Publications

A monetized tornado benefit model is developed for arbitrary weather radar network configurations. Geospatial regression analyses indicate that improvement in two key radar coverage parameters--fraction of vertical space observed and cross-range horizontal resolution--lead to better tornado warning performance as characterized by tornado detection probability and false alarm ratio. Previous experimental results showing faster volume scan rates yielding greater warning performance, including increased lead times, are also incorporated into the model. Enhanced tornado warning performance, in turn, reduces casualty rates. In combination, then, it is clearly established that better and faster radar observations reduce tornado casualty rates. Furthermore, lower false alarm ratios save costs by cutting down on people's time lost when taking shelter.

A monetized tornado benefit model is developed for arbitrary weather radar network configurations. Geospatial regression analyses indicate that improvement of two key radar parameters--fraction of vertical space observed and cross-range horizontal resolution--lead to better tornado warning performance as characterized by tornado detection probability and false alarm ratio. Previous experimental results showing faster volume scan rates yielding greater warning performance are also incorporated into the model. Enhanced tornado warning performance, in turn, reduces casualty rates. In addition, lower false alarm ratios save cost by cutting down on work and personal time lost while taking shelter. The model is run on the existing contiguous United States weather radar network as well as hypothetical future configurations. Results show that the current radars provide a tornado-based benefit of ~$490M per year. The remaining benefit pool is about $260M per year that is roughly split evenly between coverage- and rapid-scanning-related gaps.

Comprehensive thermophysical analyses of Near-Earth Asteroids (NEAs) provide important information about their physical properties, including visible albedo, diameter, composition, and thermal inertia. These details are integral to defining asteroid taxonomy and understanding how these objects interact with the solar system. Since infrared (IR) asteroid observations are not widely available, thermophysical modeling techniques have become valuable in simulating properties of different asteroid types. Several basic models that assume a spherical asteroid shape have been used extensively within the research community. As part of a program focused on developing a simulation of space-based IR sensors for asteroid search, the Near-Earth Asteroid Model (NEATM) developed by Harris, A. in 1998, was selected. This review provides a full derivation of the formulae behind NEATM, including the spectral flux density equation, consideration of the solar phase angle, and the geometry of the asteroid, Earth, and Sun system. It describes how to implement the model in software and explores the use of an ellipsoidal asteroid shape. It also applies the model to several asteroids observed by NASA's Near-Earth Object Wide-field Survey Explorer (NEOWISE) and compares the performance of the model to the observations.

