If a major disaster like Fukushima or Chernobyl ever happens again, the world would know almost straight away, thanks to an array of government and DIY radiation-monitoring programs running globally.

We study the performance of the Topological Uncertainty (TU) constructed with a trained feedforward neural network (FNN) for Anomaly Detection. Generally, meaningful information can be stored in the hidden layers of the trained FNN, and the TU implementation is one tractable recipe to extract buried information by means of the Topological Data Analysis. We explicate the concept of the TU and the numerical procedures. Then, for a concrete demonstration of the performance test, we employ the Neutron Star data used for inference of the equation of state (EoS). For the training dataset consisting of the input (Neutron Star data) and the output (EoS parameters), we can compare the inferred EoSs and the exact answers to classify the data with the label $k$. The subdataset with $k=0$ leads to the normal inference for which the inferred EoS approximates the answer well, while the subdataset with $k=1$ ends up with the unsuccessful inference. Once the TU is prepared based on the $k$-labled subdatasets, we introduce the cross-TU to quantify the uncertainty of characterizing the $k$-labeled data with the label $j$. The anomaly or unsuccessful inference is correctly detected if the cross-TU for $j=k=1$ is smaller than that for $j=0$ and $k=1$. In our numerical experiment, for various input data, we calculate the cross-TU and estimate the performance of Anomaly Detection. We find that performance depends on FNN hyperparameters, and the success rate of Anomaly Detection exceeds $90\%$ in the best case. We finally discuss further potential of the TU application to retrieve the information hidden in the trained FNN.

Researchers are always looking for new ways to improve the agility, performance, and efficiency of walking robots. Most of the time, this focus has centered on motor advancements. But a team at ETH Zurich and the Max Planck Institute for Intelligent Systems (MPI-IS) is focused on an alternative approach--artificial, electrostatically-powered musculature inspired by animal biology and human anatomy. Both two- and four-legged robots have become pretty agile over the past few years thanks to design advancements in motor technologies and artificial intelligence. For many of them, however, energy requirements and costs remain a major hurdle, especially when it comes to AI systems needed to interpret vast quantities of environmental sensor data.

Tokyo Electric Power Co. will start a demonstration project as early as Thursday to remove a small amount of radioactive debris from its wrecked nuclear power plant in Fukushima. The operator of the Fukushima No. 1 nuclear power plant, which suffered a meltdown after a massive earthquake and tsunami overwhelmed the facility in 2011, will start the experimental removal process at reactor No. 2 on Thursday, so long as necessary inspections are completed on time, an official from the company said in a news conference Monday. It's a step forward for the utility and the government, which estimates the complete decommissioning of the facility will take three to four decades. Removal of the deadly debris has proved challenging, requiring the development of a robotic arm that can fish out radioactive fuel, metal cladding and other structures in the reactor that melted, cooled and solidified together. Tepco has delayed the start of procedure in the past.

We provide high-speed implementations for simulating reservoirs described by $N$-coupled spin-torque oscillators. Here $N$ also corresponds to the number of reservoir nodes. We benchmark a variety of implementations based on CPU and GPU. Our new methods are at least 2.6 times quicker than the baseline for $N$ in range $1$ to $10^4$. More specifically, over all implementations the best factor is 78.9 for $N=1$ which decreases to 2.6 for $N=10^3$ and finally increases to 23.8 for $N=10^4$. GPU outperforms CPU significantly at $N=2500$. Our results show that GPU implementations should be tested for reservoir simulations. The implementations considered here can be used for any reservoir with evolution that can be approximated using an explicit method.

Artificial neural networks took a lot of inspiration from their biological counterparts in becoming our best machine perceptual systems. This work summarizes some of that history and incorporates modern theoretical neuroscience into experiments with artificial neural networks from the field of deep learning. Specifically, iterative magnitude pruning is used to train sparsely connected networks with 33x fewer weights without loss in performance. These are used to test and ultimately reject the hypothesis that weight sparsity alone improves image noise robustness. Recent work mitigated catastrophic forgetting using weight sparsity, activation sparsity, and active dendrite modeling. This paper replicates those findings, and extends the method to train convolutional neural networks on a more challenging continual learning task. The code has been made publicly available.

Japan's catastrophic Fukushima disaster in 2011 has resulted in a unique species of boar-pig, a new study reveals. Researchers investigating the effects of the nuclear disaster on animals in the area report that radiation has had no adverse effects on their genetics. However, wild boars (Sus scrofa leucomystax) have proliferated in the area, after being left to roam freely from the lack of humans. The boars have bred with domestic pigs (Sus scrofa domesticus) that escaped from nearby properties after farmers had to flee, creating a new hybrid species. Rare spotted wild boar observed inside the evacuated area of Fukushima, Japan, indicative of the'introgression' - the transfer of genetic information from one species to another - with domestic pigs Images from remotely-operated cameras indicate wildlife is flourishing in Fukushima's exclusion zone. Wildlife ecologist James Beasley of the University of Georgia and colleagues used a network of 106 remote cameras to capture images of the wildlife in the area over a four-month period.

Laid waste by a nuclear disaster a decade ago, Fukushima Prefecture is still struggling to recover, even as the government tries to bring people and jobs back to former ghost towns by pouring in trillions of yen to decontaminate and rebuild. But reconstruction efforts, from the mundane -- supermarkets and transport infrastructure -- to a cutting-edge hydrogen energy plant, have yet to entice more than a small fraction of the former population to return. As the country marks the 10th anniversary of the March 11, 2011 earthquake, tsunami and nuclear meltdown, parts of the prefecture are still off limits, and it remains a laggard in recovery. Its future is clouded by the 30 to 40 years it may take to decommission the crippled Fukushima No. 1 nuclear plant, near which massive amounts of treated radioactive water are in storage. The town of Namie, where a stone monument lists about 200 townspeople who died in the tsunami, emptied out overnight following the accident at the nuclear plant about 8 kilometers south.

As governments and health care agencies work to stop the spread of Covid-19 and to treat those who are infected, manufacturers in more than a dozen industries are struggling to manage the epidemic's growing impact on their supply chains. Unfortunately, many are facing a supply crisis that stems from weaknesses in their sourcing strategies that could have been corrected years ago. Just how extensive the crisis is can be seen in data released by Resilinc, a supply-chain-mapping and risk-monitoring company, which shows the number of sites of industries located in the quarantined areas of China, South Korea, and Italy, and the number of items sourced from the quarantined regions of China. After the March 2011 earthquake and tsunami in Fukushima, Japan, many multinationals learned painful lessons about the hidden weaknesses in their supply chains -- weaknesses that resulted in loss of revenue, and in some cases, market cap. While most companies could quickly assess the impacts that Fukushima had on their direct suppliers, they were blindsided by the impacts on second- and third-tier suppliers in the affected region.

When you look at the following image, you see people, objects, and buildings. It brings up memories of past experiences, similar situations you've encountered. The crowd is facing the same direction and holding up phones, which tells you that this is some kind of event. The person standing near the camera is wearing a T-shirt that hints at what the event might be. As you look at other small details, you can infer much more information from the picture.