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Analog parallel processor hardware for high speed pattern recognition
A VLSI-based analog processor for fully parallel, associative, high-speed pattern matching is reported. The processor consists of two main components: an analog memory matrix for storage of a library of patterns, and a winner-take-all (WTA) circuit for selection of the stored pattern that best matches an input pattern. An inner product is generated between the input vector and each of the stored memories. The resulting values are applied to a WTA network for determination of the closest match. Patterns with up to 22 percent overlap are successfully classified with a WTA settling time of less than 10 microsec. Applications such as star pattern recognition and mineral classification with bounded overlap patterns have been successfully demonstrated. This architecture has a potential for an overall pattern matching speed in excess of 10 exp 9 bits per second for a large memory.
Architecture Modeling on the Europa Project
In 2015 NASA chartered a partnership between the Jet Propulsion Laboratory (JPL) and the Johns Hopkins Applied Physics Laboratory (APL) to begin planning a mission to study the Jovian moon Europa. The project has adopted a Model-Based Systems Engineering (MBSE) approach to its architecting process since its early formulation, developing certain modeling practices and tools as needed, with the expectation that this process would result in a more consistent and verifiable architecture than with a more traditional document-based approach. A sound architecture is essential to provide the rationale for requirements on the system design, and to define the trade space of acceptable design points within which technical and programmatic concerns as well as project objectives can be addressed. This paper provides an overview of the framework used by the Europa project to describe the mission architecture and discusses how a system model was instrumental in providing a single-source-of-truth for this description. Several key modeling patterns to represent the architecture are presented, along with audit methods to ensure the consistency and the correctness of the model. Finally, the benefits and challenges of using a model-based approach to generate traditional requirements documents and other gate products are assessed.
Identification and functional analysis of strigolactone pathway genes regulating tillering traits in sugarcane
Abstract Saccharum officinarum and Saccharum spontaneum are two fundamental species of modern sugarcane cultivars, exhibiting divergent tillering patterns crucial for sugarcane architecture and yield. Strigolactones (SLs), a class of plant hormones, are considered to play a central role in shaping plant form and regulating tillering. Our study highlights the distinct tillering patterns observed between S. officinarum and S. spontaneum and implicates significant differences in SL levels in root exudates between the two species. Treatment with rac-GR24 (an artificial SL analog) suppressed tillering in S. spontaneum. Based on transcriptome analysis, we focused on two genes, TRANSCRIPTION ELONGATION FACTOR 1 (TEF1) and CIRCADIAN CLOCK ASSOCIATED1 (CCA1), which show higher expression in S. spontaneum or S. officinarum, respectively. While the overexpression of SoCCA1 did not lead to significant phenotypic differences, overexpression of SsTEF1 in rice stimulated tillering and inhibited plant height, demonstrating its role in tillering regulation. However, the overexpression of suggests that SoCCA1 may not be the key regulator of sugarcane tillering. Yeast one-hybrid assays identified four transcription factors (TFs) regulating SsTEF1 and four and five TFs regulating SsCCA1 and SoCCA1. This study provides a theoretical foundation for deciphering the molecular mechanisms underlying the different tillering behaviors between S. officinarum and S. spontaneum, providing valuable insights for the molecular-based design of sugarcane breeding strategies.
Inferring safety critical events from vehicle kinematics in naturalistic driving environment: Application of deep learning Algorithms
Advances in sensing technology has enabled the collection of countless terabytes of second-by-second kinematics data. Such data provides opportunities for real-time monitoring of driving behavior and identification of safety critical events (SCEs) including crashes and near crashes. The concept of volatility is relevant in this context, which identifies instability and erratic variations in driving behavior prior to involvement in SCEs. This study utilized vehicle kinematics from a large-scale naturalistic driving data to develop a deep learning approach based on 1D convolutional neural networks (CNN) for inferring SCEs. The data are unique in the sense that such accurate pre-crash data at high fidelity are not available in traditional crash repositories. This study contributes to the literature by providing a first attempt at predicting responses to SCEs by developing deep learning-based CNN architectures using novel driving volatility based kinematic thresholds for a sample of 9553 events. The key contribution lies in developing a volatility-based CNN input layout that is acceptable to CNN schemes and represents the motion kinematics such as speed, acceleration and volatility measures. Several 1D-CNN architectures were developed using layers, numbers of convolutions, layer patterns, and kernels. Shallow and deep architectures were tested, revealing higher accuracy of shallow architectures in detecting SCEs. The optimal number of epochs were identified using an early stopping method while the CNN performance was improved by increasing the number of epochs. The ensemble CNN had the highest predictive accuracy of 95.6% for detection of crashes and near crashes, which was 2.5% higher than the optimal CNN using 20% hold out test data. The ensemble CNN also outperformed classical machine learning models and model performance reported in past studies on detection of SCEs. Finally, these results have implications for identification of safety hotspots and providing real-time alerts and warnings in connected and highly automated vehicle environment including society of automotive engineers levels 3–5.
