This paper reports a case study of an application of high-resolution agent-based modeling and simulation to pandemic response planning on a university campus. In the summer of 2020, we were tasked with a COVID-19 pandemic response project to create a detailed behavioral simulation model of the entire campus population at Binghamton University. We conceptualized this problem as an agent migration process on a multilayer transportation network, in which each layer represented a different transportation mode. As no direct data were available about people's behaviors on campus, we collected as much indirect information as possible to inform the agents' behavioral rules. Each agent was assumed to move along the shortest path between two locations within each transportation layer and switch layers at a parking lot or a bus stop, along with several other behavioral assumptions. Using this model, we conducted simulations of the whole campus population behaviors on a typical weekday, involving more than 25,000 agents. We measured the frequency of close social contacts at each spatial location and identified several busy locations and corridors on campus that needed substantial behavioral intervention. Moreover, systematic simulations with varying population density revealed that the effect of population density reduction was nonlinear, and that reducing the population density to 40-45% would be optimal and sufficient to suppress disease spreading on campus. These results were reported to the university administration and utilized in the pandemic response planning, which led to successful outcomes.

Timely, accurate, and reliable information is essential for decision-makers, emergency managers, and infrastructure operators during flood events. This study demonstrates a proposed machine learning model, MaxFloodCast, trained on physics-based hydrodynamic simulations in Harris County, offers efficient and interpretable flood inundation depth predictions. Achieving an average R-squared of 0.949 and a Root Mean Square Error of 0.61 ft on unseen data, it proves reliable in forecasting peak flood inundation depths. Validated against Hurricane Harvey and Storm Imelda, MaxFloodCast shows the potential in supporting near-time floodplain management and emergency operations. The model's interpretability aids decision-makers in offering critical information to inform flood mitigation strategies, to prioritize areas with critical facilities and to examine how rainfall in other watersheds influences flood exposure in one area. The MaxFloodCast model enables accurate and interpretable inundation depth predictions while significantly reducing computational time, thereby supporting emergency response efforts and flood risk management more effectively.

The definition of anomaly detection is the identification of an unexpected event. Real-time detection of extreme events such as wildfires, cyclones, or floods using satellite data has become crucial for disaster management. Although several earth-observing satellites provide information about disasters, satellites in the geostationary orbit provide data at intervals as frequent as every minute, effectively creating a video from space. There are many techniques that have been proposed to identify anomalies in surveillance videos; however, the available datasets do not have dynamic behavior, so we discuss an anomaly framework that can work on very high-frequency datasets to find very fast-moving anomalies. In this work, we present a diffusion model which does not need any motion component to capture the fast-moving anomalies and outperforms the other baseline methods.

Since the late 1950s, when the first artificial satellite was launched, the number of Resident Space Objects has steadily increased. It is estimated that around one million objects larger than one cm are currently orbiting the Earth, with only thirty thousand larger than ten cm being tracked. To avert a chain reaction of collisions, known as Kessler Syndrome, it is essential to accurately track and predict debris and satellites' orbits. Current approximate physics-based methods have errors in the order of kilometers for seven-day predictions, which is insufficient when considering space debris, typically with less than one meter. This failure is usually due to uncertainty around the state of the space object at the beginning of the trajectory, forecasting errors in environmental conditions such as atmospheric drag, and unknown characteristics such as the mass or geometry of the space object. Operators can enhance Orbit Prediction accuracy by deriving unmeasured objects' characteristics and improving non-conservative forces' effects by leveraging data-driven techniques, such as Machine Learning. In this survey, we provide an overview of the work in applying Machine Learning for Orbit Determination, Orbit Prediction, and atmospheric density modeling.

IBM Data and AI general manager Rob Thomas discusses AI being incorporated into the workforce. The labor market may be humming right now, but there may be a dark cloud looming ahead. Over the course of the next decade, up to 800 million jobs globally could disappear due to advances in artificial intelligence and robotics, according to research from the McKinsey Global Institute, a top consulting firm. An estimated one-third of the 2030 workforce in the U.S. may need to learn new skills and find work in new occupations. The changes won't hit the country equally.

The use of robots in the workplace has more than double in just a 12 year period, displacing 50 percent of many human workers across the US, studies have found. A new interactive map provides more detail into this'robot exposure' by highlighting the top 10 metropolitan areas threatened by this machine takeover – California being listed as number one. In addition to areas most at risk, experts found that automation is displacing younger, less-educated and minority workers at the highest rates. The study and map were developed by The Century Foundation, a progressive think tank headquartered in New York City, which looked across more than 250 metropolitan areas to understand this'robot intensity'. Los Angeles, Long Beach and Santa Ana, California were ranked number one, followed by Chicago, Naperville and Joliet in Illinois.

