Understanding and manipulating articulated objects such as doors and drawers is a key skill for robots in human environments. However, it is difficult to train systems that generalize to variations of those objects. The sensory signal comes from an Azure Kinect depth camera, and the agent is a Sawyer BLACK robot. A novel per-point representation of the articulation structure of an object is proposed, called 3D Articulation Flow. A newly-developed 3D vision neural network architecture takes as input a static 3D point cloud and predicts the 3D Articulation Flow of the input under articulation motion.

Participants at the Middle East Naval Commanders Conference (MENC) held on the sidelines of the Doha International Maritime and Defence Exhibition and Conference 2022 (DIMDEX) have noted the importance of bilateral and multilateral partnerships among countries to ensure the oceans are protected from threats. While discussing'Resilience in the maritime Domain – Confronting Asymmetric Threats,' senior military officers and academia highlighted the rapid growth of technology, and artificial intelligence (AI) in modern military operations and the gradual shift towards unmanned technological revolution. Vice-Admiral Brad Cooper, Commander, US Naval Forces Central Command/5thFleet, said multilateral partnerships, especially in a vast and strategic region like the Middle East and the Gulf, would ensure the security of commerce and people. He also noted that Qatar, as a Major non-NATO ally (MNNA), would play a crucial role in deploying technologies alongside the US and other partners to ensure the region's security. "Oceans have long served as parts to new frontiers and opportunities, and they remain so today. This region has three strategic points, the Suez Canal, the Gulf of Aden and the Strait of Hormuz. Challenges to commercial vessels' security and stability and other threats can significantly impact global commerce. This is why resilience in the maritime domain matters greatly," Vice-Admiral Cooper said.

Ukraine's most sophisticated attack drone is about as stealthy as a crop duster: slow, low-flying and completely defenseless. So when the Russian invasion began, many experts expected the few drones that the Ukrainian forces managed to get off the ground would be shot down in hours. But more than two weeks into the conflict, Ukraine's drones -- Turkish-made Bayraktar TB2 models that buzz along at about half the speed of a Cessna – are not only still flying; they also shoot guided missiles at Russian missile launchers, tanks and supply trains, according to Pentagon officials. The drones have become a sort of lumbering canary in the war's coal mine, a sign of the astonishing resiliency of the Ukrainian defense forces and the larger problems that the Russians have encountered. "The performance of the Russian military has been shocking," said David Deptula, a retired three-star Air Force general who planned the U.S. air campaigns in Afghanistan in 2001 and the Persian Gulf in 1991.

The diversity of El Ni\~no events is commonly described by two distinct flavors, the Eastern Pacific (EP) and Central Pacific (CP) types. While the remote impacts, i.e. teleconnections, of EP and CP events have been studied for different regions individually, a global picture of their teleconnection patterns is still lacking. Here, we use Forman-Ricci curvature applied on climate networks constructed from 2-meter air temperature data to distinguish regional links from teleconnections. Our results confirm that teleconnection patterns are strongly influenced by the El Ni\~no type. EP events have primarily tropical teleconnections whereas CP events involve tropical-extratropical connections, particularly in the Pacific. Moreover, the central Pacific region does not have many teleconnections, even during CP events. It is mainly the eastern Pacific that mediates the remote influences for both El Ni\~no types.

This study proposes a data condensation method for multivariate kernel density estimation by genetic algorithm. First, our proposed algorithm generates multiple subsamples of a given size with replacement from the original sample. The subsamples and their constituting data points are regarded as $\it{chromosome}$ and $\it{gene}$, respectively, in the terminology of genetic algorithm. Second, each pair of subsamples breeds two new subsamples, where each data point faces either $\it{crossover}$, $\it{mutation}$, or $\it{reproduction}$ with a certain probability. The dominant subsamples in terms of fitness values are inherited by the next generation. This process is repeated generation by generation and brings the sparse representation of kernel density estimator in its completion. We confirmed from simulation studies that the resulting estimator can perform better than other well-known density estimators.