Using data tagging to improve the performance of Kanerva's sparse distributed memory
The standard formulation of Kanerva's sparse distributed memory (SDM) involves the selection of a large number of data storage locations, followed by averaging the data contained in those locations to reconstruct the stored data. A variant of this model is discussed, in which the predominant pattern is the focus of reconstruction. First, one architecture is proposed which returns the predominant pattern rather than the average pattern. However, this model will require too much storage for most uses. Next, a hybrid model is proposed, called tagged SDM, which approximates the results of the predominant pattern machine, but is nearly as efficient as Kanerva's original formulation. Finally, some experimental results are shown which confirm that significant improvements in the recall capability of SDM can be achieved using the tagged architecture.
Optimal invariant sets for atomistic machine learning
The representation of atomic configurations for machine learning models has led to numerous sets of descriptors. However, many descriptor sets are incomplete and/or functionally dependent. Incomplete sets cannot faithfully represent atomic environments. Yet complete constructions often suffer from a high degree of functional dependence, where some descriptors are functions of others. These redundant descriptors do not improve discrimination between atomic environments. We employ pattern recognition techniques to remove dependent descriptors to produce the smallest possible set that satisfies completeness. We apply this in two ways: First, we refine an existing description, the atomic cluster expansion. Second, we augment an incomplete construction, yielding a new message-passing neural network architecture that can recognize up to 5-body patterns. This architecture shows strong accuracy on state-of-the-art benchmarks while retaining low computational cost. Our results demonstrate the utility of this strategy to optimize descriptor sets across a range of descriptors and application datasets.
Achieving Lights-Out Operation of SMAP Using Ground Data System Automation
The approach used in the SMAP ground data system to provide reliable, automated capabilities to conduct unattended operations has been presented. The impacts of automation on the ground data system architecture were discussed, including the three major automation patterns identified for SMAP and how these patterns address the operations use cases. The architecture and approaches used by SMAP will set the baseline for future JPL Earth Science missions.
Patterns in interactions of variably acetylated xylans with hydrophobic cellulose surfaces
The recalcitrance of plant cell wall lignocellulosic biomass to deconstruction is a major hurdle to sustainable biofuel/bioproduct economy. A multitude of interactions stabilize lignocellulosic biomass structure. Among these, tight packing of hemicellulose-cellulose is partly responsible for biomass recalcitrance. Here, unrestrained molecular dynamics simulations are employed to understand the influence of the nature and pattern of naturally-occuring acetyl decorations of the xylan backbone on interactions with the (100) hydrophobic cellulose surface. Periodically O2-acetylated xylan (2AcX) assume twofold helical screw conformations that are stabilized by a combination of multiple hydrophobic contacts and hydrogen bonds with the hydrophobic cellulose surface. In contrast, acetylation at the O3 position in xylan obstructs interactions, thereby adopting threefold helical screw conformations that potentially preferably interact with lignin rather than cellulose. Fully acetylated xylan desorbs from the surface implying a minimum number of unsubstituted residues on the xylan backbone is required for interaction with the surface. Here, the substituted residues must form ~ 20% fewer contacts than the unsubstituted residues to sustain stable twofold helical screw xylan conformations on the cellulose surface. Thus, specific roles of macromolecular conformations of cellulose and hemicellulose in influencing the supramolecular interactions and function of plant cell walls have been determined.
