Abstract: A display substrate and a display device are provided. The display substrate includes a display area and a frame area, wherein the frame area is located around the display area, and the frame area is provided with a first barrier structure and a second barrier structure. In a direction perpendicular to the plane where the display substrate is located, the display substrate includes a base, and a driving structure layer, a light-emitting structure layer and an encapsulation structure layer, which are sequentially stacked on the base; the first barrier structure is arranged between the driving structure layer and the encapsulation structure layer, and the second barrier structure is arranged on the driving structure layer.

Abstract: Embodiments are directed to a machine learning recommendation system. The system receives a user query for generating a recommendation for one or more items with an explanation associated with recommending the one or more items. The system obtains first features of at least one user and second features of a set of items. The system provides the first features and the second features to a first machine learning network for determining a predicted score for an item. The system provides a portion of the first features and a portion of the second features to second machine learning networks for determining explainability scores for an item and generating corresponding explanation narratives. The system provides the recommendation for one or more items and corresponding explanation narratives based on ranking predicted scores and explainability scores for the items.

Abstract: The present disclosure provides a display panel and a display apparatus, and belongs to the field of display technology. The display panel includes a plurality of pixel units each including a plurality of subpixels. At least one subpixel in at least one pixel unit is adjacent to at least two support parts, an angle is formed between connection lines respectively connecting centers of the at least two support parts adjacent to the at least one subpixel and a center of the subpixel, and two angles adjacent in a first direction have different orientations.

Abstract: This application discloses a packet processing method including: receiving a first packet that includes a DA field and an SRH, where the DA field includes at least one C-SID that is sequentially arranged; determining, depending on whether another C-SID is further included after a first C-SID corresponding to the first node in the at least one C-SID, whether a first processing flavor corresponding to the first C-SID indicates a movement operation or a replacement operation; and if the first processing flavor indicates a movement operation, moving a target field that is after the first C-SID and that is in the DA field, to enable the target field to cover the first C-SID in the DA field; or if the first processing flavor indicates a replacement operation, replacing the first C-SID in the DA field with a second C-SID in the SRH.

Abstract: This application provides a packet processing method and a related device. An SR policy on a network device includes a segment list, endpoint information, and indication information, and the indication information indicates whether the segment list points to an endpoint indicated by the endpoint information in the SR policy. When receiving a service packet whose destination address matches the SR policy, the network device determines, based on the indication information in the SR policy, whether the segment list points to the endpoint of the SR policy, encapsulates the service packet based on a determining result, and forwards an encapsulated service packet according to an SR path determined based on the segment list.

Abstract: A packet sending method and device. The first node sets a next-hop of the routing information to a next-hop through which the first node reaches the first route source. The destination address of the routing information is the address prefix. When the second route source is superior to the first route source, the first node switches the next-hop of the routing information to a next-hop through which the first node reaches the second route source. Then, the first node adds, to a target packet, path information of a forwarding path from the first node to the second route source according to the switching operation, where a destination address of the target packet matches the address prefix. Finally, the first node forwards the target packet to the second route source through the forwarding path.

Abstract: Embodiments described herein provide an attentive network framework that models dynamic attributes with item and feature interactions. Specifically, the attentive network framework first encodes basket item sequences and dynamic attribute sequences with time-aware padding and time/month encoding to capture the seasonal patterns (e.g. in app recommendation, outdoor activities apps are more suitable for summer time while indoor activity apps are better for winter). Then the attentive network framework applies time-level attention modules on basket items' sequences and dynamic user attributes' sequences to capture basket items to basket items and attributes to attributes temporal sequential patterns. After that, an intra-basket attentive module is used on items in each basket to capture the correlation information among items.

Abstract: A packet sending method and device. The first node sets a next-hop of the routing information to a next-hop through which the first node reaches the first route source. The destination address of the routing information is the address prefix. When the second route source is superior to the first route source, the first node switches the next-hop of the routing information to a next-hop through which the first node reaches the second route source. Then, the first node adds, to a target packet, path information of a forwarding path from the first node to the second route source according to the switching operation, where a destination address of the target packet matches the address prefix. Finally, the first node forwards the target packet to the second route source through the forwarding path.

Abstract: A packet sending method and device. The first node sets a next-hop of the routing information to a next-hop through which the first node reaches the first route source. The destination address of the routing information is the address prefix. When the second route source is superior to the first route source, the first node switches the next-hop of the routing information to a next-hop through which the first node reaches the second route source. Then, the first node adds, to a target packet, path information of a forwarding path from the first node to the second route source according to the switching operation, where a destination address of the target packet matches the address prefix. Finally, the first node forwards the target packet to the second route source through the forwarding path.

Abstract: Implementations of the present application provide a partition splitting method. Data files in a target source partition are divided into at least two data groups. A first data group from the at least two data groups is selected as a target splitting data group. A first splitting step is performed on the target splitting data group, including: splitting the target splitting data group into a plurality of data sub-groups based on a predetermined splitting rule, wherein each data sub-group of the plurality of data sub-groups corresponds to a new partition of at least two new partitions for the target source partition, and assigning each data sub-group to the new partition corresponding to the data sub-group. A second data group from the at least two data groups is selected as the target splitting data group, wherein the second data group is different from the first data group.

