Spatially Aware Ensemble-Based Learning to Predict Weather-Related Outages in Transmission

This paper describes the implementation of prediction model for real-time assessment of weather related outages in the electric transmission system. The network data and historical outages are correlated with variety of weather sources in order to construct the knowledge extraction platform for accurate outage probability prediction. An extension of logistic regression prediction model that embeds the spatial configuration of the network was used for prediction. The results show that developed algorithm has very high accuracy and is able to differentiate the outage area from the rest of the network in 1 to 3 hours before the outage. The prediction algorithm is integrated inside weather testbed for real-time mapping of network outage probabilities using incoming weather forecast

Deep Causal Learning: Representation, Discovery and Inference

Causal learning has attracted much attention in recent years because causality reveals the essential relationship between things and indicates how the world progresses. However, there are many problems and bottlenecks in traditional causal learning methods, such as high-dimensional unstructured variables, combinatorial optimization problems, unknown intervention, unobserved confounders, selection bias and estimation bias. Deep causal learning, that is, causal learning based on deep neural networks, brings new insights for addressing these problems. While many deep learning-based causal discovery and causal inference methods have been proposed, there is a lack of reviews exploring the internal mechanism of deep learning to improve causal learning. In this article, we comprehensively review how deep learning can contribute to causal learning by addressing conventional challenges from three aspects: representation, discovery, and inference. We point out that deep causal learning is important for the theoretical extension and application expansion of causal science and is also an indispensable part of general artificial intelligence. We conclude the article with a summary of open issues and potential directions for future work

Stakeholder engagement: Defining strategic advantage for sustainable construction

This is the accepted version of the following article: Rodriguez-Melo, A. and Mansouri, S. A. (2011), Stakeholder Engagement: Defining Strategic Advantage for Sustainable Construction. Bus. Strat. Env., 20: 539–552, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/bse.715/abstract.Although sustainable development is increasingly becoming a part of business plans, it is unclear what makes the economic, social and environmental dynamics strategically compatible. This research examines which of the following in sustainable development – government policy, managerial attitude and stakeholder engagement – is the most influential on the profitability of companies in the UK construction sector. Quantitative and qualitative analyses were rendered through a survey and semi-structured interviews. Patterns of ambiguity in legislation were discovered as an obstacle for changing the sector's mind-set. Stakeholder engagement was identified as the defining factor increasing managers' awareness, helping legislation to be effectively implemented and making sustainability highly appealing to clients. These findings indicate that to gain competitive advantage, companies should embark on long-term strategic alliances which adopt the proposals of environmental non-governmental organisations and closely follow public opinion. This, strengthens brand equity, allows for premium pricing, increases market share and maximizes profit

Learning to Predict Image-based Rendering Artifacts with Respect to a Hidden Reference Image

Image metrics predict the perceived per-pixel difference between a reference image and its degraded (e. g., re-rendered) version. In several important applications, the reference image is not available and image metrics cannot be applied. We devise a neural network architecture and training procedure that allows predicting the MSE, SSIM or VGG16 image difference from the distorted image alone while the reference is not observed. This is enabled by two insights: The first is to inject sufficiently many un-distorted natural image patches, which can be found in arbitrary amounts and are known to have no perceivable difference to themselves. This avoids false positives. The second is to balance the learning, where it is carefully made sure that all image errors are equally likely, avoiding false negatives. Surprisingly, we observe, that the resulting no-reference metric, subjectively, can even perform better than the reference-based one, as it had to become robust against mis-alignments. We evaluate the effectiveness of our approach in an image-based rendering context, both quantitatively and qualitatively. Finally, we demonstrate two applications which reduce light field capture time and provide guidance for interactive depth adjustment.Comment: 13 pages, 11 figure

Fast Rates for Noisy Interpolation Require Rethinking the Effects of Inductive Bias

Good generalization performance on high-dimensional data crucially hinges on a simple structure of the ground truth and a corresponding strong inductive bias of the estimator. Even though this intuition is valid for regularized models, in this paper we caution against a strong inductive bias for interpolation in the presence of noise: While a stronger inductive bias encourages a simpler structure that is more aligned with the ground truth, it also increases the detrimental effect of noise. Specifically, for both linear regression and classification with a sparse ground truth, we prove that minimum $\ell_p$-norm and maximum $\ell_p$-margin interpolators achieve fast polynomial rates close to order $1/n$ for $p > 1$ compared to a logarithmic rate for $p = 1$. Finally, we provide preliminary experimental evidence that this trade-off may also play a crucial role in understanding non-linear interpolating models used in practice

Bridging the Career Transitional Gap Between Field Experts and University Instructors: Factors Affecting New Faculty Members’ Feelings of Preparedness of Teaching in Higher Education

This quantitative study determined factors affecting preparedness for higher education teachers who have transitioned from their expert-level fieldwork into academia. It is a common practice for new university faculty members to be recruited from their areas of expertise as clinicians and practitioners (Eret et al., 2018; Freeman & DiRamio, 2016; Savage & Pollard, 2016). Transitioning from a chosen field into a novice teacher can carry varying weights depending on university teaching appointments. Having the qualities of an experienced practitioner is highly desired to fill faculty roles, but the expertise as a practitioner does not necessarily develop the teaching skills (Eret et al., 2018; Freeman & DiRamio, 2016; Savage & Pollard, 2016). Due to the frequent hiring of faculty with limited andragogy training, university learning outcomes can be jeopardized, and the quality of the university could suffer as a result of the lack of foundational educational knowledge teachers need to successfully possess the skill sets required in the higher education classroom setting (Eret, et al., 2018; Freeman & DiRamio, 2016; Savage & Pollard, 2016).This study was completed using the Delphi process. The following research question was used to inform this study: What factors affect new faculty members’ feelings of preparedness of teaching in higher education? The theoretical framework used to guide this study was Herzberg’s Two-Factor Theory which argues that there are two factors an organization can adjust to influence workplace motivations (Herzberg et al., 1959)