A research team has successfully imaged a nova in high resolution--and the images suggest that the nova was not a single, impulsive explosion. The Center for High Angular Resolution Astronomy (CHARA Array) at Georgia State University has generated detailed images of the early stages of two nova explosions that were detected in 2021. Through near-infrared interferometry, a process that combines light from multiple telescopes, the CHARA Array was able to capture in high resolution the rapidly changing conditions of their early post-explosion phase. A nova is an astronomical phenomenon that occurs in a binary system when a white dwarf strips its companion star of hydrogen-rich gas, causing a thermonuclear runaway reaction on the white dwarf's surface. The name derives from the sudden brightening that makes it appear as though a new star has appeared in the night sky.

Principal component analysis (PCA) is one of the most fundamental tools in machine learning with broad use as a dimensionality reduction and denoising tool. In the later setting, while PCA is known to be effective at subspace recovery and is proven to aid clustering algorithms in some specific settings, its improvement of noisy data is still not well quantified in general. In this paper, we propose a novel metric called to capture the effect of PCA on high-dimensional noisy data.We show that, for data with, PCA significantly reduces the distance of data points belonging to the same community while reducing inter-community distance relatively mildly. We explain this phenomenon through both theoretical proofs and experiments on real-world data. Building on this new metric, we design a straightforward algorithm that could be used to detect outliers. Roughly speaking, we argue that points that have a do not share a with others (hence could be considered outliers).We provide theoretical justification for this simple outlier detection algorithm and use simulations to demonstrate that our method is competitive with popular outlier detection tools. Finally, we run experiments on real-world high-dimension noisy data (single-cell RNA-seq) to show that removing points from these datasets via our outlier detection method improves the accuracy of clustering algorithms. Our method is very competitive with popular outlier detection tools in this task.

Understanding how urban socio-demographic and environmental factors relate with health is essential for public health and urban planning. However, traditional statistical methods struggle with nonlinear effects, while machine learning models often fail to capture geographical (nearby areas being more similar) and topological (unequal connectivity between places) effects in an interpretable way. To address this, we propose MedGNN, a spatio-topologically explicit framework that constructs a 2-hop spatial graph, integrating positional and locational node embeddings with urban characteristics in a graph neural network. Applied to MEDSAT, a comprehensive dataset covering over 150 environmental and socio-demographic factors and six prescription outcomes (depression, anxiety, diabetes, hypertension, asthma, and opioids) across 4,835 Greater London neighborhoods, MedGNN improved predictions by over 25% on average compared to baseline methods. Using depression prescriptions as a case study, we analyzed graph embeddings via geographical principal component analysis, identifying findings that: align with prior research (e.g., higher antidepressant prescriptions among older and White populations), contribute to ongoing debates (e.g., greenery linked to higher and NO2 to lower prescriptions), and warrant further study (e.g., canopy evaporation correlated with fewer prescriptions). These results demonstrate MedGNN's potential, and more broadly, of carefully applied machine learning, to advance transdisciplinary public health research.

Principal component analysis (PCA) is one of the most fundamental tools in machine learning with broad use as a dimensionality reduction and denoising tool. In the later setting, while PCA is known to be effective at subspace recovery and is proven to aid clustering algorithms in some specific settings, its improvement of noisy data is still not well quantified in general. In this paper, we propose a novel metric called compression ratio to capture the effect of PCA on high-dimensional noisy data.We show that, for data with underlying community structure, PCA significantly reduces the distance of data points belonging to the same community while reducing inter-community distance relatively mildly. We explain this phenomenon through both theoretical proofs and experiments on real-world data. Building on this new metric, we design a straightforward algorithm that could be used to detect outliers.

We study a search problem on capturing a moving target on an infinite real line. Two autonomous mobile robots (which can move with a maximum speed of 1) are initially placed at the origin, while an oblivious moving target is initially placed at a distance $d$ away from the origin. The robots can move along the line in any direction, but the target is oblivious, cannot change direction, and moves either away from or toward the origin at a constant speed $v$. Our aim is to design efficient algorithms for the two robots to capture the target. The target is captured only when both robots are co-located with it. The robots communicate with each other only face-to-face (F2F), meaning they can exchange information only when co-located, while the target remains oblivious and has no communication capabilities. We design algorithms under various knowledge scenarios, which take into account the prior knowledge the robots have about the starting distance $d$, the direction of movement (either toward or away from the origin), and the speed $v$ of the target. As a measure of the efficiency of the algorithms, we use the competitive ratio, which is the ratio of the capture time of an algorithm with limited knowledge to the capture time in the full-knowledge model. In our analysis, we are mindful of the cost of changing direction of movement, and show how to accomplish the capture of the target with at most three direction changes (turns).

While reconstructing human poses in 3D from inexpensive sensors has advanced significantly in recent years, quantifying the dynamics of human motion, including the muscle-generated joint torques and external forces, remains a challenge. Prior attempts to estimate physics from reconstructed human poses have been hampered by a lack of datasets with high-quality pose and force data for a variety of movements. We present the AddBiomechanics Dataset 1.0, which includes physically accurate human dynamics of 273 human subjects, over 70 hours of motion and force plate data, totaling more than 24 million frames. To construct this dataset, novel analytical methods were required, which are also reported here. We propose a benchmark for estimating human dynamics from motion using this dataset, and present several baseline results.

Sam Crane was in the middle of doing Macbeth when the bullets started flying. A veteran of the British stage, Crane was on the verge of playing the lead in the London production of Harry Potter and the Cursed Child when COVID-19 shut down live performances, and by the U.K.'s third lockdown, he was itching for an audience. So instead of playing to a West End crowd, he found himself orating to a smattering of heavily armed lawbreakers inside the video game Grand Theft Auto. "If I could just request that you refrain from killing each other," he calls out amid the tomorrows and tomorrows. "And don't kill the actors either!"

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Exploring the potential of modern machine learning techniques for 3D-shape learning has earned a starring role in the new ITN GRAPES.

With startups, enterprises, and governments all pouring funds into artificial intelligence, the technology is poised to explode in the coming years. Tech and non-tech companies alike are racing to get ahead of competitors and tap into its potential, with corporate giants leading the way. AI seems to have it all: from the sci-fi-esque imagery to the big-name recognition to the enormous, universal market potential. With such a golden promise, it's little wonder that so many companies are on board the AI train. But with so much hype comes a lot of noise--how can we be sure that the current wave of hype isn't merely a passing fad?