The landscape of maritime security is rapidly evolving,
necessitating advanced surveillance and threat detection systems.
We introduce a multimodal framework employing a zero-shot
approach designed for detecting anomalies, novelty, and out-
of-distribution (OOD) events, particularly focusing on warships
and fishing vessels. This system, utilizing a pretrained model for
feature extraction, innovatively integrates high-resolution
satellite imagery, drone videos, and radio frequency data. The
fusion of these diverse data modalities into a unified latent feature
space significantly enhances precision, recall, and accuracy in
anomaly detection, while substantially reducing the noise
associated with individual modalities. This improvement stems
from the richer contextual information provided by the multimodal
fusion.
The framework features a user-centric design, allowing
operators to specify regions of interest (ROI) for monitoring
through an intuitive interface. This approach ensures that
vigilance is focused on critical areas, thus improving the relevance
and precision of threat detection. Within these ROIs, our zero-
shot transformer-based multimodal clustering algorithm
analyzes naval movement patterns, comparing them against
established normative behaviors derived from historical data.
This method efficiently filters anomalies, uncovers novel
behaviors, and enhances OOD detection by flagging significant
deviations from the training labels distribution.
Upon detecting a behavioral change within an ROI, the system
promptly issues alerts to operators. These alerts are enriched
with contextual information from the multimodal data
sources, including visual confirmations from drone footage and
satellite imagery, ensuring operators receive a comprehensive
understanding of the situation for swift and informed decision-
making.
Our proposed framework represents a significant advancement in
maritime security, setting a precedent for adaptable and scalable
surveillance systems across various domains. Its ability to
integrate new data types and sources, combined with its focus on
user-defined areas of interest, renders it a robust and versatile
tool for current and future security challenges. Empowering
operators to monitor specific maritime regions effectively and
leveraging advanced multimodal machine learning techniques for
threat detection, the proposed approach is set to revolutionize
naval surveillance and security.

Precision Maritime Monitoring: Integrated Anomaly, Novelty, and OOD Detection in Designated Naval Areas

The Navy is looking for new machine learning methods to improve fleet management, conduct passive surveillance, and perform target identification. Conventional machine learning methods leverage neural networks, SVMs, or a random forest approach. These are not the only methods. Oracle has developed an industry-leading prognostic algorithm called the Multivariate State Estimation Technique (MSET) to perform prognostic analysis used in fleet management, passive surveillance, and target identification. Oracle’s algorithm can be deployed on-premises, in the cloud, or at the edge, and it can be delivered in a user-friendly package for operators without the need for formal training. MSET is an advanced prognostic pattern recognition method that was originally developed by the DOE for high-sensitivity prognostic fault monitoring applications in commercial nuclear power and aerospace applications. Over the last 20 years, Oracle has pioneered a suite of intelligent-data-preprocessing (IDP) algorithms and automated tuning and sensitivity-optimization algorithms for a second generation MSET2, which possesses significant advantages over conventional ML algorithmic approaches, including neural nets. MSET2 advantages include higher prognostic accuracy, earlier warning of incipient anomalies in complex/dynamic/chaotic time-series signatures, lower false-alarm and missed-alarm probabilities, and much lower overhead compute cost, which is crucial for real-time dense-sensor streaming prognostics. MSET2 has been used in a broad range of safety-critical, mission-critical, and business applications for anomaly detection, early warning, counterfeit-component detection, and passive surveillance. Applied examples include NASA space shuttles, naval ship-propulsion prognostics, Oil-and-Gas exploration predictive and prescriptive maintenance, human-in-the-loop supervisory control, prognostic cyber security for SCADA assets, Utility grid assets, and enterprise IT assets in commercial datacenters. 

