Suchergebnisse

Waffen hatte die VR China schon während der 60er und 70er Jahre in alle Welt geliefert, um den dort mit "revolutionären" Ansprüchen angetretenen Parteien unter die Arme zu greifen. Militärische Ausrüstungen gingen damals beispielsweise an die "Bewaffneten Volksstreitkräfte'' in Thailand, auf den Philippinen, auf der malaiischen Halbinsel oder aber ins indische Naxalbari - um hier nur einige Beispiele zu nennen. Systematisch unterstützt wurden aber auch die Streitkräfte Nordkoreas während des Koreakriegs und Nordvietnams während des Ersten und Zweiten Indochinakriegs. Bei all diesen Lieferungen standen ideologische und politische Erwägungen im Vordergrund.

Yes, it's already time to be worried — very worried. As the wars in Ukraine and Gaza have shown, the earliest drone equivalents of "killer robots" have made it onto the battlefield and proved to be devastating weapons. But at least they remain largely under human control. Imagine, for a moment, a world of war in which those aerial drones (or their ground and sea equivalents) controlled us, rather than vice-versa. Then we would be on a destructively different planet in a fashion that might seem almost unimaginable today. Sadly, though, it's anything but unimaginable, given the work on artificial intelligence (AI) and robot weaponry that the major powers have already begun. Now, let me take you into that arcane world and try to envision what the future of warfare might mean for the rest of us.By combining AI with advanced robotics, the U.S. military and those of other advanced powers are already hard at work creating an array of self-guided "autonomous" weapons systems — combat drones that can employ lethal force independently of any human officers meant to command them. Called "killer robots" by critics, such devices include a variety of uncrewed or "unmanned" planes, tanks, ships, and submarines capable of autonomous operation. The U.S. Air Force, for example, is developing its "collaborative combat aircraft," an unmanned aerial vehicle (UAV) intended to join piloted aircraft on high-risk missions. The Army is similarly testing a variety of autonomous unmanned ground vehicles (UGVs), while the Navy is experimenting with both unmanned surface vessels (USVs) and unmanned undersea vessels (UUVs, or drone submarines). China, Russia, Australia, and Israel are also working on such weaponry for the battlefields of the future.The imminent appearance of those killing machines has generated concern and controversy globally, with some countries already seeking a total ban on them and others, including the U.S., planning to authorize their use only under human-supervised conditions. In Geneva, a group of states has even sought to prohibit the deployment and use of fully autonomous weapons, citing a 1980 U.N. treaty, the Convention on Certain Conventional Weapons, that aims to curb or outlaw non-nuclear munitions believed to be especially harmful to civilians. Meanwhile, in New York, the U.N. General Assembly held its first discussion of autonomous weapons last October and is planning a full-scale review of the topic this coming fall.For the most part, debate over the battlefield use of such devices hinges on whether they will be empowered to take human lives without human oversight. Many religious and civil society organizations argue that such systems will be unable to distinguish between combatants and civilians on the battlefield and so should be banned in order to protect noncombatants from death or injury, as is required by international humanitarian law. American officials, on the other hand, contend that such weaponry can be designed to operate perfectly well within legal constraints.However, neither side in this debate has addressed the most potentially unnerving aspect of using them in battle: the likelihood that, sooner or later, they'll be able to communicate with each other without human intervention and, being "intelligent," will be able to come up with their own unscripted tactics for defeating an enemy — or something else entirely. Such computer-driven groupthink, labeled "emergent behavior" by computer scientists, opens up a host of dangers not yet being considered by officials in Geneva, Washington, or at the U.N.For the time being, most of the autonomous weaponry being developed by the American military will be unmanned (or, as they sometimes say, "uninhabited") versions of existing combat platforms and will be designed to operate in conjunction with their crewed counterparts. While they might also have some capacity to communicate with each other, they'll be part of a "networked" combat team whose mission will be dictated and overseen by human commanders. The Collaborative Combat Aircraft, for instance, is expected to serve as a "loyal wingman" for the manned F-35 stealth fighter, while conducting high-risk missions in contested airspace. The Army and Navy have largely followed a similar trajectory in their approach to the development of autonomous weaponry.The Appeal of Robot "Swarms"However, some American strategists have championed an alternative approach to the use of autonomous weapons on future battlefields in which they would serve not as junior colleagues in human-led teams but as coequal members of self-directed robot swarms. Such formations would consist of scores or even hundreds of AI-enabled UAVs, USVs, or UGVs — all able to communicate with one another, share data on changing battlefield conditions, and collectively alter their combat tactics as the group-mind deems necessary."Emerging robotic technologies will allow tomorrow's forces to fight as a swarm, with greater mass, coordination, intelligence and speed than today's networked forces," predicted Paul Scharre, an early enthusiast of the concept, in a 2014 report for the Center for a New American Security (CNAS). "Networked, cooperative autonomous systems," he wrote then, "will be capable of true swarming — cooperative behavior among distributed elements that gives rise to a coherent, intelligent whole."As Scharre made clear in his prophetic report, any full realization of the swarm concept would require the development of advanced algorithms that would enable autonomous combat systems to communicate with each other and "vote" on preferred modes of attack. This, he noted, would involve creating software capable of mimicking ants, bees, wolves, and other creatures that exhibit "swarm" behavior in nature. As Scharre put it, "Just like wolves in a pack present their enemy with an ever-shifting blur of threats from all directions, uninhabited vehicles that can coordinate maneuver and attack could be significantly more effective than uncoordinated systems operating en masse."In 2014, however, the technology needed to make such machine behavior possible was still in its infancy. To address that critical deficiency, the Department of Defense proceeded to fund research in the AI and robotics field, even as it also acquired such technology from private firms like Google and Microsoft. A key figure in that drive was Robert Work, a former colleague of Paul Scharre's at CNAS and an early enthusiast of swarm warfare. Work served from 2014 to 2017 as deputy secretary of defense, a position that enabled him to steer ever-increasing sums of money to the development of high-tech weaponry, especially unmanned and autonomous systems.From Mosaic to ReplicatorMuch of this effort was delegated to the Defense Advanced Research Projects Agency (DARPA), the Pentagon's in-house high-tech research organization. As part of a drive to develop AI for such collaborative swarm operations, DARPA initiated its "Mosaic" program, a series of projects intended to perfect the algorithms and other technologies needed to coordinate the activities of manned and unmanned combat systems in future high-intensity combat with Russia and/or China."Applying the great flexibility of the mosaic concept to warfare," explained Dan Patt, deputy director of DARPA's Strategic Technology Office, "lower-cost, less complex systems may be linked together in a vast number of ways to create desired, interwoven effects tailored to any scenario. The individual parts of a mosaic are attritable [dispensable], but together are invaluable for how they contribute to the whole."This concept of warfare apparently undergirds the new "Replicator" strategy announced by Deputy Secretary of Defense Kathleen Hicks just last summer. "Replicator is meant to help us overcome [China's] biggest advantage, which is mass. More ships. More missiles. More people," she told arms industry officials last August. By deploying thousands of autonomous UAVs, USVs, UUVs, and UGVs, she suggested, the U.S. military would be able to outwit, outmaneuver, and overpower China's military, the People's Liberation Army (PLA). "To stay ahead, we're going to create a new state of the art… We'll counter the PLA's mass with mass of our own, but ours will be harder to plan for, harder to hit, harder to beat."To obtain both the hardware and software needed to implement such an ambitious program, the Department of Defense is now seeking proposals from traditional defense contractors like Boeing and Raytheon as well as AI startups like Anduril and Shield AI. While large-scale devices like the Air Force's Collaborative Combat Aircraft and the Navy's Orca Extra-Large UUV may be included in this drive, the emphasis is on the rapid production of smaller, less complex systems like AeroVironment's Switchblade attack drone, now used by Ukrainian troops to take out Russian tanks and armored vehicles behind enemy lines.At the same time, the Pentagon is already calling on tech startups to develop the necessary software to facilitate communication and coordination among such disparate robotic units and their associated manned platforms. To facilitate this, the Air Force asked Congress for $50 million in its fiscal year 2024 budget to underwrite what it ominously enough calls Project VENOM, or "Viper Experimentation and Next-generation Operations Model." Under VENOM, the Air Force will convert existing fighter aircraft into AI-governed UAVs and use them to test advanced autonomous software in multi-drone operations. The Army and Navy are testing similar systems.When Swarms Choose Their Own PathIn other words, it's only a matter of time before the U.S. military (and presumably China's, Russia's, and perhaps those of a few other powers) will be able to deploy swarms of autonomous weapons systems equipped with algorithms that allow them to communicate with each other and jointly choose novel, unpredictable combat maneuvers while in motion. Any participating robotic member of such swarms would be given a mission objective ("seek out and destroy all enemy radars and anti-aircraft missile batteries located within these [specified] geographical coordinates") but not be given precise instructions on how to do so. That would allow them to select their own battle tactics in consultation with one another. If the limited test data we have is anything to go by, this could mean employing highly unconventional tactics never conceived for (and impossible to replicate by) human pilots and commanders.The propensity for such interconnected AI systems to engage in novel, unplanned outcomes is what computer experts call "emergent behavior." As ScienceDirect, a digest of scientific journals, explains it, "An emergent behavior can be described as a process whereby larger patterns arise through interactions among smaller or simpler entities that themselves do not exhibit such properties." In military terms, this means that a swarm of autonomous weapons might jointly elect to adopt combat tactics none of the individual devices were programmed to perform — possibly achieving astounding results on the battlefield, but also conceivably engaging in escalatory acts unintended and unforeseen by their human commanders, including the destruction of critical civilian infrastructure or communications facilities used for nuclear as well as conventional operations.At this point, of course, it's almost impossible to predict what an alien group-mind might choose to do if armed with multiple weapons and cut off from human oversight. Supposedly, such systems would be outfitted with failsafe mechanisms requiring that they return to base if communications with their human supervisors were lost, whether due to enemy jamming or for any other reason. Who knows, however, how such thinking machines would function in demanding real-world conditions or if, in fact, the group-mind would prove capable of overriding such directives and striking out on its own.What then? Might they choose to keep fighting beyond their preprogrammed limits, provoking unintended escalation — even, conceivably, of a nuclear kind? Or would they choose to stop their attacks on enemy forces and instead interfere with the operations of friendly ones, perhaps firing on and devastating them (as Skynet does in the classic science fiction Terminator movie series)? Or might they engage in behaviors that, for better or infinitely worse, are entirely beyond our imagination?Top U.S. military and diplomatic officials insist that AI can indeed be used without incurring such future risks and that this country will only employ devices that incorporate thoroughly adequate safeguards against any future dangerous misbehavior. That is, in fact, the essential point made in the "Political Declaration on Responsible Military Use of Artificial Intelligence and Autonomy" issued by the State Department