An oil tanker associated with an Israeli billionaire has been struck by a bomb-carrying drone off the coast of Oman amid heightened tensions with Iran, an official has told the Associated Press. The attack happened on Tuesday night off the coast of Oman, the Middle East-based defence official said. The official spoke on Wednesday on condition of anonymity as they did not have authorisation to discuss the attack publicly. The United Kingdom Maritime Trade Operations, a British military organisation in the region monitoring shipping, told the AP: "We are aware of an incident and it's being investigated at this time." The official identified the vessel attacked as the Liberian-flagged oil tanker Pacific Zircon.

In 2015, I visited Fiji, Kiribati, and Tuvalu, which had just been hit by a cyclone. There, I learned a slogan -- "1.5 to stay alive" -- which refers to the 1.5 degrees Celsius (2.7 degrees Fahrenheit) threshold for global warming that, in theory, would avoid disastrous consequences. People living on the Pacific islands are well aware of the grave threat to humanity posed by climate change. Six months later, I met these new comrades again at climate negotiations in Paris. While speaking at an event, I referred to "1.5 to stay alive".

TEL AVIV--Israeli venture-capital firm OurCrowd is investing tens of millions of dollars to start an artificial-intelligence business in the United Arab Emirates, in the latest sign of deepening commercial ties between the two neighbors after they established diplomatic relations two years ago. OurCrowd said it is partnering with Abu Dhabi Investment Office, a government entity responsible for facilitating investment in the U.A.E. The two entities will invest $60 million in total for the expansion, the bulk of which would be used to set up the new AI business, named Integrated Data Intelligence Ltd., according to Jon Medved, founder and chief executive of OurCrowd. The new company will serve as a technical hub for businesses seeking AI as a service. The story of the Abraham Accords isn't one-way investment, Mr. Medved said, but of "how we build stuff together."

In the age of social media, where billions of internet users share information and opinions, the negative impact of pandemics is not limited to the physical world. It provokes a surge of incomplete, biased, and incorrect information, also known as an infodemic. This global infodemic jeopardizes measures to control the pandemic by creating panic, vaccine hesitancy, and fragmented social response. Platforms like Facebook allow advertisers to adapt their messaging to target different demographics and help alleviate or exacerbate the infodemic problem depending on their content. In this paper, we propose a minimally supervised multi-task learning framework for understanding messaging on Facebook related to the COVID vaccine by identifying ad themes and moral foundations. Furthermore, we perform a more nuanced thematic analysis of messaging tactics of vaccine campaigns on social media so that policymakers can make better decisions on pandemic control.

While physical phenomena are stated in terms of physical laws that are homogeneous in all dimensions, the mechanisms and patterns of the physical phenomena are independent of the form of the units describing the physical process. Accordingly, across different conditions, the similarity of a process may be captured through a dimensionless reformulation of the physical problem with Buckingham $\Pi$ theorem. Here, we apply Buckingham $\Pi$ theorem for creating dimensionless indices for capturing the similarity of the flood process, and in turn, these indices allow machine learning to map the likelihood of pluvial (flash) flooding over a landscape. In particular, we use these dimensionless predictors with a logistic regression machine learning (ML) model for a probabilistic determination of flood risk. The logistic regression derived flood maps compare well to 2D hydraulic model results that are the basis of the Federal Emergency Management Agency (FEMA) maps. As a result, the indices and logistic regression also provide the potential to expand existing FEMA maps to new (unmapped) areas and a wider spectrum of flood flows and precipitation events. Our results demonstrate that the new dimensionless indices capture the similarity of the flood process across different topographies and climate regions. Consequently, these dimensionless indices may expand observations of flooding (e.g., satellite) to the risk of flooding in new areas, as well as provide a basis for the rapid, real-time estimation of flood risk on a worldwide scale.

We provide a method to identify system parameters of dynamical systems, called ID-ODE -- Inference by Differentiation and Observing Delay Embeddings. In this setting, we are given a dataset of trajectories from a dynamical system with system parameter labels. Our goal is to identify system parameters of new trajectories. The given trajectories may or may not encompass the full state of the system, and we may only observe a one-dimensional time series. In the latter case, we reconstruct the full state by using delay embeddings, and under sufficient conditions, Taken's Embedding Theorem assures us the reconstruction is diffeomorphic to the original. This allows our method to work on time series. Our method works by first learning the velocity operator (as given or reconstructed) with a neural network having both state and system parameters as variable inputs. Then on new trajectories we backpropagate prediction errors to the system parameter inputs giving us a gradient. We then use gradient descent to infer the correct system parameter. We demonstrate the efficacy of our approach on many numerical examples: the Lorenz system, Lorenz96, Lotka-Volterra Predator-Prey, and the Compound Double Pendulum. We also apply our algorithm on a real-world dataset: propulsion of the Hall-effect Thruster (HET).

