Abstract: A hybrid machine learning model architecture that enhances the retrieval capabilities of recurrent models without compromising efficiency. A linear recurrent architecture is supplemented by a parallel retrieval branch that integrates retrieved knowledge into the model with relatively minimal computational overhead. The parallel retrieval branch operates alongside the primary recurrent processing pipeline. The parallel retrieval branch chunks the input sequence and embeds the chunks into one or more pool. Chunk-by-chunk causal retrieval produces results that are then injected into the primary linear pipeline using within-chunk cross-attention.

Abstract: A computer-implemented method for encoding data to be stored in a solid-state storage device. The method includes the steps of, providing a source data; calculating an entropy of the source data; applying a first encoding scheme to encode the source data into an encoded data, if the entropy is below a threshold; and applying a second data encoding scheme to encode the source data into the encoded data, if the entropy is equal to or higher than the threshold. The first encoding scheme is adapted to encode most frequently combined characters in the source data into predetermined characters to increase ratio of the predetermined characters in the encoded data. The second data encoding scheme is adapted to encode evenly distributed combined characters into a plurality of patterns with the predetermined characters, in order to increase the ratio of the predetermined characters in the encoded data.

Abstract: A computer-implemented method for encoding data to be stored in a solid-state storage device. The method includes the steps of, providing a source data; calculating an entropy of the source data; applying a first encoding scheme to encode the source data into an encoded data, if the entropy is below a threshold; and applying a second data encoding scheme to encode the source data into the encoded data, if the entropy is equal to or higher than the threshold. The first encoding scheme is adapted to encode most frequently combined characters in the source data into predetermined characters to increase ratio of the predetermined characters in the encoded data. The second data encoding scheme is adapted to encode evenly distributed combined characters into a plurality of patterns with the predetermined characters, in order to increase the ratio of the predetermined characters in the encoded data.

Abstract: A computer-implemented method and a system for configuring and implementing an artificial neural network. The method for configuring includes initializing an artificial neural network; and training, based on a training operation, the artificial neural network so as to form an adaptively deployable artificial neural network defining a plurality of nested artificial neural sub-networks. Each artificial neural sub-network is optimal for a respective resource configuration. The method for implementing includes determining an optimal configuration of the artificial neural network for deployment at an electrical device, and deploying the artificial neural network with the optimal configuration at the electrical device.

Abstract: A support device (1) and a wind generating set. The support device is used for the wind generating set, and the wind generating set comprises an electrical apparatus (3). The support device comprises: a support frame (10) which is of a hollow frame structure and comprises a plurality of beam structures (11), wherein the adjacent beam structures are connected to each other, and a relative position between at least one set of two beam structures with a connection relationship is adjustable; and a support platform (20) arranged on one surface of the support frame in a height direction of the support frame and connected to the beam structure, wherein the support platform is used for supporting the electrical apparatus. The device can be used for supporting the electrical apparatus of the wind generating set, and at the same time, the size and/or bearing capacity can be changed according to apparatus requirements, and a better universality is achieved.

Abstract: A support device (1) and a wind generating set. The support device is used for the wind generating set, and the wind generating set comprises an electrical apparatus (3). The support device comprises: a support frame (10) which is of a hollow frame structure and comprises a plurality of beam structures (11), wherein the adjacent beam structures are connected to each other, and a relative position between at least one set of two beam structures with a connection relationship is adjustable; and a support platform (20) arranged on one surface of the support frame in a height direction of the support frame and connected to the beam structure, wherein the support platform is used for supporting the electrical apparatus. The device can be used for supporting the electrical apparatus of the wind generating set, and at the same time, the size and/or bearing capacity can be changed according to apparatus requirements, and a better universality is achieved.

Abstract: Smart walking devices, assemblies, and methods of using the same. Improved partial weight bearing requirements while using a walking aid may be provided. An example smart foot assembly may include a tubular sheath with a spring assembly connected to a foot with a force sensor coupled to the spring assembly wherein during a load phase as the distal end of the foot contacts the ground, the foot moves proximally as load is increased.

Abstract: A computer-implemented method and a system for configuring and implementing an artificial neural network. The method for configuring includes initializing an artificial neural network; and training, based on a training operation, the artificial neural network so as to form an adaptively deployable artificial neural network defining a plurality of nested artificial neural sub-networks. Each artificial neural sub-network is optimal for a respective resource configuration. The method for implementing includes determining an optimal configuration of the artificial neural network for deployment at an electrical device, and deploying the artificial neural network with the optimal configuration at the electrical device.