Performance trade-offs in reconfigurable networks for HPC
Designing efficient interconnects to support high-bandwidth and low-latency communication is critical toward realizing high performance computing (HPC) and data center (DC) systems in the exascale era. At extreme computing scales, providing the requisite bandwidth through overprovisioning becomes impractical. These challenges have motivated studies exploring reconfigurable network architectures that can adapt to traffic patterns at runtime using optical circuit switching. Despite the plethora of proposed architectures, surprisingly little is known about the relative performances and trade-offs among different reconfigurable network designs. We aim to bridge this gap by tackling two key issues in reconfigurable network design. First, we study how cost, power consumption, network performance, and scalability vary based on optical circuit switch (OCS) placement in the physical topology. Specifically, we consider two classes of reconfigurable architectures: one that places OCSs between top-of-rack (ToR) switches—ToR-reconfigurable networks (TRNs)—and one that places OCSs between pods of racks—pod-reconfigurable networks (PRNs). Second, we tackle the effects of reconfiguration frequency on network performance. Our results, based on network simulations driven by real HPC and DC workloads, show that while TRNs are optimized for low fan-out communication patterns, they are less suited for carrying high fan-out workloads. PRNs exhibit better overall trade-off, capable of performing comparably to a fully non-blocking fat tree for low fan-out workloads, and significantly outperform TRNs for high fan-out communication patterns.
Fiber Reinforcement Architectures of PMCs on the Hygrothermal-Mechanical Performance for Aeropropulsion Applications
A rigid lightweight sandwich support structure, for the combustor chamber of a new generation liquid propellant rocket engine, was designed and fabricated using Polymer Matric Composite (PMC) facesheet on a Ti honeycomb or Carbon foam core. The facesheet consisted of high stiffness carbon fiber, M40JB, and high temperature Polyimides, such as PMR-11-50 and HFPE-II. Six different fiber architectures; 4HS woven fabric, uni-fabric, woven-uni hybrid, stitched woven fabric, stitched uni-fabric and tri-axial braided structures have been investigated for optimum stiffness-thickness-weight-performance design criteria for the hygrothermal-mechanical propulsion service exposure conditions including rapid heating up to 200 F/sec, maximum operating temperature of 600 F, internal pressure up to 100 psi. An extensive property and performance database including dry-wet mechanical properties at both 25 F and 600 F in various loading modes, thermal and physical properties including blistering onset condition was developed for fiber architecture down-selection and design guidelines. Various optimized process methods including vacuum bag compression molding, solvent assistant RTM (SaRTM), resin film infusion (RFI) were utilized for PMC panel fabrication depending on the architecture type. In the case of stitched woven fabric architecture, the optimal stitch pattern was chosen in terms of stitch density and yarn size, based on both in-plane mechanical properties and blistering performance. Potential reduction of the in-plane properties transverse to the line of stitching was also evaluated. Attempt to correlate the experimental results with theoretical micro-mechanics predictions will be presented.
Touch and gravitropic set-point angle interact to modulate gravitropic growth in roots
Plant roots must sense and respond to a variety of environmental stimuli as they grow through the soil. Touch and gravity represent two of the mechanical signals that roots must integrate to elicit the appropriate root growth patterns and root system architecture. Obstacles such as rocks will impede the general downwardly directed gravitropic growth of the root system and so these soil features must be sensed and this information processed for an appropriate alteration in gravitropic growth to allow the root to avoid the obstruction. We show that primary and lateral roots of Arabidopsis do appear to sense and respond to mechanical barriers placed in their path of growth in a qualitatively similar fashion. Both types of roots exhibited a differential growth response upon contacting the obstacle that directed the main axis of elongation parallel to the barrier. This growth habit was maintained until the obstacle was circumvented, at which point normal gravitropic growth was resumed. Thus, the gravitational set-point angle of the primary and lateral roots prior to encountering the barrier were 95 degrees and 136 degrees respectively and after growing off the end of the obstacle identical set-point angles were reinstated. However, whilst tracking across the barrier, quantitative differences in response were observed between these two classes of roots. The root tip of the primary root maintained an angle of 136 degrees to the horizontal as it traversed the barrier whereas the lateral roots adopted an angle of 154 degrees. Thus, this root tip angle appeared dependent on the gravitropic set-point angle of the root type with the difference in tracking angle quantitatively reflecting differences in initial set-point angle. Concave and convex barriers were also used to analyze the response of the root to tracking along a continuously varying surface. The roots maintained the a fairly fixed angle to gravity on the curved surface implying a constant resetting of this tip angle/tracking response as the curve of the surface changed. We propose that the interaction of touch and gravity sensing/response systems combine to strictly control the tropic growth of the root. Such signal integration is likely a critical part of growth control in the stimulus-rich environment of the soil. c2003 COSPAR. Published by Elsevier Ltd. All rights reserved.
