Is Dubai's glossy image under threat? Stephanie Baker had been celebrating her birthday with friends at a bar on Palm Jumeirah - Dubai's iconic man-made palm-shaped island lined with luxury hotels and beach clubs. But as the group stepped outside to head to another nearby venue, something unusual streaked across the night sky. Moments later, debris from a drone struck the five-star Fairmont hotel - Baker and her friends were standing right across the street. We all were scared, she says.

Climate change will cause an increase in the frequency and severity of flood events, prompting the need for cohesive adaptation policymaking. Designing effective adaptation policies, however, depends on managing the uncertainty of long-term climate impacts. Meanwhile, such policies can feature important normative choices that are not always made explicit. We propose that Reinforcement Learning (RL) can be a useful tool to both identify adaptation pathways under uncertain conditions while it also allows for the explicit modelling (and consequent comparison) of different adaptation priorities (e.g. economic vs. wellbeing). We use an Integrated Assessment Model (IAM) to link together a rainfall and flood model, and compute the impacts of flooding in terms of quality of life (QoL), transportation, and infrastructure damage. Our results show that models prioritising QoL over economic impacts results in more adaptation spending as well as a more even distribution of spending over the study area, highlighting the extent to which such normative assumptions can alter adaptation policy. Our framework is publicly available: https://github.com/MLSM-at-DTU/maat_qol_framework.

In full-duplex speech interaction systems, effective Acoustic Echo Cancellation (AEC) is crucial for recovering echo-contaminated speech. This paper presents a neural network-based AEC solution to address challenges in mobile scenarios with varying hardware, nonlinear distortions and long latency. We first incorporate diverse data augmentation strategies to enhance the model's robustness across various environments. Moreover, progressive learning is employed to incrementally improve AEC effectiveness, resulting in a considerable improvement in speech quality. To further optimize AEC's downstream applications, we introduce a novel post-processing strategy employing tailored parameters designed specifically for tasks such as Voice Activity Detection (VAD) and Automatic Speech Recognition (ASR), thus enhancing their overall efficacy. Finally, our method employs a small-footprint model with streaming inference, enabling seamless deployment on mobile devices. Empirical results demonstrate effectiveness of the proposed method in Echo Return Loss Enhancement and Perceptual Evaluation of Speech Quality, alongside significant improvements in both VAD and ASR results.

Active Noise Cancellation (ANC) algorithms aim to suppress unwanted acoustic disturbances by generating anti-noise signals that destructively interfere with the original noise in real time. Although recent deep learning-based ANC algorithms have set new performance benchmarks, there remains a shortage of theoretical limits to rigorously assess their improvements. To address this, we derive a unified lower bound on cancellation performance composed of two components. The first component is information-theoretic: it links residual error power to the fraction of disturbance entropy captured by the anti-noise signal, thereby quantifying limits imposed by information-processing capacity. The second component is support-based: it measures the irreducible error arising in frequency bands that the cancellation path cannot address, reflecting fundamental physical constraints. By taking the maximum of these two terms, our bound establishes a theoretical ceiling on the Normalized Mean Squared Error (NMSE) attainable by any ANC algorithm. We validate its tightness empirically on the NOISEX dataset under varying reverberation times, demonstrating robustness across diverse acoustic conditions.

Lena Raine, who composed most of the music for the beloved indie game Celeste, has released a concept album for Earthblade. Extremely OK Games announced in 2022 that it was developing Earthblade as its next project after Celeste, but it ultimately cancelled the project in January this year. Raine wrote in the description of EARTHBLADE Across the Bounds of Fate that she cobbled together "every bit of music [she'd] written for the game to the point of its cancellation in order to tell [her] own version of it." While there's no game to dictate the tracks' sequence for the album, Raine said she arranged them "into the emotional arc of their progression, much like [she] would for any soundtrack release." The composer cited older animation and film as inspiration for the album, such as the synths and live strings in Joe Hisaishi's score for Nausicaä of the Valley of the Wind, as well as Yoko Kanno's use of saxophones and percussives for Cowboy Bebop and Ghost in the Shell: Stand Alone Complex.

"Aircraft can operate safely in fog, but airports go into a kind of slow motion," says travel expert Simon Calder. "Aircraft follow a beam from the instrument landing system. This beam must not be broken, which could happen when it's foggy," he tells the BBC. And when there are high winds, for safety, aircraft have to be more spaced apart than usual. Despite Heathrow trialling artificial intelligence to help planes land in low visibility, controllers in on-ground towers still need to clearly see an aircraft arriving before issuing a landing clearance, aviation expert Scott Bateman MBE wrote on X.