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2019-2020 Assessment of the Army Research Laboratory (2021)

Chapter: 7 Crosscutting Recommendations and Exceptional Accomplishments

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Suggested Citation:"7 Crosscutting Recommendations and Exceptional Accomplishments." National Academies of Sciences, Engineering, and Medicine. 2021. 2019-2020 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/26325.
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7

Crosscutting Recommendations and Exceptional Accomplishments

CROSSCUTTING RECOMMENDATIONS

The following crosscutting conclusions, recommendations, and exceptional accomplishments are based on the projects and programs presented, as a full spectrum of projects and programs within each Army Research Laboratory (ARL) research core competency and the interrelating mapping across all research core competencies’ projects and programs were not provided to the Army Research Laboratory Technical Assessment Board (ARLTAB).

Four major changes to the Army and ARL clearly present challenges to all research core competencies. These are the recent Army doctrinal changes to multi-domain operations (MDO), the reorganization that put ARL under the newly formed Army Futures Command (AFC), the divestiture of 6.3 work to other organizations, and emphasis on “disruptive” technologies. Specifically, ARL has been charged with focusing on foundational research; targeting and conducting research to drive change within, across, and between disciplines; creating knowledge products for warfighting concepts; development of operating systems; and interacting with universities via the ARL Open Campus and the Army Research Office (ARO).

There is significant attention given to artificial intelligence (AI) and machine learning (ML) in research programs across different portfolios at ARL. At present, the scope of these research efforts is rather narrowly focused on technical specialty area. Fundamental research issues related to innovative ML techniques, AI implementation in resource-constrained environments, and trust and security of AI systems must still be addressed on a broader scale. It is also important to recognize that it is suboptimal to seek algorithmic advances in these areas without due consideration of hardware developments that are taking place in parallel. Given the potential of a disruptive impact of these technologies on Army operations, it is important to develop a comprehensive and integrative research plan in this emerging area. These technologies can have a transformational impact on key elements of future Army operations.

Crosscutting Recommendation 1: Activities in areas of artificial intelligence and machine learning are pervasive across Army Research Laboratory (ARL) research portfolios. ARL should emphasize the identification of a set of fundamental research questions that can provide a long-term focus for research in this area. Rich and disparate data sets collected across multiple research domains at ARL (materials, weapon systems, human-machine interactions, for example) could provide the context against which answers to these research questions are pursued.

Software development has advanced at a tremendous pace over the past few years. Much of the reason for this rapid development is the increasingly common practice of employing open source software to build software platforms. Because of this rapid pace, it will be difficult for ARL to remain competitive

Suggested Citation:"7 Crosscutting Recommendations and Exceptional Accomplishments." National Academies of Sciences, Engineering, and Medicine. 2021. 2019-2020 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/26325.
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within the software development space. ARL needs to be a member of GitHub1 (if it is not already), with classified information being handled appropriately.

Crosscutting Recommendation 2: The Army Research Laboratory (ARL) should develop a mechanism for collaboration between ARL and industry on software development to ensure that it continues to track the state of the art. Specifically, ARL should use and develop software platforms in collaboration with open source software libraries that will enable ARL to keep up to date and to rapidly develop software.

High-quality research cannot be pursued in a vacuum. The probability of eventual success of ARL long-range research programs will be enhanced through cognizance of outside efforts and, where appropriate, establishing formal collaborations. Establishing contacts will require, at a minimum, attendance at professional meetings and conferences and possible travel to and from leading institutions.

Crosscutting Recommendation 3: To improve career prospects of early-career researchers and improve the overall quality of the research, the Army Research Laboratory (ARL) should further encourage and facilitate all members of the research team, including junior members, to make the scientific contacts and interactions necessary to adequately place their research in the context of the entire field.

EXCEPTIONAL ACCOMPLISHMENTS

The following are the exceptional accomplishments for each core competency area.

Network and Information Sciences

The research related to imitation was found to be particularly noteworthy, drawing upon the notion that autonomous agents can learn via imitation of human “teachers,” of which the Army has many. The two approaches (reinforcement learning and inverse reinforcement learning) are not new, but the research here addresses cutting-edge technology, and the results are potentially disruptive. This represents a new way to automate. The demonstration of virtual reality was also viewed positively, with the recognition that notably high-quality experience could be disruptive for situational awareness related to Army operations. ARL could leverage the platform as a test-bed for ideas such as integration of satellite perspective, multiperson teaming, and human/agent teaming, and so on.

Some research projects were considered to be exceptionally strong and offer significant potential to contribute to U.S. Army capabilities. Research related to active defense and dynamic watermarking for cyber defense of vehicles and other cyber-physical systems was one such effort where commercial vendors are unlikely to provide solutions. This significant cyber vulnerability represents a pervasive problem in most major Army vehicles.

