Interstellar objects (ISOs), astronomical objects not gravitationally bound to the Sun, are likely representatives of primitive materials invaluable in understanding exoplanetary star systems. Due to their poorly constrained orbits with generally high inclinations and relative velocities, however, exploring ISOs with conventional human-in-the-loop approaches is significantly challenging. This paper presents Neural-Rendezvous -- a deep learning-based guidance and control framework for encountering any fast-moving objects, including ISOs, robustly, accurately, and autonomously in real-time. It uses pointwise minimum norm tracking control on top of a guidance policy modeled by a spectrally-normalized deep neural network, where its hyperparameters are tuned with a newly introduced loss function directly penalizing the state trajectory tracking error. We rigorously show that, even in the challenging case of ISO exploration, Neural-Rendezvous provides 1) a high probability exponential bound on the expected spacecraft delivery error; and 2) a finite optimality gap with respect to the solution of model predictive control, both of which are indispensable especially for such a critical space mission. In numerical simulations, Neural-Rendezvous is demonstrated to achieve a terminal-time delivery error of less than 0.2 km for 99% of the ISO candidates with realistic state uncertainty, whilst retaining computational efficiency sufficient for real-time implementation.

Neural networks have the ability to serve as universal function approximators, but they are not interpretable and don't generalize well outside of their training region. Both of these issues are problematic when trying to apply standard neural ordinary differential equations (neural ODEs) to dynamical systems. We introduce the polynomial neural ODE, which is a deep polynomial neural network inside of the neural ODE framework. We demonstrate the capability of polynomial neural ODEs to predict outside of the training region, as well as perform direct symbolic regression without additional tools such as SINDy.

Obesity is a global epidemic causing at least 2.8 million deaths per year. This complex disease is associated with significant socioeconomic burden, reduced work productivity, unemployment, and other social determinants of Health (SDoH) disparities. Objective: The objective of this study was to investigate the effects of SDoH on obesity prevalence among adults in Shelby County, Tennessee, USA using a geospatial machine-learning approach. Obesity prevalence was obtained from publicly available CDC 500 cities database while SDoH indicators were extracted from the U.S. Census and USDA. We examined the geographic distributions of obesity prevalence patterns using Getis-Ord Gi* statistics and calibrated multiple models to study the association between SDoH and adult obesity. Also, unsupervised machine learning was used to conduct grouping analysis to investigate the distribution of obesity prevalence and associated SDoH indicators. Results depicted a high percentage of neighborhoods experiencing high adult obesity prevalence within Shelby County. In the census tract, median household income, as well as the percentage of individuals who were black, home renters, living below the poverty level, fifty-five years or older, unmarried, and uninsured, had a significant association with adult obesity prevalence. The grouping analysis revealed disparities in obesity prevalence amongst disadvantaged neighborhoods. More research is needed that examines linkages between geographical location, SDoH, and chronic diseases. These findings, which depict a significantly higher prevalence of obesity within disadvantaged neighborhoods, and other geospatial information can be leveraged to offer valuable insights informing health decision-making and interventions that mitigate risk factors for increasing obesity prevalence.

The volume contains selected contributions from the Machine Learning Challenge "Discover the Mysteries of the Maya", presented at the Discovery Challenge Track of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021). Remote sensing has greatly accelerated traditional archaeological landscape surveys in the forested regions of the ancient Maya. Typical exploration and discovery attempts, beside focusing on whole ancient cities, focus also on individual buildings and structures. Recently, there have been several successful attempts of utilizing machine learning for identifying ancient Maya settlements. These attempts, while relevant, focus on narrow areas and rely on high-quality aerial laser scanning (ALS) data which covers only a fraction of the region where ancient Maya were once settled. Satellite image data, on the other hand, produced by the European Space Agency's (ESA) Sentinel missions, is abundant and, more importantly, publicly available. The "Discover the Mysteries of the Maya" challenge aimed at locating and identifying ancient Maya architectures (buildings, aguadas, and platforms) by performing integrated image segmentation of different types of satellite imagery (from Sentinel-1 and Sentinel-2) data and ALS (lidar) data.

Bot Image’s ProstatID program recognizes and measures the volume of the prostate gland and highlights suspicious lesions that could be harboring tumor cells.

Last week OpenAI moved DALL-E 2, the image generation tool, into Beta (the company hopes to expand its current user base to 1 million) while granting users the "the right to reprint, sell, and merchandise" images they generate with DALL-E. This is useful for users who wish to use the generated images for commercial purposes, like making illustrations for children's books. Other openly available AI image generation models face similar problems. Also, it's not clear if OpenAI violated any IP laws for just training on these Internet images and then commercializing their model. While the UK is exploring allowing commercial use of models trained on public but trademarked data, the U.S. may not follow suit.

Gizmodo is 20 years old! To celebrate the anniversary, we're looking back at some of the most significant ways our lives have been thrown for a loop by our digital tools. Like so many others after 9/11, I felt spiritually and existentially lost. It's hard to believe now, but I was a regular churchgoer at the time. Watching those planes smash into the World Trade Center woke me from my extended cerebral slumber and I haven't set foot in a church since, aside from the occasional wedding or baptism. I didn't realize it at the time, but that godawful day triggered an intrapersonal renaissance in which my passion for science and philosophy was resuscitated. My marriage didn't survive this mental reboot and return to form, but it did lead me to some very positive places, resulting in my adoption of secular Buddhism, meditation, and a decade-long stint with vegetarianism.

Artificial intelligence guru Jack Clark has written the longest, most interesting Twitter thread on AI policy that I've ever read. After a brief initial introductory tweet on August 6, Clark went on to post an additional 79 tweets in this thread. It was a real tour de force. Because I'm currently finishing up a new book on AI governance, I decided to respond to some of his thoughts on the future of governance for artificial intelligence (AI) and machine learning (ML). Clark is a leading figure in the field of AI science and AI policy today. He is the co-founder of Anthropic, an AI safety and research company, and he previously served as the Policy Director of OpenAI. So, I take seriously what he has to say on AI governance matters and really learned a lot from his tweetstorm. But I also want to push back on a few things. Specifically, several of the issues that Clark raises about AI governance are not unique to AI per se; they are broadly applicable to many other emerging technology sectors, and even some traditional ones. Below, I will refer to this as my "general critique" of Clark's tweetstorm. On the other hand, Clark correctly points to some issues that are unique to AI/ML and which really do complicate the governance of computational systems.

Asia-Pacific has embraced AI and is on track to one single goal -- leadership. The region likely recognizes the benefit of pouring heart, soul, and investment into making world-changing artificial intelligence. The response has been overwhelming. Here is what we're expecting from AI in APAC in the coming years. According to research from IDC, the spending on AI in APAC will almost double by 2025.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. In a recent closed-door meeting with leaders of the agency's counterterrorism center, the CIA's No. 2 official made clear that fighting al-Qaida and other extremist groups would remain a priority -- but that the agency's money and resources would be increasingly shifted to focusing on China. The CIA drone attack that killed al-Qaida's leader showed that fighting terrorism is hardly an afterthought. But it didn't change the message the agency's deputy director, David Cohen, delivered at that meeting weeks earlier: While the U.S. will continue to go after terrorists, the top priority is trying to better understand and counter Beijing.