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.

In this paper, we provide a novel perspective on the underlying structure of real-world data with ground-truth clusters via characterization of an abundantly observed yet often overlooked density-geometry correlation, that manifests itself as a multi-layered manifold structure. We leverage this correlation to design CoreSPECT (Core Space Projection based Enhancement of Clustering Techniques), a general framework that improves the performance of generic clustering algorithms. Our framework boosts the performance of clustering algorithms by applying them to strategically selected regions, then extending the partial partition to a complete partition for the dataset using a novel neighborhood graph based multi-layer propagation procedure. We provide initial theoretical support of the functionality of our framework under the assumption of our model, and then provide large-scale real-world experiments on 19 datasets that include standard image datasets as well as genomics datasets. We observe two notable improvements. First, CoreSPECT improves the NMI of K-Means by 20% on average, making it competitive to (and in some cases surpassing) the state-of-the-art manifold-based clustering algorithms, while being orders of magnitude faster. Secondly, our framework boosts the NMI of HDBSCAN by more than 100% on average, making it competitive to the state-of-the-art in several cases without requiring the true number of clusters and hyper-parameter tuning. The overall ARI improvements are higher.

Scientists have released the first X-ray images of a mysterious, sphere-shaped object recovered in Colombia, which locals claim is of alien origin. The so-called'UFO' was spotted in March over the town of Buga, zig-zagging through the sky in a way that defies the movement of conventional aircraft. The object was recovered shortly after it landed and has since been analyzed by scientists, who discovered it features three layers of metal-like material and 18 microspheres surrounding a central nucleus they are calling'a chip.' Dr Jose Luis Velazquez, a radiologist who examined the sphere, reported finding'no welds or joints,' which would typically indicate human fabrication. He and his team concluded: 'It is of artificial origin, in that it shows no evidence of welding, and its internal structure is composed of high-density elements. More testing is needed to establish its origin.'

Agent-based models (ABMs) are valuable for modelling complex, potentially out-of-equilibria scenarios. However, ABMs have long suffered from the Lucas critique, stating that agent behaviour should adapt to environmental changes. Furthermore, the environment itself often adapts to these behavioural changes, creating a complex bi-level adaptation problem. Recent progress integrating multi-agent reinforcement learning into ABMs introduces adaptive agent behaviour, beginning to address the first part of this critique, however, the approaches are still relatively ad hoc, lacking a general formulation, and furthermore, do not tackle the second aspect of simultaneously adapting environmental level characteristics in addition to the agent behaviours. In this work, we develop a generic two-layer framework for ADaptive AGEnt based modelling (ADAGE) for addressing these problems. This framework formalises the bi-level problem as a Stackelberg game with conditional behavioural policies, providing a consolidated framework for adaptive agent-based modelling based on solving a coupled set of non-linear equations. We demonstrate how this generic approach encapsulates several common (previously viewed as distinct) ABM tasks, such as policy design, calibration, scenario generation, and robust behavioural learning under one unified framework. We provide example simulations on multiple complex economic and financial environments, showing the strength of the novel framework under these canonical settings, addressing long-standing critiques of traditional ABMs.

We develop a novel two-layer approach for optimising mortgage relief products through a simulated multi-agent mortgage environment. While the approach is generic, here the environment is calibrated to the US mortgage market based on publicly available census data and regulatory guidelines. Through the simulation layer, we assess the resilience of households to exogenous income shocks, while the optimisation layer explores strategies to improve the robustness of households to these shocks by making novel mortgage assistance products available to households. Households in the simulation are adaptive, learning to make mortgage-related decisions (such as product enrolment or strategic foreclosures) that maximize their utility, balancing their available liquidity and equity. We show how this novel two-layer simulation approach can successfully design novel mortgage assistance products to improve household resilience to exogenous shocks, and balance the costs of providing such products through post-hoc analysis. Previously, such analysis could only be conducted through expensive pilot studies involving real participants, demonstrating the benefit of the approach for designing and evaluating financial products.

The outer layer of the cerebral hemisphere is termed the cerebral cortex. This is inter-connected via pathways that run sub-cortically. It is these connections as well as the connections from the cerebral cortex to the brainstem, spinal cord and nuclei deep within the cerebral hemisphere that form the white matter of the cerebral hemisphere. The deep nuclei include structures such as the basal ganglia and the thalamus. The main difference between cerebrum and cerebral cortex is that cerebrum is the largest part of the brain whereas cerebral cortex is the outer layer of the cerebrum.

Here, α is the learning rate. Using this we can control the rate at which the weights get trained and updated. In order to perform the partial differentiation, we need to understand the Chain Rule. In the diagram above, the highlighted part shows the flow of information, from last layer to the Layer # 5. We know that, back propagation helps us in optimise/update weights and biases by using the below relation: Now, we already have the weights with us, usually we decide α using hyperparameter tuning. The only part left to calculate is the partial differentiation term.

Manifold learning now plays a very important role in machine learning and many relevant applications. Although its superior performance in dealing with nonlinear data distribution, data sparsity is always a thorny knot. There are few researches to well handle it in manifold learning. In this paper, we propose Hierarchic Neighbors Embedding (HNE), which enhance local connection by the hierarchic combination of neighbors. After further analyzing topological connection and reconstruction performance, three different versions of HNE are given. The experimental results show that our methods work well on both synthetic data and high-dimensional real-world tasks. HNE develops the outstanding advantages in dealing with general data. Furthermore, comparing with other popular manifold learning methods, the performance on sparse samples and weak-connected manifolds is better for HNE.

NASA has announced a new round of funding for 18 futuristic projects that could help propel humans further into our solar system and beyond. Many of the ideas'sound like the stuff of science fiction,' the agency acknowledged, but they're not too crazy to one day become a reality. Among those that received funding are micro-probes that take after spiders to safely fly through the air, as well as a futuristic'smart suit' with self-healing skin to protect astronauts. Among those that were funded are micro-probes that take after spiders to safely fly through the air, as well as a futuristic'smart suit' with self-healing skin to protect astronauts (pictured) The cutting edge technologies are part of NASA's Innovative Advanced Concepts (NIAC) Program, which awards applicants up to $500,000 to develop their ideas. There are 12 Phase I ideas, like the smart suit, which are awarded $125,000 over nine months.

Technology is designed to improve and streamline every facet of life, and that inevitably includes areas most people would never even think about. A random issue for many, perhaps, but for the agriculture industry, a new development in this field is a big deal. Researchers from Cambridge University have developed the first robotic lettuce leaf peeling system, which not only demonstrates advances in automation, but addresses increasing food and labor demands. After harvesting, lettuces must have their outer layers removed. This is a time-consuming, menial task currently performed by farm workers, whose man power is better used elsewhere.