Naval Prognostic Applications for MSET2 Algorithm

Predictive analytics for Naval safety data often involves small and imbalanced sets. To cope with categorical data sets of this nature, the usual remedies often include replicating or oversampling the minority class to bring the numbers into balance, trimming the larger categories to achieve the desired balance or simply choosing a modeling method that is less sensitive to the imbalance. However, by creating a synthetic version of the data based on the characteristics of the original set, the new problems created by oversampling or using less data can be avoided. A more diverse set of data can ultimately be used in model training that will allow use of a broader range of modeling options and marginally increase the generalizability of the resultant solution. This paper will walk through a method for constructing a synthetic data set using an adversarial generative technique which generates a data set of arbitrary size with the same apparent modeling characteristics of the original set. This generated data can also be used for other purposes aside from directly modeling, including as a highly effective anonymization technique for the purpose of collaboration with other researchers while using sensitive data sets.

Generation and Utilization of Synthetic Data to Enhance Type I and Type II Error Resistance for Small Datasets

Autonomous Systems consist of different systems working together so they can sense, plan, and act to a dynamic environment. There is much confusion on what autonomous systems are. This session will explain what autonomous systems are and how machine teaching and simulation can collaborate to create autonomous systems. Machine Teaching is critical to autonomous systems. It is based on reinforcement learning. The differences between machine teaching and machine learning will also be clarified.

Understanding Autonomous Systems

Reinforcement learning (RL) is an effective method for solving problems that require autonomous agents to learn the best way to act in complex environments. RLlib is a powerful tool for applying reinforcement learning to problems where there are multiple agents or when agents must take on multiple roles. There exist many resources for learning about RLlib from a theoretical or academic perspective, but there is a lack of materials for learning how to use RLlib to solve your own practical problems. This tutorial helps to fill that gap. We show you how to apply reinforcement learning to your problem by taking you through a custom environment and demonstrating how to apply RLlib to that environment. We make the code available on GitHub.

Tutorial: RLlib for Deep Hierarchical Multiagent Reinforcement Learning

Although Convolutional Neural Networks (CNNs) achieve high accuracy on image recognition tasks, they lack robustness against realistic corruptions and fail catastrophically when deliberately attacked. Previous work suggests that CNNs with representations similar to primary visual cortex (V1) are more robust to adversarial attacks on images than current adversarial defense techniques. However, these approaches require training on large-scale neural recordings or handcrafting classical neuroscientific models. Motivated by long-standing evidence that neural activity in V1 and other sensory areas is sparse, we develop a class of hybrid CNNs, called LCANets, which feature a frontend with recurrent lateral competition. We demonstrate competitive accuracy of LCANets on action recognition datasets and show that LCANets are significantly more robust to image corruptions and adversarial examples than both standard CNNs and adversarially-trained CNNs. Our results demonstrate that recurrent lateral competition plays a large role in forming sparse and robust V1-like representations when incorporated into CNNs.

CNNs With a Neuromorphic Sparse Coding Frontend are More Robust to Adversarial Attacks

Today, essentially all computing is done with Boolean logic and binary numbers, requiring all manner of error correction codes to keep the most significant bits straight. But in the realm of machine learning, what if instead every bit was equally (in)significant? Consider two randomly generated strings thousands of zeros and ones long, about 50/50 split. Normally, about half the bits will be different between the two strings. If the number of differences is less than half though, the strings as a whole must be related. Even a large number of errant bits will not change the strings’ similarity. This creation and measurement of similarity between very large strings of bits (or numbers) is called hyperdimensional computing (HDC). 

Though not quantum mechanical, a kind of superposition of information across each of the thousands of bits enables machine learning capabilities even Deep Neural Networks (DNN) struggle with, such as learning from a single example or combining the learning from several different sensor modalities. One can even dynamically reprogram swarms of robots in a contested environment – the new program intelligible from a single binary string even with 30-50% noise.

What if we started training artificial neural networks (ANN) to learn and recall these strings: not memorizating or autoencoding but rather “resonating” like a tuning fork? This “resonance” training paradigm suddenly enables even more capabilities not possible with traditional DNNs today such as learning new categories “on the fly” without retraining the whole network from scratch. Such attractor state networks (ASN) lend themselves to modular design, allowing new categories to be added or shared amongst other ASNs. This combining of HDC with “resonance” ANNs is a potential game changer for machine learning, establishing mathematically rigorous engineering design rules instead of the unintelligible black boxes of DNNs today.

