Abstract: A post-task-completion retention period for which a computing resource is to be retained, without de-activating the resource, on behalf of a set of requesters of machine learning tasks is determined at a machine learning service. A first task, identified at the service prior to expiration of the retention period at a first computing resource at which a second task has completed, is initiated at the first computing resource. In response to obtaining an indication of a third task and determining that a threshold criterion associated with the retention period satisfies a criterion, the third task is initiated at an additional computing resource. The additional computing resource is de-activated after the third task completes, without waiting for the retention period to expire.

Abstract: Microcapsules including a polymeric shell and an internal phase including a non-photopolymerizable reactive diluent are provided for use in microcapsule imaging sheets. Imaging sheets including microcapsules which include a non-photopolymerizable reactive diluent, and which exhibit improvements in color development (Dmax), discoloration (yellowing) resistance, mechanical properties (e.g., peel strength), and temperature latitude of imaging sheets including leuco dyes.

Abstract: Parameters of a pool of computing resources to be utilized for machine learning tasks from a set of entities are stored, including a category of the computing resources, and a post-task-completion retention period during which, after completion of a task, at least a portion of data stored at the resource is not to be deleted. A compute instance of the pool is assigned to a task requested from the set of entities after determining that one or more configuration settings of the instance satisfy a preference indicated in the request for the task, and that the retention period of the instance relative to a completion of an earlier task on the instance has not expired. A result of the task is stored.

Abstract: The present application discloses a signal processing method, an electronic device, a storage medium, and a program product. The signal processing method comprises: when a data symbol in a signal stream is received, performing signal processing according to the data symbol and a first channel estimation value to obtain a demodulated data symbol, wherein the first channel estimation value is obtained according to a previous signal symbol of the data symbol.

Abstract: A post-task-completion retention period for which a computing resource is to be retained, without de-activating the resource, on behalf of a set of requesters of machine learning tasks is determined at a machine learning service. A first task, identified at the service prior to expiration of the retention period at a first computing resource at which a second task has completed, is initiated at the first computing resource. In response to obtaining an indication of a third task and determining that a threshold criterion associated with the retention period satisfies a criterion, the third task is initiated at an additional computing resource. The additional computing resource is de-activated after the third task completes, without waiting for the retention period to expire.

Abstract: A gain adjustment control method, device and apparatus, and a non-transitory computer-readable storage medium are disclosed. The method may include: calculating energy of a full-bandwidth physical downlink shared channel (PDSCH) transmission signal in an intermediate frequency digital domain or a baseband digital domain according to a set calculation period; determining a gain control adjustment parameter according to the calculated energy of the full-bandwidth PDSCH transmission signal; and controlling and adjusting a gain of a receiver according to the determined gain control adjustment parameter and a gain adjustment rule.

Abstract: Parameters of a pool of computing resources to be utilized for machine learning tasks from a set of entities are stored, including a category of the computing resources, and a post-task-completion retention period during which, after completion of a task, at least a portion of data stored at the resource is not to be deleted. A compute instance of the pool is assigned to a task requested from the set of entities after determining that one or more configuration settings of the instance satisfy a preference indicated in the request for the task, and that the retention period of the instance relative to a completion of an earlier task on the instance has not expired. A result of the task is stored.

Abstract: Primer layers having improved reflectivity and thermal insulation properties are provided for use in microencapsulated thermal imaging sheets. Primer layers having a polymeric binder, of about 3% to about 60% by weight, relative to the total weight of the primer layer, of a white particulate, a primer substrate, and hollow particulate particles improve the reflectivity, color development, color density, and thermal insulation of microcapsule imaging sheets including the same. A black back-coat applied to the substrate on a side opposite the primer layer further enhances the Dmax and E10 of microcapsule imaging sheets and prevents premature exposure of microcapsule imaging sheets in stacked media.

Abstract: Embodiments provide approaches to selectively integrate with various providers. For example, a user in a location where an on-demand provider is available may attempt to utilize services provided by the on-demand provider in a different location where the on-demand provider has limited or no services available, but where one or more other on-demand providers may be available. The user may attempt to access services provided by the on-demand provider using an application provided by the on-demand provider. However, in the situation where the user is in the location where services provided by the on-demand provider are not available, the user would have to secure other means of accessing those services. In such a situation the user may be able to utilize the application to request services with at least one partner on-demand provider.

Abstract: This disclosure describes a card-scan system that can update a card-scan machine learning model to improve card-character predictions for character-bearing cards by using an active-learning technique that learns from card-scan representations indicating corrections by users to predicted card characters. In particular, the disclosed systems can use a client device to capture and analyze a set of card images of a character-bearing card to predict card characters using a card-scan machine learning model. The disclosed systems can further receive card-scan gradients representing one or more corrections to incorrectly predicted card characters. Based on the card-scan gradients, the disclosed systems can generate active-learning metrics and retrain or update the card-scan machine learning model based on such active-learning metrics.

