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.

Intel has introduced what it claims(Opens in a new window) is the world's very first real-time deepfake detector. FakeCatcher is said to have a 96% accuracy rate and works by analyzing blood flow in video pixels using innovative photoplethysmography (PPG(Opens in a new window)). Ilke Demir, the senior staff research scientist in Intel Labs, designed the FakeCatcher detector in collaboration with Umur Ciftci from the State University of New York at Binghamton. The real-time detector uses Intel hardware and software and runs on a server and interfaces through a web-based platform. FakeCatcher is different from most deep learning-based detectors in the fact that it looks for authentic clues in real videos rather than looking at raw data to spot signs of inauthenticity.

Ever wondered how come your favourite celebrities are morphed into another person and made to speak something which they have never really spoken? Well, to combat this Intel has come up with its own product which will detect these fake videos, also called deep faking. The company claims its tech called FakeCatcher will detect fake videos with a 96% accuracy rate and will give results in milliseconds. This detector has been designed by Demir in collaboration with Umur Ciftci from the State University of New York at Binghamton. Wondering how this will happen?

FakeCatcher can detect deepfakes in real time by analysing pixels in a video to look for signs of blood flow. Intel has developed an AI that it says can detect in real time whether a video has been manipulated using deepfake technology. FakeCatcher, part of the chipmaker's responsible AI work, claims to detect deepfakes within milliseconds and with a 96pc accuracy rate. "Deepfake videos are everywhere now," said Intel scientist Ilke Demir, who designed FakeCatcher with Umur Ciftci from the State University of New York at Binghamton. "You have probably already seen them; videos of celebrities doing or saying things they never actually did."

Enter increasingly affordable and sophisticated drones and miniaturized geophysical sensors. The Binghamton team's first focus: the Russian-made PFM-1 mine, a device just five inches across, made largely of plastic, and shaped like a butterfly. Designed to be dropped from the air in large numbers, they flutter gently to the ground like flocks of birds, and await the unwary. Designed mainly to maim, not kill, they are difficult to spot with a magnetometer, because they contain little metal. And because they resemble plastic toys, many children handle them, and get blown up.

Armed with a newly minted undergraduate degree in geology, Jasper Baur is in the mining business. Not those mines where we extract metals or minerals; the kind that kill and maim thousands of people every year. As a freshman at upstate New York's Binghamton University in 2016, Baur started working with two geophysics professors, Alex Nikulin and Timothy de Smet, to look into employing instrument-equipped drones to speed the slow, hazardous task of finding land mines. Baur stuck with the research all the way through college; now a grad student in volcanology at Columbia University's Lamont-Doherty Earth Observatory, he is still pursuing it. "It seemed like a really relevant and impactful use of science," he said.

In the assignment problem, items must be assigned to agents who have unit demands, based on agents' ordinal preferences. Often the goal is to design a mechanism that is both fair and efficient. In this paper, we first prove that, unfortunately, the desirable efficiency notions rank-maximality, ex-post favoring-higher-ranks, and ex-ante favoring-higher-ranks, which aim to allocate each item to agents who rank it highest over all the items, are incompatible with the desirable fairness notions strong equal treatment of equals (SETE) and sd-weak-envy-freeness (sd-WEF) simultaneously. In light of this, we propose novel properties of efficiency based on a subtly different notion to favoring higher ranks, by favoring "eagerness" for remaining items and aiming to guarantee that each item is allocated to agents who rank it highest among remaining items. We prove that the eager Boston mechanism satisfies ep-FERI and sd-WSP, and that the uniform probabilistic respecting eagerness mechanism satisfies ea-FERI. We also prove that both mechanisms satisfy SETE and sd-WEF, and show that no mechanism can satisfy stronger versions of envyfreeness and strategyproofness while simultaneously maintaining SETE, and either ep-FERI or ea-FERI. X. Guo and Y. Cao are with Key Laboratory of High Confidence Software Technologies (MOE), Department of Computer Science and Technology, Peking University, Beijing 100871, China (e-mail: guoxiaoxi@pku.edu.cn; S. Sikdar is with Department of Computer Science, Binghamton University (email: ssikdar@binghamton.edu). H. Wang is with School of Computer Science and Cyber Engineering, Guangzhou University, China, and Key Laboratory of High Confidence Software Technologies (MOE), Department of Computer Science and Technology, Peking University, Beijing 100871, China (whpxhy@pku.edu.cn). This serves as a useful model for a variety of problems where the items may be either indivisible such as houses (Shapley and Scarf, 1974), dormitory rooms (Chen and Sönmez, 2002), and school choice without priorities (Miralles, 2009); or divisible such as natural resources like land and water (Segal-Halevi, 2016), and computational resources in cloud computing (Ghodsi et al., 2011, 2012; Grandl et al., 2014).

It's making data look uncannily realistic" Recently, Bo Zhao, along with his team of researchers at the University of Washington, argued that with the widespread use of geographic information systems, Google Earth, and other satellite imagery systems, location spoofing has become much more sophisticated. Yifan Sun, a student in the UW Department of Geography, Shaozeng Zhang and Chunxue Xu of Oregon State University, and Chengbin Deng of Binghamton University, co-authored the report. Zhao, along with his team, deployed machine learning algorithms that take in the satellite images of urban areas to learn their characteristics and then impose them into the base map of another city to produce a deep fake image as output. "As technology continues to evolve, this study aims to encourage a more holistic understanding of geographic data and information so that we can demystify the question of absolute reliability of satellite images or other geospatial data. We also want to have more future-oriented thinking to take countermeasures such as fact-checking when necessary," Zhao said.

BEGIN ARTICLE PREVIEW: BINGHAMTON, NY — Emergencies, by their very nature, are hard to predict. When and where the next crime, fire or vehicle accident will happen is often a matter of random chance. What can be measured, however, is how long it takes for emergency services personnel to consider a particular incident to be resolved — for instance, suspects apprehended, flames extinguished or damaged cars removed from the street. New York City is among the large urban areas that maintain those kinds of statistics, and a team of researchers at Binghamton University, State University of New York has used deep-learning techniques to analyze the numbers and suggest improved public safety through re-allocation of resources. Arti Ramesh and Anand Seetharam — both assistant professors in the Department of Computer Science at the Thomas J. Watson College of Engineering and Applied Science — worked with PhD students Gissella Bejarano, MS &

One of the most obvious changes is in food. Dining halls are moving far beyond standard fare, working with students and local partners to devise healthy and diverse options. Some are incorporating the lessons of Silicon Valley by introducing on-demand and delivery services – with at least one using robots for those deliveries. Binghamton University has partnered with a local Indian restaurateur to offer authentic dishes, with a website that offers students directions to local halal and Asian grocers. The University of Coventry in the UK has brought in a truck that offers vegan food.