Visualizing Deep Networks by Optimizing with Integrated Gradients

Understanding and interpreting the decisions made by deep learning models is valuable in many domains. In computer vision, computing heatmaps from a deep network is a popular approach for visualizing and understanding deep networks. However, heatmaps that do not correlate with the network may mislead human, hence the performance of heatmaps in providing a faithful explanation to the underlying deep network is crucial. In this paper, we propose I-GOS, which optimizes for a heatmap so that the classification scores on the masked image would maximally decrease. The main novelty of the approach is to compute descent directions based on the integrated gradients instead of the normal gradient, which avoids local optima and speeds up convergence. Compared with previous approaches, our method can flexibly compute heatmaps at any resolution for different user needs. Extensive experiments on several benchmark datasets show that the heatmaps produced by our approach are more correlated with the decision of the underlying deep network, in comparison with other state-of-the-art approaches

Regional coordination in medical emergencies and major incidents; plan, execute and teach

<p>Abstract</p> <p>Background</p> <p>Although disasters and major incidents are difficult to predict, the results can be mitigated through planning, training and coordinated management of available resources. Following a fire in a disco in Gothenburg, causing 63 deaths and over 200 casualties, a medical disaster response centre was created. The center was given the task to coordinate risk assessments, disaster planning and training of staff within the region and on an executive level, to be the point of contact (POC) with authority to act as "gold control," <it>i.e</it>. to take immediate strategic command over all medical resources within the region if needed. The aim of this study was to find out if the centre had achieved its tasks by analyzing its activities.</p> <p>Methods</p> <p>All details concerning alerts of the regional POC was entered a web-based log by the duty officer. The data registered in this database was analyzed during a 3-year period.</p> <p>Results</p> <p>There was an increase in number of alerts between 2006 and 2008, which resulted in 6293 activities including risk assessments and 4473 contacts with major institutions or key persons to coordinate or initiate actions. Eighty five percent of the missions were completed within 24 h. Twenty eight exercises were performed of which 4 lasted more than 24 h. The centre also offered 145 courses in disaster and emergency medicine and crisis communication.</p> <p>Conclusion</p> <p>The data presented in this study indicates that the center had achieved its primary tasks. Such regional organization with executive, planning, teaching and training responsibilities offers possibilities for planning, teaching and training disaster medicine by giving immediate feed-back based on real incidents.</p

Lighting Consumption Optimization in a Residential House Based on Electricity Price

The increment of the electricity consumption around the world has led many efforts on the network operators to reduce the consumption in the demand side and encourage to increase the use of renewable energies. Since the buildings have a significant part in energy consumption, and lighting systems have an important role in the energy consumption of the buildings, the optimization of the lighting system should be effective. Hence, the focus of this paper is to minimize the lamps consumption of a residential house based on electricity price and try to take advantages from photovoltaic generation as much as possible. The methodology of this work is proposed as a linear optimization problem that manages the generation of a renewable energy re-source, which supplies a part of the energy consumption of the house. For the case studies, the amount of the renewable energy generation, total consumption of building, consumption of the lights, and electricity price are considered.info:eu-repo/semantics/publishedVersio