Defense Advanced Research Projects Agency

We measured the elastic properties and intrinsic breaking strength of free-standing monolayer graphene membranes by nanoindentation in an atomic force microscope. The force-displacement behavior is interpreted within a framework of nonlinear elastic stress-strain response, and yields second- and third-order elastic stiffnesses of 340 newtons per meter (N m(-1)) and -690 Nm(-1), respectively. The breaking strength is 42 N m(-1) and represents the intrinsic strength of a defect-free sheet. These quantities correspond to a Young's modulus of E = 1.0 terapascals, third-order elastic stiffness of D = -2.0 terapascals, and intrinsic strength of sigma(int) = 130 gigapascals for bulk graphite. These experiments establish graphene as the strongest material ever measured, and show that atomically perfect nanoscale materials can be mechanically tested to deformations well beyond the linear regime.

This review describes a new paradigm of electronics based on the spin degree of freedom of the electron. Either adding the spin degree of freedom to conventional charge-based electronic devices or using the spin alone has the potential advantages of nonvolatility, increased data processing speed, decreased electric power consumption, and increased integration densities compared with conventional semiconductor devices. To successfully incorporate spins into existing semiconductor technology, one has to resolve technical issues such as efficient injection, transport, control and manipulation, and detection of spin polarization as well as spin-polarized currents. Recent advances in new materials engineering hold the promise of realizing spintronic devices in the near future. We review the current state of the spin-based devices, efforts in new materials fabrication, issues in spin transport, and optical spin manipulation.

Wireless microsensor networks have been identified as one of the most important technologies for the 21st century. This paper traces the history of research in sensor networks over the past three decades, including two important programs of the Defense Advanced Research Projects Agency (DARPA) spanning this period: the Distributed Sensor Networks (DSN) and the Sensor Information Technology (SensIT) programs. Technology trends that impact the development of sensor networks are reviewed, and new applications such as infrastructure security, habitat monitoring, and traffic control are presented. Technical challenges in sensor network development include network discovery, control and routing, collaborative signal and information processing, tasking and querying, and security. The paper concludes by presenting some recent research results in sensor network algorithms, including localized algorithms and directed diffusion, distributed tracking in wireless ad hoc networks, and distributed classification using local agents.

Explainability is essential for users to effectively understand, trust, and manage powerful artificial intelligence applications.

The emerging discipline of software risk management is described. It is defined as an attempt to formalize the risk-oriented correlates of success into a readily applicable set of principles and practices. Its objectives are to identify, address, and eliminate risk items before they become either threats to successful software operation or major sources of software rework. The basic concepts are set forth, and the major steps and techniques involved in software risk management are explained. Suggestions for implementing risk management are provided.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Gripping and holding of objects are key tasks for robotic manipulators. The development of universal grippers able to pick up unfamiliar objects of widely varying shape and surface properties remains, however, challenging. Most current designs are based on the multifingered hand, but this approach introduces hardware and software complexities. These include large numbers of controllable joints, the need for force sensing if objects are to be handled securely without crushing them, and the computational overhead to decide how much stress each finger should apply and where. Here we demonstrate a completely different approach to a universal gripper. Individual fingers are replaced by a single mass of granular material that, when pressed onto a target object, flows around it and conforms to its shape. Upon application of a vacuum the granular material contracts and hardens quickly to pinch and hold the object without requiring sensory feedback. We find that volume changes of less than 0.5% suffice to grip objects reliably and hold them with forces exceeding many times their weight. We show that the operating principle is the ability of granular materials to transition between an unjammed, deformable state and a jammed state with solid-like rigidity. We delineate three separate mechanisms, friction, suction, and interlocking, that contribute to the gripping force. Using a simple model we relate each of them to the mechanical strength of the jammed state. This advance opens up new possibilities for the design of simple, yet highly adaptive systems that excel at fast gripping of complex objects.

Reproducible dry and wet transfer techniques were developed to improve the transfer of large-area monolayer graphene grown on copper foils by chemical vapor deposition (CVD). The techniques reported here allow transfer onto three different classes of substrates: substrates covered with shallow depressions, perforated substrates, and flat substrates. A novel dry transfer technique was used to make graphene-sealed microchambers without trapping liquid inside. The dry transfer technique utilizes a polydimethylsiloxane frame that attaches to the poly(methyl methacrylate) spun over the graphene film, and the monolayer graphene was transferred onto shallow depressions with 300 nm depth. The improved wet transfer onto perforated substrates with 2.7 μm diameter holes yields 98% coverage of holes covered with continuous films, allowing the ready use of Raman spectroscopy and transmission electron microscopy to study the intrinsic properties of CVD-grown monolayer graphene. Additionally, monolayer graphene transferred onto flat substrates has fewer cracks and tears, as well as lower sheet resistance than previous transfer techniques. Monolayer graphene films transferred onto glass had a sheet resistance of ∼980 Ω/sq and a transmittance of 97.6%. These transfer techniques open up possibilities for the fabrication of various graphene devices with unique configurations and enhanced performance.