Another noteworthy project was in the area of narrative generation as it relates to human-robot interaction. The focus of this effort was in developing an understanding and explanation of visual scenery by extracting information and adding captions to video stills that describe relevant scene information and ultimately what transpired in the sum of correlated scene(s). This result alone, if successful, could provide the warfighter a significant workload reduction in processing full-motion video or still-frame imagery.

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1 GitHub is a major open source group—see https://github.com/.

Suggested Citation:"7 Crosscutting Recommendations and Exceptional Accomplishments." National Academies of Sciences, Engineering, and Medicine. 2021. 2019-2020 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/26325.
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While not the focus of the ongoing work, this research could potentially also save communications bandwidth by the transmittal of the textual descriptions of the scenes instead of the full-motion video.

Computational and Atmospheric Sciences

The artificial intelligence/machine learning at the edge: inferencing engines on field programmable gate array (FPGA) project concerns development of AI/ML inference engines that can be deployed as a digital chip (application-specific integrated circuit, or ASIC) in Army environments even when network connectivity is not available (e.g., for higher precision targeting). Further, when network connectivity is regained, learning online and training could resume to further enhance target solution quality. Such an ASIC-based online/off-line device could have multiple applications across the cross-functional teams if the underlying systems design and engineering research and development (R&D) were to be successful. The technical approach involving a software framework, extensible instruction set architecture, and algorithm redesign/refactoring of convolutional neural nets (CNNs) seeks to reduce computational costs by an order of magnitude and increase efficiencies to meet space, weight, power, and time-to-solution constraints. This project spans the software-hardware space to deliver performance, portability, and programmability across multiple applications, future CNN algorithms, and future field programmable gate array (FPGA) architectures. Initial results seem encouraging, and there are many possible pathways to transition to the field while advancing the basic research. With improved direction, resources, and leveraging of related research, there is the potential for outstanding successes.

The work on improving numerical weather prediction over short time frames through assimilation of radar observations represents innovative research in support of an impactful real-world application and is especially noteworthy. ARL—collaborating with the Combat Capabilities Development Command (CCDC) Aviation and Missile Center, formerly known as AMRDEC—is developing a new mesoscale modeling capability that will provide forecasts in data-sparse regions such as the test facility on Kwajalein Atoll in the Pacific Ocean. Owing to the site’s unique remote location, current Department of Defense (DoD) weather capabilities cannot meet these needs. The ARL approach assimilates radar observations already available at the location into the widely used weather research and forecasting (WRF) prediction model. Radar measurements of reflectivity are then used to infer rates of latent heating for input to WRF. Assessments of the new modeling capability demonstrate increases in probability of correct prediction of weather phenomena through assimilation of radar data by factors up to one order of magnitude.

Human Sciences

ARL has developed robust, contextualized human-autonomy teaming research laboratories, and has developed state-of-the-art synthetic task environments and data collection platforms through Cyber-Human Integrated Modeling and Experimentation Range Army (CHIMERA) and Information for Mixed Squads Laboratory (INFORMS). INFORMS has the potential to gather data that do not exist anywhere else on platoon-size teams. This could lead to very interesting science on platoon-size interactions, shared mental models, and attention allocation. In addition, the CHIMERA laboratory has outstanding metrics collection capabilities for cyber-human systems studies. ARL has significant experience and investment in neurophysiological measures to infer human states, as well as instrumented laboratories and simulation capabilities. Such advanced facilities promise to provide the ecological validity and experimental control needed to generate empirical evidence to address critical research questions and advance the science of human-technology integration.

A significant achievement includes the collection and management of large data sets. Ambitious data collection activities—over time, between and within subjects, in the field—with target populations have created a number of large data sets that will be used to drive ML and simulation. The team has access to

Suggested Citation:"7 Crosscutting Recommendations and Exceptional Accomplishments." National Academies of Sciences, Engineering, and Medicine. 2021. 2019-2020 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/26325.
×

good information technology infrastructure to store and protect these organized and time-stamped infrastructure components. These researchers are pioneering something new.

The group has established a unique and valuable niche for engineering advances for neurophysiological monitoring in dynamic tasks, such as ambulatory electroencephalogram (EEG) and eye-gaze tracking. Specific contributions include novel algorithms for achieving accurate, reliable, and online detection of evoked response signals in the EEG while subjects are engaged in complex, operationally relevant activities. The group has also made novel contributions to the hardware and software technologies for EEG monitoring, particularly in mobile scenarios, which are prone to contamination from motion and environmental noise sources. The group has taken a rigorous approach to addressing these problems, developing a novel test-bed for isolating and eliminating sources of noise through innovative electrode and signal processing strategies. The group has also created unique testing platforms for studying human performance in complex tasks involving teaming among groups of humans and autonomous vehicles in ecologically valid settings. These platforms are generating data sets that are unique and exceedingly rich in measuring physiological and behavioral aspects of human performance, spanning multiple time scales and modalities. In addition to supporting immediate questions, these data sets could be leveraged to support secondary analyses within and beyond ARL.

