The idea of Subspace Learning Machine (SLM) has been a powerful tool for Machine Learning (ML), and it has been successfully applied to the task of image classification. Recently, a novel SLM method was proposed, which (i) projects high-dimensional feature vectors into a 1D feature subspace, and (ii) partitions it into two disjoint sets. SLM with soft partitioning (SLM/SP) extends this approach by learning an adaptive Soft Decision Tree (SDT) structure using local greedy subspace partitioning. After meeting the stopping criteria for all child nodes and determining the tree structure, it updates all Projection Vectors (PVs) globally. It enables efficient training, high classification accuracy, and a small model size. It is applied to experimental data to show its performance as a lightweight and high-performance classification method.
Feature classification and regression tasks for high dimensional data have been handled by many well-known algorithms like feed-forward multi-level perceptron, decision tree, support vector machine, and many others. Recently, a new approach called subspace learning machine (SLM) has been found which finds a balance between simplicity and effectiveness by partitioning an input feature space into multiple discriminant subspaces in a hierarchical manner. The technique has been extended in many directions to handle high dimensional data. We will emphasize the significance of these developments and present experimental results.
Recently, deep reinforcement learning (DRL) algorithms have been adapted for real-time control and policy-based decision for robots, drones, and autonomous vehicles. Traditional implementations of DRL use general purpose computing (CPU) and graphical processing units (GPU). In current work, we present HardCompress as an optimized hardware configuration for neural network (DNN) accelerators using High Level Synthesis (HLS) techniques.
The tactical edge, with its complicated electromagnetic environment is a very important part of the defense operations. In general, it contains a mix of friendly and adversarial radio frequency signal sources. A method for distinguishing the signals in the tactical arena will be very useful for telling blue and red teams apart. The function data analysis (FDA) methods offer a promising approach to find their underlying signatures. The FDA contains techniques for understanding and analyzing large and complex datasets with hidden underlying properties. It is particularly useful in situations in which one records the data continuously during a time interval or intermittently at several discrete time points. It can also uncover nonlinear functional dependence hidden in such data.
In current work, we use FDA techniques to uncover the hidden continuous functions in the noisy field data. The measured data is a result of the combination of the signal and noise introduced by solar, atmospheric, and other electromagnetic signals present in the surrounding. The report consists of general theory behind FDA (Section 2), steps in the analysis of the field data (Section 3), and numerical results (Section 4). Finally, in Section 5 we summarize the results and point out the next steps.
One needs a good communication cost model [1, 2] for optimizing the off-loaded computation in a tactical environment. Recently, we presented a mathematical cost model protocol for optimizing those computations [3]. It applies to the Autonomous Mobile Agents (AMA) in the field communicating via a resource-constrained multi-node tactical network. In the present work, we will include the delay and recast the situation as a Linear Programming optimization problem.
Quantum sensing is an important application of Quantum Information techniques. In this work, we present analytical results for the Quantum Fisher Information of a single Qutrit in the Λ, V, and Ξ configurations of a qutrit with the equidistant energy levels
Quantum sensing is an important application of Quantum Information techniques. In this work, we present a mathematical model for a single Qutrit in Λ (Lambda) configuration and its quantum Fisher information.
Quantum sensing is an important application of Quantum Information techniques. In this work, a mathematical model for a single Qutrit in three different representations is presented and their Shannon Mutual Information is compared.
One needs a good communication cost model to understand how best to optimize the off-loaded computation in a tactical environment. In practical scenarios, complications also arise from the in-band and out-of-band channel congestion interference. This can happen due to the intentional and/or unintentional adversary equipment and will affect both the nature of algorithms and the sequence of steps in their execution. As a first step towards solution of the above problems, we present a model for a multi-node tactical network with resource constraints with and without presence of some adversary nodes.
The extraction of useful information from large, disparate, and heterogeneous data sets requires a good set of theoretical and computational tools. The methods based on the ideas of Information Geometry (IG) offer an understanding of the hidden patterns inherent in the data as well as help in their visualization. Fisher Information is such a tool. It has been used widely in many areas of social and economic research leading to an improved understand of trends and hidden patterns. Here we outline its usefulness in understanding large and disparate data sets.
Quantum sensing is an important application of Quantum Information (QI) techniques and usually entangled Qubits are used for this task. The sensor sensitivity depends on QI metrics like Shannon Mutual Information (SMI). In this work we present a mathematical model for calculating 1-Qutrit SMI and compare that with 1-Qubit SMI. Qutrit in cascade configuration is found to have higher SMI and so it a better Quantum Sensor for constant fields.
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