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.

The null space of the $k$-th order Laplacian $\mathbf{\mathcal L}_k$, known as the {\em $k$-th homology vector space}, encodes the non-trivial topology of a manifold or a network. Understanding the structure of the homology embedding can thus disclose geometric or topological information from the data. The study of the null space embedding of the graph Laplacian $\mathbf{\mathcal L}_0$ has spurred new research and applications, such as spectral clustering algorithms with theoretical guarantees and estimators of the Stochastic Block Model. In this work, we investigate the geometry of the $k$-th homology embedding and focus on cases reminiscent of spectral clustering. Namely, we analyze the {\em connected sum} of manifolds as a perturbation to the direct sum of their homology embeddings. We propose an algorithm to factorize the homology embedding into subspaces corresponding to a manifold's simplest topological components. The proposed framework is applied to the {\em shortest homologous loop detection} problem, a problem known to be NP-hard in general. Our spectral loop detection algorithm scales better than existing methods and is effective on diverse data such as point clouds and images.

Dealing with missing values and incomplete time series is a labor-intensive and time-consuming inevitable task when handling data coming from real-world applications. Effective spatio-temporal representations would allow imputation methods to reconstruct missing temporal data by exploiting information coming from sensors at different locations. However, standard methods fall short in capturing the nonlinear time and space dependencies existing within networks of interconnected sensors and do not take full advantage of the available - and often strong - relational information. Notably, most of state-of-the-art imputation methods based on deep learning do not explicitly model relational aspects and, in any case, do not exploit processing frameworks able to adequately represent structured spatio-temporal data. Conversely, graph neural networks have recently surged in popularity as both expressive and scalable tools for processing sequential data with relational inductive biases. In this work, we present the first assessment of graph neural networks in the context of multivariate time series imputation. In particular, we introduce a novel graph neural network architecture, named GRIL, which aims at reconstructing missing data in the different channels of a multivariate time series by learning spatial-temporal representations through message passing. Preliminary empirical results show that our model outperforms state-of-the-art methods in the imputation task on relevant benchmarks with mean absolute error improvements often higher than 20%.