Review on the Conflicts between Offshore Wind Power and Fishery Rights: Marine Spatial Planning in Taiwan

In recent years, Taiwan has firmly committed itself to pursue the green energy transition and a nuclear-free homeland by 2025, with an increase in renewable energy from 5% in 2016 to 20% in 2025. Offshore wind power (OWP) has become a sustainable and scalable renewable energy source in Taiwan. Maritime Spatial Planning (MSP) is a fundamental tool to organize the use of the ocean space by different and often conflicting multi-users within ecologically sustainable boundaries in the marine environment. MSP is capable of definitively driving the use of offshore renewable energy. Lessons from Germany and the UK revealed that MSP was crucial to the development of OWP. This paper aims to evaluate how MSP is able to accommodate the exploitation of OWP in Taiwan and contribute to the achievement of marine policy by proposing a set of recommendations. It concludes that MSP is emerging as a solution to be considered by government institutions to optimize the multiple use of the ocean space, reduce conflicts and make use of the environmental and economic synergies generated by the joint deployment of OWP facilities and fishing or aquaculture activities for the conservation and protection of marine environments.Peer Reviewe

ICML - On the Number of Linear Regions of Convolutional Neural Networks

One fundamental problem in deep learning is understanding the outstanding performance of deep Neural Networks (NNs) in practice. One explanation for the superiority of NNs is that they can realize a large class of complicated functions, i.e., they have powerful expressivity. The expressivity of a ReLU NN can be quantified by the maximal number of linear regions it can separate its input space into. In this paper, we provide several mathematical results needed for studying the linear regions of CNNs, and use them to derive the maximal and average numbers of linear regions for one-layer ReLU CNNs. Furthermore, we obtain upper and lower bounds for the number of linear regions of multi-layer ReLU CNNs. Our results suggest that deeper CNNs have more powerful expressivity than their shallow counterparts, while CNNs have more expressivity than fully-connected NNs per parameter.Comment: International Conference on Machine Learning (ICML) 202

ReSup: Reliable Label Noise Suppression for Facial Expression Recognition

Because of the ambiguous and subjective property of the facial expression recognition (FER) task, the label noise is widely existing in the FER dataset. For this problem, in the training phase, current FER methods often directly predict whether the label of the input image is noised or not, aiming to reduce the contribution of the noised data in training. However, we argue that this kind of method suffers from the low reliability of such noise data decision operation. It makes that some mistakenly abounded clean data are not utilized sufficiently and some mistakenly kept noised data disturbing the model learning process. In this paper, we propose a more reliable noise-label suppression method called ReSup (Reliable label noise Suppression for FER). First, instead of directly predicting noised or not, ReSup makes the noise data decision by modeling the distribution of noise and clean labels simultaneously according to the disagreement between the prediction and the target. Specifically, to achieve optimal distribution modeling, ReSup models the similarity distribution of all samples. To further enhance the reliability of our noise decision results, ReSup uses two networks to jointly achieve noise suppression. Specifically, ReSup utilize the property that two networks are less likely to make the same mistakes, making two networks swap decisions and tending to trust decisions with high agreement. Extensive experiments on three popular benchmarks show that the proposed method significantly outperforms state-of-the-art noisy label FER methods by 3.01% on FERPlus becnmarks. Code: https://github.com/purpleleaves007/FERDenois