Creating written products is essential to modern life, including writings about one's identity and personal experiences. However, writing is often a difficult activity that requires extensive effort to frame the central ideas, the pursued approach to communicate the central ideas, e.g., using analogies, metaphors, or other possible means, the needed presentation structure, and the actual verbal expression. Large Language Models, a recently emerged approach in Machine Learning, can offer a significant help in reducing the effort and improving the quality of written products. This paper proposes a new computational approach to explore prompts that given as inputs to a Large Language Models can generate cues to improve the considered written products. Two case studies on improving write-ups, one based on an analogy and one on a metaphor, are also presented in the paper.

Asteroid diameters are traditionally difficult to estimate. When a direct measurement of the diameter cannot be made through either occultation or direct radar observation, the most common method is to approximate the diameter from infrared observations. Once the diameter is known, a comparison with visible light observations can be used to find the visible geometric albedo of the body. One of the largest datasets of asteroid albedos comes from the NEOWISE mission, which measured asteroid albedos both in the visible and infrared. We model these albedos as a function of proper elements available from the Asteroid Families Portal using an ensemble of neural networks. We find that both the visible and infrared geometric albedos are significantly correlated with asteroid position in the belt and occur in both asteroid families and in the background belt. We find that the ensemble's prediction reduces the average error in albedo by about 37% compared to a model that simply adopts an average albedo, with no regard for the dynamical state of the body. We then use this model to predict albedos for the half million main belt asteroids with proper elements available in the Asteroid Families Portal and provide the results in a catalog. Finally, we show that several presently categorized asteroid families exist within much larger groups of asteroids of similar albedos - this may suggest that further improvements in family identification can be made.

It has been recently recognized that the surface of sub-km asteroids in contact with the space environment is not fine-grained regolith but consists of centimeter to meter-scale rocks. Here we aim to understand how the rocky morphology of minor bodies react to the well known space erosion agents on the Moon. We deploy a neural network and map a total of ~130,000 fragmented boulders scattered across the lunar surface and visually identify a dozen different desintegration morphologies corresponding to different failure modes. We find that several fragmented boulder morphologies are equivalent to morphologies observed on asteroid Bennu, suggesting that these morphologies on the Moon and on asteroids are likely not diagnostic of their formation mechanism. Our findings suggest that the boulder fragmentation process is characterized by an internal weakening period with limited morphological signs of damage at rock scale until a sudden highly efficient impact shattering event occurs. In addition, we identify new morphologies such as breccia boulders with an advection-like erosion style. We publicly release the produced fractured boulder catalog along with this paper.

An increasingly common area of study in procedural content generation is the creation of level segments: short pieces that can be used to form larger levels. Previous work has used basic concatenation to form these larger levels. However, even if the segments themselves are completable and well-formed, concatenation can fail to produce levels that are completable and can cause broken in-game structures (e.g. malformed pipes in Mario). We show this with three tile-based games: a side-scrolling platformer, a vertical platformer, and a top-down roguelike. Additionally, we present a Markov chain and a tree search algorithm that finds a link between two level segments, which uses filters to ensure completability and unbroken in-game structures in the linked segments. We further show that these links work well for multi-segment levels. We find that this method reliably finds links between segments and is customizable to meet a designer's needs.

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In this paper we investigate Charon's craters size distribution using a deep learning model. This is motivated by the recent results of Singer et al. (2019) who, using manual cataloging, found a change in the size distribution slope of craters smaller than 12 km in diameter, translating into a paucity of small Kuiper Belt objects. These results were corroborated by Robbins and Singer (2021), but opposed by Morbidelli et al. (2021), necessitating an independent review. Our MaskRCNN-based ensemble of models was trained on Lunar, Mercurian, and Martian crater catalogues and both optical and digital elevation images. We use a robust image augmentation scheme to force the model to generalize and transfer-learn into icy objects. With no prior bias or exposure to Charon, our model find best fit slopes of q =-1.47+-0.33 for craters smaller than 10 km, and q =-2.91+-0.51 for craters larger than 15 km. These values indicate a clear change in slope around 15 km as suggested by Singer et al. (2019) and thus independently confirm their conclusions. Our slopes however are both slightly flatter than those found more recently by Robbins and Singer (2021). Our trained models and relevant codes are available online on github.com/malidib/ACID .

We are in an era of large catalogs and, thus, statistical analysis tools for large data sets, such as machine learning, play a fundamental role. One example of such a survey is the Sloan Moving Object Catalog (MOC), which lists the astrometric and photometric information of all moving objects captured by the Sloan field of view. One great advantage of this telescope is represented by its set of five filters, allowing for taxonomic analysis of asteroids by studying their colors. However, until now, the color variation produced by the change of phase angle of the object has not been taken into account. In this paper, we address this issue by using absolute magnitudes for classification. We aim to produce a new taxonomic classification of asteroids based on their magnitudes that is unaffected by variations caused by the change in phase angle. We selected 9481 asteroids with absolute magnitudes of Hg, Hi and Hz, computed from the Sloan Moving Objects Catalog using the HG12 system. We calculated the absolute colors with them. To perform the taxonomic classification, we applied a unsupervised machine learning algorithm known as fuzzy C-means. This is a useful soft clustering tool for working with {data sets where the different groups are not completely separated and there are regions of overlap between them. We have chosen to work with the four main taxonomic complexes, C, S, X, and V, as they comprise most of the known spectral characteristics. We classified a total of 6329 asteroids with more than 60% probability of belonging to the assigned taxonomic class, with 162 of these objects having been characterized by an ambiguous classification in the past. By analyzing the sample obtained in the plane Semimajor axis versus inclination, we identified 15 new V-type asteroid candidates outside the Vesta family region.

En route to a recent conference on how procurement networks are reshaping the aviation industry, I realized how amazing it was to be arriving in the historic city of Athens, the cradle of Western civilization. My enthusiasm owes not only to the city's timeless beauty, though that's reason enough to visit. Athens, it turns out, is ideally suited for such a gathering because it holds a unique place in the imagination for all of us who've ever wanted to fly. When you're fortunate enough to have as many Greek family members as I do, you learn about the ancient legends. So the fable of Icarus is well known to me.

From Harry Potter and Romeo and Juliet to the stories of Oedipus and Icarus, almost every tale told conforms to one of just six plots, researchers have claimed. A major new analysis of over 1,700 stories identified the core plots'which form the building blocks of complex narratives'. Researchers used complex data-mining to locate words linked to positive or negative emotion in each story to reveal the set of arcs. A major new analysis of over 1,700 stories identified the core plots'which form the building blocks of complex narratives'. Shown, the plot of Harry Potter and the Deathly Hallows, which researchers found has the'rise, fall rise' plot.

Automatic transfer of learned knowledge from one task or domain to another offers great potential to simplify and expedite the construction and deployment of intelligent systems. In practice however, there are many barriers to achieving this goal. In this article, we present a prototype system for the real-world context of transferring knowledge of American football from video observation to control in a game simulator. We trace an example play from the raw video through execution and adaptation in the simulator, highlighting the system's component algorithms along with issues of complexity, generality, and scale. We then conclude with a discussion of the implications of this work for other applications, along with several possible improvements.