Will Gulf states join war? What is the UAE's Barakah nuclear plant, nearly hit by a drone? A drone attack that caused a fire close to the Barakah Nuclear Energy Plant in the United Arab Emirates has raised further concerns about nuclear security and military escalation in the Gulf as discussions of peace between Iran and the United States hang in the balance. Barakah was the first nuclear power station to be built on the Arabian Peninsula. What is the Barakah Nuclear Energy Plant? Barakah is a nuclear energy plant located in Al Dhafra, the largest municipal region of the emirate of Abu Dhabi.

The United Arab Emirates is investigating the source of a drone strike which triggered a fire near a nuclear power station, officials have said. The country's defence ministry said three drones had entered the UAE from the western border direction on Sunday. While two were intercepted, the third drone struck an electrical generator outside the inner perimeter of the Barakah Nuclear Power Plant in Abu Dhabi. No injuries were reported and there was no impact on radiological safety levels, local authorities said. The country's defence ministry said in a statement that investigations were under way to determine the source of the attacks.

Could the war trigger a hunger crisis? How well do you know Iran? Drone strike sparks fire on perimeter of UAE's Barakah nuclear power plant A drone strike has sparked a fire on the perimeter of the Barakah Nuclear Energy Plant in the United Arab Emirates (UAE), raising new concerns over a potential new regional escalation amid a fragile ceasefire between Iran and the United States. Authorities in Abu Dhabi said the blaze broke out at an electrical generator outside the plant's inner perimeter in the Al Dhafra region on Sunday. No injuries were reported, and officials said radiation levels remained normal.

How well do you know Iran? A fragile ceasefire in the US-Israel war on Iran is coming under growing strain as several Gulf countries have reported drone attacks. Qatar said on Sunday that a drone struck a cargo ship in Qatari waters, sparking a fire, while Kuwait and the United Arab Emirates said they repelled drone attacks. Qatar's Ministry of Defence said the freighter had been arriving in the country's waters from the UAE capital, Abu Dhabi, and was hit by a drone northeast of the port of Mesaieed. "The vessel continued its journey toward Mesaieed Port after the fire was brought under control," the ministry said. The United Kingdom Maritime Trade Operations (UKMTO) said a bulk carrier reported being struck by an "unknown projectile", and a small fire had been extinguished, but there were no casualties from the incident.

In order to expand the polynomial surface fitting in 3D dimensional space into the high dimensional feature space using a neural network with parameter Θ, we define f1(gω):= g and f2(cυ):= c, where f means MLP layer. Then, the multiplication of real numbers gω cυ in the polynomial function is represented as g c, i.e., gω cυ:= g c, and the orders ω,υ [0,1,...,τ]. Then, the final bivariate function used in our hyper surface fitting is Nθ,τ(G,C) = Θ(G C), where Gand C are high dimensional features of the 3D point clouds extracted by the two different modules, which are introduced in Sec.3.3 and Sec.3.4 of the paper, respectively. The other terms except the principal terms in the polynomial equation are not used in the estimation of the normal. Based on this, we use the max-pooling over all features from the hyper surface fitting 2 Figure 1: Visualization of the contribution of each 3D point to estimate the normal of the query point (black).

Prior studies [18, 12] show that GAN often experiences generation failures with severely degraded generation performance when only limited training data is available. Specifically, with limited training data, the discriminator tends to discriminate via meaningless shortcuts by merely focusing on easy-to-discriminate image locations and spectra instead of holistic understanding of images. This can be viewed clearly in Figure 1, where the Gini Coefficient [4] of discriminator's spatial attentions increases quickly along the training iteration (when only limited training data is available). Note that the Gini coefficient [4] is negatively correlated with equality, i.e., the discriminator will pay more unevenly distributed attention to each spatial location while the Gini coefficient increases from '0' to '1'. For image generation with GAN, the large Gini coefficient (of discriminator's spatial attentions) thus means that the discriminator starts to focus on certain spatial locations (easy to discriminate) while ignoring other spatial locations (hard to discriminate), ultimately leading to an over-confident discriminator and training collapse. In another word, the Gini coefficient [4] of '0' expresses perfect equality where all values are the same (i.e., where the discriminator pays the same attention to every spatial location) while '1' expresses maximal inequality among values (i.e., the discriminator focuses on only one location while all others are ignored).