in February 2023. Many prominent security and technology officials are, however, all too aware of the potential risks of emergent behavior in future robotic weaponry and continue to issue warnings against the rapid utilization of AI in warfare.Of particular note is the final report that the National Security Commission on Artificial Intelligence issued in February 2021. Co-chaired by Robert Work (back at CNAS after his stint at the Pentagon) and Eric Schmidt, former CEO of Google, the commission recommended the rapid utilization of AI by the U.S. military to ensure victory in any future conflict with China and/or Russia. However, it also voiced concern about the potential dangers of robot-saturated battlefields."The unchecked global use of such systems potentially risks unintended conflict escalation and crisis instability," the report noted. This could occur for a number of reasons, including "because of challenging and untested complexities of interaction between AI-enabled and autonomous weapon systems [that is, emergent behaviors] on the battlefield." Given that danger, it concluded, "countries must take actions which focus on reducing risks associated with AI-enabled and autonomous weapon systems."When the leading advocates of autonomous weaponry tell us to be concerned about the unintended dangers posed by their use in battle, the rest of us should be worried indeed. Even if we lack the mathematical skills to understand emergent behavior in AI, it should be obvious that humanity could face a significant risk to its existence, should killing machines acquire the ability to think on their own. Perhaps they would surprise everyone and decide to take on the role of international peacekeepers, but given that they're being designed to fight and kill, it's far more probable that they might simply choose to carry out those instructions in an independent and extreme fashion.If so, there could be no one around to put an R.I.P. on humanity's gravestone.This article was republished with permission from Tom Dispatch

Ali Abbas (" From the Editors… ") is an associate professor in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana–Champaign. He received an M.S. in electrical engineering (1998), an M.S. in engineering economic systems and operations research (2001), a Ph.D. in management science and engineering (2003), and a Ph.D. (minor) in electrical engineering, all from Stanford University. He worked as a lecturer in the Department of Management Science and Engineering at Stanford and worked in Schlumberger Oilfield Services from 1991 to 1997, where he held several international positions in wireline logging, operations management, and international training. He has also worked on several consulting projects for mergers and acquisitions in California, and cotaught several executive seminars on decision analysis at Strategic Decisions Group in Menlo Park, California. His research interests include utility theory, decision making with incomplete information and preferences, dynamic programming, and information theory. Dr. Abbas is a senior member of the Institute of Electrical and Electronic Engineers (IEEE) and a member of the Institute for Operations Research and the Management Sciences (INFORMS). He is also an associate editor for Decision Analysis and Operations Research and coeditor of the DA column in education for Decision Analysis Today. Address: Department of Industrial and Enterprise Systems Engineering, College of Engineering, University of Illinois at Urbana–Champaign, 117 Transportation Building, MC-238, 104 South Mathews Avenue, Urbana, IL 61801; e-mail: aliabbas@uiuc.edu . Matthew D. Bailey (" Eliciting Patients' Revealed Preferences: An Inverse Markov Decision Process Approach ") is an assistant professor of business analytics and operations in the School of Management at Bucknell University, and he is an adjunct research investigator with Geisinger Health System. He received his Ph.D. in industrial and operations engineering from the University of Michigan. His primary research interest is in sequential decision making under uncertainty with applications to health-care operations and medical decision making. He is a member of the Institute for Operations Research and the Management Sciences (INFORMS) and the Institute of Industrial Engineers (IIE). Address: School of Management, Bucknell University, 308 Taylor Hall, Lewisburg, PA 17837; e-mail: matt.bailey@bucknell.edu . Anthony M. Barrett (" Cost Effectiveness of On-Site Chlorine Generation for Chlorine Truck Attack Prevention ") is a risk analyst at ABS Consulting in Arlington, Virginia. He holds a Ph.D. in engineering and public policy from Carnegie Mellon University, and he also was a postdoctoral research associate at the Homeland Security Center for Risk and Economic Analysis of Terrorism Events (CREATE) at the University of Southern California. His research interests include risk analysis, risk management, and public policies in a wide variety of areas, including terrorism, hazardous materials, energy and the environment, and natural hazards. Address: ABS Consulting, 1525 Wilson Boulevard, Suite 625, Arlington, VA 22209; e-mail: abarrett@absconsulting.com . Manel Baucells (" From the Editors… ") is a full professor at the Department of Economics and Business of Universitat Pompeu Fabra, Barcelona. He was an associate professor and head of the Managerial Decision Sciences Department at IESE Business School. He earned his Ph.D. in management from the University of California, Los Angeles (UCLA) and holds a degree in mechanical engineering from Polytechnic University of Catalonia (UPC). His research and consulting activities cover multiple areas of decision making including group decisions, consumer decisions, uncertainty, complexity, and psychology. He acts as associate editor for the top journals Management Science, Operations Research, and Decision Analysis. He has received various prizes and grants for his research. In 2001, he won the student paper competition of the Decision Analysis Society. He is the only IESE professor having won both the Excellence Research Award and the Excellence Teaching Award. He has been visiting professor at Duke University, UCLA, London Business School, and Erasmus University. Address: Universitat Pompeu Fabra, Ramon Trias Fargas 25-27, 08005 Barcelona, Spain; e-mail: manel.baucells@upf.edu . J. Eric Bickel (" Scoring Rules and Decision Analysis Education ") is an assistant professor in both the Operations Research/Industrial Engineering Group (Department of Mechanical Engineering) and the Department of Petroleum and Geosystems Engineering at the University of Texas at Austin. In addition, Professor Bickel is a fellow in both the Center for International Energy and Environmental Policy and the Center for Petroleum Asset Risk Management. He holds an M.S. and Ph.D. from the Department of Engineering-Economic Systems at Stanford University and a B.S. in mechanical engineering with a minor in economics from New Mexico State University. His research interests include the theory and practice of decision analysis and its application in the energy and climate-change arenas. His research has addressed the modeling of probabilistic dependence, value of information, scoring rules, calibration, risk preference, education, decision making in sports, and climate engineering as a response to climate change. Prior to joining the University of Texas at Austin, Professor Bickel was an assistant professor at Texas A&M University and a senior engagement manager for Strategic Decisions Group. He has consulted around the world in a range of industries, including oil and gas, electricity generation/transmission/delivery, energy trading and marketing, commodity and specialty chemicals, life sciences, financial services, and metals and mining. Address: Graduate Program in Operations Research, The University of Texas at Austin, 1 University Station, C2200, Austin, TX 78712-0292; e-mail: ebickel@mail.utexas.edu . Vicki M. Bier (" From the Editors… ") holds a joint appointment as a professor in the Department of Industrial and Systems Engineering and the Department of Engineering Physics at the University of Wisconsin–Madison, where she has directed the Center for Human Performance and Risk Analysis (formerly the Center for Human Performance in Complex Systems) since 1995. She has more than 20 years of experience in risk analysis for the nuclear power, chemical, petrochemical, and aerospace industries. Before returning to academia, she spent seven years as a consultant at Pickard, Lowe and Garrick, Inc. While there, her clients included the U.S. Nuclear Regulatory Commission, the U.S. Department of Energy, and a number of nuclear utilities, and she prepared testimony for Atomic Safety and Licensing Board hearings on the safety of the Indian Point nuclear power plants. Dr. Bier's current research focuses on applications of risk analysis and related methods to problems of security and critical infrastructure protection, under support from the Department of Homeland Security. Dr. Bier received the Women's Achievement Award from the American Nuclear Society in 1993, and was elected a Fellow of the Society for Risk Analysis in 1996, from which she received the Distinguished Achievement Award in 2007. She has written a number of papers and book chapters related to uncertainty analysis and decision making under uncertainty, and is the author of two scholarly review articles on risk communication. She served as the engineering editor for Risk Analysis from 1997 through 2001, and has served as a councilor of both the Society for Risk Analysis and the Decision Analysis Society, for which she is currently vice president and president elect. Dr. Bier has also served as a member of both the Radiation Advisory Committee and the Homeland Security Advisory Committee of the U.S. Environmental Protection Agency's Science Advisory Board. Address: Department of Industrial and Systems Engineering, University of Wisconsin–Madison, 1513 University Avenue, Madison, WI 53706; e-mail: bier@engr.wisc.edu . Samuel E. Bodily (" Darden's Luckiest Student: Lessons from a High-Stakes Risk Experiment ") is the John Tyler Professor of Business Administration at the University of Virginia's Darden School of Business and has published textbooks and more than 40 articles in journals ranging from Harvard Business Review to Management Science. His publications relate to decision and risk analysis, forecasting, strategy modeling, revenue management, and eStrategy. He has edited special issues of Interfaces on decision and risk analysis and strategy modeling and analysis. Professor Bodily has published well over 100 cases, including a couple of the 10 best-selling cases at Darden. He received the Distinguished Casewriter Wachovia Award from Darden in 2005 and three other best case or research Wachovia awards. He is faculty leader for an executive program on Strategic Thinking and Action. He is the course head of, and teaches in, a highly valued first-year MBA course in decision analysis, has a successful second-year elective on Management Decision Models, and has taught eStrategy and Strategy. He is a past winner of the Decision Sciences International Instructional Award and has served as chair of the INFORMS Decision Analysis Society. He has taught numerous executive education programs for Darden and private companies, has consulted widely for business and government entities, and has served as an expert witness. Professor Bodily was on the faculties of MIT Sloan School of Management and Boston University and has been a visiting professor at INSEAD Singapore, Stanford University, and the University of Washington. He has a Ph.D. degree and an S.M. degree from the Massachusetts Institute of Technology and a B.S. degree in physics from Brigham Young University. Address: Darden School of Business, 100 Darden Boulevard, Charlottesville, VA 22903; e-mail: bodilys@virginia.edu . David Budescu (" From the Editors… ") is the Anne Anastasi Professor of Psychometrics and Quantitative Psychology at Fordham University. He held positions at the University of Illinois and the University of Haifa, and visiting positions at Carnegie Mellon University, University of Gotheborg, the Kellog School at Northwestern University, the Hebrew University, and the Israel Institute of Technology (Technion). His research is in the areas of human judgment, individual and group decision making under uncertainty and with incomplete and vague information, and statistics for the behavioral and social sciences. He is or was on the editorial boards of Applied Psychological Measurement; Decision Analysis; Journal of Behavioral Decision Making; Journal of Mathematical Psychology; Journal of Experimental Psychology: Learning, Memory and Cognition (2000–2003); Multivariate Behavioral Research; Organizational Behavior and Human Decision Processes (1992–2002); and Psychological Methods (1996–2000). He is past president of the Society for Judgment and Decision Making (2000–2001), fellow of the Association for Psychological Science, and an elected member of the Society of Multivariate Experimental Psychologists. Address: Department of Psychology, Fordham University, Bronx, New York, NY 10458; e-mail: budescu@fordham.edu . John C. Butler (" From the Editors… ") is a clinical associate professor of finance and the academic director of the Energy Management and Innovation Center in the McCombs School of Business at the University of Texas