Evidence for instances of astrophysical ‘fine tuning’ (or ‘coincidences’) is thought by some to lend support to the design argument (i.e. the argument that our universe has been designed by some deity). We assess some of the relevant empirical and conceptual issues. We argue that astrophysical fine tuning calls for some explanation, but this explanation need not appeal to the design argument. A...

This paper tests a learning-based model of strategic teaching in repeated games with incomplete information. The repeated game has a long-run player whose type is unknown to a group of short-run players. The proposed model assumes a fraction of ‘short-run’ players follow a one-parameter learning model (self-tuning EWA). In addition, some ‘long-run’ players are myopic while others are sophistica...

In this paper, a novel technique is presented to solve tolerance design problems. To achieve the desired performance tolerance, the technique uses a subtle, but significant, change in the design: the addition of a tuning parameter in place of an increase in component precision. Statistical models are used to develop a framework for the tuning parameter design method. Also developed is a new, di...

Tiling, a key transformation for optimizing programs, has been widely studied in the literature. Parameterized tiled code is important for auto-tuning systems since they often execute a large number of runs with dynamically varied tile sizes. In this paper, we present a comparative study of three recently developed approaches to parametric tiling of imperfectly nested loops.

The issue of tuning in Indian classical music has been, historically, a matter of theoretical debate. In this paper, we study its contemporary practice in sung performances of Carnatic and Hindustani music following an empiric and quantitative approach. To do so, we select stable fundamental frequencies, estimated via a standard algorithm, and construct interval histograms from a pool of record...

This paper refers to our prosperous development of algorithms for detecting keywords in continuous speech. Two different approaches to define confidence measures are introduced. As an advantage, these definitions are theoretically calculable without artful tuning. Moreover, two distinct decoding algorithms are presented, that incorporate these confidence measures into the search procedure. One ...

A class of modi®ed state space self-tuning controllers of the minimum state error variance type was considered. A suitable chosen structure of the proposed controller allows the tracking of a time-varying reference input and makes a possibility of applying this solution to nonlinear and non-stationar y plants. The advantage in using the proposed algorithm for nonlinear systems’ control is demon...

Problem of parameterization of multi-output autoregressive regressive Gaussian model (ARX) is studied in the context of prior design of adaptive controllers. The substantial role of prior distribution of unknown parameters on the parameterization is demonstrated. Among several parameterizations a nontraditional one is advocated which • makes it possible to model the system output entry-wise, th...

Many papers have been published on the knowledge base completion task in the past few years. Most of these introduce novel architectures for relation learning that are evaluated on standard datasets such as FB15k and WN18. This paper shows that the accuracy of almost all models published on the FB15k can be outperformed by an appropriately tuned baseline — our reimplementation of the DistMult m...

Despite the good results provided by Dynamic Classifier Selection (DCS) mechanisms based on local accuracy in a large number of applications, the performances are still capable of improvement. As the selection is performed by computing the accuracy of each classifier in a neighbourhood of the test pattern, performances depend on the shape and size of such a neighbourhood, as well as the local d...