteristic.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task on a network input to generate a network output. In one aspect, one of the systems includes a neural network configured to perform the machine learning task, the neural network including one or more expert neural network blocks that each include router that performs expert-choice routing between multiple expert neural networks.

Abstract: A deep state space generative model is augmented with intervention prediction. The state space model provides a principled way to capture the interactions among observations, interventions, critical event occurrences, true states, and associated uncertainty. The state space model can include a discrete-time hazard rate model that provides flexible fitting of general survival time distributions. The state space model can output a joint prediction of event risk, observation and intervention trajectories based on patterns in temporal progressions, and correlations between past measurements and interventions.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for extracting entities from conversation transcript data. One of the methods includes obtaining a conversation transcript sequence, processing the conversation transcript sequence using a span detection neural network configured to generate a set of text token spans; and for each text token span: processing a span representation using an entity name neural network to generate an entity name probability distribution over a set of entity names, each probability in the entity name probability distribution representing a likelihood that a corresponding entity name is a name of the entity referenced by the text token span; and processing the span representation using an entity status neural network to generate an entity status probability distribution over a set of entity statuses.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network to perform any one or more of a variety of machine learning tasks. For example, the neural network can be configured as a generative neural network, e.g., an autoregressive generative neural network.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network to perform any one or more of a variety of machine learning tasks. For example, the neural network can be configured as a generative neural network, e.g., an autoregressive generative neural network.

Abstract: A method includes providing a first set of data objects to a first skip router of a neural network (NN). The NN includes a first NN layer and a second NN layer. The first set of data objects is subdivided into a first set of skip objects and a first set of non-skip objects based on a first skip logic implemented by the first skip router and a first context of each data object in the first set of data objects. A first set of processed objects is generated based on the first set of non-skip objects and a first layer logic implemented by the first NN layer. Predictions are generated based on a second set of data objects and a second layer logic implemented by the second NN layer. The second set of data objects includes the first set of processed objects and the first set of skip objects.

Abstract: A decoder for decoding to obtain a binary data sequence is provided. The decoder includes a first inductance element, a first state element, a second inductance element, a second state element, an inductance sequence generator, a first comparison unit and a second comparison unit. The inductance sequence generator is configured to generate an inductance sequence including a plurality of inductance values and to adjust each inductance value of the plurality of inductance values of the inductance sequence at the first inductance element and at the second inductance element.

Abstract: A method for reconfiguration of a vortex density in a rare earth manganate, to a non-volatile impedance switch having reconfigurable impedance, and to the use thereof as micro-inductance is disclosed. A unique voltage-time profile is applied between a first and a second electrically conductive contact attached to the rare earth manganate, such that the rare earth manganate passes through an ordering temperature in a region of an electric field forming between the two electrically conductive contacts during a cooling process during and after application of the voltage pulse or the voltage ramp, and the vortex density is thus influenced and adjusted locally in the region of the electric field forming between the two electrically conductive contacts.

Abstract: Provided are a display substrate and a preparation method thereof, and a display apparatus. The display substrate includes a display region and at least one hole region located in the display region, the hole region includes a function hole and a partition region surrounding the function hole, the partition region is provided with at least one partition dam surrounding the function hole; the partition dam includes a first partition layer disposed on a base substrate and a second partition layer disposed on a side of the first partition layer away from the base substrate, at least one partition layer includes a second sub-layer and a third sub-layer disposed on a side of the second sub-layer away from the base substrate, the third sub-layer has a protrusion with respect to a sidewall of the second sub-layer, and the protrusion and the sidewall of the second sub-layer form an inwardly recessed structure.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing neural architecture search for machine learning models. In one aspect, a method comprises receiving training data for a machine learning, generating a plurality of candidate neural networks for performing the machine learning task, wherein each candidate neural network comprises a plurality of instances of a layer block composed of a plurality of layers, for each candidate neural network, selecting a respective type for each of the plurality of layers from a set of layer types that comprises, training the candidate neural network and evaluating performance scores for the trained candidate neural networks as applied to the machine learning task, and determining a final neural network for performing the machine learning task based at least on the performance scores for the candidate neural networks.

Abstract: According to various aspects, a memristive structure is provided including: a first electrode, a second electrode, and a memristive element arranged between the first electrode and the second electrode; wherein the memristive element includes a memristive material that has a ferroelectric polarization capability, and wherein the memristive material has a crystalline microstructure configured to suppress a substantial ferroelectric switching of the memristive element in response to a voltage drop over the memristive element applied via the first electrode and the second electrode.

Abstract: The present invention discloses a compound as a sodium channel regulator, and this type of compound can effectively inhibit and block the activity of Nav1.8 ion channel, a subtype of voltage-gated sodium channels, and can be used as a specific Nav1.8 inhibitor and to prepare drugs for the treatment of pain diseases mediated by Nav1.8 such as of intestinal pain, neuropathic pain, musculoskeletal pain, acute pain, inflammatory pain, cancer pain, primary pain, multiple sclerosis, Charcot-Marie-Tooth syndrome, incontinence, and cardiac arrhythmia, with broad application prospects.

Abstract: A method and apparatus include receiving, by a device, medical information associated with a user. Inquiry information is determined based on the medical information associated with the user and a reinforcement learning model. The inquiry information is provided to permit response information to be received. The response information is received based on providing the inquiry information. Diagnosis information is determined based on the medical information and the response information using a machine learning model. The diagnosis information is provided to a set of devices via a network.