Apex Reference Manual 3.0 Beta
Apex is a toolkit for constructing software that behaves intelligently and responsively in demanding task environments. Reflecting its origin at NASA where Apex continues to be developed, current applications include: a) Providing autonomous mission management and tactical control capabilities for unmanned aerial vehicles including an autonomous surveillance helicopter and a simulation prototype of an unmanned fixed-wing aircraft to be used for wildfire mapping; b) Simulating human air traffic controllers, pilots and astronauts to help predict how people might respond to changes in equipment or procedures; and c) Predicting the precise duration and sequence of routine human behaviors based on a human-computer interaction engineering technique called CPM-GOMS. Among Apex s components are a set of implemented reasoning services, such as those for reactive planning and temporal pattern recognition; a software architecture that embeds and integrates these services and allows additional reasoning elements to be added as extensions; a formal language for specifying agent knowledge; a simulation environment to facilitate prototyping and analysis; and Sherpa, a set of tools for visualizing autonomy logic and runtime behavior. In combination, these are meant to provide a flexible and usable framework for creating, testing, and deploying intelligent agent software. Overall, our goal in developing Apex is to lower economic barriers to developing intelligent software agents. New ideas about how to extend or modify the system are evaluated in terms of their impact in reducing the time, expertise, and inventiveness required to build and maintain applications. For example, potential enhancements to the AI reasoning capabilities in the system are reviewed not only for usefulness and distinctiveness, but also for their impact on the readability and general usability of Apex s behavior representation language (PDL) and on the transparency of resulting behavior. A second central part of our approach is to iteratively refine Apex based on lessons learned from as diverse a set of applications as possible. Many applications have been developed by users outside the core development team including engineers, researchers, and students. Usability is thus a central concern for every aspect of Apex visible to a user, including PDL, Sherpa, the Apex installation process, APIs, and user documentation. Apex users vary in their areas of expertise and in their familiarity with autonomy technology. Focusing on usability, a development philosophy summarized by the project motto "Usable Autonomy," has been important part of enabling diverse users to employ Apex successfully and to provide feedback needed to guide iterative, user-centered refinement.
The INTERSECT Open Federated Architecture for the Laboratory of the Future
A federated instrument-to-edge-to-center architecture is needed to autonomously collect, transfer, store, process, curate, and archive scientific data and reduce human-in-the-loop needs with (a) common interfaces to leverage community and custom software, (b) pluggability to permit adaptable solutions, reuse, and digital twins, and (c) an open standard to enable adoption by science facilities world-wide. The Selfdriven Experiments for Science/Interconnected Science Ecosystem (INTERSECT) Open Architecture enables science breakthroughs using intelligent networked systems, instruments and facilities with autonomous experiments, “self-driving” laboratories, smart manufacturing and artificial intelligence (AI) driven design, discovery and evaluation. It creates an open federated architecture for the laboratory of the future using a novel approach, consisting of (1) science use case design patterns, (2) a system of systems architecture, and (3) a microservice architecture.
Autonomous exploration system: Techniques for interpretation of multispectral data
An on-board autonomous exploration system that fuses data from multiple sensors, and makes decisions based on scientific goals is being developed using a series of artificial neural networks. Emphasis is placed on classifying minerals into broad geological categories by analyzing multispectral data from an imaging spectrometer. Artificial neural network architectures are being investigated for pattern matching and feature detection, information extraction, and decision making. As a first step, a stereogrammetry net extracts distance data from two gray scale stereo images. For each distance plane, the output is the probable mineral composition of the region, and a list of spectral features such as peaks, valleys, or plateaus, showing the characteristics of energy absorption and reflection. The classifier net is constructed using a grandmother cell architecture: an input layer of spectral data, an intermediate processor, and an output value. The feature detector is a three-layer feed-forward network that was developed to map input spectra to four geological classes, and will later be expanded to encompass more classes. Results from the classifier and feature detector nets will help to determine the relative importance of the region being examined with regard to current scientific goals of the system. This information is fed into a decision making neural net along with data from other sensors to decide on a plan of activity. A plan may be to examine the region at higher resolution, move closer, employ other sensors, or record an image and transmit it back to Earth.