The Director's Office at MIT Lincoln Laboratory (MIT LL) requested a comprehensive study on artificial intelligence (AI) focusing on present applications and future science and technology (S&T) opportunities in the Cyber Security and Information Sciences Division (Division 5). This report elaborates on the main results from the study. Since the AI field is evolving so rapidly, the study scope was to look at the recent past and ongoing developments to lead to a set of findings and recommendations. It was important to begin with a short AI history and a lay-of-the-land on representative developments across the Department of Defense (DoD), intelligence communities (IC), and Homeland Security. These areas are addressed in more detail within the report. A main deliverable from the study was to formulate an end-to-end AI canonical architecture that was suitable for a range of applications. The AI canonical architecture, formulated in the study, serves as the guiding framework for all the sections in this report. Even though the study primarily focused on cyber security and information sciences, the enabling technologies are broadly applicable to many other areas. Therefore, we dedicate a full section on enabling technologies in Section 3. The discussion on enabling technologies helps the reader clarify the distinction among AI, machine learning algorithms, and specific techniques to make an end-to-end AI system viable. In order to understand what is the lay-of-the-land in AI, study participants performed a fairly wide reach within MIT LL and external to the Laboratory (government, commercial companies, defense industrial base, peers, academia, and AI centers). In addition to the study participants (shown in the next section under acknowledgements), we also assembled an internal review team (IRT). The IRT was extremely helpful in providing feedback and in helping with the formulation of the study briefings, as we transitioned from datagathering mode to the study synthesis. The format followed throughout the study was to highlight relevant content that substantiates the study findings, and identify a set of recommendations. An important finding is the significant AI investment by the so-called "big 6" commercial companies. These major commercial companies are Google, Amazon, Facebook, Microsoft, Apple, and IBM. They dominate in the AI ecosystem research and development (R&D) investments within the U.S. According to a recent McKinsey Global Institute report, cumulative R&D investment in AI amounts to about $30 billion per year. This amount is substantially higher than the R&D investment within the DoD, IC, and Homeland Security. Therefore, the DoD will need to be very strategic about investing where needed, while at the same time leveraging the technologies already developed and available from a wide range of commercial applications. As we will discuss in Section 1 as part of the AI history, MIT LL has been instrumental in developing advanced AI capabilities. For example, MIT LL has a long history in the development of human language technologies (HLT) by successfully applying machine learning algorithms to difficult problems in speech recognition, machine translation, and speech understanding. Section 4 elaborates on prior applications of these technologies, as well as newer applications in the context of multi-modalities (e.g., speech, text, images, and video). An end-to-end AI system is very well suited to enhancing the capabilities of human language analysis. Section 5 discusses AI's nascent role in cyber security. There have been cases where AI has already provided important benefits. However, much more research is needed in both the application of AI to cyber security and the associated vulnerability to the so-called adversarial AI. Adversarial AI is an area very critical to the DoD, IC, and Homeland Security, where malicious adversaries can disrupt AI systems and make them untrusted in operational environments. This report concludes with specific recommendations by formulating the way forward for Division 5 and a discussion of S&T challenges and opportunities. The S&T challenges and opportunities are centered on the key elements of the AI canonical architecture to strengthen the AI capabilities across the DoD, IC, and Homeland Security in support of national security.

Human trafficking is a form of modern-day slavery affecting an estimated 40 million victims worldwide, primarily through the commercial sexual exploitation of women and children. In the last decade, the advertising of victims has moved from the streets to websites on the Internet, providing greater efficiency and anonymity for sex traffickers. This shift has allowed traffickers to list their victims in multiple geographic areas simultaneously, while also improving operational security by using multiple methods of electronic communication with buyers; complicating the ability of law enforcement to disrupt these illicit organizations. In this paper, we address this issue and present a novel unsupervised and scalable template matching algorithm for analyzing and detecting complex organizations operating on adult service websites. The algorithm uses only the advertisement content to uncover signature patterns in text that are indicative of organized activities and organizational structure. We apply this method to a large corpus of adult service advertisements retrieved from backpage.com, and show that the networks identified through the algorithm match well with surrogate truth data derived from phone number networks in the same corpus. Further exploration of the results show that the proposed method provides deeper insights into the complex structures of sex trafficking organizations, not possible through networks derived from phone numbers alone. This method provides a powerful new capability for law enforcement to more completely identify and gather evidence about trafficking networks and their operations.

This paper is motivated by the need to ensure fast microgrid stability. Modeling for purposes of establishing stability criterion and possible implementations are described. In particular, this paper proposes that highly heterogeneous microgrids comprising both conventional equipment and equipment based on rapidly emerging new technologies can be modeled as purely electric networks in order to provide intuitive insight into the issues of network stability. It is shown that the proposed model is valid for representing fast primary dynamics of diverse components (gensets, loads, PVs), assuming that slower variables are regulated by the higher-level controllers. Based on this modeling approach, an intuitively-appealing criterion is introduced requiring that components or their combined representations must behave as closed-loop passive electrical circuits. Implementing this criterion is illustrated using typical commercial feeder microgrid. Notably, these set the basis for standards which should be required for groups of components (sub grids) to ensure no fast instabilities in complex microgrids. Building the need for incrementally passive and monotonic characteristics into standards for network components may clarify the system level analysis and integration of microgrids.