The field of thermoelectrics has progressed enormously and is now growing steadily because of recently demonstrated advances and strong global demand for cost-effective, pollution-free forms of energy conversion. Rapid growth and exciting innovative breakthroughs in the field over the last 10-15 years have occurred in large part due to a new fundamental focus on nanostructured materials. As a result of the greatly increased research activity in this field, a substantial amount of new data--especially related to materials--have been generated. Although this has led to stronger insight and understanding of thermoelectric principles, it has also resulted in misconceptions and misunderstanding about some fundamental issues. This article sets out to summarize and clarify the current understanding in this field; explain the underpinnings of breakthroughs reported in the past decade; and provide a critical review of various concepts and experimental results related to nanostructured thermoelectrics. We believe recent achievements in the field augur great possibilities for thermoelectric power generation and cooling, and discuss future paths forward that build on these exciting nanostructuring concepts.

Electric double-layer capacitors (DLCs) can have high storage capacity, but their porous electrodes cause them to perform like resistors in filter circuits that remove ripple from rectified direct current. We have demonstrated efficient filtering of 120-hertz current with DLCs with electrodes made from vertically oriented graphene nanosheets grown directly on metal current collectors. This design minimized electronic and ionic resistances and produced capacitors with RC time constants of less than 200 microseconds, in contrast with ~1 second for typical DLCs. Graphene nanosheets have a preponderance of exposed edge planes that greatly increases charge storage as compared with that of designs that rely on basal plane surfaces. Capacitors constructed with these electrodes could be smaller than the low-voltage aluminum electrolyte capacitors that are typically used in electronic devices.

Dramatic success in machine learning has led to a new wave of AI applications (for example, transportation, security, medicine, finance, defense) that offer tremendous benefits but cannot explain their decisions and actions to human users. DARPA's explainable artificial intelligence (XAI) program endeavors to create AI systems whose learned models and decisions can be understood and appropriately trusted by end users. Realizing this goal requires methods for learning more explainable models, designing effective explanation interfaces, and understanding the psychologic requirements for effective explanations. The XAI developer teams are addressing the first two challenges by creating ML techniques and developing principles, strategies, and human‐computer interaction techniques for generating effective explanations. Another XAI team is addressing the third challenge by summarizing, extending, and applying psychologic theories of explanation to help the XAI evaluator define a suitable evaluation framework, which the developer teams will use to test their systems. The XAI teams completed the first of this 4‐year program in May 2018. In a series of ongoing evaluations, the developer teams are assessing how well their XAM systems' explanations improve user understanding, user trust, and user task performance.

The search for water on the surface of the anhydrous Moon had remained an unfulfilled quest for 40 years. However, the Moon Mineralogy Mapper (M3) on Chandrayaan-1 has recently detected absorption features near 2.8 to 3.0 micrometers on the surface of the Moon. For silicate bodies, such features are typically attributed to hydroxyl- and/or water-bearing materials. On the Moon, the feature is seen as a widely distributed absorption that appears strongest at cooler high latitudes and at several fresh feldspathic craters. The general lack of correlation of this feature in sunlit M3 data with neutron spectrometer hydrogen abundance data suggests that the formation and retention of hydroxyl and water are ongoing surficial processes. Hydroxyl/water production processes may feed polar cold traps and make the lunar regolith a candidate source of volatiles for human exploration.

A protocol that supports the sharing of resources that exist in different packet switching networks is presented. The protocol provides for variation in individual network packet sizes, transmission failures, sequencing, flow control, end-to-end error checking, and the creation and destruction of logical process-to-process connections. Some implementation issues are considered, and problems such as internetwork routing, accounting, and timeouts are exposed.

Rapid progress in unconstrained face recognition has resulted in a saturation in recognition accuracy for current benchmark datasets. While important for early progress, a chief limitation in most benchmark datasets is the use of a commodity face detector to select face imagery. The implication of this strategy is restricted variations in face pose and other confounding factors. This paper introduces the IARPA Janus Benchmark A (IJB-A), a publicly available media in the wild dataset containing 500 subjects with manually localized face images. Key features of the IJB-A dataset are: (i) full pose variation, (ii) joint use for face recognition and face detection benchmarking, (iii) a mix of images and videos, (iv) wider geographic variation of subjects, (v) protocols supporting both open-set identification (1:N search) and verification (1:1 comparison), (vi) an optional protocol that allows modeling of gallery subjects, and (vii) ground truth eye and nose locations. The dataset has been developed using 1,501,267 million crowd sourced annotations. Baseline accuracies for both face detection and face recognition from commercial and open source algorithms demonstrate the challenge offered by this new unconstrained benchmark.