at Austin, and the secretary/treasurer of the INFORMS Decision Analysis Society. Butler received his Ph.D. in management science and information systems from the University of Texas in 1998. His research interests involve the use of decision science models to support decision making, with a particular emphasis on decision and risk analysis models with multiple performance criteria. Butler has consulted with a number of organizations regarding the application of decision analysis tools to a variety of practical problems. Most of his consulting projects involve use of Visual Basic for Applications and Excel to implement complex decision science models in a user-friendly format. Address: Center for Energy Management and Innovation, McCombs School of Business, The University of Texas at Austin, Austin, TX 78712-1178; e-mail: john.butler2@mccombs.utexas.edu . Philippe Delquié (" From the Editors… ") is an associate professor of decision sciences at the George Washington University and holds a Ph.D. from the Massachusetts Institute of Technology. Professor Delquié's teaching and research are in decision, risk, and multicriteria analysis. His work focuses on the interplay of behavioral and normative theories of choice, with the aim of improving managerial decision making and risk taking. His research addresses issues in preference assessment, value of information, nonexpected utility models of choice under risk, and risk measures. Prior to joining the George Washington University, he held academic appointments at INSEAD, the University of Texas at Austin, and École Normale Supérieure, France, and visiting appointments at Duke University's Fuqua School of Business. Address: Department of Decision Sciences, George Washington University, Funger Hall, Suite 415, Washington, DC 20052; e-mail: delquie@gwu.edu . Zeynep Erkin (" Eliciting Patients' Revealed Preferences: An Inverse Markov Decision Process Approach ") is a Ph.D. candidate in the Department of Industrial Engineering at the University of Pittsburgh. She received her M.S. and B.S. degrees in industrial engineering from the University of Pittsburgh and Middle East Technical University, Turkey, in 2008 and 2006, respectively. Her research interests include maintenance optimization and medical decision making. Address: Department of Industrial Engineering, University of Pittsburgh, 3600 O'Hara Street, Pittsburgh, PA 15261; e-mail: zee2@pitt.edu . Peter I. Frazier (" Paradoxes in Learning and the Marginal Value of Information ") is an assistant professor in the School of Operations Research and Information Engineering at Cornell University. He received a Ph.D. in operations research and financial engineering from Princeton University in 2009. His research interest is in the optimal acquisition of information, with applications in simulation, medicine, operations management, neuroscience, and information retrieval. He teaches courses in simulation and statistics. Address: School of Operations Research and Information Engineering, Cornell University, Ithaca, NY 14853; e-mail: pf98@cornell.edu . L. Robin Keller (" From the Editors… ") is a professor of operations and decision technologies in the Merage School of Business at the University of California, Irvine. She received her Ph.D. and M.B.A. in management science and her B.A. in mathematics from the University of California, Los Angeles. She has served as a program director for the Decision, Risk, and Management Science Program of the U.S. National Science Foundation (NSF). Her research is on decision analysis and risk analysis for business and policy decisions and has been funded by NSF and the U.S. Environmental Protection Agency. Her research interests cover multiple-attribute decision making, riskiness, fairness, probability judgments, ambiguity of probabilities or outcomes, risk analysis (for terrorism, environmental, health, and safety risks), time preferences, problem structuring, cross-cultural decisions, and medical decision making. She is currently Editor-in-Chief of Decision Analysis, published by the Institute for Operations Research and the Management Sciences (INFORMS). She is a Fellow of INFORMS and has held numerous roles in INFORMS, including board member and chair of the INFORMS Decision Analysis Society. She is a recipient of the George F. Kimball Medal from INFORMS. She has served as the decision analyst on three National Academy of Sciences committees. Address: The Paul Merage School of Business, University of California, Irvine, Irvine, CA 92697-3125; e-mail: lrkeller@uci.edu . Lisa M. Maillart (" Eliciting Patients' Revealed Preferences: An Inverse Markov Decision Process Approach ") is an associate professor in the Industrial Engineering Department at the University of Pittsburgh. Prior to joining the faculty at the University of Pittsburgh, she served on the faculty of the Department of Operations in the Weatherhead School of Management at Case Western Reserve University. She received her M.S. and B.S. in industrial and systems engineering from Virginia Tech, and her Ph.D. in industrial and operations engineering from the University of Michigan. Her primary research interest is in sequential decision making under uncertainty, with applications in medical decision making and maintenance optimization. She is a member of the Institute for Operations Research and the Management Sciences (INFORMS), the Society of Medical Decision Making (SMDM), and the Institute of Industrial Engineers (IIE). Address: Department of Industrial Engineering, University of Pittsburgh, 3600 O'Hara Street, Pittsburgh, PA 15261; e-mail: maillart@pitt.edu . Jason R. W. Merrick (" From the Editors… ") is an associate professor in the Department of Statistical Sciences and Operations Research at Virginia Commonwealth University. He has a D.Sc. in operations research from the George Washington University. He teaches courses in decision analysis, risk analysis, and simulation. His research is primarily in the area of decision analysis and Bayesian statistics. He has worked on projects ranging from assessing maritime oil transportation and ferry system safety, the environmental health of watersheds, and optimal replacement policies for rail tracks and machine tools, and he has received grants from the National Science Foundation, the Federal Aviation Administration, the United States Coast Guard, the American Bureau of Shipping, British Petroleum, and Booz Allen Hamilton, among others. He has also performed training for Infineon Technologies, Wyeth Pharmaceuticals, and Capital One Services. He is an associate editor for Decision Analysis and Operations Research. He is the information officer for the Decision Analysis Society. Address: Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA 23284; e-mail: jrmerric@vcu.edu . Phillip E. Pfeifer (" Darden's Luckiest Student: Lessons from a High-Stakes Risk Experiment ") is the Richard S. Reynolds Professor of Business at the University of Virginia's Darden School of Business, where he teaches courses in decision analysis and direct marketing. A graduate of Lehigh University and the Georgia Institute of Technology, his teaching has won student awards and has been recognized in Business Week's Guide to the Best Business Schools. He is an active researcher in the areas of decision making and direct marketing, and he currently serves on the editorial review board of the Journal of Interactive Marketing, which named him their best reviewer of 2008. In 2004 he was recognized as the Darden School's faculty leader in terms of external case sales, and in 2006 he coauthored a managerial book, Marketing Metrics: 50+ Metrics Every Executive Should Master, published by Wharton School Publishing, which was named best marketing book of the year by Strategy + Business. Address: Darden School of Business; 100 Darden Boulevard; Charlottesville, VA 22903; e-mail: pfeiferp@virginia.edu . Warren B. Powell (" Paradoxes in Learning and the Marginal Value of Information ") is a professor in the Department of Operations Research and Financial Engineering at Princeton University, where he has taught since 1981. He is the director of CASTLE Laboratory (Princeton University), which specializes in the development of stochastic optimization models and algorithms with applications in transportation and logistics, energy, health, and finance. The author or coauthor of more than 160 refereed publications, he is an INFORMS Fellow, and the author of Approximate Dynamic Programming: Solving the Curses of Dimensionality, published by John Wiley and Sons. His primary research interests are in approximate dynamic programming for high-dimensional applications and optimal learning (the efficient collection of information), and their application in energy systems analysis and transportation. He is a recipient of the Wagner prize and has twice been a finalist in the Edelman competition. He has also served in a variety of editorial and administrative positions for INFORMS, including INFORMS Board of Directors, area editor for Operations Research, president of the Transportation Science Section, and numerous prize and administrative committees. Address: Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08544; e-mail: powell@princeton.edu . Mark S. Roberts (" Eliciting Patients' Revealed Preferences: An Inverse Markov Decision Process Approach "), M.D., M.P.P., is professor and chair of health policy and management, and he holds secondary appointments in medicine, industrial engineering, and clinical and translational science. A practicing general internist, he has conducted research in decision analysis and the mathematical modeling of disease for more than 25 years, and he has expertise in cost effectiveness analysis, mathematical optimization and simulation, and the measurement and inclusion of patient preferences into decision problems. He has used decision analysis to examine clinical, costs, policy and allocation questions in liver transplantation, vaccination strategies, operative interventions, and the use of many medications. His recent research has concentrated in the use of mathematical methods from operations research and management science, including Markov decision processes, discrete-event simulation, and integer programming, to problems in health care. Address: Department of Health Policy and Management, University of Pittsburgh, Graduate School of Public Health, 130 De Soto Street, Pittsburgh, PA 15261; e-mail: robertsm@upmc.edu . Ahti Salo (" From the Editors… ") is a professor of systems analysis at the Systems Analysis Laboratory of Aalto University. His research interests include topics in portfolio decision analysis, multicriteria decision making, risk management, efficiency analysis, and technology foresight. He is currently president of the Finnish Operations Research Society (FORS) and represents Europe and the Middle East in the INFORMS International Activities Committee. Professor Salo has been responsible for the methodological design and implementation of numerous high-impact decision and policy processes, including FinnSight 2015, the national foresight exercise of the Academy of Finland and the National Funding Agency for Technology and Innovations (Tekes). Address: Aalto University, Systems Analysis Laboratory, P.O. Box 11100, 00076 Aalto, Finland; e-mail: ahti.salo@tkk.fi . Andrew J. Schaefer (" Eliciting Patients' Revealed Preferences: An Inverse Markov Decision Process Approach ") is an associate professor of industrial engineering and Wellington C. Carl Fellow at the University of Pittsburgh. He has courtesy appointments in bioengineering, medicine, and clinical and translational science. He received his Ph.D. in industrial and systems engineering from Georgia Tech in 2000. His research interests include the application of stochastic optimization methods to health-care problems, as well as stochastic optimization techniques, in particular, stochastic integer programming. He is interested in patient-oriented decision making in contexts such as end-stage liver disease, HIV/AIDS, sepsis, and diabetes. He also models health-care systems, including operating rooms and intensive-care units. He is an associate editor for INFORMS Journal on Computing and IIE Transactions. Address: Department of Industrial Engineering, University of Pittsburgh, 3600 O'Hara Street, Pittsburgh, PA 15261; e-mail: Schaefer@pitt.edu . George Wu (" From the Editors… ") has been on the faculty of the University of Chicago Booth School of Business since September 1997. His degrees include A.B. (applied mathematics, 1985), S.M. (applied mathematics, 1987), and Ph.D. (decision sciences, 1991), all from Harvard University. Prior to joining the faculty at the University of Chicago, Professor Wu was on the faculty at Harvard Business School. Wu worked as a decision analyst at Procter & Gamble prior to starting graduate school. His research interests include descriptive and prescriptive aspects of decision making, in particular, decision making involving risk, cognitive biases in bargaining and negotiation, and managerial and organizational decision making. Professor Wu is a coordinating editor for Theory and Decision, an advisory editor for Journal of Risk and Uncertainty, on the editorial boards of Decision Analysis and Journal of Behavioral Decision Making, and a former department editor of Management Science. Address: Booth School of Business, University of Chicago, 5807 South Woodlawn Avenue, Chicago, IL 60637; e-mail: wu@chicagobooth.edu .