Abstract: Embodiments described herein provide an attentive network framework that models dynamic attributes with item and feature interactions. Specifically, the attentive network framework first encodes basket item sequences and dynamic attribute sequences with time-aware padding and time/month encoding to capture the seasonal patterns (e.g. in app recommendation, outdoor activities apps are more suitable for summer time while indoor activity apps are better for winter). Then the attentive network framework applies time-level attention modules on basket items' sequences and dynamic user attributes' sequences to capture basket items to basket items and attributes to attributes temporal sequential patterns. After that, an intra-basket attentive module is used on items in each basket to capture the correlation information among items.

Abstract: A packet sending method and device. The first node sets a next-hop of the routing information to a next-hop through which the first node reaches the first route source. The destination address of the routing information is the address prefix. When the second route source is superior to the first route source, the first node switches the next-hop of the routing information to a next-hop through which the first node reaches the second route source. Then, the first node adds, to a target packet, path information of a forwarding path from the first node to the second route source according to the switching operation, where a destination address of the target packet matches the address prefix. Finally, the first node forwards the target packet to the second route source through the forwarding path.

Abstract: Embodiments are directed to a machine learning recommendation system. The system receives a user query for generating a recommendation for one or more items with an explanation associated with recommending the one or more items. The system obtains first features of at least one user and second features of a set of items. The system provides the first features and the second features to a first machine learning network for determining a predicted score for an item. The system provides a portion of the first features and a portion of the second features to second machine learning networks for determining explainability scores for an item and generating corresponding explanation narratives. The system provides the recommendation for one or more items and corresponding explanation narratives based on ranking predicted scores and explainability scores for the items.

Abstract: Implementations of the present application provide a partition splitting method. Data files in a target source partition are divided into at least two data groups. A first data group from the at least two data groups is selected as a target splitting data group. A first splitting step is performed on the target splitting data group, including: splitting the target splitting data group into a plurality of data sub-groups based on a predetermined splitting rule, wherein each data sub-group of the plurality of data sub-groups corresponds to a new partition of at least two new partitions for the target source partition, and assigning each data sub-group to the new partition corresponding to the data sub-group. A second data group from the at least two data groups is selected as the target splitting data group, wherein the second data group is different from the first data group.

Abstract: There is provided a deep brain stimulation system having k focuses, which comprises: a first electrode and a second electrode, used for connecting one side of a brain scalp; a third electrode and a fourth electrode, used for connecting the other side of the brain scalp; and a first signal generation unit, a second signal generation unit, . . . , a (2k?1)th signal generation unit and a 2kth signal generation unit, wherein the k is a positive integer greater than or equal to 2; and each of the signal generation units is used for providing the following currents for the first electrode to the fourth electrode: a first current and a second current, which are interfered at a deep portion of a brain to form a first focus; and a (2k?1)th current and a 2kth current, which are interfered at the deep portion of the brain to form a kth focus.

Abstract: Implementations of the present application provide a partition splitting method. Data files in a target source partition are divided into at least two data groups. A first data group from the at least two data groups is selected as a target splitting data group. A first splitting step is performed on the target splitting data group, including: splitting the target splitting data group into a plurality of data sub-groups based on a predetermined splitting rule, wherein each data sub-group of the plurality of data sub-groups corresponds to a new partition of at least two new partitions for the target source partition, and assigning each data sub-group to the new partition corresponding to the data sub-group. A second data group from the at least two data groups is selected as the target splitting data group, wherein the second data group is different from the first data group.

Abstract: Implementations of the present application provide a partition splitting method. Data files in a target source partition are divided into at least two data groups. A first data group from the at least two data groups is selected as a target splitting data group. A first splitting step is performed on the target splitting data group, including: splitting the target splitting data group into a plurality of data sub-groups based on a predetermined splitting rule, wherein each data sub-group of the plurality of data sub-groups corresponds to a new partition of at least two new partitions for the target source partition, and assigning each data sub-group to the new partition corresponding to the data sub-group. A second data group from the at least two data groups is selected as the target splitting data group, wherein the second data group is different from the first data group.

Abstract: Implementations of the present application provide a partition splitting method. Data files in a target source partition are divided into at least two data groups. A first data group from the at least two data groups is selected as a target splitting data group. A first splitting step is performed on the target splitting data group, including: splitting the target splitting data group into a plurality of data sub-groups based on a predetermined splitting rule, wherein each data sub-group of the plurality of data sub-groups corresponds to a new partition of at least two new partitions for the target source partition, and assigning each data sub-group to the new partition corresponding to the data sub-group. A second data group from the at least two data groups is selected as the target splitting data group, wherein the second data group is different from the first data group.

Abstract: The present disclosure discloses a deep brain stimulation system having k focuses, which comprises: a first electrode and a second electrode, used for connecting one side of a brain scalp; a third electrode and a fourth electrode, used for connecting the other side of the brain scalp; and a first signal generation unit, a second signal generation unit, . . . , a (2k?1)th signal generation unit and a 2kth signal generation unit, wherein the k is a positive integer greater than or equal to 2; and each of the signal generation units is used for providing the following currents for the first electrode to the fourth electrode: a first current and a second current, which are interfered at a deep portion of a brain to form a first focus; and a (2k?1)th current and a 2kth current, which are interfered at the deep portion of the brain to form a kth focus; for the first current, the second current, . . .