During the spring, a troublesome pattern plays out as marine birds along the California coast die from domoic acid poisoning, which is caused by harmful algal blooms. An early clue indicates when and where this problem starts spreading: rescued California brown pelicans, red-throated loons, and other species start turning up at wildlife rehabilitation centers with signs of neurological disease. Yet, though they pepper the state map, these centers are not interconnected enough to nip the issue in the bud. When staffers at one center diagnose a sick bird, others another 40 miles up the road might not be privy to that information. So researchers at UC Davis recently tested an early detection system that uses artificial intelligence to classify admissions to rehabilitation centers, in the hope of sending wildlife agencies and researchers warnings about growing problems among marine birds and many other kinds of animals.

Accurate traffic state prediction is the foundation of transportation control and guidance. It is very challenging due to the complex spatiotemporal dependencies in traffic data. Existing works cannot perform well for multi-step traffic prediction that involves long future time period. The spatiotemporal information dilution becomes serve when the time gap between input step and predicted step is large, especially when traffic data is not sufficient or noisy. To address this issue, we propose a multi-spatial graph convolution based Seq2Seq model. Our main novelties are three aspects: (1) We enrich the spatiotemporal information of model inputs by fusing multi-view features (time, location and traffic states) (2) We build multiple kinds of spatial correlations based on both prior knowledge and data-driven knowledge to improve model performance especially in insufficient or noisy data cases. (3) A spatiotemporal attention mechanism based on reachability knowledge is novelly designed to produce high-level features fed into decoder of Seq2Seq directly to ease information dilution. Our model is evaluated on two real world traffic datasets and achieves better performance than other competitors.

Japan plans to revise its Medium Term Defense Program earlier than originally scheduled as it looks to boost spending to counter China's growing assertiveness in surrounding waters and prepare for contingencies in the Taiwan Strait, government sources said Friday. The program, which covers the five years through fiscal 2023, could be updated within the year, with Prime Minister Yoshihide Suga and Defense Minister Nobuo Kishi having agreed earlier this month that some changes are necessary, the sources said. Discussions between officials including at the Defense Ministry and the National Security Secretariat are already underway, with budget issues set to be reviewed by the Finance Ministry. The revision would seek to fulfill Suga's promise to U.S. President Joe Biden during their meeting in Washington in April that Japan would bolster its defense capabilities to strengthen the alliance between their countries and maintain security in the Indo-Pacific region. In a joint statement issued after the meeting, the leaders singled out China for actions that are "inconsistent with the international rules-based order, including the use of economic and other forms of coercion."

Let me tell you a little-known fact. Look at companies that use a gaming mindset to help you grow and monetize your company. Snapchat or Uber might be the next big thing. They will likely look more like a gaming studio that uses the best user acquisition, retention, and revenue strategies from the gaming industry. The video game industry is more important than the movie and music industries.

Motional, a joint autonomous vehicle venture between Aptiv and Hyundai, is expanding its operations in California. The company plans to start public road mapping and testing of its robotaxi in Los Angeles this month. Motional is currently testing the AV in Boston, Pittsburgh, Las Vegas (including driverless tests) and Singapore. The company and partner Lyft plan to start a robotaxi service in several US markets in 2023. Extensive road mapping and testing are essential precursors for that to happen.

In estimations of ocean heat content – important when assessing and predicting the effects of climate change – calculations have often presented the rate of warming as a gradual rise from the mid 20th century to today. However, new research from UC Santa Barbara scientists Timothy DeVries and Aaron Bagnell could overturn that assumption, suggesting the ocean maintained a relatively steady temperature throughout most of the 20th century, before embarking on a steep rise. The newly discovered dynamics may have significant implications for what we might expect in the future. "There wasn't an onset of an imbalance until about 1990, which is later than most estimates," said DeVries, an associate professor in the Department of Geography, and a co-author on a paper that appears in the journal Nature Communications. According to the study, the period from 1950 to1990 saw temperature fluctuations in the water column but no net warming.

Ocean fronts can cause the accumulation of nutrients and affect the propagation of underwater sound, so high-precision ocean front detection is of great significance to the marine fishery and national defense fields. However, the current ocean front detection methods either have low detection accuracy or most can only detect the occurrence of ocean front by binary classification, rarely considering the differences of the characteristics of multiple ocean fronts in different sea areas. In order to solve the above problems, we propose a semantic segmentation network called location and seasonality enhanced network (LSENet) for multi-class ocean fronts detection at pixel level. In this network, we first design a channel supervision unit structure, which integrates the seasonal characteristics of the ocean front itself and the contextual information to improve the detection accuracy. We also introduce a location attention mechanism to adaptively assign attention weights to the fronts according to their frequently occurred sea area, which can further improve the accuracy of multi-class ocean front detection. Compared with other semantic segmentation methods and current representative ocean front detection method, the experimental results demonstrate convincingly that our method is more effective.

Champ Suthipongchai is a General Partner at Creative Ventures, a method-driven venture capital firm based in the San Francisco Bay Area. Among these, only 5% are decacorns, commanding a valuation of $10 billion or more. Unbeknownst to many, one-third of the decacorns are deep tech companies, commanding more than $500 billion in aggregate valuation. They are not always the household names we hear, but they are already among us. Deep tech is nothing new.

After months of testing, Citizen, the crime and neighborhood watch app, is releasing Protect, a subscription-based feature that lets users contact virtual agents for help if they feel they're in danger. According to Citizen, the feature can connect users with a Protect agent either through video, audio, or text available around the clock. The company said audio and text-only communication allows users to discreetly call for help "in difficult situations" where they might not be able to or are scared to be seen calling 911. Protect began beta testing earlier this year as the feature has been available to 100,000 users, Citizen said. The new feature comes as Citizen currently has more than 8 million users who have sent out more than billion alerts in major U.S. cities including New York, Los Angeles, Chicago, Atlanta, Houston and the San Francisco Bay Area.

The power system is undergoing rapid evolution with the roll-out of advanced metering infrastructure and local energy applications (e.g. electric vehicles) as well as the increasing penetration of intermittent renewable energy at both transmission and distribution level, which characterizes the peak load demand with stronger randomness and less predictability and therefore poses a threat to the power grid security. Since storing large quantities of electricity to satisfy load demand is neither economically nor environmentally friendly, effective peak demand management strategies and reliable peak load forecast methods become essential for optimizing the power system operations. To this end, this paper provides a timely and comprehensive overview of peak load demand forecast methods in the literature. To our best knowledge, this is the first comprehensive review on such topic. In this paper we first give a precise and unified problem definition of peak load demand forecast. Second, 139 papers on peak load forecast methods were systematically reviewed where methods were classified into different stages based on the timeline. Thirdly, a comparative analysis of peak load forecast methods are summarized and different optimizing methods to improve the forecast performance are discussed. The paper ends with a comprehensive summary of the reviewed papers and a discussion of potential future research directions.