Deforestation is one of the contributing factors to climate change. Climate change has a serious impact on human life, and it occurs due to emission of greenhouse gases, such as carbon dioxide, to the atmosphere. It is important to know the causes of deforestation for mitigation efforts, but there is a lack of data-driven research studies to predict these deforestation drivers. In this work, we propose a contrastive learning architecture, called Multimodal SuperCon, for classifying drivers of deforestation in Indonesia using satellite images obtained from Landsat 8. Multimodal SuperCon is an architecture which combines contrastive learning and multimodal fusion to handle the available deforestation dataset. Our proposed model outperforms previous work on driver classification, giving a 7% improvement in accuracy in comparison to a state-of-the-art rotation equivariant model for the same task.

We propose VDL-Surrogate, a view-dependent neural-network-latent-based surrogate model for parameter space exploration of ensemble simulations that allows high-resolution visualizations and user-specified visual mappings. Surrogate-enabled parameter space exploration allows domain scientists to preview simulation results without having to run a large number of computationally costly simulations. Limited by computational resources, however, existing surrogate models may not produce previews with sufficient resolution for visualization and analysis. To improve the efficient use of computational resources and support high-resolution exploration, we perform ray casting from different viewpoints to collect samples and produce compact latent representations. This latent encoding process reduces the cost of surrogate model training while maintaining the output quality. In the model training stage, we select viewpoints to cover the whole viewing sphere and train corresponding VDL-Surrogate models for the selected viewpoints. In the model inference stage, we predict the latent representations at previously selected viewpoints and decode the latent representations to data space. For any given viewpoint, we make interpolations over decoded data at selected viewpoints and generate visualizations with user-specified visual mappings. We show the effectiveness and efficiency of VDL-Surrogate in cosmological and ocean simulations with quantitative and qualitative evaluations. Source code is publicly available at https://github.com/trainsn/VDL-Surrogate.

There is so much technology emerging, from the metaverse to AI to gene editing, that it can be overwhelming. Yet the CTO who fails to keep an eye on what's around the corner could find their business disrupted – or even kaput When the then head of IT at US VHS rental chain Blockbuster first read about an experimental video-on-demand technology being trialled in London at the beginning of the 1990s, did it cross their mind that this would eventually destroy their business? Or if the CEO of monolithic US bookstore chain Borders first read about an upstart online bookseller called Amazon getting going in 1994, could they have foreseen themselves going bankrupt 17 years later? The problem is that with so much emerging technology, how can you focus on the technologies which will affect your business the most? All we can do is sketch out the technologies we believe will have the most profound effect on business for you.

Millions of shipments of nuclear and other radiological materials are moved in the U.S. every year for good reasons, including health care, power generation, research and manufacturing. But there remains the threat that bad actors in possession of stolen or illegally produced nuclear materials or weapons will try to smuggle them across borders for nefarious purposes. Texas A&M University researchers are making it harder for them to succeed. If border agents intercept illicit nuclear materials, investigators need to know who produced them and where they came from. Fortunately, nuclear materials carry certain forensic markers that can reveal valuable information, much like fingerprints can identify criminals.

The graph represents a network of 1,298 Twitter users whose tweets in the requested range contained "#selfdrivingcars", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 27 July 2022 at 12:51 UTC. The requested start date was Wednesday, 27 July 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 12-day, 21-hour, 51-minute period from Wednesday, 13 July 2022 at 22:43 UTC to Tuesday, 26 July 2022 at 20:34 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

State company tested how artificial intelligence could minimise electricity disruptions and now looks to expand the technology.

The Energy Stocks Package is based on the I Know First algorithm and is designed for investors and analysts who need recommendations for the best performing stocks for the whole Energy Industry. Package Name: Energy Stocks Forecast Recommended Positions: Long Forecast Length: 3 Days (7/24/22 – 7/27/22) I Know First Average: 6.85% In this 3 Days forecast for the Energy Stocks Forecast Package, there were many high performing trades and the algorithm correctly predicted 9 out of 10 trades. The greatest return came from MTDR at 16.65%. CPE and EPM also performed well for this time horizon with returns of 10.55% and 9.2%, respectively.

The news: DeepMind says its AlphaFold tool has successfully predicted the structure of nearly all proteins known to science. It's a massive boost to the existing database of 1 million proteins it released last year, and includes structures for plants, bacteria, animals, and many other organisms. Why it matters: The expanded database opens up huge opportunities for AlphaFold to have impact on important issues such as sustainability, fuel, food insecurity, and neglected diseases, according to Demis Hassabis, DeepMind's founder and CEO. Scientists could use the findings to better understand diseases, and to speed innovation in drug discovery and biology, he added. AI for protein folding represents such a major advance that it was chosen as one of MIT Technology Review's 10 Breakthrough Technologies this year.

When robots operate in real-world off-road environments with unstructured terrains, the ability to adapt their navigational policy is critical for effective and safe navigation. However, off-road terrains introduce several challenges to robot navigation, including dynamic obstacles and terrain uncertainty, leading to inefficient traversal or navigation failures. To address these challenges, we introduce a novel approach for adaptation by negotiation that enables a ground robot to adjust its navigational behaviors through a negotiation process. Our approach first learns prediction models for various navigational policies to function as a terrain-aware joint local controller and planner. Then, through a new negotiation process, our approach learns from various policies' interactions with the environment to agree on the optimal combination of policies in an online fashion to adapt robot navigation to unstructured off-road terrains on the fly. Additionally, we implement a new optimization algorithm that offers the optimal solution for robot negotiation in real-time during execution. Experimental results have validated that our method for adaptation by negotiation outperforms previous methods for robot navigation, especially over unseen and uncertain dynamic terrains.

Internet of Underwater Things (IoUT) have gained rapid momentum over the past decade with applications spanning from environmental monitoring and exploration, defence applications, etc. The traditional IoUT systems use machine learning (ML) approaches which cater the needs of reliability, efficiency and timeliness. However, an extensive review of the various studies conducted highlight the significance of data privacy and security in IoUT frameworks as a predominant factor in achieving desired outcomes in mission critical applications. Federated learning (FL) is a secured, decentralized framework which is a recent development in machine learning, that will help in fulfilling the challenges faced by conventional ML approaches in IoUT. This paper presents an overview of the various applications of FL in IoUT, its challenges, open issues and indicates direction of future research prospects.