Materials and Manufacturing Sciences

The advanced solid-state lasers group continues to be one of the “crown jewels” for ARL by driving infrared laser technology with the recent achievement of lasing at 3 microns in new, low phonon energy hosts such as barium fluoride and yttrium lithium fluoride. The sensor protection scientific team is commended for the clever iridium chemistry that is being pursued to develop broadband reverse saturable absorption materials and the GMR filters. Among the impressive accomplishments of the integrated photonics research team is the dramatic improvement in the performance of optical frequency combs and the successful demonstration of electrically steerable phased arrays.

The scientific quality of the work in diamond electronics is excellent and makes a significant contribution to the field. The effort is currently focused on improving device performance by introducing transition metal oxides or boron nitride to stabilize the diamond surface. The group has achieved state-of-the-art direct current (DC) characteristics utilizing solid oxide cap layers. ARL has an impressive development of two important optical power device types for the Army—the improvement and standardization of high-voltage power devices and the creation of UV emitters and detectors for covert communications and sensing. All the work is on optical power devices is of high technical quality comparable to the best peer laboratories.

The team working on aqueous lithium-ion battery (LIB) materials and systems is making exceptional advancements in the science and technology (S&T) of electrical energy storage with lithium-ion batteries. Aqueous electrolytes are nonflammable and thus dramatically safer than conventional electrolytes for military use. This ARL team has advanced the science of ultra-concentrated aqueous electrolytes that has enabled the use of high-voltage electrodes that previously were incompatible with aqueous systems. Work by the ARL team spans a broad range from computational modeling of interfacial chemistry to fabrication of cells of a size (approximately 5 Ah) suitable for field use. This is a wide range of activity for a relatively small group that has deservedly garnered positive international recognition. Work on wireless energy transmission is also making excellent progress with capabilities, for local wireless energy transmission (centimeters to meters) using electronic and acoustical waves, and is among the best in the world.

Suggested Citation:"7 Crosscutting Recommendations and Exceptional Accomplishments." National Academies of Sciences, Engineering, and Medicine. 2021. 2019-2020 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/26325.
×

Propulsion Sciences

The program on tribology and lubrication science is well formulated and aimed at understanding fundamental physics. This program has demonstrated commendable use of experimental facilities in fundamental research. Application of this capability to study the failure mechanisms for fuel pumps is an excellent application of fundamental capability in resolution of relevant problems—for example, operation using different fuels and under different atmospheric conditions.

A particular exemplar is the microstructure deep learning (DL) that is developing AI/ML approaches by fusing simulation and experimental data. The molecular dynamics modeling in both the computational design of shape memory polymer actuation and the tailorable and multifunctional dynamic polymer networks can provide data to implement AI methods. Dynamic polymer networks research is a unique combination of morphing, self-healing, and shape memory. The non-equilibrium molecular motor research is on the leading edge of bio-hybrid basic research and will help inform ARL to shape its future portfolio.

The tilt rotor development versus quadcopter introduces a new capability—the ability to change attitude and apply force without lateral motion. When added to a quadrotor small-unmanned aerial system (sUAS), the tilting and force capability showed promise over pure quadrotor variants, enabling a new level of control. This development offers capabilities beyond a standard UAS, potentially aiding future designs, and could lead to disruptive, game-changer functionalities. The interplay between modeling, simulation, and testing in this project was excellent.

Suggested Citation:"7 Crosscutting Recommendations and Exceptional Accomplishments." National Academies of Sciences, Engineering, and Medicine. 2021. 2019-2020 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/26325.
×
Page 108
Suggested Citation:"7 Crosscutting Recommendations and Exceptional Accomplishments." National Academies of Sciences, Engineering, and Medicine. 2021. 2019-2020 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/26325.
×
Page 109
Suggested Citation:"7 Crosscutting Recommendations and Exceptional Accomplishments." National Academies of Sciences, Engineering, and Medicine. 2021. 2019-2020 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/26325.
×
Page 110
Suggested Citation:"7 Crosscutting Recommendations and Exceptional Accomplishments." National Academies of Sciences, Engineering, and Medicine. 2021. 2019-2020 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/26325.
×
Page 111
Suggested Citation:"7 Crosscutting Recommendations and Exceptional Accomplishments." National Academies of Sciences, Engineering, and Medicine. 2021. 2019-2020 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/26325.
×
Page 112
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The National Academies of Sciences, Engineering, and Medicine's Army Research Laboratory Technical Assessment Board (ARLTAB) provides biennial assessments of the scientific and technical quality of the Army Research Laboratory (ARL). These assessments include the development of findings and recommendations related to the quality of ARL's research, development, and analysis programs. 2019-2020 Assessment of the Army Research Laboratory reviews the following research core competencies of ARL: human sciences, network and information sciences, computational sciences, materials and manufacturing sciences, and propulsion sciences. This biennial report summarizes the findings of the ARLTAB from reviews conducted in 2019 and 2020.

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