Ayman Al Zawahiri, the terrorist killed in a U.S. drone strike in Afghanistan Monday, was a top deputy to al Qaeda leader Usama bin Laden before taking the helm of the organization after his predecessor's death in 2011. A drone strike on a Kabul home took him out over the weekend, Fox News reported earlier. Taliban spokesman Zabihullah Mujahid confirmed and condemned the attack on Twitter, calling it "a clear violation of international principles," according to a translation of the thread. However, the 2020 Doha Agreement, which preceded the Biden administration's highly criticized withdrawal of U.S. troops from Afghanistan last year, called for the Taliban to combat terrorism within the country. Al Zawahiri was also a doctor and founder of the Egyptian Islamic Jihad terror group, which later merged with al-Qaeda, according to authorities.

CloudFactory, a global leader in human-in-the-loop artificial intelligence (AI), announced that Pieter Nel has joined as Chief Technology Officer (CTO). Nel brings more than 20 years of experience, across three continents, in technology strategy and software engineering management at fast-growth technology companies. As CTO, he will lead the technology and machine learning (ML) teams, continuously evolving CloudFactory's platform as a key enabler for clients' successful AI deployments. "Considering all successful AI deployments include humans in the loop, CloudFactory is positioned perfectly to support clients with our experienced annotation workforce and building the infrastructure to enable human-in-the-loop AI deployments." Nel previously served as CTO at Ocrolus, where he scaled the New York company's human-in-the-loop AI document processing product.

For millennia, we've been told that asking questions was the path to enlightenment. But in the surveillance age, it might land you in jail. That's the danger of a new search tactic that police are increasingly turning to in their constant campaign to transform our phones and devices into evidence against us: keyword warrants. One Denver court may soon rule on whether they can continue as a policing tactic--and in the post-Roe era, the wrong decision could put abortion seekers in unprecedented danger. Police have used web browser history and search engine data in their investigations for about as long as the data has existed, but keyword warrants are different--a digital dragnet to find every user who searches for a specific person, place or thing.

Over the weekend, debris from an out-of-control Chinese rocket crashed to Earth over the Indian and Pacific oceans. There had been fears that pieces of the 23-tonne Long March 5B booster could come down over a populated area, but experts had said the probability of this was extremely low. Nevertheless, NASA hit out at China by accusing Beijing of not sharing the'specific trajectory information' needed to calculate where possible debris might fall. Elsewhere at the weekend, a 10ft (3m) piece of space junk – thought to be from one of Elon Musk's spacecrafts – crashed into a farmer's property in Australia at around 15,500mph (25,000km/h). The object, believed to be part of the SpaceX Crew-1 craft, was found in a sheep paddock by a farmer living on a large property in the Snowy Mountains in New South Wales.

There's nothing like migration to reveal how things that seem natural may be artifacts of culture. When I left India for college in England, I was surprised to find that pinching my Adam's apple didn't mean, as I had thought it meant everywhere, "on my honor." I learned to expect only mockery at the side-to-side tilts of the head with which I expressed degrees of agreement or disagreement, and trained myself to keep to the Aristotelian binary of nod and shake. Around that time, I also learned--from watching the British version of "The Office"--that the word "cringe" could be an adjective, as in the phrase "so cringe." It turned out that there was a German word for the feeling inspired by David Brent, the cringe-making boss played by Ricky Gervais in the show: Fremdschämen--the embarrassment one feels when other people have, perhaps obliviously, embarrassed themselves.

Health monitoring applications increasingly rely on machine learning techniques to learn end-user physiological and behavioral patterns in everyday settings. Considering the significant role of wearable devices in monitoring human body parameters, on-device learning can be utilized to build personalized models for behavioral and physiological patterns, and provide data privacy for users at the same time. However, resource constraints on most of these wearable devices prevent the ability to perform online learning on them. To address this issue, it is required to rethink the machine learning models from the algorithmic perspective to be suitable to run on wearable devices. Hyperdimensional computing (HDC) offers a well-suited on-device learning solution for resource-constrained devices and provides support for privacy-preserving personalization. Our HDC-based method offers flexibility, high efficiency, resilience, and performance while enabling on-device personalization and privacy protection. We evaluate the efficacy of our approach using three case studies and show that our system improves the energy efficiency of training by up to $45.8\times$ compared with the state-of-the-art Deep Neural Network (DNN) algorithms while offering a comparable accuracy.

