Logic mechanisms operate to define the position of at least one mechanical output based on the position of at least one mechanical input. Some mechanisms are configured to determine, based on the input position(s), whether a path to transmit motion to an output exists or does not exist. Some mechanisms are configured to determine, based on the input position(s), whether or not motion of a driven element can be accommodated without moving an output. Some mechanisms are configured to determine, based on the input position(s), whether or not one or more elements are constrained to transmit motion to an output.

Logic mechanisms operate to define the position of at least one mechanical output based on the position of at least one mechanical input. Some mechanisms are configured to determine, based on the input position(s), whether a path to transmit motion to an output exists or does not exist. Some mechanisms are configured to determine, based on the input position(s), whether or not motion of a driven element can be accommodated without moving an output. Some mechanisms are configured to determine, based on the input position(s), whether or not one or more elements are constrained to transmit motion to an output.

A system and method for identification of key driver responsible for bringing changes in a microbial population is provided. The method involves construction of microbial association networks with each microbial taxa as nodes and their associations as edges and subsequent identification of crucial ‘driver’ nodes involved in the studied disease progression. While comparing a particular node between two networks, this method takes individual nodes and their associations into account as well as the identity of their interacting partners. A taxon in the diseased state with an altered set of associations while still being increasingly important for the whole network necessarily holds a key significance in microbial interplay. Using this rationale, this methodology computes a score to quantify this change for each node and calculates its statistical significance. Subsequently, ‘driver’ nodes are identified using the score coupled with other network parameters and a critical score for the ‘driver’ nodes is calculated to quantify its importance.

A system and method for identification of key driver responsible for bringing changes in a microbial population is provided. The method involves construction of microbial association networks with each microbial taxa as nodes and their associations as edges and subsequent identification of crucial ‘driver’ nodes involved in the studied disease progression. While comparing a particular node between two networks, this method takes individual nodes and their associations into account as well as the identity of their interacting partners. A taxon in the diseased state with an altered set of associations while still being increasingly important for the whole network necessarily holds a key significance in microbial interplay. Using this rationale, this methodology computes a score to quantify this change for each node and calculates its statistical significance. Subsequently, ‘driver’ nodes are identified using the score coupled with other network parameters and a critical score for the ‘driver’ nodes is calculated to quantify its importance.

Disclosed are a partitioning method, an encoder, a decoder and a computer storage medium. The method includes: determining location information of a point of a point cloud to be partitioned; when i is less than or equal to M−1, determining right-shift number N.sub.i of ith LOD layer in the point cloud, M representing a preset maximum quantity of layers for LOD partitioning; for the ith LOD layer, shifting location information of the point rightwards by N.sub.i-digit, performing storing in a preset storage area based on right-shifted location information; determining location information of a parent point corresponding to a current point in the ith LOD layer; according to determined location information of the parent point, searching the preset storage area for a neighbor point of the parent point; partitioning the current point into an (i+1)th LOD layer, or the neighbor point into the ith LOD layer.

Disclosed are a partitioning method, an encoder, a decoder and a computer storage medium. The method includes: determining location information of a point of a point cloud to be partitioned; when i is less than or equal to M−1, determining right-shift number N.sub.i of ith LOD layer in the point cloud, M representing a preset maximum quantity of layers for LOD partitioning; for the ith LOD layer, shifting location information of the point rightwards by N.sub.i-digit, performing storing in a preset storage area based on right-shifted location information; determining location information of a parent point corresponding to a current point in the ith LOD layer; according to determined location information of the parent point, searching the preset storage area for a neighbor point of the parent point; partitioning the current point into an (i+1)th LOD layer, or the neighbor point into the ith LOD layer.

A processing-in-memory (PIM) device may include a plurality of memory banks configured to provide plural groups of weight data, a global buffer configured to provide plural sets of vector data, and a plurality of multiplication/accumulation (MAC) operators configured to perform MAC operations of the plural groups of weigh data and the plural sets of vector data. Each of the plurality of MAC operators includes a plurality of multiple operation circuits. Each of the plurality of multiple operation circuits is configured to perform an arithmetic operation in a first operation mode, a second operation mode, or a third operation mode according to first to third selection signals.

The present disclosure relates to neural network processing apparatus and method. An exemplary neural network processing apparatus includes: a memory for storing data; an operation unit to perform an operation on a first batch of tensors; a statistics determination unit communicatively coupled to the operation unit and configured to receive an output from the operation unit and determine statistics from the output; and a normalization unit communicatively coupled to the memory and the statistics determination unit and configured to receive a second batch of tensors from the memory and the statistics from the statistics determination unit and to normalize the second batch of tensors using the statistics.

The present disclosure relates to neural network processing apparatus and method. An exemplary neural network processing apparatus includes: a memory for storing data; an operation unit to perform an operation on a first batch of tensors; a statistics determination unit communicatively coupled to the operation unit and configured to receive an output from the operation unit and determine statistics from the output; and a normalization unit communicatively coupled to the memory and the statistics determination unit and configured to receive a second batch of tensors from the memory and the statistics from the statistics determination unit and to normalize the second batch of tensors using the statistics.

Computer processing and outcome prediction systems and methods used to generate algorithm time prediction polynomials, inverse algorithm time prediction polynomials, determine race conditions, determine when a non-linear algorithm can be treated as if it were linear, as well as automatically generate parallel and quantum solutions from classical software or from the relationship between monotonic attribute values.