With few exceptions, most soldiers do not wish for death on the battlefield.While some warrior cultures, like the Norse, revered dying in battle as an honourable end, and some jihadists today believe in heavenly rewards for martyrs, these are outliers. The reality is that the prospect of being shot or blown to pieces is terrifying, making recruitment a persistent challenge.A recent BBC article highlighted the increasing difficulty of recruiting new soldiers in Ukraine. After two-and-a-half years of war and more than 500,000 Russian and Ukrainian casualties, volunteers are scarce.Consequently, Ukraine introduced a law requiring all men aged 25 to 60 to register their details in an electronic database for potential conscription. Conscription officers actively seek those avoiding registration, driving many into hiding.In Odesa, the feared mobilization squads are known for pulling people off buses and from train stations, taking them directly to enlistment centres. These reluctant recruits understandably fear becoming another statistic in the front line "meat grinder" with Russia.Throughout history, rulers and politicians have faced the challenge of convincing ordinary citizens to enlist for war. How do leaders persuade their populace to take up arms and risk their lives? As societies have become better educated and informed, these tactics have evolved. Leaders often appeal to extreme nationalism, dehumanize the enemy, and create an atmosphere of existential threats, false flags, and outright lies.Consider Putin's "Denazification" of Ukraine, Israel's claims of decapitated babies, and America's claim of weapons of mass destruction (WMDs). Not to mention the excuse of needing to protect citizens through regime change, as seen in Libya. The truth usually emerges, but often long after the damage is done.Notable examples include the Pentagon Papers scandal in 1971, which revealed significant information about the Gulf of Tonkin incident, showing that the U.S. government had misrepresented the events that led to the escalation of the Vietnam War, and the unfounded WMD claims that led to the invasion of Iraq.Convincing the populace is one thing, but recruiting soldiers requires a deeper indoctrination. Training recruits to follow orders without question involves repetitive military drills. These drills condition recruits to respond to commands promptly and without hesitation. As a soldier, you're not expected to judge the morality of your actions; you execute orders precisely as given. If this indoctrination fails, there is always the threat of court-martial, imprisonment, or even facing a firing squad.That said, given the effort required to craft narratives, fear-mongering, lies, and indoctrination necessary to mobilize the populace for war, wouldn't it be simpler to eliminate the need for citizens' permission or soldiers altogether? Recent technological advances might offer warmongers a solution.Artificial Intelligence (AI) will revolutionize weaponry like no previous innovation in history. A recent report by the Quincy Institute, which I support, highlights Silicon Valley's entry into the weapons industry. It quotes former Google CEO Eric Schmidt, "Every once in a while, a new weapon, a new technology comes along that changes things. Einstein wrote a letter to Roosevelt in the 1930s saying that there is this new technology — nuclear weapons — that could change war, which it clearly did. I would argue that (AI-powered) autonomy and decentralized, distributed systems are that powerful."AI can achieve what humans cannot, parsing millions of inputs, identify patterns, and alerting commanders at unimaginable speeds. Military experts assert that the side that most effectively shortens the "kill chain" — the time between identifying and destroying a target — wins.AI may be the most revolutionary technology for conducting war, but it's not alone. Kamikaze drones, now used in the Ukraine war, will one day swarm battlefields. Additionally, many other sci-fi-esque technologies, like Direct Energy Weapons (DEW), including high-power microwaves, particle beam weapons, and lasers are being tested by the U.S., the U.K., Israel, and Russia.Mimicking "Terminators" is not far off either. Witness the advancements by companies like Boston Dynamics. Their humanlike robots can run, jump, and move much like humans. Equipping them with machine guns or flame-throwers and mass-producing a few hundred thousand of them is a scary thought. (Boston Dynamics reached out to the Star following publication to note that it does not support the weaponization of robots). The future of war will also encompass fifth-generation warfare, primarily conducted through nonkinetic military actions like social engineering, misinformation, and cyberattacks. When paired with AI and fully autonomous systems, these methods can be as damaging as kinetic warfare. Consider the movie "Leave the World Behind," which explores societal collapse when all communication networks are shut down by a cyberattack.Adding AI to any of these weapons creates autonomous systems capable of making decisions without human intervention, leaving the decision to kill to an algorithm. Given that we still don't fully understand how AI learns and arrives at conclusions, the risks of catastrophic malfunctions should not be underestimated.AI lacks a moral compass; it simply aims to complete its task. As author and AI expert Max Tegmark explains, "a machine does not need to be malevolent; it simply needs to be competent at achieving its goals to be a potential threat." If innocent humans are in the way of completing its task, tough luck.What are the implications of these advancements for decisions about wars? It's hard to predict, but one outcome might be that with less need to recruit soldiers, there's less need to "sell" the public on engaging in war. In the U.S., the "Military Industrial Complex" has already managed to bypass Congress (contravening the U.S. Constitution) when deciding to go to war, giving the president tremendous power.Relegating soldiers to museums alongside crossbows and muskets, would make declaring war much less controversial — no body bags on the evening news — and therefore easier to wage. The only "soldiers" needed will be the young kids sitting at computer consoles conducting destruction of lives and property like in video games. No more 18-year-olds dying somewhere in the mud. Only the innocent civilians caught in the crossfire of this new kind of warfare will pay the price.As the U.S. has engaged in decades of wars that have destroyed lives and economies without achieving their stated goals, and have significantly contributed to the nation's enormous debt, public attitudes — especially among Gen-Z — have become increasingly critical and less accepting of war.As rapper Cardi B mockingly said about recent draft legislation, "I just read an article saying that the House just passed a bill that they're going to automatically register men from 18 to 26 for war. All I want to say is to America, good luck with that. These new little n——— are TikTokkers, baby. These mother f——— ain't going to fight no war. You might as well just keep investing money and get guns. This is a new America, baby."The introduction of technologies that might make soldiers obsolete may give warmongers who manipulate the decision-making process at the highest levels an easier path to wage more senseless wars. That would be a tragedy.This article was republished with permission from the Toronto Star.