We introduce a new large-scale video dataset designed to assess the performance of diverse visual event recognition algorithms with a focus on continuous visual event recognition (CVER) in outdoor areas with wide coverage. Previous datasets for action recognition are unrealistic for real-world surveillance because they consist of short clips showing one action by one individual [15, 8]. Datasets have been developed for movies [11] and sports [12], but, these actions and scene conditions do not apply effectively to surveillance videos. Our dataset consists of many outdoor scenes with actions occurring naturally by non-actors in continuously captured videos of the real world. The dataset includes large numbers of instances for 23 event types distributed throughout 29 hours of video. This data is accompanied by detailed annotations which include both moving object tracks and event examples, which will provide solid basis for large-scale evaluation. Additionally, we propose different types of evaluation modes for visual recognition tasks and evaluation metrics along with our preliminary experimental results. We believe that this dataset will stimulate diverse aspects of computer vision research and help us to advance the CVER tasks in the years ahead.

This paper deals with a relatively new area of radio-frequency (RF) technology based on microelectro-mechanical systems (MEMS). RF MEMS provides a class of new devices and components which display superior high-frequency performance relative to conventional (usually semiconductor) devices, and which enable new system capabilities. In addition, MEMS devices are designed and fabricated by techniques similar to those of very large-scale integration, and can be manufactured by traditional batch-processing methods. In this paper, the only device addressed is the electrostatic microswitch - perhaps the paradigm RF-MEMS device. Through its superior performance characteristics, the microswitch is being developed in a number of existing circuits and systems, including radio front-ends, capacitor banks, and time-delay networks. The superior performance combined with ultra-low-power dissipation and large-scale integration should enable new system functionality as well. Two possibilities addressed here are quasi-optical beam steering and electrically reconfigurable antennas.

In this era, information technology is revolutionizing almost every domain of technology and society, whereas the ‘complexity revolution’ is occurring in science at a silent pace. In this paper, we look at the impact of the two, in the context of supply-chain networks. With the advent of information technology, supply chains have acquired a complexity almost equivalent to that of biological systems. However, one of the major challenges that we are facing in supply-chain management is the deployment of coordination strategies that lead to adaptive, flexible and coherent collective behaviour in supply chains. The main hurdle has been the lack of the principles that govern how supply chains with complex organizational structure and function arise and develop, and what organizations and functionality are attainable, given specific kinds of lower-level constituent entities. The study of Complex Adaptive Systems (CAS), has been a research effort attempting to find common characteristics and/or formal distinctions among complex systems arising in diverse domains (like biology, social systems, ecology and technology) that might lead to a better understanding of how complexity occurs, whether it follows any general scientific laws of nature, and how it might be related to simplicity. In this paper, we argue that supply chains should be treated as a CAS. With this recognition, we propose how various concepts, tools and techniques used in the study of CAS can be exploited to characterize and model supply-chain networks. These tools and techniques are based on the fields of nonlinear dynamics, statistical physics and information theory.

UV response of ZnO nanowire nanosensor has been studied under ambient condition. By utilizing Schottky contact instead of Ohmic contact in device fabrication, the UV sensitivity of the nanosensor has been improved by four orders of magnitude, and the reset time has been drastically reduced from approximately 417 to approximately 0.8 s. By further surface functionalization with function polymers, the reset time has been reduced to approximately 20 ms even without correcting the electronic response of the measurement system. These results demonstrate an effective approach for building high response and fast reset UV detectors.

The DARPA's Explainable Artificial Intelligence (XAI) program endeavors to create AI systems whose learned models and decisions can be understood and appropriately trusted by end users. This talk will summarize the XAI program and present highlights from these Phase 1 evaluations.

We present a method to design controllers for safety specifications in hybrid systems. The hybrid system combines discrete event dynamics with nonlinear continuous dynamics: the discrete event dynamics model linguistic and qualitative information and naturally accommodate mode switching logic, and the continuous dynamics model the physical processes themselves, such as the continuous response of an aircraft to the forces of aileron and throttle. Input variables model both continuous and discrete control and disturbance parameters. We translate safety specifications into restrictions on the system's reachable sets of states. Then, using analysis based on optimal control and game theory for automata and continuous dynamical systems, we derive Hamilton-Jacobi equations whose solutions describe the boundaries of reachable sets. These equations are the heart of our general controller synthesis technique for hybrid systems, in which we calculate feedback control laws for the continuous and discrete variables, which guarantee that the hybrid system remains in the "safe subset" of the reachable set. We discuss issues related to computing solutions to Hamilton-Jacobi equations. Throughout, we demonstrate out techniques on examples of hybrid automata modeling aircraft conflict resolution, autopilot flight mode switching, and vehicle collision avoidance.

We report the synthesis and thermoelectric characterization of composite nanocrystals composed of a tellurium core functionalized with the conducting polymer poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS). Solution processed nanocrystal films electronically out perform both PEDOT:PSS and unfunctionalized Te nanorods while retaining a polymeric thermal conductivity, resulting in a room temperature ZT ∼ 0.1. This combination of electronic and thermal transport indicates the potential for tailored transport in nanoscale organic/inorganic heterostructures.