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Real-time decoding of question-and-answer speech dialogue using human cortical activity.
Natural communication often occurs in dialogue, differentially engaging auditory and sensorimotor brain regions during listening and speaking. However, previous attempts to decode speech directly from the human brain typically consider listening or speaking tasks in isolation. Here, human participants listened to questions and responded aloud with answers while we used high-density electrocorticography (ECoG) recordings to detect when they heard or said an utterance and to then decode the utterance's identity. Because certain answers were only plausible responses to certain questions, we could dynamically update the prior probabilities of each answer using the decoded question likelihoods as context. We decode produced and perceived utterances with accuracy rates as high as 61% and 76%, respectively (chance is 7% and 20%). Contextual integration of decoded question likelihoods significantly improves answer decoding. These results demonstrate real-time decoding of speech in an interactive, conversational setting, which has important implications for patients who are unable to communicate
Reducing Errors in Excel Models with Component-Based Software Engineering
Model errors are pervasive and can be catastrophic. We can reduce model
errors and time to market by applying Component-Based Software Engineering
(CBSE) concepts to Excel models. CBSE assembles solutions from pre-built,
pre-tested components rather than written from formulas. This is made possible
by the introduction of LAMBDA. LAMBDA is an Excel function that creates
functions from Excel's formulas. CBSE-compliant LAMBDA functions can be reused
in any project just like any Excel function. They also look exactly like
Excel's native functions such as SUM(). This makes it possible for even junior
modelers to leverage CBSE-compliant LAMBDAs to develop models quicker with
fewer errors.Comment: 27 page
copulaedas: An R Package for Estimation of Distribution Algorithms Based on Copulas
The use of copula-based models in EDAs (estimation of distribution
algorithms) is currently an active area of research. In this context, the
copulaedas package for R provides a platform where EDAs based on copulas can be
implemented and studied. The package offers complete implementations of various
EDAs based on copulas and vines, a group of well-known optimization problems,
and utility functions to study the performance of the algorithms. Newly
developed EDAs can be easily integrated into the package by extending an S4
class with generic functions for their main components. This paper presents
copulaedas by providing an overview of EDAs based on copulas, a description of
the implementation of the package, and an illustration of its use through
examples. The examples include running the EDAs defined in the package,
implementing new algorithms, and performing an empirical study to compare the
behavior of different algorithms on benchmark functions and a real-world
problem
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