The successful installation, commissioning, and operation of the Pierre Auger Observatory would not have been possible without the strong commitment and effort from the technical and admin-istrative staff in Malargtie. We are very grateful to the following agencies and organizations for financial support: Comision Nacional de Energla Atomica, Agencia Nacional de Promocion Cientffica y Tec-nologica (ANPCyT) , Consejo Nacional de Investigaciones Cientfficas y Tecnicas (CONICET) , Gobierno de la Provincia de Mendoza, Municipalidad de Malargtie, NDM Holdings and Valle Las Leilas, in gratitude for their continuing cooperation over land access, Argentina; the Australian Research Council; Conselho Nacional de Desenvolvimento Cientffico e Tecnologico (CNPq) , Fi-nanciadora de Estudos e Projetos (FINEP) , FundagAo de Amparo a Pesquisa do Estado de Rio de Janeiro (FAPERJ) , SAo Paulo Research Foundation (FAPESP) Grants No. 2010/07359-6 and No. 1999/05404-3, Ministerio de Ciencia e Tecnologia (MCT) , Brazil; Grant No.MSMT CR LG15014, LO1305 and LM2015038 and the Czech Science Foundation Grant No. 14-17501S, Czech Republic; Centre de Calcul IN2P3/CNRS, Centre National de la Recherche Scientifique (CNRS) , Conseil Regional Ile-de-France, Departement Physique Nucleaire et Corpusculaire (PNC-IN2P3/CNRS) , Departement Sciences de l'Univers (SDU-INSU/CNRS) , Institut Lagrange de Paris (ILP) Grant No. LABEX ANR-10-LABX-63, within the Investissements d'Avenir Programme Grant No. ANR-11-IDEX-0004-02, France; Bundesministerium fur Bildung und Forschung (BMBF) , Deutsche Forschungsgemeinschaft (DFG) , Finanzministerium Baden-Wurttemberg, Helmholtz Al-liance for Astroparticle Physics (HAP) , Helmholtz-Gemeinschaft Deutscher Forschungszentren (HGF) , Ministerium fur Wissenschaft und Forschung, Nordrhein Westfalen, Ministerium fur Wis-senschaft, Forschung und Kunst, Baden-Wurttemberg, Germany; Istituto Nazionale di Fisica Nu-cleare (INFN) ,Istituto Nazionale di Astrofisica (INAF) , Ministero dell'Istruzione, dell'Universita e della Ricerca (MIUR) , Gran Sasso Center for Astroparticle Physics (CFA) , CETEMPS Cen-ter of Excellence, Ministero degli Affari Esteri (MAE) , Italy; Consejo Nacional de Ciencia y Tecnologia (CONACYT) No. 167733, Mexico; Universidad Nacional Autonoma de Mexico (UNAM) , PAPIIT DGAPA-UNAM, Mexico; Ministerie van Onderwijs, Cultuur en Wetenschap, Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) , Stichting voor Fundamenteel Onderzoek der Materie (FOM) , Netherlands; National Centre for Research and Development, Grants No. ERA-NET-ASPERA/01/11 and No. ERA-NET-ASPERA/02/11, National Science Cen-tre, Grants No. 2013/08/M/ST9/00322, No. 2013/08/M/ST9/00728 and No. HARMONIA 5 - 2013/10/M/ST9/00062, Poland; Portuguese national funds and FEDER funds within Programa Operacional Factores de Competitividade through Fundagdo para a Ciencia e a Tecnologia (COM-PETE) , Portugal; Romanian Authority for Scientific Research ANCS, CNDI-UEFISCDI partner-ship projects Grants No. 20/2012 and No.194/2012 and PN 16 42 01 02; Slovenian Research Agency, Slovenia; Comunidad de Madrid, Fondo Europeo de Desarrollo Regional (FEDER) funds, Ministerio de Economia y Competitividad, Xunta de Galicia, European Community 7th Frame-work Program, Grant No. FP7-PEOPLE-2012-IEF-328826, Spain; Science and Technology Fa-cilities Council, United Kingdom; Department of Energy, Contracts No. DE-AC02-07CH11359, No. DE-FR02-04ER41300, No. DE-FG02-99ER41107 and No. DE-SC0011689, National Science Foundation, Grant No. 0450696, The Grainger Foundation, U.S.A. ; NAFOSTED, Vietnam; Marie Curie-IRSES/EPLANET, European Particle Physics Latin American Network, European Union 7th Framework Program, Grant No. PIRSES-2009-GA-246806; and UNESCO. ; The Auger Muon Infill Ground Array (AMIGA) is part of the AugerPrime upgrade of the Pierre Auger Observatory. It consists of particle counters buried 2.3m underground next to the water-Cherenkov stations that form the 23.5 km2 large infilled array. The reduced distance between detectors in this denser area allows the lowering of the energy threshold for primary cosmic ray reconstruction down to about 1017 eV. At the depth of 2.3m the electromagnetic component of cosmic ray showers is almost entirely absorbed so that the buried scintillators provide an independent and direct measurement of the air showers muon content. This work describes the design and implementation of the AMIGA embedded system, which provides centralized control, data acquisition and environment monitoring to its detectors. The presented system was firstly tested in the engineering array phase ended in 2017, and lately selected as the final design to be installed in all new detectors of the production phase. The system was proven to be robust and reliable and has worked in a stable manner since its first deployment. ; Comision Nacional de Energla Atomica ; ANPCyT ; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET) ; Gobierno de la Provincia de Mendoza ; Municipalidad de Malargtie ; NDM Holdings ; Australian Research Council ; Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ) ; Financiadora de Inovacao e Pesquisa (Finep) ; Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) 2010/07359-6 1999/05404-3 ; Ministerio de Ciencia e Tecnologia (MCT) , Brazil ; Grant Agency of the Czech Republic Czech Republic Government 14-17501S ; Centre de Calcul IN2P3/CNRS ; Centre National de la Recherche Scientifique (CNRS) Region Ile-de-France Departement Physique Nucleaire et Corpusculaire PNC-IN2P3/CNRS Departement Sciences de l'Univers (SDU-INSU/CNRS) French National Research Agency (ANR) LABEX ANR-10-LABX-63 ANR-11-IDEX-0004-02 ; Federal Ministry of Education & Research (BMBF) ; German Research Foundation (DFG) Finanzministerium Baden-Wurttemberg, Helmholtz Al-liance for Astroparticle Physics (HAP) Helmholtz Association Ministerium fur Wissenschaft und Forschung, Nordrhein Westfalen Ministerium fur Wis-senschaft, Forschung und Kunst, Baden-Wurttemberg, Germany ; Istituto Nazionale di Fisica Nucleare (INFN) Istituto Nazionale Astrofisica (INAF) ; Ministry of Education, Universities and Research (MIUR) ; Gran Sasso Center for Astroparticle Physics (CFA) ; CETEMPS Cen-ter of Excellence ; Ministry of Foreign Affairs and International Cooperation (Italy) ; Consejo Nacional de Ciencia y Tecnologia (CONACyT) 167733 ; Mexico; Universidad Nacional Autonoma de Mexico (UNAM) ; Programa de Apoyo a Proyectos de Investigacion e Innovacion Tecnologica (PAPIIT) ; Universidad Nacional Autonoma de Mexico ; Ministerie van Onderwijs, Cultuur en Wetenschap Netherlands Organization for Scientific Research (NWO) FOM (The Netherlands) Netherlands Government ; National Centre for Research & Development, Poland ERA-NET-ASPERA/01/11 ERA-NET-ASPERA/02/11 ; National Science Centre, Poland 2013/08/M/ST9/00322 2013/08/M/ST9/00728 HARMONIA 5 - 2013/10/M/ST9/00062 ; Portuguese national funds ; FEDER funds within Programa Operacional Factores de Competitividade through Fundagdo para a Ciencia e a Tecnologia (COM-PETE) , Portugal ; Romanian Authority for Scientific Research ANCS ; Consiliul National al Cercetarii Stiintifice (CNCS) Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii (UEFISCDI) 20/2012 194/2012 PN 16 42 01 02 ; Slovenian Research Agency - Slovenia ; Comunidad de Madrid ; Fondo Europeo de Desarrollo Regional (FEDER) funds, Ministerio de Economia y Competitividad,Xunta de Galicia ,European Community 7th Frame-work Program FP7-PEOPLE-2012-IEF-328826 ; UK Research & Innovation (UKRI) ; Science & Technology Facilities Council (STFC) ; United States Department of Energy (DOE) DE-FG02-99ER41107 DE-SC0011689 ; National Science Foundation (NSF) 0450696 ; Grainger Foundation, U.S.A ; National Foundation for Science & Technology Development (NAFOSTED) ; Marie Curie-IRSES/EPLANET ; European Particle Physics Latin American Network European Commission PIRSES-2009-GA-246806 ; UNESCO ; MSMT CR LG15014 LO1305 LM2015038

