Systems and methods are described for quantifying the response of a biological system to one or more perturbations. First and second datasets corresponding to a response of a biological system to first and second treatments are received. A plurality of computational network models that represent the biological system are provided, each model including nodes representing a plurality of biological entities and edges representing relationships between the nodes in the model. A first set of scores is generated, representing the perturbation of the biological system based on the first dataset and the plurality of models, and a second set of scores representing the perturbation of the biological system based on the second dataset and the plurality of computational models. One or more biological impact factors are generated based on each of the first set and second set of scores that represent the biological impact of the perturbation on the biological system.

A device configured to emulate a correlithm object processing system comprises a memory and one or more processors. The memory stores a mapping table that includes multiple context value entries, multiple corresponding source value entries, and multiple corresponding target value entries. Each context value entry includes a correlithm object. The one or more processors receive at least one input source value and a context input value. The one or more processors identify a context value entry from the mapping table that matches the context input value based at least in part upon n-dimensional distances between the context input value and each of the context value entries. The one or more processors identify a portion of the source value entries corresponding to the identified context value entry, and further identifies a source value entry that matches the input source value. The one or more processors identify a target value entry corresponding to the identified source value entry.

A device configured to emulate a correlithm object processing system comprises a memory and one or more processors. The memory stores a mapping table that includes multiple context value entries, multiple corresponding source value entries, and multiple corresponding target value entries. Each context value entry includes a correlithm object. The one or more processors receive at least one input source value and a context input value. The one or more processors identify a context value entry from the mapping table that matches the context input value based at least in part upon n-dimensional distances between the context input value and each of the context value entries. The one or more processors identify a portion of the source value entries corresponding to the identified context value entry, and further identifies a source value entry that matches the input source value. The one or more processors identify a target value entry corresponding to the identified source value entry.

A non-transitory computer-readable recording medium stores therein a learning program for causing a computer to execute a process comprising: referring to, at time of learning a computation model that is a target of deep learning and has a plurality of nodes, a storage unit in which route information that indicates a calculation route followed by a tensor in each stage of learning prior to the time of learning, and statistical information regarding a position of a decimal point used in the calculation route are associated with each other; acquiring, when executing each piece of calculation processing set in each of the plurality of nodes at the time of learning, the statistical information corresponding to the route information that reaches each of the plurality of nodes; and executing the each piece of calculation processing using the position of the decimal point specified by the acquired statistical information.

A non-transitory computer-readable recording medium stores therein a learning program for causing a computer to execute a process comprising: referring to, at time of learning a computation model that is a target of deep learning and has a plurality of nodes, a storage unit in which route information that indicates a calculation route followed by a tensor in each stage of learning prior to the time of learning, and statistical information regarding a position of a decimal point used in the calculation route are associated with each other; acquiring, when executing each piece of calculation processing set in each of the plurality of nodes at the time of learning, the statistical information corresponding to the route information that reaches each of the plurality of nodes; and executing the each piece of calculation processing using the position of the decimal point specified by the acquired statistical information.

Systems, apparatuses and methods may provide for technology that identifies a cognitive space that is to be a compressed representation of activations of a neural network, maps a plurality of activations of the neural network to a cognitive initial point and a cognitive destination point in the cognitive space and generates a first cognitive trajectory through the cognitive space, wherein the first cognitive trajectory traverses the cognitive space from the cognitive initial point to the cognitive destination point.

Systems, apparatuses and methods may provide for technology that identifies a cognitive space that is to be a compressed representation of activations of a neural network, maps a plurality of activations of the neural network to a cognitive initial point and a cognitive destination point in the cognitive space and generates a first cognitive trajectory through the cognitive space, wherein the first cognitive trajectory traverses the cognitive space from the cognitive initial point to the cognitive destination point.

A computer-implemented method for generating a simulated environment in which the behaviour of one or more individuals is modelled, the method comprising: defining a state of the environment at a first point in time; receiving an input defining an action to be performed by one or more individuals in the simulated environment; and in response to the input, updating the state of the environment based on a social-ecological model, wherein the social-ecological model simulates the behaviour of people within the environment and how the people respond to changes associated with said action, wherein the social-ecological model is a machine learning model that is trained using data reflective of real-life past events, the social-ecological model being configured to accept as input a parameterised dataset describing the state of the environment at the first point in time and to output an updated dataset that describes the updated state of the environment.

The present disclosure provides systems and methods for the management and/or evaluation of machine-learned models based on locally logged data. In one example, a user computing device can obtain a machine-learned model (e.g., from a server computing device) and can evaluate at least one performance metric for the machine-learned model. In particular, the at least one performance metric for the machine-learned model can be evaluated relative to data that is stored locally at the user computing device. The user computing device and/or the server computing device can determine whether to activate the machine-learned model on the user computing device based at least in part on the at least one performance metric. In another example, the user computing device can evaluate a plurality of machine-learned models against locally stored data. At least one of the models can be selected based on the evaluated performance metrics.

Systems, apparatuses and methods may provide for technology that identifies a cognitive space that is to be a compressed representation of activations of a neural network, maps a plurality of activations of the neural network to a cognitive initial point and a cognitive destination point in the cognitive space and generates a first cognitive trajectory through the cognitive space, wherein the first cognitive trajectory traverses the cognitive space from the cognitive initial point to the cognitive destination point