Abstract: A computer implemented method of executing a plurality of discrete software modules each including a machine learning algorithm as an executable software component configurable to approximate a function relating a domain data set to a range data set; a data store; and a message handler as an executable software component arranged to receive input data and communicate output data for the module, wherein the message handler is adapted to determine domain parameters for the algorithm based on the input data and to generate the output data based on a result generated by the algorithm, the method including providing a communication channel between modules in order that at least part of output data for a first module constitutes at least part of input data for a second module so as to create a network of modules for combining machine learning algorithms to refine the approximation of the function.

Abstract: A computer implemented method of executing a plurality of discrete software modules each including a machine learning algorithm as an executable software component configurable to approximate a function relating a domain data set to a range data set; a data store; and a message handler as an executable software component arranged to receive input data and communicate output data for the module, wherein the message handler is adapted to determine domain parameters for the algorithm based on the input data and to generate the output data based on a result generated by the algorithm, each module having associated a metric of resource utilization by the module, the method including receiving a request for a machine learning task; and selecting a module from the plurality of modules for the task based on the metric associated with the module.

Abstract: A computer implemented method of executing a software module includes a machine learning algorithm as an executable software component configurable to approximate a function relating a domain data set to a range data set; a data store; and a message handler as an executable software component arranged to receive input data and communicate output data for the module, wherein the message handler is adapted to determine domain parameters for the algorithm based on the input data and to generate the output data based on a result generated by the algorithm, the method including generating a message as input data for the module, the message including instructions for execution by the module to effect a modification of the machine learning algorithm of the module.

Abstract: A computer implemented method of validating use of a computing resource by a an executing requester software module from a plurality of discrete software modules, the method including validating a characteristic of the requester software module; generating a first transaction defining criteria for consumption of the computing resource by the requester software module, the first transaction being encrypted with a private key from a public key/private key pair and being added as part of a block of transactions to a blockchain data structure; generating a subsequent encrypted transaction corresponding to a request of the requester software module to consume the computing resource, the subsequent transaction referring to the first transaction, wherein the subsequent transaction is validated by a transaction miner computing component from a plurality of miners by authenticating the transaction using the public key and verifying compliance with the criteria defined in each transaction.

Abstract: A computer implemented method of executing a plurality of discrete software modules each including a machine learning algorithm as an executable software component configurable to approximate a function relating a domain data set to a range data set; a data store; and a message handler as an executable software component arranged to receive input data and communicate output data for the module, wherein the message handler is adapted to determine domain parameters for the algorithm based on the input data and to generate the output data based on a result generated by the algorithm, the method including providing a communication channel between modules in order that at least part of output data for a first module constitutes at least part of input data for a second module so as to create a network of modules for combining machine learning algorithms to refine the approximation of the function.

Abstract: A computer implemented method of executing a machine learning algorithm includes providing the machine learning algorithm as an executable software component configured to receive machine learning parameters and generate a machine learning result, wherein the machine learning algorithm is operable with a data store; providing a message handler as an executable software component arranged to receive input data and communicate output data for the module, wherein the message handler is adapted to determine the machine learning parameters based on the input data and to generate the output data based on the machine learning result so as to provide a common interface via the input and output data for the machine learning algorithm taken from a set of heterogeneous algorithms.

Abstract: A computer implemented method of executing a software module includes a machine learning algorithm as an executable software component configurable to approximate a function relating a domain data set to a range data set; a data store; and a message handler as an executable software component arranged to receive input data and communicate output data for the module, wherein the message handler is adapted to determine domain parameters for the algorithm based on the input data and to generate the output data based on a result generated by the algorithm, the method including generating a message as input data for the module, the message including instructions for execution by the module to effect a modification of the machine learning algorithm of the module.

Abstract: A computer implemented method of validating use of a computing resource by a an executing requester software module from a plurality of discrete software modules, the method including validating a characteristic of the requester software module; generating a first transaction defining criteria for consumption of the computing resource by the requester software module, the first transaction being encrypted with a private key from a public key/private key pair and being added as part of a block of transactions to a blockchain data structure; generating a subsequent encrypted transaction corresponding to a request of the requester software module to consume the computing resource, the subsequent transaction referring to the first transaction, wherein the subsequent transaction is validated by a transaction miner computing component from a plurality of miners by authenticating the transaction using the public key and verifying compliance with the criteria defined in each transaction.

Abstract: A computer implemented method for identifying a signal in a sequence of numerical values, the method including: converting the sequence of numerical values into a vector sequence by converting each value in the sequence to a normalized vector representation; generating a vector field comprising the vector sequence as a multi-dimensional data structure such that vectors in the vector sequence are plotted in sequence in the vector field; modeling a simulated flow of particles through the vector field such that a flow of each particle is influenced by vectors in the vector field; generating one or more static representations of the particles, each representation being generated at a particular point in time; and performing feature detection on each of the one or more static representations to identify features in the representation corresponding to the signal.