After receiving more than 18,300 applications, NASA has finally announced its new class of astronauts – some of whom could move on to deep-space missions aboard the Orion spacecraft. The space agency introduced 12 men and women today on stage at the Johnson Space Center in Houston, during an event that was attended by Vice President Mike Pence. Vice President Pence wished'Godspeed' to the new class, and revealed the Trump administration will be reopening the National Space Council, with Pence as a chair, in efforts to'ensure that America will never again lose our lead in space exploration and space innovation technology.' The lineup includes: Kayla Barron, Zena Cardon, Raja Chari, Matthew Dominick, Bob Hines, Dr Warren'Woody' Hoburg, Jonny Kim, Robb Kulin, Jasmin Moghbeli, Loral O'Hara, Dr Frank Rubio, Jessica Watkins The chosen few will undergo two years of training, after which they will be assigned to various missions, including research on the International Space Station, launches aboard commercial spacecraft, and even deep-space exploration. After brief introductions from Johnson Center Director Ellen Ochoa and the showing of a video from current astronauts welcoming the newcomers, Flight Operations Director Brian Kelly introduced the new candidates one by one, in alphabetical order. The lineup includes: Kayla Barron, Zena Cardon, Raja Chari, Matthew Dominick, Bob Hines, Dr Warren'Woody' Hoburg, Jonny Kim, Robb Kulin, Jasmin Moghbeli, Loral O'Hara, Dr Frank Rubio, Jessica Watkins.

For more than 40 years, no single director has more defined what we think of when we think of the movies than Steven Spielberg. To date, his feature films have grossed 4.3 billion in North America and 9.2 billion worldwide, more than any other filmmaker in history by a comfortable margin. His movies have been nominated for 128 Academy Awards and won 32, and Spielberg personally has been nominated for 16 Oscars, winning three (Best Director for Saving Private Ryan, and Best Director and Best Picture for Schindler's List). And if that's not enough, Spielberg has also presided over at least two of the most transformative changes of the last 50 years in the movie industry: the creation of the summer blockbuster (with Jaws) and the proliferation of computer-generated imagery in visual effects (with Jurassic Park). To be sure, Spielberg has not done any of this alone. With George Lucas and Harrison Ford, he helped create Indiana Jones. With Tom Hanks, he established an ongoing creative partnership (and lifelong friendship). His longtime producer Kathleen Kennedy -- the woman currently shepherding the revival of Star Wars -- got her start as Spielberg's secretary. Just about every one of his films have been tightly edited by Michael Kahn and majestically scored by John Williams. And he's collaborated with a small stable of top-flight screenwriters, including David Koepp, Richard Curtis, Eric Roth, Lawrence Kasdan, Steven Zaillian, Tom Stoppard, Tony Kushner, Joel and Ethan Coen, and, on his newest film The BFG, Melissa Mathison. When we go to a Spielberg movie, we know we will see a film made with consummate craft and exhilarating visual style -- few directors know better how to harness the tools of pure cinema. But I would argue the artistic constant that has informed Spielberg's career and success more than any other has been his seemingly limitless capacity for empathy. "Movies are like a machine that generates empathy," the late Roger Ebert once said. "It lets you understand a bit more about different hopes, aspirations, dreams and fears. It helps us to identify with the people who are sharing this journey with us." Ebert might as well have been describing Spielberg's entire career, and I know that because, like a crazy person, I screened all 29 of Spielberg's theatrical feature films in chronological order, and then ranked them from worst to best. I also skipped 1983's Twilight Zone: The Movie, since Spielberg directed just one of five segments in the film.) By my count, only three of Spielberg's movies are irredeemably bad.

Real-time optimization of the dynamic flow of vehicle traffic through a network of signalized intersections is an important practical problem. In this paper, we take a decentralized, schedule-driven coordination approach to address the challenge of achieving scalable network-wide optimization. To be locally effective, each intersection is controlled independently by an on-line scheduling agent. At each decision point, an agent constructs a schedule that optimizes movement of the observable traffic through the intersection, and uses this schedule to determine the best control action to take over the current look-ahead horizon. Decentralized coordination mechanisms, limited to interaction among direct neighbors to ensure scalability, are then layered on top of these asynchronously operating scheduling agents to promote overall performance. As a basic protocol, each agent queries for newly planned output flows from its upstream neighbors to obtain an optimistic projection of future demand. This projection may incorporate non-local influence from indirect neighbors depending on horizon length. Two additional mechanisms are then introduced to dampen ``nervousness'' and dynamic instability in the network, by adjusting locally determined schedules to better align with those of neighbors. We present simulation results on two traffic networks of tightly-coupled intersections that demonstrate the ability of our approach to establish traffic flows with lower average vehicle wait times than both a simple isolated control strategy and other contemporary coordinated control strategies that use moving average forecast or traditional offset calculation.