Small electrical networks (i.e., microgrids) and machine models (synchronous generators, induction motors) can be simulated fairly easily, on sequential processes. However, running a large simulation on a single process becomes infeasible because of complexity and timing issues. Scalability becomes an increasingly important issue for larger simulations, and the platform for running such large simulations, like the MIT Supercloud, becomes more important. The distributed computing network used to simulate an electrical network as the physical system presents new challenges, however. Different simulation models, different time steps, and different computation times for each process in the distributed computing network introduce new challenges not present with typical problems that are addressed with high performance computing techniques. A distributed computing network is established for some example electrical networks, and then adjustments are made in the parallel simulation set-up to alleviate the new kinds of challenges that come with modeling and simulating a physical system as diverse as an electrical network. Also, methods are shown to simulate the same electrical network in hundreds of milliseconds, as opposed to several seconds--a dramatic speedup once the simulation is parallelized.

Autonomous fuel synthesis in remote locations remains the Holy Grail of fuel delivery logistics. The burdened cost of delivering fuel to remote locations is often significantly more expensive than the purchase price. Here it is shown that newly developed solid aluminum metal fuel is suited for remote production of liquid diesel fuels. On a volumetric basis, aluminum has more than twice the energy of diesel fuel, making it a superb structural energy vector for remote applications. Once aluminum is treated with gallium, water of nearly any purity is used to rapidly oxidize the aluminum metal which spontaneously evolves hydrogen and heat in roughly equal energetic quantities. The benign byproduct of the reaction could, in theory, be taken to an off-site facility and recycled back into aluminum using standard smelting processes or it could be left onsite as a high-value waste. The hydrogen can easily be used as a feedstock for diesel fuel, via Fischer-Tropsch (FT) reaction mechanisms, while the heat can be leveraged for other processes, including synthesis gas compression. It is shown that as long as a carbon source, such as diesel fuel, is already present, additional diesel can be made by recovering and recycling the CO2 in the diesel exhaust. The amount of new diesel that can be made is directly related to the fraction of available CO2 that is recovered, with 100% recovery being equivalent to doubling the diesel fuel. The volume of aluminum required to accomplish this is lower than simply bringing twice as much diesel and results in a 50% increase in volumetric energy density. That is, 50% fewer fuel convoys would be required for fuel delivery. Moreover, aluminum has the potential to be exploited as a structural fuel that can be used as pallets, containers, etc., before being consumed to produce diesel. Furthermore, FT diesel production via aluminum and CO2 can be achieved without sacrificing electrical power generation.

Airport wind information is critical for ensuring safe aircraft operations and for managing runway configurations. Airports across the National Airspace System (NAS) are served by a wide variety of wind sensing systems that have been deployed over many decades. This analysis presents a survey of existing systems and user requirements, identifies potential shortfalls, and offers recommendations for improvements to support the long-term goals of the FAA NextGen system.

A flow-injection analysis tandem mass spectrometry (FIA MSMS) method was developed for rapid quantitative analysis of 10 different inorganic and organic explosives. Performance is optimized by tailoring the ionization method (APCI/ESI), de-clustering potentials, and collision energies for each specific analyte. In doing so, a single instrument can be used to detect urea nitrate, potassium chlorate, 2,4,6-trinitrotoluene, 2,4,6-trinitrophenylmethylnitramine, triacetone triperoxide, hexamethylene triperoxide diamine, pentaerythritol tetranitrate, 1,3,5-trinitroperhydro-1,3,5-triazine, nitroglycerin, and octohy-dro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine with sensitivities all in the picogram per milliliter range. In conclusion, FIA APCI/ESI MSMS is a fast (<1 min/sample), sensitive (~pg/mL LOQ), and precise (intraday RSD < 10%) method for trace explosive detection that can play an important role in criminal and attributional forensics, counterterrorism, and environmental protection areas, and has the potential to augment or replace several of the existing explosive detection methods.