Full list of authors: Acharyya, A.; Adam, R.; Adams, C.; Agudo, I.; Aguirre-Santaella, A.; Alfaro, R.; Alfaro, J.; Alispach, C.; Aloisio, R.; Alves Batista, R.; Amati, L.; Ambrosi, G.; Angüner, E. O.; Antonelli, L. A.; Aramo, C.; Araudo, A.; Armstrong, T.; Arqueros, F.; Asano, K.; Ascasíbar, Y. Ashley, M.; Balazs, C.; Ballester, O.; Baquero Larriva, A.; Barbosa Martins, V.; Barkov, M.; Barres de Almeida, U.; Barrio, J. A.; Bastieri, D.; Becerra, J.; Beck, G.; Becker Tjus, J.; Benbow, W.; Benito, M.; Berge, D.; Bernardini, E.; Bernlöhr, K.; Berti, A.; Bertucci, B.; Beshley, V.; Biasuzzi, B.; Biland, A.; Bissaldi, E.; Biteau, J.; Blanch, O.; Blazek, J.; Bocchino, F.; Boisson, C.; Bonneau Arbeletche, L.; Bordas, P.; Bosnjak, Z.; Bottacini, E.; Bozhilov, V.; Bregeon, J.; Brill, A.; Bringmann, T.; Brown, A. M.; Brun, P.; Brun, F.; Bruno, P.; Bulgarelli, A.; Burton, M.; Burtovoi, A.; Buscemi, M.; Cameron, R.; Capasso, M.; Caproni, A.; Capuzzo-Dolcetta, R.; Caraveo, P.; Carosi, R.; Carosi, A.; Casanova, S.; Cascone, E.; Cassol, F.; Catalani, F.; Cauz, D.; Cerruti, M.; Chadwick, P.; Chaty, S.; Chen, A.; Chernyakova, M.; Chiaro, G.; Chiavassa, A.; Chikawa, M.; Chudoba, J.; Çolak, M.; Conforti, V.; Coniglione, R.; Conte, F.; Contreras, J. L.; Coronado-Blazquez, J.; Costa, A.; Costantini, H.; Cotter, G.; Cristofari, P.; D'Aimath, A.; D'Ammando, F.; Damone, L. A.; Daniel, M. K.; Dazzi, F.; De Angelis, A.; De Caprio, V.; de Cássia dos Anjos, R.; de Gouveia Dal Pino, E. M.; De Lotto, B.; De Martino, D.; de Oña Wilhelmi, E.; De Palma, F.; de Souza, V.; Delgado, C.; Delgado Giler, A. G.; della Volpe, D.; Depaoli, D.; Di Girolamo, T.; Di Pierro, F.; Di Venere, L.; Diebold, S.; Dmytriiev, A.; Domínguez, A.; Donini, A.; Doro, M.; Ebr, J.; Eckner, C.; Edwards, T. D. P.; Ekoume, T. R. N.; Elsässer, D.; Evoli, C.; Falceta-Goncalves, D.; Fedorova, E.; Fegan, S.; Feng, Q.; Ferrand, G.; Ferrara, G.; Fiandrini, E.; Fiasson, A.; Filipovic, M.; Fioretti, V.; Fiori, M.; Foffano, L.; Fontaine, G.; Fornieri, O.; Franco, F. J.; Fukami, S.; Fukui, Y.; Gaggero, D.; Galaz, G.; Gammaldi, V.; Garcia, E.; Garczarczyk, M.; Gascon, D.; Gent, A.; Ghalumyan, A.; Gianotti, F.; Giarrusso, M.; Giavitto, G.; Giglietto, N.; Giordano, F.; Giuliani, A.; Glicenstein, J.; Gnatyk, R.; Goldoni, P.; González, M. M.; Gourgouliatos, K.; Granot, J.; Grasso, D.; Green, J.; Grillo, A.; Gueta, O.; Gunji, S.; Halim, A.; Hassan, T.; Heller, M.; Hernández Cadena, S.; Hiroshima, N.; Hnatyk, B.; Hofmann, W.; Holder, J.; Horan, D.; Hörandel, J.; Horvath, P.; Hovatta, T.; Hrabovsky, M.; Hrupec, D.; Hughes, G.; Humensky, T. B.; Hütten, M.; Iarlori, M.; Inada, T.; Inoue, S.; Iocco, F.; Iori, M.; Jamrozy, M.; Janecek, P.; Jin, W.; Jouvin, L.; Jurysek, J.; Karukes, E.; Katarzyński, K.; Kazanas, D.; Kerszberg, D.; Kherlakian, M. C.; Kissmann, R.; Knödlseder, J.; Kobayashi, Y.; Kohri, K.; Komin, N.; Kubo, H.; Kushida, J.; Lamanna, G.; Lapington, J.; Laporte, P.; Leigui de Oliveira, M. A.; Lenain, J.; Leone, F.; Leto, G.; Lindfors, E.; Lohse, T.; Lombardi, S.; Longo, F.; Lopez, A.; López, M.; López-Coto, R.; Loporchio, S.; Luque-Escamilla, P. L.; Mach, E.; Maggio, C.; Maier, G.; Mallamaci, M.; Malta Nunes de Almeida, R.; Mandat, D.; Manganaro, M.; Mangano, S.; Manicò, G.; Marculewicz, M.; Mariotti, M.; Markoff, S.; Marquez, P.; Martí, J.; Martinez, O.; Martínez, M.; Martínez, G.; Martínez-Huerta, H.; Maurin, G.; Mazin, D.; Mbarubucyeye, J. D.; Medina Miranda, D.; Meyer, M.; Miceli, M.; Miener, T.; Minev, M.; Miranda, J. M.; Mirzoyan, R.; Mizuno, T.; Mode, B.; Moderski, R.; Mohrmann, L.; Molina, E.; Montaruli, T.; Moralejo, A.; Morcuende-Parrilla, D.; Morselli, A.; Mukherjee, R.; Mundell, C.; Nagai, A.; Nakamori, T.; Nemmen, R.; Niemiec, J.; Nieto, D.; Nikołajuk, M.; Ninci, D.; Noda, K.; Nosek, D.; Nozaki, S.; Ohira, Y.; Ohishi, M.; Ohtani, Y.; Oka, T.; Okumura, A.; Ong, R. A.; Orienti, M.; Orito, R.; Orlandini, M.; Orlando, S.; Orlando, E.; Ostrowski, M.; Oya, I.; Pagano, I.; Pagliaro, A.; Palatiello, M.; Pantaleo, F. R.; Paredes, J. M.; Pareschi, G.; Parmiggiani, N.; Patricelli, B.; Pavletić, L.; Pe'er, A.; Pecimotika, M.; Pérez-Romero, J.; Persic, M.; Petruk, O.; Pfrang, K.; Piano, G.; Piatteli, P.; Pietropaolo, E.; Pillera, R.; Pilszyk, B.; Pintore, F.; Pohl, M.; Poireau, V.; Prado, R. R.; Prandini, E.; Prast, J.; Principe, G.; Prokoph, H.; Prouza, M.; Przybilski, H.; Pühlhofer, G.; Pumo, M. L.; Queiroz, F.; Quirrenbach, A.; Rainò, S.; Rando, R.; Razzaque, S.; Recchia, S.; Reimer, O.; Reisenegger, A.; Renier, Y.; Rhode, W.; Ribeiro, D.; Ribó, M.; Richtler, T.; Rico, J.; Rieger, F.; Rinchiuso, L.; Rizi, V.; Rodriguez, J.; Rodriguez Fernandez, G.; Rodriguez Ramirez, J. C.; Rojas, G.; Romano, P.; Romeo, G.; Rosado, J.; Rowell, G.; Rudak, B.; Russo, F.; Sadeh, I.; Sæther Hatlen, E.; Safi-Harb, S.; Salesa Greus, F.; Salina, G.; Sanchez, D.; Sánchez-Conde, M.; Sangiorgi, P.; Sano, H.; Santander, M.; Santos, E. M.; Santos-Lima, R.; Sanuy, A.; Sarkar, S.; Saturni, F. G.; Sawangwit, U.; Schussler, F.; Schwanke, U.; Sciacca, E.; Scuderi, S.; Seglar-Arroyo, M.; Sergijenko, O.; Servillat, M.; Seweryn, K.; Shalchi, A.; Sharma, P.; Shellard, R. C.; Siejkowski, H.; Silk, J.; Siqueira, C.; Sliusar, V.; Słowikowska, A.; Sokolenko, A.; Sol, H.; Spencer, S.; Stamerra, A.; Stanič, S.; Starling, R.; Stolarczyk, T.; Straumann, U.; Strišković, J.; Suda, Y.; Suomijarvi, T.; Świerk, P.; Tavecchio, F.; Taylor, L.; Tejedor, L. A.; Teshima, M.; Testa, V.; Tibaldo, L.; Todero Peixoto, C. J.; Tokanai, F.; Tonev, D.; Tosti, G.; Tosti, L.; Tothill, N.; Truzzi, S.; Travnicek, P.; Vagelli, V.; Vallage, B.; Vallania, P.; van Eldik, C.; Vandenbroucke, J.; Varner, G. S.; Vassiliev, V.; Vázquez Acosta, M.; Vecchi, M.; Ventura, S.; Vercellone, S.; Vergani, S.; Verna, G.; Viana, A.; Vigorito, C. F.; Vink, J.; Vitale, V.; Vorobiov, S.; Vovk, I.; Vuillaume, T.; Wagner, S. J.; Walter, R.; Watson, J.; Weniger, C.; White, R.; White, M.; Wiemann, R.; Wierzcholska, A.; Will, M.; Williams, D. A.; Wischnewski, R.; Yanagita, S.; Yang, L.; Yoshikoshi, T.; Zacharias, M.; Zaharijas, G.; Zakaria, A. A.; Zampieri, L.; Zanin, R.; Zaric, D.; Zavrtanik, M.; Zavrtanik, D.; Zdziarski, A. A.; Zech, A.; Zechlin, H.; Zhdanov, V. I.; Živec, M.--Published by IOP Publishing Ltd on behalf of Sissa Medialab. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. ; We provide an updated assessment of the power of the Cherenkov Telescope Array (CTA) to search for thermally produced dark matter at the TeV scale, via the associated gamma-ray signal from pair-annihilating dark matter particles in the region around the Galactic centre. We find that CTA will open a new window of discovery potential, significantly extending the range of robustly testable models given a standard cuspy profile of the dark matter density distribution. Importantly, even for a cored profile, the projected sensitivity of CTA will be sufficient to probe various well-motivated models of thermally produced dark matter at the TeV scale. This is due to CTA's unprecedented sensitivity, angular and energy resolutions, and the planned observational strategy. The survey of the inner Galaxy will cover a much larger region than corresponding previous observational campaigns with imaging atmospheric Cherenkov telescopes. CTA will map with unprecedented precision the large-scale diffuse emission in high-energy gamma rays, constituting a background for dark matter searches for which we adopt state-of-the-art models based on current data. Throughout our analysis, we use up-to-date event reconstruction Monte Carlo tools developed by the CTA consortium, and pay special attention to quantifying the level of instrumental systematic uncertainties, as well as background template systematic errors, required to probe thermally produced dark matter at these energies. © 2021 The Author(s). ; We gratefully acknowledge financial support from the following agencies and organisations: State Committee of Science of Armenia, Armenia; The Australian Research Council, Astronomy Australia Ltd, The University of Adelaide, Australian National University, Monash University, The University of New South Wales, The University of Sydney, Western Sydney University, Australia; Federal Ministry of Education, Science and Research, and Innsbruck University, Austria; Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq), Fundacao de Amparo a Pesquisa do Estado do Rio de Janeiro (FAPERJ), Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP), Ministry of Science, Technology, Innovations and Communications (MCTIC), and Instituto Serrapilheira, Brasil; Ministry of Education and Science, National RI Roadmap Project DO1-153/28.08.2018, Bulgaria; The Natural Sciences and Engineering Research Council of Canada and the Canadian Space Agency, Canada; CONICYT-Chile grants CATA AFB 170002, ANID PIA/APOYO AFB 180002, ACT 1406, FONDECYT-Chile grants, 1161463, 1170171, 1190886, 1171421, 1170345, 1201582, Gemini-ANID 32180007, Chile; Croatian Science Foundation, Rudjer Boskovic Institute, University of Osijek, University of Rijeka, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Zagreb, Faculty of Electrical Engineering and Computing, Croatia; Ministry of Education, Youth and Sports, MEYS LM2015046, LM2018105, LTT17006, EU/MEYS CZ.02.1.01/0.0/0.0/16_013/0001403, CZ.02.1.01/0.0/0.0/18_046/0016007 and CZ.02.1.01/0.0/0.0/16_019/0000754, Czech Republic; Academy of Finland (grant nr.317636, 320045, 317383 and 320085), Finland; Ministry of Higher