Future google
In computer science, future, promise, and delay refer to constructs used for synchronization in some concurrent programming languages. They describe an object that acts as a proxy for a result that is initially unknown, usually because the computation of its value is yet incomplete. The term promise was proposed in 1976 by Daniel P. Friedman and David Wise, and Peter Hibbard called it eventual. A somewhat similar concept future was introduced in 1977 in a paper by Henry Baker and Carl Hewitt. The terms future, promise, and delay are often used interchangeably, although some differences in usage between future and promise are treated below. Specifically, when usage is distinguished, a future is a read-only placeholder view of a variable, while a promise is a writable, single assignment container which sets the value of the future. Notably, a future may be defined without specifying which specific promise will set its value, and different possible promises may set the value of a given future, though this can be done only once for a given future. In other cases a future and a promise are created together and associated with each other: the future is the value, the promise is the function that sets the value – essentially the return value (future) of an asynchronous function (promise). Setting the value of a future is also called resolving, fulfilling, or binding it. …

CogSciK google
Computational models of decisionmaking must contend with the variance of context and any number of possible decisions that a defined strategic actor can make at a given time. Relying on cognitive science theory, the authors have created an algorithm that captures the orientation of the actor towards an object and arrays the possible decisions available to that actor based on their given intersubjective orientation. This algorithm, like a traditional K-means clustering algorithm, relies on a core-periphery structure that gives the likelihood of moves as those closest to the cluster’s centroid. The result is an algorithm that enables unsupervised classification of an array of decision points belonging to an actor’s present state and deeply rooted in cognitive science theory. …

Linear Unified LASSO (LLASSO) google
We propose a rescaled LASSO, by premultipying the LASSO with a matrix term, namely linear unified LASSO (LLASSO) for multicollinear situations. Our numerical study has shown that the LLASSO is comparable with other sparse modeling techniques and often outperforms the LASSO and elastic net. Our findings open new visions about using the LASSO still for sparse modeling and variable selection. We conclude our study by pointing that the LLASSO can be solved by the same efficient algorithm for solving the LASSO and suggest to follow the same construction technique for other penalized estimators. …

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