INTERSECT Architecture Specification: Microservice Architecture (V.0.5)
Oak Ridge National Laboratory (ORNL)’s Self-driven Experiments for Science / Interconnected Science Ecosystem (INTERSECT) architecture project, titled “An Open Federated Architecture for the Laboratory of the Future”, creates an open federated hardware/software architecture for the laboratory of the future using a novel system of systems (SoS) and microservice architecture approach, connecting scientific instruments, robot-controlled laboratories and edge/center computing/data resources to enable autonomous experiments, “self-driving” laboratories, smart manufacturing, and artificial intelligence (AI)-driven design, discovery and evaluation. The project describes science use cases as design patterns that identify and abstract the involved hardware/software components and their interactions in terms of control, work and data flow. It creates a SoS architecture of the federated hardware/software ecosystem that clarifies terms, architectural elements, the interactions between them and compliance. It further designs a federated microservice architecture, mapping science use case design patterns to the SoS architecture with loosely coupled microservices, standardized interfaces and multi programming language support. The primary deliverable of this project is an INTERSECT Open Architecture Specification, containing the science use case design pattern catalog, the federated SoS architecture specification and the microservice architecture specification. This document represents the microservice architecture specification of the INTERSECT Open Architecture Specification.
INTERSECT Architecture Specification: Microservice Architecture (V.0.9)
Oak Ridge National Laboratory (ORNL)’s Self-driven Experiments for Science / Interconnected Science Ecosystem (INTERSECT) architecture project, titled “An Open Federated Architecture for the Laboratory of the Future”, creates an open federated hardware/software architecture for the laboratory of the future using a novel system of systems (SoS) and microservice architecture approach, connecting scientific instruments, robot-controlled laboratories and edge/center computing/data resources to enable autonomous experiments, “self-driving” laboratories, smart manufacturing, and artificial intelligence (AI)-driven design, discovery and evaluation. The project describes science use cases as design patterns that identify and abstract the involved hardware/software components and their interactions in terms of control, work and data flow. It creates a SoS architecture of the federated hardware/software ecosystem that clarifies terms, architectural elements, the interactions between them and compliance. It further designs a federated microservice architecture, mapping science use case design patterns to the SoS architecture with loosely coupled microservices, standardized interfaces and multi programming language support. The primary deliverable of this project is an INTERSECT Open Architecture Specification, containing the science use case design pattern catalog, the federated SoS architecture specification and the microservice architecture specification. This document represents the microservice architecture specification of the INTERSECT Open Architecture Specification.
Controlled patterning of crystalline domains by frontal polymerization
Materials with hierarchical architectures that combine soft and hard material domains with coalesced interfaces possess superior properties compared with their homogeneous counterparts. These architectures in synthetic materials have been achieved through deterministic manufacturing strategies such as 3D printing, which require an a priori design and active intervention throughout the process to achieve architectures spanning multiple length scales. Here we harness frontal polymerization spin mode dynamics to autonomously fabricate patterned crystalline domains in poly(cyclooctadiene) with multiscale organization. This rapid, dissipative processing method leads to the formation of amorphous and semi-crystalline domains emerging from the internal interfaces generated between the solid polymer and the propagating cure front. The size, spacing and arrangement of the domains are controlled by the interplay between the reaction kinetics, thermochemistry and boundary conditions. Small perturbations in the fabrication conditions reproducibly lead to remarkable changes in the patterned microstructure and the resulting strength, elastic modulus and toughness of the polymer. Furthermore, this ability to control mechanical properties and performance solely through the initial conditions and the mode of front propagation represents a marked advancement in the design and manufacturing of advanced multiscale materials. Drawing inspiration from biological systems in which structural complexity develops through dissipative reaction–diffusion processes, this study explores a transformative synthetic manufacturing strategy aimed at harnessing the principles underpinning morphogenic growth, unlocking new avenues for advanced materials design and fabrication. Synthetic coupled reaction-transport processes offer a versatile yet relatively underexplored method to manipulate the spatial attributes of synthetic materials10. Here we introduce an innovative manufacturing approach based on frontal ring-opening metathesis polymerization (FROMP) that draws parallels with morphogenic growth and development, enabling the formation of patterned microstructures within polymeric materials.