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The Relationship Among Chronotype, Hardiness, Affect, and Talent and Their Effects on Performance in a Military Context
No full textIndividual preference for morning or evening activities (chronotype), affect, hardiness, and talent are associated with a variety of performance outcomes. This longitudinal study was designed to investigate the degree to which these variables are associated with academic, physical, and military performance. Self-reported measures of chronotype, affect, and hardiness were collected from 1149 cadets from the Class of 2016 upon entry to the United States Military Academy. Talent, a composite of academic, leadership, and physical fitness scores were drawn from cadet records. Academic, military, and physical performance measures were collected at graduation 4 years later. The results indicated that a morning orientation was associated with better physical and military performance. Higher talent scores, as well as lower levels of negative affect, were associated with better performance across all three performance measures. Hardiness was only associated with military performance. The findings suggest that a morning orientation and less negative affect may result in better performance overall within a challenging and structured military environment. Future studies of chronotype shifts may provide further insight into associated performance benefits
SARS-CoV-2 aerosol risk models for the Airplane Seating Assignment Problem
No full textTransmission of SARS-CoV-2 between passengers on airplanes is a significant concern and reducing the transmission of SARS-CoV-2 or other viruses aboard aircraft could save lives. Solving the Airplane Seating Assignment Problem (ASAP) produces seating arrangements that minimize transmission risks between passengers aboard an aircraft, but the chosen risk model affects the optimal seating arrangement. We analyze previous risk models and introduce two new risk models, masked and unmasked, based on previous experiments performed aboard real aircraft to test aerosol dispersion of SARS-CoV-2 sized particles. We make recommendations on when each risk model is applicable and the types of seating arrangements that are optimal for each risk model
Runtime Monitoring of Deep Neural Networks Using Top-Down Context Models Inspired by Predictive Processing and Dual Process Theory
No full textDeep neural networks (DNNs) have achieved near-human level accuracy on many datasets across different domains. But they are known to produce incorrect predictions with high confidence on inputs far from the training distribution. This challenge of lack of calibration of DNNs has limited the adoption of deep learning models in high-assurance systems such as autonomous driving, air traffic management, cyber security, and medical diagnosis. The problem of detecting when an input is outside the training distribution of a machine learning model, and hence, its prediction on this input cannot be trusted, has received significant attention recently. Several techniques based on statistical, geometric, topological, or relational signatures have been developed to detect the out-of-distribution (OOD) or novel inputs. In this paper, we present a runtime monitor based on predictive processing and dual process theory. We posit that the bottom-up deep neural networks can be monitored using top-down context models comprising two layers. The first layer is a feature density model that learns the joint distribution of the original DNN’s inputs, outputs, and the model’s explanation for its decisions. The second layer is a graph Markov neural network that captures an even broader context. We demonstrate the efficacy of our monitoring architecture in recognizing out-of-distribution and out-of-context inputs on the image classification and object detection tasks
Developing Critical Thinking Military Officers
Get PDFCritical thinking is frequently identified as an important trait for military officers. This paper examines critical thinking from a historical, pedagogical, and warfighting perspective. The author uses his experience teaching mathematical reasoning at the Naval Postgraduate School to provide helpful advice for educators charged with teaching deductive and inductive reasoning. The paper argues that critical thinking should be taught early in an officer\u27s career. It emphasizes a systematic and Socratic instructional approach along with the importance of equipping students with the necessary tools to evaluate problem-solving techniques and critique their associated solutions. Finally, the paper discusses Augmented Intelligence and the growing need to adopt a more holistic view of the combined Human and Machine-Learning decision making system
Powerful Narratives: Weaponized Harmony and the Soft Power Tools of China’s Rise to Global Primacy
No full textThis project explores how the People’s Republic of China (PRC) might use information and other soft-power mechanisms to rise as the dominant hegemonic power by 2035. We acknowledge that fourteen years is an ambitious timeframe within which to upset the balance of power across the globe without incurring the devastating results of a world war, but that is exactly the PRC’s ambition. We use the Threatcasting foresight methodology to explore nearly two dozen possible and probable future scenarios that might appear should the PRC and the Chinese Communist Party (CCP) continue to seek a Chinese-dominated world order. Specifically, we attempt to answer the question, “How does China employ information during the competition phase to advance its position on the global stage as the preponderant world power?” These imagined futures are models of the complex interactions between geopolitical, economic, social, and natural systems, and provide a sophisticated and relatable nuance when seen through the eyes of a person, in a place, experiencing a threat. We provide observations and recommendations about how the United States and allies could disrupt, mitigate, or recover from these future threats
Computational complexity reduction of deep neural networks
Get PDFDeep neural networks (DNN) have been widely used and play a major role in the field of computer vision and autonomous navigation. However, these DNNs are computationally complex and their deployment over resource-constrained platforms is difficult without additional optimizations and customization.
