From Chaos Comes Order: Ordering Event Representations for Object Recognition and Detection

Today, state-of-the-art deep neural networks that process events first convert them into dense, grid-like input representations before using an off-the-shelf network. However, selecting the appropriate representation for the task traditionally requires training a neural network for each representation and selecting the best one based on the validation score, which is very time-consuming. This work eliminates this bottleneck by selecting representations based on the Gromov-Wasserstein Discrepancy (GWD) between raw events and their representation. It is about 200 times faster to compute than training a neural network and preserves the task performance ranking of event representations across multiple representations, network backbones, datasets, and tasks. Thus finding representations with high task scores is equivalent to finding representations with a low GWD. We use this insight to, for the first time, perform a hyperparameter search on a large family of event representations, revealing new and powerful representations that exceed the state-of-the-art. Our optimized representations outperform existing representations by 1.7 mAP on the 1 Mpx dataset and 0.3 mAP on the Gen1 dataset, two established object detection benchmarks, and reach a 3.8% higher classification score on the mini N-ImageNet benchmark. Moreover, we outperform state-of-the-art by 2.1 mAP on Gen1 and state-of-the-art feed-forward methods by 6.0 mAP on the 1 Mpx datasets. This work opens a new unexplored field of explicit representation optimization for event-based learning

From Chaos Comes Order: Ordering Event Representations for Object Recognition and Detection

Today, state-of-the-art deep neural networks that process events first convert them into dense, grid-like input representations before using an off-the-shelf network. However, selecting the appropriate representation for the task traditionally requires training a neural network for each representation and selecting the best one based on the validation score, which is very time-consuming. This work eliminates this bottleneck by selecting representations based on the Gromov-Wasserstein Discrepancy (GWD) between raw events and their representation. It is about 200 times faster to compute than training a neural network and preserves the task performance ranking of event representations across multiple representations, network backbones, datasets, and tasks. Thus finding representations with high task scores is equivalent to finding representations with a low GWD. We use this insight to, for the first time, perform a hyperparameter search on a large family of event representations, revealing new and powerful representations that exceed the state-of-the-art. Our optimized representations outperform existing representations by 1.7 mAP on the 1 Mpx dataset and 0.3 mAP on the Gen1 dataset, two established object detection benchmarks, and reach a 3.8% higher classification score on the mini N-ImageNet benchmark. Moreover, we outperform state-of-the-art by 2.1 mAP on Gen1 and state-of-the-art feed-forward methods by 6.0 mAP on the 1 Mpx datasets. This work opens a new unexplored field of explicit representation optimization for event-based learning.Comment: 15 pages, 11 figures, 2 tables, ICCV 2023 Camera Ready pape

Smart municipal energy grid within electricity market

A smart municipal energy grid including electricity and heat production infrastructure and electricity demand response has been modeled in HOMER case study with the aim of decreasing total yearly community energy costs. The optimal configurations of used technologies (photovoltaic plants, combined heat and power plants, wind power plants) and sizing, with minimal costs, are presented and compared using three scenarios of average electricity market price 3.5 c€/kWh, 5 c€/kWh and 10 c€/kWh. Smart municipal energy grids will have an important role in future electricity markets, due to their flexibility to utilize excess electricity production from CHP and variable renewable energy sources through heat storage. This flexibility enables the levelized costs of energy within smart municipal energy grids to decrease below electricity market prices even in case of fuel price disturbances. With initial costs in the range 0- 3,931,882 €, it has been shown that economical and environmental benefits of smart municipal energy grids are: the internal rate of return in the range 6.87-15.3%, and CO2 emissions in the range from -4,885,203 to 5,165,780 kg/year. The resulting realistic number of hours of operation of combined heat and power plants obtained by simulations is in the range 2,410- 7,849 hours/year.This is the peer-reviewed version of the article: Batas-Bjelic, I., Rajakovic, N., Duic, N., 2017. Smart municipal energy grid within electricity market. Energy 137, 1277–1285. [https://doi.org/10.1016/j.energy.2017.06.177