Hyperdimensional computing can be implemented in traditional computing hardware, but large size, weight, and power savings are possible by including memristors crossbars into the ML ensemble. The Pipeline for Novel Neuromorphic Hardware (NeuroPipe) Applied Research for Advancement of Priorities (ARAP) is working to bring not only novel neuromorphic computing hardware like memristors to the field but also develop the next generation of machine learning algorithms such as these.


NeuroPipe ARAP Hardware & Algorithms


Robots are increasingly deployed to solve complex real-world tasks that require  continuous processing of their observations only to be constrained by their on-board resources.  Energy-efficient control of mobile robots is one of the limiting factor in having robots deployed  to perform in-space assembly of structures. Spiking Neural Networks (SNNs) deployed on  neuromorphic processors provide an energy-efficient solution that could also facilitate building  intelligence similar to living creatures for robots. 
Neuromorphic architectures imitate the behavior and function of biological nervous  systems by implementing networks of artificial neurons and synapses that benefit from low  power consumption. These computing platforms emulate their biological counterparts by  distributing both computation and memory among a large number of computation units, each  passing information, via asynchronous spikes, to hundreds or thousands of other computational  units, like neurons would do through synapses. 
Over time, different digital neuromorphic architectures like Intel’s neuromorphic  technology, code named Loihi [1], have replicated many characteristics of SNNs in hardware.  Despite the advantages of neuromorphic hardware (e.g., high speed and energy efficiency)  there are few algorithms that utilize it for in situ robot control. Recent work by Michaelis et al. [2], proved the theoretical feasibility of using Intel’s neuromorphic technology for trajectory  generation on hardware, but fails to employ the strategy with robotic hardware (simulated or in  situ). Michaelis’ work presents a biologically plausible approach that also promised a control  relevant timescale.  
His work is based on Reservoir Computing, specifically, Liquid State Machines,  implemented as biologically plausible anisotropic networks. In Michaelis’ work for trajectory  generation, the liquid layer is implemented as an anisotropic network, based on a biologically  
inspired rule for forming spatially asymmetric non-plastic connections, similar to the ones that  have been observed to activate in human and mammal brains [3]. It is believed that correlated  spatial anisotropic connectivity is what makes learning faster and more efficient. Michaelis et 
Distribution A — Approved for public release, distribution is unlimited 
al. [2], make use of this anisotropic network idea from Spreizer et al. [3] and show the feasibility  of using an architecture that integrates it for trajectory generation.  
In this work we close the gap between theoretical formulation and practical applications  by implementing the missing components to enable Kinova Gen3, a 7 degree of freedom  robotic arm, to use this type of neural network on Intel Kapho Bay Loihi for trajectory  generation.  
Demonstrating neuromorphic capabilities for robotic control is of high interest at NRL  due to the appeal of high performance and energy efficiency that neuromorphic hardware  presents. The success of this strategy has a big impact on different projects at NRL that are investigating: neuromorphically controlled multi-limbed robotic systems for Naval ship  maintenance tasks, neuromorphically controlled multi-limbed space truss walking robot that  can autonomously assemble truss elements to construct large space structures in orbit, and the  capabilities of memristor-based neuromorphic processors. 

References: 

[1] Davies, M., Srinivasa, N., Lin, T.-H., Chinya, G., Cao, Y., Choday, S. H., et al. “Loihi: a  neuromorphic manycore processor with on-chip learning.” IEEE Micro (2018). 
[2] Michaelis, Carlo, Andrew B. Lehr, and Christian Tetzlaff. “Robust trajectory generation for  robotic control on the neuromorphic research chip Loihi.” Frontiers in neurorobotics 14 (2020). 
[3] Spreizer, Sebastian, Ad Aertsen, and Arvind Kumar. “From space to time: Spatial  inhomogeneities lead to the emergence of spatiotemporal sequences in spiking neuronal  networks.” PLoS computational biology 15.10 (2019).
Distribution A — Approved for public release, distribution is unlimited 
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Neuromorphic Hardware Anisotropic Reservoir Trajectory Generation and execution with Robotic Arm