Abstract: Embodiments provide approaches to selectively integrate with various providers based on reliability factors, user preferences, and/or other criteria. For example, a user in a location where an on-demand provider is available may attempt to utilize services provided by the on-demand provider in a different location where the on-demand provider has limited or no services available, but where one or more other on-demand providers may be available. The user may attempt to access services provided by the on-demand provider using an application provided by the on-demand provider. However, in the situation where the user is in the location where services provided by the on-demand provider are not available, the user would have to secure other means of accessing those services. In such a situation the user may be able to utilize the application to request services with at least one partner on-demand provider.

Abstract: A post-task-completion retention period for which a computing resource is to be retained, without de-activating the resource, on behalf of a set of requesters of machine learning tasks is determined at a machine learning service. A first task, identified at the service prior to expiration of the retention period at a first computing resource at which a second task has completed, is initiated at the first computing resource. In response to obtaining an indication of a third task and determining that a threshold criterion associated with the retention period satisfies a criterion, the third task is initiated at an additional computing resource. The additional computing resource is de-activated after the third task completes, without waiting for the retention period to expire.

Abstract: This disclosure describes a card-scan system that can update a card-scan machine learning model to improve card-character predictions for payment cards, driver licenses, or other character-bearing cards by using an active-learning technique that learns from card-scan representations indicating corrections by users to predicted card characters. In particular, the disclosed systems can use a client device to capture and analyze a set of card images of a character-bearing card to predict card characters using a card-scan machine learning model. The disclosed systems can further receive card-scan gradients representing one or more corrections to incorrectly predicted card characters. Based on the card-scan gradients, the disclosed systems can generate active-learning metrics and retrain or update the card-scan machine learning model based on such active-learning metrics.

Abstract: Microcapsules including a color-filtering shell and a core comprising a leuco dye or dye precursor, a photoinitiator or photosensitizer, and a photohardenable or photosoftenable material are provided for use in microcapsule imaging sheets. The imaging sheets including such microcapsules reduce undesirable crosstalk among the microcapsules of various colors and provide significantly improved color fidelity of the image thus reproduced.

Abstract: A post-task-completion retention period for which a computing resource is to be retained, without de-activating the resource, on behalf of a set of requesters of machine learning tasks is determined at a machine learning service. A first task, identified at the service prior to expiration of the retention period at a first computing resource at which a second task has completed, is initiated at the first computing resource. In response to obtaining an indication of a third task and determining that a threshold criterion associated with the retention period satisfies a criterion, the third task is initiated at an additional computing resource. The additional computing resource is de-activated after the third task completes, without waiting for the retention period to expire.

Abstract: Hydrocarbonbackbone polymers with pendant quaternary ammonium groups and methods of making and using same. A polymer can be made by a ring-opening polymerization of quaternary ammonium bearing monomer(s), and, optionally, non-quaternary ammonium bearing monomer(s). A film including a polymer can be used as an anion exchange membrane in a device, such as, for example, a battery, a fuel cell, or the like.

Abstract: Microcapsules including a polymeric shell and an internal phase including a non-photopolymerizable reactive diluent are provided for use in microcapsule imaging sheets. Imaging sheets including microcapsules which include a non-photopolymerizable reactive diluent, and which exhibit improvements in color development (Dmax), discoloration (yellowing) resistance, mechanical properties (e.g., peel strength), and temperature latitude of imaging sheets including leuco dyes.

Abstract: Primer layers having improved reflectivity and thermal insulation properties are provided for use in microencapsulated thermal imaging sheets. Primer layers having a polymeric binder, of about 3% to about 60% by weight, relative to the total weight of the primer layer, of a white particulate, a primer substrate, and hollow particulate particles improve the reflectivity, color development, color density, and thermal insulation of microcapsule imaging sheets including the same. A black back-coat applied to the substrate on a side opposite the primer layer further enhances the Dmax and E10 of microcapsule imaging sheets and prevents premature exposure of microcapsule imaging sheets in stacked media.

Abstract: The present invention provides a knowledge graph construction method and device. The method includes: obtaining structured data, where the structured data includes a first entity name of a first entity and attribute information corresponding to the first entity name, and the attribute information includes a first attribute and a first attribute value; performing, based on measurement of a similarity between the first entity and a second entity in a knowledge graph, entity alignment processing on the first entity, where the measurement of the similarity includes at least one of the following types: measurement of a character similarity, measurement of a structure similarity of a classification tree on which an entity is located, and measurement of an attribute similarity; and importing the structured data into the knowledge graph according to an entity alignment processing result. Embodiments may ensure correctness of data in the knowledge graph.

Abstract: A touch panel is provided in the present disclosure, including: a substrate, a conductive trace structure, and a light-shielding structure. The substrate includes a visible area and a peripheral area, and the visible area is surrounded by the peripheral area. A conductive trace structure is disposed on the visible area. The light-shielding structure includes a first material layer and a second material layer, in which the optical density of the light-shielding structure is lower than 4, the first material layer is disposed on the peripheral area, and the second material layer is disposed on the first material layer. A method of manufacturing a touch panel is provided in some embodiments of the present disclosure for the effects of saving cost and improving wire drift.