Post-Westgate SWAT : C4ISTAR Architectural Framework for Autonomous Network Integrated Multifaceted Warfighting Solutions Version 1.0 : A Peer-Reviewed Monograph

Police SWAT teams and Military Special Forces face mounting pressure and challenges from adversaries that can only be resolved by way of ever more sophisticated inputs into tactical operations. Lethal Autonomy provides constrained military/security forces with a viable option, but only if implementation has got proper empirically supported foundations. Autonomous weapon systems can be designed and developed to conduct ground, air and naval operations. This monograph offers some insights into the challenges of developing legal, reliable and ethical forms of autonomous weapons, that address the gap between Police or Law Enforcement and Military operations that is growing exponentially small. National adversaries are today in many instances hybrid threats, that manifest criminal and military traits, these often require deployment of hybrid-capability autonomous weapons imbued with the capability to taken on both Military and/or Security objectives. The Westgate Terrorist Attack of 21st September 2013 in the Westlands suburb of Nairobi, Kenya is a very clear manifestation of the hybrid combat scenario that required military response and police investigations against a fighting cell of the Somalia based globally networked Al Shabaab terrorist group.Comment: 52 pages, 6 Figures, over 40 references, reviewed by a reade

Elektroentsefalograafiline perspektiiv kindlustunde neuraalsetele mehhanismidele

Neural mechanisms responsible for feelings of certainty in reasoning and decision-making remain unclear. This thesis attempts to address this problem by examining the role of error-related EEG potentials (error-related negativity - ERN, error positivity - Pe) in decision confidence. The amplitude of these potentials has been shown to correlate with error detection and confidence ratings in simple perceptual decisions. In order to test whether this pattern holds in more complex decisions, we investigated activity changes in ERN and Pe in response to manipulations of decision confidence in an arithmetic reasoning task. In an EEG experiment, 49 participants had to quickly respond whether an equation (e.g. 9 * 7 = 65) is correct or incorrect and then report their decision confidence. Task difficulty and response fluency were varied as manipulations of confidence. The results indicated that ERN and Pe did not mediate the effect of task difficulty on confidence. Response fluency impacted confidence only for simple decisions, and this effect was partially mediated by ERN. These results suggest that Pe could be an index of implicit cognitive control, whereas ERN tracks decision confidence in simple decisions and is susceptible to response fluency manipulations

Contextual Centrality: Going Beyond Network Structures

Centrality is a fundamental network property which ranks nodes by their structural importance. However, structural importance may not suffice to predict successful diffusions in a wide range of applications, such as word-of-mouth marketing and political campaigns. In particular, nodes with high structural importance may contribute negatively to the objective of the diffusion. To address this problem, we propose contextual centrality, which integrates structural positions, the diffusion process, and, most importantly, nodal contributions to the objective of the diffusion. We perform an empirical analysis of the adoption of microfinance in Indian villages and weather insurance in Chinese villages. Results show that contextual centrality of the first-informed individuals has higher predictive power towards the eventual adoption outcomes than other standard centrality measures. Interestingly, when the product of diffusion rate $p$ and the largest eigenvalue $\lambda_1$ is larger than one and diffusion period is long, contextual centrality linearly scales with eigenvector centrality. This approximation reveals that contextual centrality identifies scenarios where a higher diffusion rate of individuals may negatively influence the cascade payoff. Further simulations on the synthetic and real-world networks show that contextual centrality has the advantage of selecting an individual whose local neighborhood generates a high cascade payoff when $p \lambda_1 < 1$. Under this condition, stronger homophily leads to higher cascade payoff. Our results suggest that contextual centrality captures more complicated dynamics on networks and has significant implications for applications, such as information diffusion, viral marketing, and political campaigns

Application of a stochastic snowmelt model for probabilistic decisionmaking

A stochastic form of the snowmelt runoff model that can be used for probabilistic decision-making was developed. The use of probabilistic streamflow predictions instead of single valued deterministic predictions leads to greater accuracy in decisions. While the accuracy of the output function is important in decisionmaking, it is also important to understand the relative importance of the coefficients. Therefore, a sensitivity analysis was made for each of the coefficients

Aeronautical Engineering. A continuing bibliography with indexes, supplement 156

This bibliography lists 288 reports, articles and other documents introduced into the NASA scientific and technical information system in December 1982

Neural representation of complex motion in the primate cortex

This dissertation is concerned with how information about the environment is represented by neural activity in the primate brain. More specifically, it contains several studies that explore the representation of visual motion in the brains of humans and nonhuman primates through behavioral and physiological measures. The majority of this work is focused on the activity of individual neurons in the medial superior temporal area (MST) – a high-level, extrastriate area of the primate visual cortex. The first two studies provide an extensive review of the scientific literature on area MST. The area’s prominent role at the intersection of low-level, bottom-up, sensory processing and high-level, top-down mechanisms is highlighted. Furthermore, a specific article on how information about self-motion and object motion can be decoded from a population of MSTd neurons is reviewed in more detail. The third study describes a published and annotated dataset of MST neurons’ responses to a series of different motion stimuli. This dataset is analyzed using a variety of different analysis approaches in the fifth study. Classical tuning curve approaches confirm that MST neurons have large, but well-defined spatial receptive fields and are independently tuned for linear and spiral motion, as well as speed. We also confirm that the tuning for spiral motion is position invariant in a majority of MST neurons. A bias-free characterization of receptive field profiles based on a new stimulus that generates smooth, complex motion patterns turned out to be predictive of some of the tuning properties of MST neurons, but was generally less informative than similar approaches have been in earlier visual areas. The fifth study introduces a new motion stimulus that consists of hexgonal segments and presents an optimization algorithm for an adaptive online analysis of neurophysiological recordings. Preliminary physiological data and simulations show these tools to have a strong potential in characterizing the response functions of MST neurons. The final study describes a behavioral experiment with human subjects that explores how different stimulus features, such as size and contrast, affect motion perception and discusses what conclusions can be drawn from that about the representation of visual motion in the human brain. Together these studies highlight the visual motion processing pathway of the primate brain as an excellent model system for studying more complex relations of neural activity and external stimuli. Area MST in particular emerges as a gateway between perception, cognition, and action planning.2021-11-1

Topology beyond the horizon: how far can it be probed?

The standard cosmological model does not determine the spatial topology of the universe. This article revisits the signature of a non-trivial topology on the properties of the cosmic microwave background anisotropies. We show that the correlation function of the coefficients of the expansion of the temperature and polarization anisotropies in spherical harmonics, encodes a topological signature that can be used to distinguish a multi-connected space from an infinite space on sizes larger than the last scattering surface. The effect of the instrumental noise and of a galactic cut are estimated. We thus establish boundaries for the size of the biggest torus dintinguisable with temperature and polarization CMB data. We also describe the imprint of the spatial topology on the 3-point function and on non-Gaussianity.Comment: 20 pages, 24 figure