In this work we present a spiking neural network for the adaptive control of a simulated Kinova Gen3 7-DOF arm. This work extends the Recurrent Error-driven Adaptive Control Hierarchy (REACH), developed as a computational model of the primate motor control system, which had previously been implemented for the operational space control of a 3-DOF arm restricted to the horizontal plane. The REACH model is divided into kinematics control, responsible for transforming the control signal from end-effector (hand) space to joint-space, and dynamics control, responsible for accounting for the effects of gravity and inertia on the system. Additionally, a nonlinear adaptive control component learns online to correct for errors that arise during movement as a result of internal model inaccuracies or external perturbations. Due to the increased complexity of the 7-DOF system, the process for training the REACH model required a different approach than the 3-DOF implementation. Here, we utilize a hybrid approach to training that takes advantage of both deep learning and the circuit building methods from the Neural Engineering Framework (NEF). The NEF allows us to both quickly build highly optimized components performing computations on low-dimensional input, as well as integrate these networks directly with those trained using deep learning, which provides a more exhaustive sampling of high-dimensional state space. We analyze the performance of the spiking control network on a benchmark task compared to both an analytic controller and a rate-mode version of the control network. Lastly, we discuss the process of training the network to run on the highly-efficient neuromorphic chip from Intel, the Loihi, and provide initial results from simulation in a Loihi emulator.

Adaptive spiking control of a 7 DOF arm

Synaptic plasticity poses itself as a powerful method of self-regulated unsupervised learning in neural networks. A recent resurgence of interest has developed in utilizing Artificial Neural Networks (ANNs) together with synaptic plasticity for intra-lifetime learning. Plasticity has been shown to improve the learning capabilities of these networks in generalizing to novel environmental circumstances. However, the long-term stability of these trained networks has yet to be examined. This work demonstrates that utilizing plasticity together with ANNs leads to instability beyond the pre-specified lifespan used during training. This instability can lead to the dramatic decline of reward seeking behavior, or quickly lead to reaching environment terminal states. This behavior is shown to hold consistent for several plasticity rules on two different environments across many training time-horizons: a cart-pole balancing problem and a quadrupedal locomotion problem. We present a solution to this instability through the use of spiking neurons.

Stable Lifelong Learning: Spiking neurons as a solution to instability in plastic neural networks

DARPA’s Real Time Machine Learning (RTML) program seeks to reduce the design costs associated with developing ASICs tailored for emerging ML applications by developing a means of automatically generating novel chip designs based on ML frameworks. The goal of the RTML program is to create a compiler – or software platform – that can ingest ML frameworks like TensorFlow and Pytorch and, based on the objectives of the specific ML algorithms or systems, generate hardware design configurations and standard Verilog code optimized for the specific need. Throughout the lifetime of the program, RTML will explore the compiler’s capabilities across two critical, high-bandwidth application areas: 5G networks and image processing.

Real Time Machine Learning: Automated Flows Enabling Concept to Chip

VeriGOOD-ML is an automated methodology for generating machine learning (ML) hardware for DNN and non-DNN ML algorithms. The VeriGOOD-ML generator takes as input a high-level description of an ML algorithm in a standard format such as ONNX, and outputs Verilog RTL code, with no human in the loop. The Verilog RTL is then translated through a back-end design flow to GDSII, driven by a design planning approach that is well tailored to the macro-intensive nature of ML platforms. VeriGOOD-ML uses three approaches to build ML hardware: (1) the TABLA platform uses a dataflow architecture that is well suited to non-DNN ML algorithms; (2) the GeneSys platform, with a systolic array and a SIMD array, is optimized for implementing DNNs; and (3) the Axiline approach synthesizes small ML algorithms by hardcoding the structure of the algorithm into hardware, thus trading off flexibility for performance and power.

The Verilog generator is complemented by an approach that performs design space exploration of the configurations of each hardware engine to determine the best implementation of the hardware. The performance metrics of the implemented hardware are significantly affected by the configuration of the hardware platform and the physical implementation from RTL to GDS. Key metrics include the power, performance, and area (PPA) of the hardware engine, and the power, execution time, and accuracy of the ML algorithm at the system level. The talk overviews the overall methodology, from architecture to back-end design to hardware implementation, and presents the results of VeriGOOD-ML on a set of ML benchmarks.

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