366,598 research outputs found
Micro-bias and macro-performance
We use agent-based modeling to investigate the effect of conservatism and
partisanship on the efficiency with which large populations solve the density
classification task--a paradigmatic problem for information aggregation and
consensus building. We find that conservative agents enhance the populations'
ability to efficiently solve the density classification task despite large
levels of noise in the system. In contrast, we find that the presence of even a
small fraction of partisans holding the minority position will result in
deadlock or a consensus on an incorrect answer. Our results provide a possible
explanation for the emergence of conservatism and suggest that even low levels
of partisanship can lead to significant social costs.Comment: 11 pages, 5 figure
One Dimensional ary Density Classification Using Two Cellular Automaton Rules
Suppose each site on a one-dimensional chain with periodic boundary condition
may take on any one of the states , can you find out the most
frequently occurring state using cellular automaton? Here, we prove that while
the above density classification task cannot be resolved by a single cellular
automaton, this task can be performed efficiently by applying two cellular
automaton rules in succession.Comment: Revtex, 4 pages, uses amsfont
Sound Event Detection in Synthetic Audio: Analysis of the DCASE 2016 Task Results
As part of the 2016 public evaluation challenge on Detection and
Classification of Acoustic Scenes and Events (DCASE 2016), the second task
focused on evaluating sound event detection systems using synthetic mixtures of
office sounds. This task, which follows the `Event Detection - Office
Synthetic' task of DCASE 2013, studies the behaviour of tested algorithms when
facing controlled levels of audio complexity with respect to background noise
and polyphony/density, with the added benefit of a very accurate ground truth.
This paper presents the task formulation, evaluation metrics, submitted
systems, and provides a statistical analysis of the results achieved, with
respect to various aspects of the evaluation dataset
Anomalous pattern based clustering of mental tasks with subject independent learning – some preliminary results
In this paper we describe a new method for EEG signal classification in which the classification of one subject’s EEG signals is based on features learnt from another subject. This method applies to the power spectrum density data and assigns class-dependent information weights to individual features. The informative features appear to be rather similar among different subjects, thus supporting the view that there are subject independent general brain patterns for the same mental task. Classification is done via clustering using the intelligent k-means algorithm with the most informative features from a different subject. We experimentally compare our method with others.</jats:p
Breast density classification with deep convolutional neural networks
Breast density classification is an essential part of breast cancer
screening. Although a lot of prior work considered this problem as a task for
learning algorithms, to our knowledge, all of them used small and not
clinically realistic data both for training and evaluation of their models. In
this work, we explore the limits of this task with a data set coming from over
200,000 breast cancer screening exams. We use this data to train and evaluate a
strong convolutional neural network classifier. In a reader study, we find that
our model can perform this task comparably to a human expert
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