Education and Research, CNRS-INSU and CNRS-IN2P3, CEA-Irfu, ANR, Regional Council Ile de France, Labex ENIGMASS, OSUG2020, P2IO and OCEVU, France; Max Planck Society, BMBF, DESY, Helmholtz Association, Germany; Department of Atomic Energy, Department of Science and Technology, India; Istituto Nazionale di Astrofisica (INAF), Istituto Nazionale di Fisica Nucleare (INFN), MIUR, Istituto Nazionale di Astrofisica (INAF-OABRERA) Grant Fondazione Cariplo/Regione Lombardia ID 2014-1980/RST_ERC, Italy; ICRR, University of Tokyo, JSPS, MEXT, Japan; Netherlands Research School for Astronomy (NOVA), Netherlands Organization for Scientific Research (NWO), Netherlands; University of Oslo, Norway; Ministry of Science and Higher Education, DIR/WK/2017/12, the National Centre for Research and Development and the National Science Centre, UMO-2016/22/M/ST9/00583, Poland; Slovenian Research Agency, grants P1-0031, P1-0385, I0-0033, J1-9146, J1-1700, N1-0111, and the Young Researcher program, Slovenia; South African Department of Science and Technology and National Research Foundation through the South African Gamma-Ray Astronomy Programme, South Africa; The Spanish Ministry of Science and Innovation and the Spanish Research State Agency (AEI) through grants AYA2016-79724-C4-1-P, AYA2016-80889-P, AYA2016-76012-C3-1-P, BES-2016-076342, ESP2017-87055-C2-1-P, FPA2017-82729-C6-1-R, FPA2017-82729-C6-2-R, FPA2017-82729-C6-3-R, FPA2017-82729-C6-4-R, FPA2017-82729-C6-5-R, FPA2017-82729-C6-6-R, PGC2018-095161-B-I00, PGC2018-095512-B-I00; the \Centro de Excelencia Severo Ochoa"program through grants no. SEV-2015-0548, SEV-2016-0597, SEV-2016-0588, SEV-2017-0709; the "Unidad de Excelencia Maria de Maeztu" program through grant no. MDM-2015-0509; the "Ramon y Cajal" programme through grants RYC-2013-14511, RyC-2013-14660, RYC-2017-22665; and the MultiDark Consolider Network FPA2017-90566-REDC. Atraccion de Talento contract no. 2016-T1/TIC-1542 granted by the Comunidad de Madrid; the "Postdoctoral Junior Leader Fellowship" programme from La Caixa Banking Foundation, grants no. LCF/BQ/LI18/11630014 and LCF/BQ/PI18/11630012; the "Programa Operativo" FEDER2014-2020, Consejeria de Economia y Conocimiento de la Junta de Andalucia (ref. 1257737), PAIDI 2020 (ref. P18-FR-1580), and Universidad de Jaen; the Spanish AEI EQC2018-005094-P FEDER 2014-2020; the European Union's "Horizon 2020" research and innovation programme under Marie Sklodowska-Curie grant agreement no. 665919; and the ESCAPE project with grant no. GA:824064, Spain; Swedish Research Council, Royal Physiographic Society of Lund, Royal Swedish Academy of Sciences, The Swedish National Infrastructure for Computing (SNIC) at Lunarc (Lund), Sweden; State Secretariat for Education, Research and Innovation (SERI) and Swiss National Science Foundation (SNSF), Switzerland; Durham University, Leverhulme Trust, Liverpool University, University of Leicester, University of Oxford, Royal Society, Science and Technology Facilities Council, U.K.; U.S. National Science Foundation, U.S. Department of Energy, Argonne National Laboratory, Barnard College, University of California, University of Chicago, Columbia University, Georgia Institute of Technology, Institute for Nuclear and Particle Astrophysics (INPAC-MRPI program), Iowa State University, the Smithsonian Institution, Washington University McDonnell Center for the Space Sciences, The University of Wisconsin and the Wisconsin Alumni Research Foundation, U.S.A. The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreements No 262053 and No 317446. This project is receiving funding from the European Union's Horizon 2020 research and innovation programs under agreement No 676134. ; Peer reviewed

All authors: Psaltis, Dimitrios; Medeiros, Lia; Christian, Pierre; Özel, Feryal; Akiyama, Kazunori; Alberdi, Antxon; Alef, Walter; Asada, Keiichi; Azulay, Rebecca; Ball, David; Baloković, Mislav; Barrett, John; Bintley, Dan; Blackburn, Lindy; Boland, Wilfred; Bower, Geoffrey C.; Bremer, Michael; Brinkerink, Christiaan D.; Brissenden, Roger; Britzen, Silke Broguiere, Dominique; Bronzwaer, Thomas; Byun, Do-Young; Carlstrom, John E.; Chael, Andrew; Chan, Chi-kwan; Chatterjee, Shami; Chatterjee, Koushik; Chen, Ming-Tang; Chen, Yongjun; Cho, Ilje; Conway, John E.; Cordes, James M.; Crew, Geoffrey B.; Cui, Yuzhu; Davelaar, Jordy; De Laurentis, Mariafelicia; Deane, Roger; Dempsey, Jessica; Desvignes, Gregory; Dexter, Jason; Eatough, Ralph P.; Falcke, Heino; Fish, Vincent L.; Fomalont, Ed; Fraga-Encinas, Raquel; Friberg, Per; Fromm, Christian M.; Gammie, Charles F.; García, Roberto; Gentaz, Olivier; Goddi, Ciriaco; Gómez, José L.; Gu, Minfeng; Gurwell, Mark; Hada, Kazuhiro; Hesper, Ronald; Ho, Luis C.; Ho, Paul; Honma, Mareki; Huang, Chih-Wei L.; Huang, Lei; Hughes, David H.; Inoue, Makoto; Issaoun, Sara; James, David J.; Jannuzi, Buell T.; Janssen, Michael; Jiang, Wu; Jimenez-Rosales, Alejandra; Johnson, Michael D.; Jorstad, Svetlana; Jung, Taehyun; Karami, Mansour; Karuppusamy, Ramesh; Kawashima, Tomohisa; Keating, Garrett K.; Kettenis, Mark; Kim, Jae-Young; Kim, Junhan; Kim, Jongsoo; Kino, Motoki; Koay, Jun Yi; Koch, Patrick M.; Koyama, Shoko; Kramer, Michael; Kramer, Carsten; Krichbaum, Thomas P.; Kuo, Cheng-Yu; Lauer, Tod R.; Lee, Sang-Sung; Li, Yan-Rong; Li, Zhiyuan; Lindqvist, Michael; Lico, Rocco; Liu, Jun; Liu, Kuo; Liuzzo, Elisabetta; Lo, Wen-Ping; Lobanov, Andrei P.; Lonsdale, Colin; Lu, Ru-Sen; Mao, Jirong; Markoff, Sera; Marrone, Daniel P.; Marscher, Alan P.; Martí-Vidal, Iván; Matsushita, Satoki; Mizuno, Yosuke; Mizuno, Izumi; Moran, James M.; Moriyama, Kotaro; Moscibrodzka, Monika; Müller, Cornelia; Musoke, Gibwa; Mus Mejías, Alejandro; Nagai, Hiroshi; Nagar, Neil M.; Narayan, Ramesh; Narayanan, Gopal; Natarajan, Iniyan; Neri, Roberto; Noutsos, Aristeidis; Okino, Hiroki; Olivares, Héctor; Oyama, Tomoaki; Palumbo, Daniel C. M.; Park, Jongho; Patel, Nimesh; Pen, Ue-Li; Piétu, Vincent; Plambeck, Richard; PopStefanija, Aleksandar; Prather, Ben; Preciado-López, Jorge A.; Ramakrishnan, Venkatessh; Rao, Ramprasad; Rawlings, Mark G.; Raymond, Alexander W.; Ripperda, Bart; Roelofs, Freek; Rogers, Alan; Ros, Eduardo; Rose, Mel; Roshanineshat, Arash; Rottmann, Helge; Roy, Alan L.; Ruszczyk, Chet; Ryan, Benjamin R.; Rygl, Kazi L. J.; Sánchez, Salvador; Sánchez-Arguelles, David; Sasada, Mahito; Savolainen, Tuomas; Schloerb, F. Peter; Schuster, Karl-Friedrich; Shao, Lijing; Shen, Zhiqiang; Small, Des; Sohn, Bong Won; SooHoo, Jason; Tazaki, Fumie; Tilanus, Remo P. J.; Titus, Michael; Torne, Pablo; Trent, Tyler; Traianou, Efthalia; Trippe, Sascha; van Bemmel, Ilse; van Langevelde, Huib Jan; van Rossum, Daniel R.; Wagner, Jan; Wardle, John; Ward-Thompson, Derek; Weintroub, Jonathan; Wex, Norbert; Wharton, Robert; Wielgus, Maciek; Wong, George N.; Wu, Qingwen; Yoon, Doosoo; Young, André; Young, Ken; Younsi, Ziri; Yuan, Feng; Yuan, Ye-Fei; Zhao, Shan-Shan; EHT Collaboration ; The 2017 Event Horizon Telescope (EHT) observations of the central source in M87 have led to the first measurement of the size of a black-hole shadow. This observation offers a new and clean gravitational test of the black-hole metric in the strong-field regime. We show analytically that spacetimes that deviate from the Kerr metric but satisfy weak-field tests can lead to large deviations in the predicted black-hole shadows that are inconsistent with even the current EHT measurements. We use numerical calculations of regular, parametric, non-Kerr metrics to identify the common characteristic among these different parametrizations that control the predicted shadow size. We show that the shadow-size measurements place significant constraints on deviation parameters that control the second post-Newtonian and higher orders of each metric and are, therefore, inaccessible to weak-field tests. The new constraints are complementary to those imposed by observations of gravitational waves from stellar-mass sources. © 2020 American Physical Society. ; The authors of the present paper thank the following organizations and programs: the Academy of Finland (Projects No. 274477, No. 284495, No. 312496); the Advanced European Network of E-infrastructures for Astronomy with the SKA (AENEAS) project, supported by the European Commission Framework Programme Horizon 2020 Research and Innovation action under Grant Agreement No. 731016; the Alexander von Humboldt Stiftung; the Black Hole Initiative at Harvard University, through a grant (No. 60477) from the John Templeton Foundation; the China Scholarship Council; Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT, Chile, via PIA ACT172033, Fondecyt Projects No. 1171506 and No. 3190878, BASAL AFB-170002, ALMA-conicyt 31140007); Consejo