322 research outputs found
Data Mining the Brain to Decode the Mind
In recent years, neuroscience has begun to transform itself into a âbig dataâ enterprise with the importation of computational and statistical techniques from machine learning and informatics. In addition to their translational applications such as brain-computer interfaces and early diagnosis of neuropathology, these tools promise to advance new solutions to longstanding theoretical quandaries. Here I critically assess whether these promises will pay off, focusing on the application of multivariate pattern analysis (MVPA) to the problem of reverse inference. I argue that MVPA does not inherently provide a new answer to classical worries about reverse inference, and that the method faces pervasive interpretive problems of its own. Further, the epistemic setting of MVPA and other decoding methods contributes to a potentially worrisome shift towards prediction and away from explanation in fundamental neuroscience
Uncertainty-Aware Principal Component Analysis
We present a technique to perform dimensionality reduction on data that is
subject to uncertainty. Our method is a generalization of traditional principal
component analysis (PCA) to multivariate probability distributions. In
comparison to non-linear methods, linear dimensionality reduction techniques
have the advantage that the characteristics of such probability distributions
remain intact after projection. We derive a representation of the PCA sample
covariance matrix that respects potential uncertainty in each of the inputs,
building the mathematical foundation of our new method: uncertainty-aware PCA.
In addition to the accuracy and performance gained by our approach over
sampling-based strategies, our formulation allows us to perform sensitivity
analysis with regard to the uncertainty in the data. For this, we propose
factor traces as a novel visualization that enables to better understand the
influence of uncertainty on the chosen principal components. We provide
multiple examples of our technique using real-world datasets. As a special
case, we show how to propagate multivariate normal distributions through PCA in
closed form. Furthermore, we discuss extensions and limitations of our
approach
PerfVis: Pervasive Visualization in Immersive AugmentedReality for Performance Awareness
Developers are usually unaware of the impact of code changes to the
performance of software systems. Although developers can analyze the
performance of a system by executing, for instance, a performance test to
compare the performance of two consecutive versions of the system, changing
from a programming task to a testing task would disrupt the development flow.
In this paper, we propose the use of a city visualization that dynamically
provides developers with a pervasive view of the continuous performance of a
system. We use an immersive augmented reality device (Microsoft HoloLens) to
display our visualization and extend the integrated development environment on
a computer screen to use the physical space. We report on technical details of
the design and implementation of our visualization tool, and discuss early
feedback that we collected of its usability. Our investigation explores a new
visual metaphor to support the exploration and analysis of possibly very large
and multidimensional performance data. Our initial result indicates that the
city metaphor can be adequate to analyze dynamic performance data on a large
and non-trivial software system.Comment: ICPE'19 vision, 4 pages, 2 figure, conferenc
Ethnoontology: Ways of worldâbuilding across cultures
This article outlines a program of ethnoontology that brings together empirical research in the ethnosciences with ontological debates in philosophy. First, we survey empirical evidence from heterogeneous cultural contexts and disciplines. Second, we propose a model of crossâcultural relations between ontologies beyond a simple divide between universalist and relativist models. Third, we argue for an integrative model of ontology building that synthesizes insights from different fields such as biological taxonomy, cognitive science, cultural anthropology, and political ecology. We conclude by arguing that a program of ethnoontology provides philosophers both with insights about traditional issues such as debates about natural kinds and with novel strategies for connecting philosophy with pressing global issues such as the conservation of local environments and the selfâdetermination of Indigenous communities
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