Tropical cyclone (TC) is an extreme tropical weather system and its trajectory can be described by a variety of spatio-temporal data. Effective mining of these data is the key to accurate TCs track forecasting. However, existing methods face the problem that the model complexity is too high or it is difficult to efficiently extract features from multi-modal data. In this paper, we propose the Dual-Branched spatio-temporal Fusion Network (DBF-Net) -- a novel multi-horizon tropical cyclone track forecasting model which fuses the multi-modal features efficiently. DBF-Net contains a TC features branch that extracts temporal features from 1D inherent features of TCs and a pressure field branch that extracts spatio-temporal features from reanalysis 2D pressure field. Through the encoder-decoder-based architecture and efficient feature fusion, DBF-Net can fully mine the information of the two types of data, and achieve good TCs track prediction results. Extensive experiments on historical TCs track data in the Northwest Pacific show that our DBF-Net achieves significant improvement compared with existing statistical and deep learning TCs track forecast methods.

We present an end-to-end differentiable neural network architecture to perform anomaly detection in multivariate time series by incorporating a Sequential Probability Ratio Test on the prediction residual. The architecture is a cascade of dynamical systems designed to separate linearly predictable components of the signal such as trends and seasonality, from the non-linear ones. The former are modeled by local Linear Dynamic Layers, and their residual is fed to a generic Temporal Convolutional Network that also aggregates global statistics from different time series as context for the local predictions of each one. The last layer implements the anomaly detector, which exploits the temporal structure of the prediction residuals to detect both isolated point anomalies and set-point changes. It is based on a novel application of the classic CUMSUM algorithm, adapted through the use of a variational approximation of f-divergences. The model automatically adapts to the time scales of the observed signals. It approximates a SARIMA model at the get-go, and auto-tunes to the statistics of the signal and its covariates, without the need for supervision, as more data is observed. The resulting system, which we call STRIC, outperforms both state-of-the-art robust statistical methods and deep neural network architectures on multiple anomaly detection benchmarks.

Markov chain Monte Carlo (MCMC) methods are often used in clustering since they guarantee asymptotically exact expectations in the infinite-time limit. In finite time, though, slow mixing often leads to poor performance. Modern computing environments offer massive parallelism, but naive implementations of parallel MCMC can exhibit substantial bias. In MCMC samplers of continuous random variables, Markov chain couplings can overcome bias. But these approaches depend crucially on paired chains meetings after a small number of transitions. We show that straightforward applications of existing coupling ideas to discrete clustering variables fail to meet quickly. This failure arises from the "label-switching problem": semantically equivalent cluster relabelings impede fast meeting of coupled chains. We instead consider chains as exploring the space of partitions rather than partitions' (arbitrary) labelings. Using a metric on the partition space, we formulate a practical algorithm using optimal transport couplings. Our theory confirms our method is accurate and efficient. In experiments ranging from clustering of genes or seeds to graph colorings, we show the benefits of our coupling in the highly parallel, time-limited regime.

The United States Navy announced the launch of a new joint fleet of unmanned drones in the Middle East with allied nations to patrol vast swaths of volatile waters as tensions simmer with Iran. Vice Admiral Brad Cooper, who leads the 5th Fleet, said 100 unmanned drones, both sailing and submersible, would dramatically multiply the surveillance capacities of the US Navy, allowing it to keep a close eye on waters critical to the flow of global oil and shipping. Trade at sea has been targeted in recent years as Tehran's nuclear deal with world powers collapsed. "By using unmanned systems, we can just simply see more. They're high reliability and remove the human factor," Cooper said on the sidelines of a defence exhibition in Abu Dhabi, adding the systems are "the only way to cover on whatever gaps that we have today".

Multi-model ensemble analysis integrates information from multiple climate models into a unified projection. However, existing integration approaches based on model averaging can dilute fine-scale spatial information and incur bias from rescaling low-resolution climate models. We propose a statistical approach, called NN-GPR, using Gaussian process regression (GPR) with an infinitely wide deep neural network based covariance function. NN-GPR requires no assumptions about the relationships between models, no interpolation to a common grid, no stationarity assumptions, and automatically downscales as part of its prediction algorithm. Model experiments show that NN-GPR can be highly skillful at surface temperature and precipitation forecasting by preserving geospatial signals at multiple scales and capturing inter-annual variability. Our projections particularly show improved accuracy and uncertainty quantification skill in regions of high variability, which allows us to cheaply assess tail behavior at a 0.44$^\circ$/50 km spatial resolution without a regional climate model (RCM). Evaluations on reanalysis data and SSP245 forced climate models show that NN-GPR produces similar, overall climatologies to the model ensemble while better capturing fine scale spatial patterns. Finally, we compare NN-GPR's regional predictions against two RCMs and show that NN-GPR can rival the performance of RCMs using only global model data as input.