Nacional de Ciencia y Tecnologia (CONACYT, Mexico, Projects No. 104497, No. 275201, No. 279006, No. 281692); the Delaney Family via the Delaney Family John A. Wheeler Chair at Perimeter Institute; Direccion General de Asuntos del Personal Academico, Universidad Nacional Autonoma de Mexico (DGAPA-UNAM, project IN112417); the European Research Council Synergy Grant "BlackHoleCam: Imaging the Event Horizon of Black Holes" (Grant No. 610058); the Generalitat Valenciana postdoctoral grant APOSTD/2018/177 and GenT Program (project CIDEGENT/2018/021); the Gordon and Betty Moore Foundation (Grants No. GBMF-3561, No. GBMF-5278); the Istituto Nazionale di Fisica Nucleare (INFN) sezione di Napoli, iniziative specifiche TEONGRAV; the International Max Planck Research School for Astronomy and Astrophysics at the Universities of Bonn and Cologne; the Jansky Fellowship program of the National Radio Astronomy Observatory (NRAO); the Japanese Government (Monbukagakusho:MEXT) Scholarship; the Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for JSPS Research Fellowship (JP17J08829); the Key Research Program of Frontier Sciences, Chinese Academy of Sciences (CAS, Grants No. QYZDJ-SSW-SLH057, No. QYZDJSSW-SYS008, No. ZDBS-LY-SLH011); the Leverhulme Trust Early Career Research Fellowship; the Max-Planck-Gesellschaft (MPG); the Max Planck Partner Group of the MPG and the CAS; the MEXT/JSPS KAKENHI (Grants No. 18KK0090, No. JP18K13594, No. JP18K03656, No. JP18H03721, No. 18K03709, No. 18H01245, No. 25120007); the MIT International Science and Technology Initiatives (MISTI) Funds; the Ministry of Science and Technology (MOST) of Taiwan (105-2112-M-001-025-MY3, 106-2112-M-001-011, 106-2119-M-001-027, 107-2119-M-001-017, 107-2119-M-001-020, and 107-2119-M-110-005); the National Aeronautics and Space Administration (NASA, Fermi Guest Investigator Grant No. 80NSSC17K0649 and Hubble Fellowship Grant No. HST-HF2-51431.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under Contract No. NAS5-26555); the National Institute of Natural Sciences (NINS) of Japan; the National Key Research and Development Program of China (Grants No. 2016YFA0400704, No. 2016YFA0400702); the National Science Foundation (NSF, Grants No. AST-0096454, No. AST-0352953, No. AST-0521233, No. AST-0705062, No. AST0905844, No. AST-0922984, No. AST-1126433, No. AST-1140030, No. DGE-1144085, No. AST-1207704, No. AST-1207730, No. AST-1207752, No. MRI-1228509, No. OPP-1248097, No. AST-1310896, No. AST-1312651, No. AST-1337663, No. AST-1440254, No. AST-1555365, No. AST-1715061, No. AST-1615796, No. AST-1716327, No. OISE-1743747, No. AST-1816420); the Natural Science Foundation of China (Grants No. 11573051, No. 11633006, No. 11650110427, No. 10625314, No. 11721303, No. 11725312, No. 11933007); the Natural Sciences and Engineering Research Council of Canada (NSERC, including a Discovery Grant and the NSERC Alexander Graham Bell Canada Graduate Scholarships-Doctoral Program); the National Youth Thousand Talents Program of China; the National Research Foundation of Korea (the Global PhD Fellowship Grant: Grants No. NRF-2015H1A2A1033752, No. 2015-R1D1A1A01056807, the Korea Research Fellowship Program: NRF-2015H1D3A1066561); the Netherlands Organization for Scientific Research (NWO) VICI award (Grant No. 639.043.513) and Spinoza Prize SPI 78-409; the New Scientific Frontiers with Precision Radio Interferometry Fellowship awarded by the South African Radio Astronomy Observatory (SARAO), which is a facility of the National Research Foundation (NRF), an agency of the Department of Science and Technology (DST) of South Africa; the Onsala Space Observatory (OSO) national infrastructure, for the provisioning of its facilities/observational support (OSO receives funding through the Swedish Research Council under Grant No. 2017-00648) the Perimeter Institute for Theoretical Physics (research at Perimeter Institute is supported by the Government of Canada through the Department of Innovation, Science and Economic Development and by the Province of Ontario through the Ministry of Research, Innovation and Science); the Russian Science Foundation (Grant No. 17-12-01029); the Spanish Ministerio de Economia y Competitividad (Grants No. AYA2015-63939-C2-1-P, No. AYA2016-80889-P, No. PID2019-108995GB-C21); the State Agency for Research of the Spanish MCIU through the "Center of Excellence Severo Ochoa" award for the Instituto de Astrofisica de Andalucia (SEV-2017-0709); the Toray Science Foundation; the Consejeria de Economia, Conocimiento, Empresas y Universidad of the Junta de Andalucia (Grant No. P18-FR-1769), the Consejo Superior de Investigaciones Cientificas (Grant No. 2019AEP112); the U.S. Department of Energy (DOE) through the Los Alamos National Laboratory [operated by Triad National Security, LLC, for the National Nuclear Security Administration of the U.S. DOE (Contract No. 89233218CNA000001)]; the Italian Ministero dell'Istruzione, dell'Universita e della Ricerca through the grant Progetti Premiali 2012-iALMA (CUP C52I13000140001); the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 730562 RadioNet; ALMA North America Development Fund; the Academia Sinica;Chandra TM6-17006X; the GenT Program (Generalitat Valenciana) Project CIDEGENT/2018/021. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF Grant No. ACI-1548562, and CyVerse, supported by NSF Grants No. DBI-0735191, No. DBI-1265383, and No. DBI-1743442. We thank the staff at the participating observatories, correlation centers, and institutions for their enthusiastic support. ALMA is a partnership of the European Southern Observatory (ESO; Europe, representing its member states), NSF, and National Institutes of Natural Sciences of Japan, together with National Research Council (Canada), Ministry of Science and Technology (MOST; Taiwan), Academia Sinica Institute of Astronomy and Astrophysics (ASIAA; Taiwan), and Korea Astronomy and Space Science Institute (KASI; Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, Associated Universities, Inc. (AUI)/NRAO, and the National Astronomical Observatory of Japan (NAOJ). The NRAO is a facility of the NSF operated under cooperative agreement by AUI. APEX is a collaboration between the Max-Planck-Institut fur Radioastronomie (Germany), ESO, and the Onsala Space Observatory (Sweden). The SMA is a joint project between the SAO and ASIAA and is funded by the Smithsonian Institution and the Academia Sinica. The JCMT is operated by the East Asian Observatory on behalf of the NAOJ, ASIAA, and KASI, as well as the Ministry of Finance of China, Chinese Academy of Sciences, and the National Key R&D Program (No. 2017YFA0402700) of China. Additional funding support for the JCMT is provided by the Science and Technologies Facility Council (UK) and participating universities in the UK and Canada. The LMT is a project operated by the Instituto Nacional de Astrofisica, Optica, y Electronica (Mexico) and the University of Massachusetts at Amherst (USA). The IRAM 30-m telescope on Pico Veleta, Spain, is operated by IRAM and supported by CNRS (Centre National de la Recherche Scientifique, France), MPG (Max-PlanckGesellschaft, Germany) and IGN (Instituto Geografico Nacional, Spain). The SMT is operated by the Arizona Radio Observatory, a part of the Steward Observatory of the University of Arizona, with financial support of operations from the State of Arizona and financial support for instrumentation development from the NSF. The SPT is supported by the National Science Foundation through Grant No. PLR-1248097. Partial support is also provided by the NSF Physics Frontier Center Grant No. PHY1125897 to the Kavli Institute of Cosmological Physics at the University of Chicago, the Kavli Foundation and the Gordon and Betty Moore Foundation Grant No. GBMF 947. The SPT hydrogen maser was provided on loan from the GLT, courtesy of ASIAA. The EHTC has received generous donations of FPGA chips from Xilinx Inc., under the Xilinx University Program. The EHTC has benefited from technology shared under open-source license by the Collaboration for Astronomy Signal Processing and Electronics Research (CASPER). The EHT project is grateful to T4Science and Microsemi for their assistance with hydrogen masers. This research has made use of NASA's Astrophysics Data System. We gratefully acknowledge the support provided by the extended staff of the ALMA, both from the inception of the ALMA Phasing Project through the observational campaigns of 2017 and 2018. We would like to thank A. Deller and W. Brisken for EHT-specific support with the use of DiFX. We acknowledge the significance that Maunakea, where the SMA and JCMT EHT stations are located, has for the indigenous Hawaiian people.