In recent years, the role of artificially intelligent (AI) agents has evolved from being basic tools to socially intelligent agents working alongside humans towards common goals. In such scenarios, the ability to predict future behavior by observing past actions of their human teammates is highly desirable in an AI agent. Goal-oriented human behavior is complex, hierarchical, and unfolds across multiple timescales. Despite this observation, relatively little attention has been paid towards using multi-timescale features to model such behavior. In this paper, we propose an LSTM network architecture that processes behavioral information at multiple timescales to predict future behavior. We demonstrate that our approach for modeling behavior in multiple timescales substantially improves prediction of future behavior compared to methods that do not model behavior at multiple timescales. We evaluate our architecture on data collected in an urban search and rescue scenario simulated in a virtual Minecraft-based testbed, and compare its performance to that of a number of valid baselines as well as other methods that do not process inputs at multiple timescales.

This paper aims to develop resilient transmission mechanisms to suitably distribute traffic across multiple paths in an arbitrary millimeter-wave (mmWave) network. The main contributions include: (a) the development of proactive transmission mechanisms that build resilience against network disruptions in advance, while achieving a high end-to-end packet rate; (b) the design of a heuristic path selection algorithm that efficiently selects (in polynomial time in the network size) multiple proactively resilient paths with high packet rates; and (c) the development of a hybrid scheduling algorithm that combines the proposed path selection algorithm with a deep reinforcement learning (DRL) based online approach for decentralized adaptation to blocked links and failed paths. To achieve resilience to link failures, a stateof-the-art Soft Actor-Critic DRL algorithm, which adapts the information flow through the network, is investigated. The proposed scheduling algorithm robustly adapts to link failures over different topologies, channel and blockage realizations while offering a superior performance to alternative algorithms. M. G. Dogan and C. Fragouli are with the Electrical and Computer Engineering Department at the University of California, Los Angeles, CA 90095 USA (e-mail: {minedogan96, christina.fragouli}@ucla.edu). The research carried out at UCLA was supported in part by the Army Research Laboratory under Co-Operative Agreement W911NF-17-2-0196 and by the U.S. National Science Foundation (NSF) awards 442521-FC-22071 and 442521-FC-21454. M. Cardone is with the Electrical and Computer Engineering Department of the University of Minnesota, MN 55404 USA (e-mail: cardo089@umn.edu). The work of M. Cardone was supported in part by the NSF under Grants CCF-2045237 and CNS-2146838. Part of this work was presented at the 2021 IEEE Military Communications Conference [1] and at the 2022 IEEE International Symposium on Information Theory [2]. Millimeter Wave (mmWave) (and beyond) is an enabling technology that is playing an increasingly important role in our wireless infrastructure by expanding the available spectrum and enabling multi-gigabit services [3]-[5]. A number of use cases are currently built around multihop mmWave networks, such as Facebook's Terragraph network [6] that uses flexible mmWave backbones to connect clusters of base stations. Other example scenarios include private networks, such as in shopping centers, airports and enterprises; mmWave mesh networks that use mmWave links as backhaul in dense urban scenarios; military applications employing mobile hot spots; and mmWave based vehicle-to-everything (V2X) services, such as cooperative perception [7]-[9].

The geometry of 4-string contact interaction of closed string field theory is characterized using machine learning. We obtain Strebel quadratic differentials on 4-punctured spheres as a neural network by performing unsupervised learning with a custom-built loss function. This allows us to solve for local coordinates and compute their associated mapping radii numerically. We also train a neural network distinguishing vertex from Feynman region. As a check, 4-tachyon contact term in the tachyon potential is computed and a good agreement with the results in the literature is observed. We argue that our algorithm is manifestly independent of number of punctures and scaling it to characterize the geometry of $n$-string contact interaction is feasible.

Mobile robots extract information from its environment to understand their current situation to enable intelligent decision making and autonomous task execution. In our previous work, we introduced the concept of Situation Graphs (S-Graphs) which combines in a single optimizable graph, the robot keyframes and the representation of the environment with geometric, semantic and topological abstractions. Although S-Graphs were built and optimized in real-time and demonstrated state-of-the-art results, they are limited to specific structured environments with specific hand-tuned dimensions of rooms and corridors. In this work, we present an advanced version of the Situational Graphs (S-Graphs+), consisting of the five layered optimizable graph that includes (1) metric layer along with the graph of free-space clusters (2) keyframe layer where the robot poses are registered (3) metric-semantic layer consisting of the extracted planar walls (4) novel rooms layer constraining the extracted planar walls (5) novel floors layer encompassing the rooms within a given floor level. S-Graphs+ demonstrates improved performance over S-Graphs efficiently extracting the room information while simultaneously improving the pose estimate of the robot, thus extending the robots situational awareness in the form of a five layered environmental model.