Abstract: A computer implemented method of executing a plurality of discrete software modules each including a machine learning algorithm as an executable software component configurable to approximate a function relating a domain data set to a range data set; a data store; and a message handler as an executable software component arranged to receive input data and communicate output data for the module, wherein the message handler is adapted to determine domain parameters for the algorithm based on the input data and to generate the output data based on a result generated by the algorithm, each module having associated a metric of resource utilization by the module, the method including receiving a request for a machine learning task; and selecting a module from the plurality of modules for the task based on the metric associated with the module.

Abstract: A method of manufacturing an article with integral active electronic component uses an additive manufacturing process to: a) form a non-electrically conductive substrate; b) form a non-electrically conductive perforated layer having an aperture; c) form electrically conductive anode and cathode elements spaced in the aperture; d) deposit a conductive electrical connection to each of the elements suitable for imparting an electrical potential difference between the elements; e) form a non-electrically conductive sealing layer atop the perforated layer so as to retain and seal the aperture in the perforated layer.

Abstract: A computer implemented method for identifying a signal in a sequence of numerical values, the method including: converting the sequence of numerical values into a vector sequence by converting each value in the sequence to a normalized vector representation; generating a vector field comprising the vector sequence as a multi-dimensional data structure such that vectors in the vector sequence are plotted in sequence in the vector field; modeling a simulated flow of particles through the vector field such that a flow of each particle is influenced by vectors in the vector field; generating one or more static representations of the particles, each representation being generated at a particular point in time; and performing feature detection on each of the one or more static representations to identify features in the representation corresponding to the signal.

Abstract: A method of manufacturing an article with integral active electronic component includes using an additive manufacturing process to: a) form a non-electrically conductive substrate; b) form a non-electrically conductive perforated layer having an aperture; c) form electrically conductive anode and cathode elements spaced in the aperture; d) deposit a conductive electrical connection to each of the elements suitable for imparting an electrical potential difference between the elements; and e) form a non-electrically conductive sealing layer atop the perforated layer so as to retain and seal the aperture in the perforated layer.

Abstract: An additively manufactured apparatus having a gas filled sealed cavity containing at least two additively manufactured cathodes and an additively manufactured anode spaced from the cathodes such that a continuous electric discharge of the gas stimulated between at least one of the cathodes and the anode provides a Boolean function output at the anode corresponding to electrical input signals at two of the cathodes.

Abstract: A method of manufacturing an article with integral active electronic component comprising: using an additive manufacturing process to: a) form a non-electrically conductive substrate; b) form a non-electrically conductive perforated layer having an aperture; c) form electrically conductive anode and cathode elements spaced in the aperture; d) deposit a conductive electrical connection to each of the elements suitable for imparting an electrical potential difference between the elements; e) form a non-electrically conductive sealing layer atop the perforated layer so as to retain and seal the aperture in the perforated layer.

Abstract: An additively manufactured apparatus having a gas filled sealed cavity containing at least two additively manufactured cathodes and an additively manufactured anode spaced from the cathodes such that a continuous electric discharge of the gas stimulated between at least one of the cathodes and the anode provides a Boolean function output at the anode corresponding to electrical input signals at two of the cathodes.

Abstract: A network of devices permits data to be stored on the devices and subsequently searched and accessed from any other one of the devices. A plurality of channels are defined to distribute a plurality of access points throughout the network. A mechanism securely assigns authorizations to users to read or write from or to specified ones of the channels, which authorizations are capable of being checked by each of the access points. To write into a channel, a request is made to one of the access points which checks if the requesting user is authorized to write onto the requested channel. If appropriate, the data is associated with the requested channel. To search for data from a particular channel or group of channels, a search request is made to one of the access points which first checks the requesting user is authorized to read from the requested channel or group of channels.

Abstract: A method of manufacturing an article with integral active electronic component comprising: using an additive manufacturing process to: a) form a non-electrically conductive substrate; b) form a non-electrically conductive perforated layer having an aperture; c) form electrically conductive anode and cathode elements spaced in the aperture; d) deposit a conductive electrical connection to each of the elements suitable for imparting an electrical potential difference between the elements; e) form a non-electrically conductive sealing layer atop the perforated layer so as to retain and seal the aperture in the perforated layer.

Abstract: The present invention relates to distributed systems in which resource utilisation decisions depend upon the semi-automatic categorisation of resource descriptions stored in the distributed system. In the principal embodiment, the resource descriptions are web service descriptions which are augmented with tags (i.e. descriptive words or phrases) entered by users and/or by web service administrators. The initial use of automatic categorisation of these descriptions, followed by a user-driven fine-tuning of the automatically-generated categories enables the rapid creation of reliable categorisation of the resource descriptions, which in turns results in better resource utilisation decisions and hence a more efficient use of the resources of the distributed system.

Abstract: A peer-to-peer network providing a distributed document store is disclosed. A problem with known distributed document stores is that search engines are unable to respond quickly to changes in the stored documents or the addition or removal of peers. In the described embodiment, the peers in the network send search queries to one another and each keeps a record of which peers most frequently respond to those queries, and the quality of the responses. The peers each maintain a data structure 46 including connection weights to each of the other peers which depend on that record. By then forwarding search queries to peers selected on the basis of the connection weights, rapid retrieval of relevant documents is enabled. Search queries are generated automatically by peers as well as being generated by users.