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  • Day: October 6, 2020

    Paper by Yakov Nekrich Accepted for ACM-SIAM SODA21 Symposium

    A paper by Associate Professor Yakov Nekrich, Computer Science, has been accepted for the 61st ACM-SIAM Symposium on Discrete Algorithms 2021 (SODA21), which will take place virtually January 10-13, 2021.

    Nekrich is sole author of the accepted article, “New Data Structures for Orthogonal Range Reporting and Range Minima Queries.” An extended version of the paper is available for download on ArXiv.

    The annual ACM-SIAM Symposium on Discrete Algorithms (SODA) is an academic conference in the fields of algorithm design and discrete mathematics. It is considered among the top conferences for research in algorithms.

    Paper Abstract

    In this paper we present new data structures for two extensively studied variants of the orthogonal range searching problem.
    First, we describe a data structure that supports two-dimensional orthogonal range minima queries in O(n) space and O(logεn) time, where n is the number of points in the data structure and ε is an arbitrarily small positive constant. Previously known linear-space solutions for this problem require O(log1+εn) (Chazelle, 1988) or O(lognloglogn) time (Farzan et al., 2012). A modification of our data structure uses space O(nloglogn) and supports range minima queries in time O(loglogn). Both results can be extended to support three-dimensional five-sided reporting queries.

    Next, we turn to the four-dimensional orthogonal range reporting problem and present a data structure that answers queries in optimal O(logn/loglogn+k) time, where k is the number of points in the answer. This is the first data structure that achieves the optimal query time for this problem. Our results are obtained by exploiting the properties of three-dimensional shallow cuttings.

    The Society for Industrial and Applied Mathematics (SIAM) is an international community of 14,500+ individual members. Almost 500 academic, manufacturing, research and development, service and consulting organizations, government, and military organizations worldwide are institutional members.

    What Lies Ahead: Cooperative, Data-Driven Automated Driving

    Kuilin Zhang

    Associate Professor Kuilin Zhang, Civil and Environmental Engineering and affiliated associate professor, Computer Science, was featured in a recent article on Michigan Tech News. The article appears below. Link to the original article here.

    By Kelley Christensen, September 28, 2020.

    Networked data-driven vehicles can adapt to road hazards at longer range, increasing safety and preventing slowdowns.

    Vehicle manufacturers offer smart features such as lane and braking assist to aid drivers in hazardous situations when human reflexes may not be fast enough. But most options only provide immediate benefits to a single vehicle. What if entire groups of vehicles could respond? What if instead of responding solely to the vehicle immediately in front of us, our cars reacted proactively to events happening hundreds of meters ahead?

    What if, like a murmuration of starlings, our cars and trucks moved cooperatively on the road in response to each vehicle’s environmental sensors, reacting as a group to lessen traffic jams and protect the humans inside?

    This question forms the basis of Kuilin Zhang’s National Science Foundation CAREER Award research. Zhang, an associate professor of civil and environmental engineering at Michigan Technological University, has published “A distributionally robust stochastic optimization-based model predictive control with distributionally robust chance constraints for cooperative adaptive cruise control under uncertain traffic conditions” in the journal Transportation Research Part B: Methodological.

    The paper is coauthored with Shuaidong Zhao ’19, now a senior quantitative analyst at National Grid, where he continues to conduct research on the interdependency between smart grid and electric vehicle transportation systems.

    Vehicle Platoons Operate in Sync

    Creating vehicle systems adept at avoiding traffic accidents is an exercise in proving Newton’s First Law: An object in motion remains so unless acted on by an external force. Without much warning of what’s ahead, car accidents are more likely because drivers don’t have enough time to react. So what stops the car? A collision with another car or obstacle — causing injuries, damage and in the worst case, fatalities.

    But cars communicating vehicle-to-vehicle can calculate possible obstacles in the road at increasing distances — and their synchronous reactions can prevent traffic jams and car accidents.

    “On the freeway, one bad decision propagates other bad decisions. If we can consider what’s happening 300 meters in front of us, it can really improve road safety. It reduces congestion and accidents.”Kuilin Zhang

    Zhang’s research asks how vehicles connect to other vehicles, how those vehicles make decisions together based on data from the driving environment and how to integrate disparate observations into a network.

    Zhang and Zhao created a data-driven, optimization-based control model for a “platoon” of automated vehicles driving cooperatively under uncertain traffic conditions. Their model, based on the concept of forecasting the forecasts of others, uses streaming data from the modeled vehicles to predict the driving states (accelerating, decelerating or stopped) of preceding platoon vehicles. The predictions are integrated into real-time, machine-learning controllers that provide onboard sensed data. For these automated vehicles, data from controllers across the platoon become resources for cooperative decision-making. 

    CAREER Award 

    Kuilin Zhang won an NSF CAREER Award in 2019 for research on connected, autonomous vehicles and predictive modeling

    Proving-Grounds Ready

    The next phase of Zhang’s CAREER Award-supported research is to test the model’s simulations using actual connected, autonomous vehicles. Among the locations well-suited to this kind of testing is Michigan Tech’s Keweenaw Research Center, a proving ground for autonomous vehicles, with expertise in unpredictable environments.

    Ground truthing the model will enable data-driven, predictive controllers to consider all kinds of hazards vehicles might encounter while driving and create a safer, more certain future for everyone sharing the road.

    Tomorrow Needs Mobility

    Michigan Technological University is a public research university, home to more than 7,000 students from 54 countries. Founded in 1885, the University offers more than 120 undergraduate and graduate degree programs in science and technology, engineering, forestry, business and economics, health professions, humanities, mathematics, and social sciences. Our campus in Michigan’s Upper Peninsula overlooks the Keweenaw Waterway and is just a few miles from Lake Superior.

    Kuilin Zhang

    About the Researcher: Kuilin Zhang

    • Data-driven optimization and control models for connected and automated vehicles (CAVs)
    • Big traffic data analytics using machine learning
    • Mobile and crowd sensing of dynamic traffic systems
    • Dynamic network equilibrium and optimization
    • Modeling and simulation of large-scale complex systems
    • Freight logistics and supply chain systems
    • Impact of plug-in electric vehicles to smart grid and transportation network systems
    • Interdependency and resiliency of large-scale networked infrastructure systems
    • Vehicular Ad-hoc Networks (VANETs)
    • Smart Cities
    • Cyber-Physical Systems

    Guy Hembroff Presents Invited Talk at Bahiana Medical School, Brazil

    Associate Professor Guy Hembroff, director of Michigan Tech’s Health Informatics graduate program, presented an invited virtual talk to physicians, residents, and medical students at the Bahiana Medical School, Salvador, Brazil, on September 25, 2020.

    Hembroff spoke about, “The Challenges and Opportunities of Artificial Intelligence in Disease Prevention and Monitoring.”

    BAHIANA (Bahia School of Medicine and Public Health) is a private, nonprofit, educational, cultural, scientific and healthcare institution. Its main purpose is “teaching, research and the spread of knowledge and special services in the fields of health, science and culture in general.” Learn more here.