A method for sorting digital images, the method comprising the steps of: a) Receiving a digital image acquired with an electronic device associated with a user, the digital image being sent using the electronic device to an electronic image sorting system; b) Generating, using an image analysis portion of the electronic image sorting system, one or more initial identifiers based on the contents of the digital image; c) Sending the one or more initial identifiers to an identifier analysis portion of the electronic image sorting system, wherein the identifier analysis portion includes an electronic database of one or more rules adapted to generate one or more additional identifiers based on the one or more initial identifiers; d) Sending, using the electronic image sorting system, the one or more initial identifiers and/or the one or more additional identifiers, to the electronic device to allow the user to generate one or more selected identifiers from the one or more initial identifiers and/or the one or more additional identifiers; and e) Modifying the one or more rules based on the one or more selected identifiers.

A method for sorting digital images, the method comprising the steps of: a) Receiving a digital image acquired with an electronic device associated with a user, the digital image being sent using the electronic device to an electronic image sorting system; b) Generating, using an image analysis portion of the electronic image sorting system, one or more initial identifiers based on the contents of the digital image; c) Sending the one or more initial identifiers to an identifier analysis portion of the electronic image sorting system, wherein the identifier analysis portion includes an electronic database of one or more rules adapted to generate one or more additional identifiers based on the one or more initial identifiers; d) Sending, using the electronic image sorting system, the one or more initial identifiers and/or the one or more additional identifiers, to the electronic device to allow the user to generate one or more selected identifiers from the one or more initial identifiers and/or the one or more additional identifiers; and e) Modifying the one or more rules based on the one or more selected identifiers.

Apparatuses, methods of operating apparatuses, and corresponding computer programs are disclosed. In the apparatuses input circuitry receives input data comprising at least one data element and shift circuitry generates, for each data element of the input data, a bit-map giving a one-hot encoding representation of the data element, wherein a position of a set bit in the bit-map is dependent on the data element. Summation circuitry generates a position summation value for each position in the bit-map, wherein each position summation value is a sum across all bit-maps generated by the shift circuitry from the input data. Maximum identification circuitry determines at least one largest position summation value generated by the summation circuitry and output circuitry to generate an indication of at least one data element corresponding to the at least one largest position summation value. The statistical mode of the data elements in the input data is thereby efficiently determined.

[Problem] To suppress increases in the size of a fully indexable dictionary while making it possible for a target bit stream to be subjected to two types of selection operation employing the fully indexable dictionary. [Solution] An information processing device (100) is provided with a storage unit (10) which stores a data structure (11) used to represent a bit stream formed using a first value and a second value. The data structure (11) includes: first data specifying the positions on the bit stream of all or some succession segments including a succession of one or more of the first value or the second value; second data specifying, for some of the succession segments, the number of first values that have appeared on the bit stream from the beginning of the bit stream as far as the succession segment; and third data specifying, for some of the succession segments, the number of second values that have appeared on the bit stream from the beginning of the bit stream as far as the succession segment.

[Problem] To suppress increases in the size of a fully indexable dictionary while making it possible for a target bit stream to be subjected to two types of selection operation employing the fully indexable dictionary. [Solution] An information processing device (100) is provided with a storage unit (10) which stores a data structure (11) used to represent a bit stream formed using a first value and a second value. The data structure (11) includes: first data specifying the positions on the bit stream of all or some succession segments including a succession of one or more of the first value or the second value; second data specifying, for some of the succession segments, the number of first values that have appeared on the bit stream from the beginning of the bit stream as far as the succession segment; and third data specifying, for some of the succession segments, the number of second values that have appeared on the bit stream from the beginning of the bit stream as far as the succession segment.

This disclosure describes adaptive loop filtering (ALF). There is symmetry in the filter coefficients, and this symmetry may be leveraged so that the clipping function is performed on symmetrical input samples, rather than with respect to all samples used in the filter. In this way, the example techniques may reduce the number of operations performed by a video coder, thereby reducing the amount of time needed to perform the ALF process.

This disclosure describes adaptive loop filtering (ALF). There is symmetry in the filter coefficients, and this symmetry may be leveraged so that the clipping function is performed on symmetrical input samples, rather than with respect to all samples used in the filter. In this way, the example techniques may reduce the number of operations performed by a video coder, thereby reducing the amount of time needed to perform the ALF process.

A computing device to implement fast floating-point adder tree for the neural network applications is disclosed. The fast float-point adder tree comprises a data preparation module, a fast fixed-point Carry-Save Adder (CSA) tree, and a normalization module. The floating-point input data comprises a sign bit, exponent part and fraction part. The data preparation module aligns the fraction part of the input data and prepares the input data for subsequent processing. The fast adder uses a signed fixed-point CSA tree to quickly add a large number of fixed-point data into 2 output values and then uses a normal adder to add the 2 output values into one output value. The fast adder uses for a large number of operands is based on multiple levels of fast adders for a small number of operands. The output from the signed fixed-point Carry-Save Adder tree is converted to a selected floating-point format.

A feed handler is configured to receive a transaction entry from a data feed, the transaction entry indicating at least a floating-point value amount, the data feed associated with a transaction target. The feed handler modifies a locally stored priority queue based on the transaction entry by converting the floating-point value amount in transaction entry to an integer value amount based on a ratio between two underlying components indicated by the transaction target; and storing the integer value amount in a corresponding entry in the locally stored priority queue.

A local extremum calculator detects a local maximum sample and a local minimum sample of a digital audio signal. A number-of-sample detector detects a sample interval between the local maximum sample and the local minimum sample. A difference value calculator calculates difference values between adjacent samples. A correction value calculator calculates a first correction value by multiplying the difference value between the local maximum sample and a first adjacent sample by a coefficient and calculates a second correction value by multiplying the difference value between the local minimum sample and a second adjacent sample by the coefficient. When a periodic signal detector detects that the digital audio signal is a single sine wave, an adder/subtractor does not add the first correction value to the first adjacent sample, and does not subtract the second correction value from the second adjacent sample.