The manifold Helmholtzian (1-Laplacian) operator $\Delta_1$ elegantly generalizes the Laplace-Beltrami operator to vector fields on a manifold $\mathcal M$. In this work, we propose the estimation of the manifold Helmholtzian from point cloud data by a weighted 1-Laplacian $\mathbf{\mathcal L}_1$. While higher order Laplacians ave been introduced and studied, this work is the first to present a graph Helmholtzian constructed from a simplicial complex as an estimator for the continuous operator in a non-parametric setting. Equipped with the geometric and topological information about $\mathcal M$, the Helmholtzian is a useful tool for the analysis of flows and vector fields on $\mathcal M$ via the Helmholtz-Hodge theorem. In addition, the $\mathbf{\mathcal L}_1$ allows the smoothing, prediction, and feature extraction of the flows. We demonstrate these possibilities on substantial sets of synthetic and real point cloud datasets with non-trivial topological structures; and provide theoretical results on the limit of $\mathbf{\mathcal L}_1$ to $\Delta_1$.

Ocean scientists are using robot submariness to detect barrels of toxic chemicals under the sea. Thousands of barrels of DDT and other substances are believed submerged in the Pacific Ocean near Los Angeles, but authorities aren't sure where or how many. To get an idea, researchers have launched two'underwater Roombas,' Remote Environmental Monitoring UnitS (REMUS) that can operate in waters ranging from 80 feet to about 20,000 feet. The vehicles take 12 hours to recharge, so while one is scanning the seafloor with its sonar the other is powering up and passing along its findings. Ocean scientists are using'underwater Roombas' to scan the ocean floor for barrels of toxic chemicals, including the banned pesticide DDT.

Civil liberties activists are suing a company that provides facial recognition services to law enforcement agencies and private companies around the world, contending that Clearview AI illegally stockpiled data on 3 billion people without their knowledge or permission. The lawsuit, filed in Alameda County Superior Court in the San Francisco bay area, says the New York company violates California's constitution and seeks a court order to bar it from collecting biometric information in California and requiring it to delete data on Californians. The lawsuit says the company has built "the most dangerous" facial recognition database in the nation, has fielded requests from more than 2,000 law enforcement agencies and private companies and has amassed a database nearly seven times larger than the FBI's. Separately, the Chicago Police Department stopped using the New York company's software last year after Clearview AI was sued in Cook County by the American Civil Liberties Union. The California lawsuit was filed by four activists and the groups Mijente and Norcal Resist.

Extracting interesting scenarios from real-world data as well as generating failure cases is important for the development and testing of autonomous systems. We propose efficient mechanisms to both characterize and generate testing scenarios using a state-of-the-art driving simulator. For any scenario, our method generates a set of possible driving paths and identifies all the possible safe driving trajectories that can be taken starting at different times, to compute metrics that quantify the complexity of the scenario. We use our method to characterize real driving data from the Next Generation Simulation (NGSIM) project, as well as adversarial scenarios generated in simulation. We rank the scenarios by defining metrics based on the complexity of avoiding accidents and provide insights into how the AV could have minimized the probability of incurring an accident. We demonstrate a strong correlation between the proposed metrics and human intuition.

While all eyes were on the dramatic descent of NASA's Perseverance rover last month, a team sent a robot into another alien world, one closer to home: the deep sea. With its towering undersea mountains, dramatic geological features, and unique creatures--many of which remain mysterious--the deep sea is the last uncharted environment on Earth. Sinking any intrepid explorer into blackened waters means facing freezing temperatures and crushing pressure. Ever listened to the sound of metal creaking under pressure? Without protection, puny electronic components in a robot don't have a chance.

San Francisco-based Ample announced a new battery charging technology that refuels electric vehicles from any automaker in just 10 minutes – three times faster than traditional systems. Using Modular Battery system, AI-powered robots remove the depleted battery and replace it with a fully charged unit – Ample says its batteries are like Lego-blocks that can accommodate any vehicle. Ample, started by Ex-Tesla and Google engineers, has constructed five battery swap stations in the San Francisco Bay Area, which can fit in two parking spots, specifically for Uber drivers. The technology comes as Tesla had promised deliver electric vehicle battery swapping stations in 2013, but the Elon Musk-owned company did not deliver - so the start-up moved to make it happen. 'Hopefully this is what convinces people finally that electric cars are the future,' Musk said, rallying a crowd at a splashy demo in 2013, also noting that battery of a Tesla Model S could be swapped in about 90 seconds. However, Musk said in 2015 that Tesla owners were not interested in swapping batteries and pulled the plug on pursuing a batter swapping station.

This silicone rubber robot can withstand the pressures in the ocean's deepest abyss A silicone robot has survived a journey to 10,900 metres below the ocean's surface in the Mariana trench, where the crushing pressure can implode all but the strongest enclosures. This device could lead to lighter and more nimble submersible designs. A team led by Guorui Li at Zhejiang University in China based the robot's design on snailfish, which have relatively delicate, soft bodies and are among the deepest living fish. They have been observed swimming at depths of more than 8000 metres. The submersible robot looks a bit a manta ray and is 22 centimetres long and 28 centimetres in wingspan.

We now turn to understanding the impact that COVID-19 had on the personal productivity and well-being of information workers as their work practices were impacted by remote work. This chapter overviews people's productivity, satisfaction, and work patterns, and shows that the challenges and benefits of remote work are closely linked. Looking forward, the infrastructure surrounding work will need to evolve to help people adapt to the challenges of remote and hybrid work.

In real-world applications of large-scale time series, one often encounters the situation where the temporal patterns of time series, while drifting over time, differ from one another in the same dataset. In this paper, we provably show under such heterogeneity, training a forecasting model with commonly used stochastic optimizers (e.g. SGD) potentially suffers large gradient variance, and thus requires long time training. To alleviate this issue, we propose a sampling strategy named Subgroup Sampling, which mitigates the large variance via sampling over pre-grouped time series. We further introduce SCott, a variance reduced SGD-style optimizer that co-designs subgroup sampling with the control variate method. In theory, we provide the convergence guarantee of SCott on smooth non-convex objectives. Empirically, we evaluate SCott and other baseline optimizers on both synthetic and real-world time series forecasting problems, and show SCott converges faster with respect to both iterations and wall clock time. Additionally, we show two SCott variants that can speed up Adam and Adagrad without compromising generalization of forecasting models.

The game is as wide as the Pacific Ocean at the beginning of a rookie's R6 tenure. There's always a new operator to learn, a new gadget interaction to test, a new strategy to try, and the horizon is constantly expanding. Put bluntly, players must do a lot of homework and suffer tens, if not hundreds of hours worth of hard-learned lessons before they feel they can contribute competently. On one hand, the game's complexity is its biggest appeal, on the other, it's a significant barrier to expanding its player base. While that number is massive, numbering 70 as of this year, could this ongoing expanse and evolution eventually case that player base to stagnate?