302 research outputs found
Human Pheromones: Integrating Neuroendocrinology and Ethology
The effect of sensory input on hormones is essential to any explanation of mammalian behavior, including aspects of physical attraction. The chemical signals we send have direct and developmental effects on hormone levels in other people. Since we don't know either if, or how, visual cues might have direct and developmental effects on hormone levels in other people, the biological basis for the development of visually perceived human physical attraction is currently somewhat questionable. In contrast, the biological basis for the development of physical attraction based on chemical signals is well detailed
On the Predictability of Talk Attendance at Academic Conferences
This paper focuses on the prediction of real-world talk attendances at
academic conferences with respect to different influence factors. We study the
predictability of talk attendances using real-world tracked face-to-face
contacts. Furthermore, we investigate and discuss the predictive power of user
interests extracted from the users' previous publications. We apply Hybrid
Rooted PageRank, a state-of-the-art unsupervised machine learning method that
combines information from different sources. Using this method, we analyze and
discuss the predictive power of contact and interest networks separately and in
combination. We find that contact and similarity networks achieve comparable
results, and that combinations of different networks can only to a limited
extend help to improve the prediction quality. For our experiments, we analyze
the predictability of talk attendance at the ACM Conference on Hypertext and
Hypermedia 2011 collected using the conference management system Conferator
Adaptive kNN using Expected Accuracy for Classification of Geo-Spatial Data
The k-Nearest Neighbor (kNN) classification approach is conceptually simple -
yet widely applied since it often performs well in practical applications.
However, using a global constant k does not always provide an optimal solution,
e.g., for datasets with an irregular density distribution of data points. This
paper proposes an adaptive kNN classifier where k is chosen dynamically for
each instance (point) to be classified, such that the expected accuracy of
classification is maximized. We define the expected accuracy as the accuracy of
a set of structurally similar observations. An arbitrary similarity function
can be used to find these observations. We introduce and evaluate different
similarity functions. For the evaluation, we use five different classification
tasks based on geo-spatial data. Each classification task consists of (tens of)
thousands of items. We demonstrate, that the presented expected accuracy
measures can be a good estimator for kNN performance, and the proposed adaptive
kNN classifier outperforms common kNN and previously introduced adaptive kNN
algorithms. Also, we show that the range of considered k can be significantly
reduced to speed up the algorithm without negative influence on classification
accuracy
Extracting Interpretable Local and Global Representations from Attention on Time Series
This paper targets two transformer attention based interpretability methods
working with local abstraction and global representation, in the context of
time series data. We distinguish local and global contexts, and provide a
comprehensive framework for both general interpretation options. We discuss
their specific instantiation via different methods in detail, also outlining
their respective computational implementation and abstraction variants.
Furthermore, we provide extensive experimentation demonstrating the efficacy of
the presented approaches. In particular, we perform our experiments using a
selection of univariate datasets from the UCR UEA time series repository where
we both assess the performance of the proposed approaches, as well as their
impact on explainability and interpretability/complexity. Here, with an
extensive analysis of hyperparameters, the presented approaches demonstrate an
significant improvement in interpretability/complexity, while capturing many
core decisions of and maintaining a similar performance to the baseline model.
Finally, we draw general conclusions outlining and guiding the application of
the presented methods.Comment: Paper: 54 Pages excluding references, 19 Figures, 30 Tables +
Appendix: 12 Pages, 23 Table
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