In this manuscript, we describe an overview of DNN architecture and propose methods to reduce computational complexity in order to accelerate training and inference speeds to fit them on edge computing platforms with low computational resources
Leader Loss: Russian Junior Officer Casualties
Get PDFOpen-source researchers at Killed in Ukraine have confirmed 800-plusRussian senior lieutenants and captains KIA. When the loss of wounded inaction (WIA) is added, it is likely that half of all competent ground-fightingcompany commanders in the Russian force in Ukraine are either KIA or WIA.Russia may be running out of missiles, but these can be bought andmanufactured; what they are more certainly lacking is able tactical leaders. Why does this matter? First, tactical leaders are essential to executecombined arms, and company commanders lead the fight by synchronizingfires, movement, and supporting units. A company commander is also thehighest-level officer who knows each soldier in their unit, and can drive theexecution of a mission by his presence. This matters to the Russian army.Motivation and the will to fight have deteriorated over time
Bias and Bifurcation in the Telling of the History of Social Psychology.
Get PDFThe demand for understanding human behavior during World War II, created an unprecedented approach to social scientific research that required cross-disciplinary collaboration among anthropologists, sociologists, and psychologists. For many, this was a first opportunity to work with scholars from other academic disciplines (Dallenbach, 1946). In addition to the challenging nature of measuring an attitude, these assignments led psychologists and sociologists to envision research problems in ways that they had never imagined, and to experiment with new methodologies in research design and data analysis (Smith, 1984). What resulted from these innovations was a new ability to quantify human attitudes and morale, which would eventually lead to the emergence of a new field of psychology, called “Social Psychology” in the years following the war (Triplet, 1992). This article explains the ways in which historians and practitioners characterize the causal and/or correlative relationship between the research conducted by social scientists on behalf of the United States Government during WWII and the emergence of Social Psychology as an independent discipline in the years following WWII. Both substantive and methodological advances were made in social science research during this time, which created the conditions for the evolution of Social Psychology as an academic and a scientific discipline (Allport & Schmeidler, 1943; Allport & Veltfort, 1943). I illustrate the extent to which the methodological innovations are overlooked in the retelling of this history.https://digitalcommons.usmalibrary.org/books/1051/thumbnail.jp
The Tactical Considerations of Augmented and Mixed Reality Implementation
No full textThe U.S. Army, NATO armies, and other advanced nations actively seek to implement augmented reality (AR) and mixed reality (MR) support for their operational forces. These platforms are intended to improve tactical awareness, target acquisition, and situational awareness and also to develop information upstream for commanders to act upon. The United States’ example is the integrated visual augmentation system (IVAS), which provides an integrated suite of situational awareness capabilities to enable better decision-making and increase soldier tactical fighting ability.1 In the light of rapid developments and hurdles faced in fielding for the United States and its allies, we would like to add to the Army discourse the need to identify potential operational weaknesses in the AR/MR systems. The operational environment will test any equipment’s durability and reliability. A central question we investigate is the tactical value on the battlefield and whether the system losing full or partial functionality changes the system from a capability enhancement into something that obstructs or prevents mission success. We identify multiple areas and research topics for investigation in order for AR devices to become a combat multiplier
Unsupervised Machine Learning Approaches to Nuclear Particle Type Classification
Get PDFHistorically, nuclear science and radiation detection fields of research used Pulse Shape Discrimination (PSD) to label gamma-ray and neutron interactions. However, PSD’s effectiveness relies greatly on the existence of distinguishable differences in an interaction’s measured pulse shape. In the fields of machine learning and data analytics, clustering algorithms provide ways to group samples with similar features without the need for labels. Clustering gamma-ray and neutron interactions may mitigate PSD’s pitfalls, since clustering methods view the total waveform rather than just the area under the tail and the total area under the pulse. However, traditional clustering methods, such as the k-means clustering algorithm, suffer from poor performance on high dimensional data. This study explores unsupervised machine learning methods using Deep Neural Networks (DNN) to cluster gamma-ray and neutron interaction measurements collected with an organic scintillation detector, in order to perform binary labeling of gamma-rays and neutrons. Using various network architectures, this research demonstrates the effectiveness of using autoencoder-based neural networks to cluster gamma-ray and neutron interactions when compared to shallow clustering algorithms. The results reveal the effectiveness of autoencoders on high energy gamma-ray and neutron pulses with an energy deposit greater than 0.80 MeVee whilst greatly outperforming k-means comparatively in all cases