Visual inertial odometry (VIO) is widely used for the state estimation of multicopters, but it may function poorly in environments with few visual features or in overly aggressive flights. In this work, we propose a perception-aware collision avoidance trajectory planner for multicopters, that may be used with any feature-based VIO algorithm. Our approach is able to fly the vehicle to a goal position at fast speed, avoiding obstacles in an unknown stationary environment while achieving good VIO state estimation accuracy. The proposed planner samples a group of minimum jerk trajectories and finds collision-free trajectories among them, which are then evaluated based on their speed to the goal and perception quality. Both the motion blur of features and their locations are considered for the perception quality. Our novel consideration of the motion blur of features enables automatic adaptation of the trajectory's aggressiveness under environments with different light levels. The best trajectory from the evaluation is tracked by the vehicle and is updated in a receding horizon manner when new images are received from the camera. Only generic assumptions about the VIO are made, so that the planner may be used with various existing systems. The proposed method can run in real-time on a small embedded computer on board. We validated the effectiveness of our proposed approach through experiments in both indoor and outdoor environments. Compared to a perception-agnostic planner, the proposed planner kept more features in the camera's view and made the flight less aggressive, making the VIO more accurate. It also reduced VIO failures, which occurred for the perception-agnostic planner but not for the proposed planner. The ability of the proposed planner to fly through dense obstacles was also validated. The experiment video can be found at https://youtu.be/qO3LZIrpwtQ.

We present a self-supervised approach for learning to predict traversable paths for wheeled mobile robots that require good traction to navigate. Our algorithm, termed WayFAST (Waypoint Free Autonomous Systems for Traversability), uses RGB and depth data, along with navigation experience, to autonomously generate traversable paths in outdoor unstructured environments. Our key inspiration is that traction can be estimated for rolling robots using kinodynamic models. Using traction estimates provided by an online receding horizon estimator, we are able to train a traversability prediction neural network in a self-supervised manner, without requiring heuristics utilized by previous methods. We demonstrate the effectiveness of WayFAST through extensive field testing in varying environments, ranging from sandy dry beaches to forest canopies and snow covered grass fields. Our results clearly demonstrate that WayFAST can learn to avoid geometric obstacles as well as untraversable terrain, such as snow, which would be difficult to avoid with sensors that provide only geometric data, such as LiDAR. Furthermore, we show that our training pipeline based on online traction estimates is more data-efficient than other heuristic-based methods.

LiDAR (short for "Light Detection And Ranging" or "Laser Imaging, Detection, And Ranging") technology can be used to provide detailed three-dimensional elevation maps of urban and rural landscapes. To date, airborne LiDAR imaging has been predominantly confined to the environmental and archaeological domains. However, the geographically granular and open-source nature of this data also lends itself to an array of societal, organizational and business applications where geo-demographic type data is utilised. Arguably, the complexity involved in processing this multi-dimensional data has thus far restricted its broader adoption. In this paper, we propose a series of convenient task-agnostic tile elevation embeddings to address this challenge, using recent advances from unsupervised Deep Learning. We test the potential of our embeddings by predicting seven English indices of deprivation (2019) for small geographies in the Greater London area. These indices cover a range of socio-economic outcomes and serve as a proxy for a wide variety of downstream tasks to which the embeddings can be applied. We consider the suitability of this data not just on its own but also as an auxiliary source of data in combination with demographic features, thus providing a realistic use case for the embeddings. Having trialled various model/embedding configurations, we find that our best performing embeddings lead to Root-Mean-Squared-Error (RMSE) improvements of up to 21% over using standard demographic features alone. We also demonstrate how our embedding pipeline, using Deep Learning combined with K-means clustering, produces coherent tile segments which allow the latent embedding features to be interpreted.

This paper discusses creating and analysing a new dataset for data mining and text analytics research, contributing to a joint Leeds University research project for the Corpus of National Dialects. This report investigates machine learning classifiers to classify samples of French dialect text across various French-speaking countries. Following the steps of the CRISP-DM methodology, this report explores the data collection process, data quality issues and data conversion for text analysis. Finally, after applying suitable data mining techniques, the evaluation methods, best overall features and classifiers and conclusions are discussed.