The month before, the administration released oil from the country’s strategic reserve and the president promised to get more tankers filled with liquified natural gas to Europe. Last week, President Joe Biden resumed leasing federal lands for oil and gas drilling and boosted ethanol-rich fuels, despite pollution concerns. He added that it was paramount to “ensure the solutions for the long-term problems don’t create a dependence on an authoritarian regime.”įor now, fossil fuels remain the quickest option to replace Russian energy and mitigate its impact on the global energy market. “We have complete dependence on China for batteries for electric vehicles,” said Robbie Diamond, the president of Securing America’s Energy Future, a nonprofit group that advocates for green transportation. Some worry the race to electrify could intensify U.S. The situation highlights some of the bigger challenges around the world’s shift to green energy, which is not without its own geopolitical wrinkles. Natalia Kolesnikova / AFP via Getty Images file A worker stands at the Utrenneye field, the resource base for Novatek's Arctic LNG 2 project, in the Gydan Peninsula on the Kara Sea shore line in the Arctic circle on Nov. “What probably came as a surprise for Russia is how decisive and united Europe has become in its resolve to walk away from that dependency,” Tatarenko said of Russian gas. Countries like Germany, which pulled out of a huge pipeline project with Russia, are building more terminals to receive it. The United States plans to provide Europe with more liquified natural gas (LNG), which is more carbon intensive than piped gas. Tatarenko, who has family members still living in Ukraine, said the energy consequences of Russia’s invasion have been swift. Scale with p=0.“The moral imperative here is to cut off the supply of Russian fossil fuels as quickly as possible for Europe,” said Oleksiy Tatarenko, a Ukrainian policy expert who leads a climate and industry program at the Rocky Mountain Institute, a nonprofit organization that works to promote green energy. Predictions compared to traditional approaches (0.15$\pm$0.95 in a 5 point Shift explanation allows a user to have more confidence in true positive However, the results from our reader study indicate that these modelsĪre generally looking at the correct features. Low overlap with ground truth pathology masks for models with reasonably highĪccuracy. Using traditional attribution maps or our proposed method. We conduct a reader study with two radiologists assessing 240Ĭhest X-ray predictions to identify which ones are false positives (half are) ![]() We use this method to study chest X-ray classifiers and evaluate their Specific input image to exaggerate or curtail the features used for prediction. Update (Latent Shift) that can transform the latent representation of a Given an arbitrary classifier, we propose a simple autoencoder and gradient However, current approaches are difficult to implement as they are Transform input images to increase or decrease features which cause the ![]() Thus, there is a pressing need to develop improved models for modelĮxplainability and introspection. ![]() Prediction explanation is important,Įspecially in medical imaging, for avoiding the unintended consequences ofĭeploying AI systems when false positive predictions can impact patient care. Lungren, Akshay Chaudhari Download PDF Abstract: Motivation: Traditional image attribution methods struggle to satisfactorilyĮxplain predictions of neural networks. Authors: Joseph Paul Cohen, Rupert Brooks, Sovann En, Evan Zucker, Anuj Pareek, Matthew P.
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