Long Response to Scheuer-Yariv: "A Classical Key-Distribution System based on Johnson (like) noise - How Secure?", physics/0601022

This is the longer (partially unpublished) version of response; the shorter version (http://arxiv.org/abs/physics/0605013) is published in Physics Letters A. We point out that the claims in the comment-paper of Scheuer and Yariv are either irrelevant or incorrect. We first clarify what the security of a physically secure layer means. The idealized Kirchoff-loop-Johnson-like-noise (KLJN) scheme is totally secure therefore it is more secure than idealized quantum communication schemes which can never be totally secure because of the inherent noise processes in those communication schemes and the statistical nature of eavesdropper detection based on error statistics. On the other hand, with sufficient resources, a practical/non-ideal realization of the KLJN cipher can arbitrarily approach the idealized limit and outperform even the idealized quantum communicator schemes because the non-ideality-effects are determined and controlled by the design. The cable resistance issue analyzed by Scheuer and Yariv is a good example for that because the eavesdropper has insufficient time window to build a sufficient statistics and the actual information leak can be designed. We show that Scheuer's and Yariv's numerical result of 1% voltage drop supports higher security than that of quantum communicators. Moreover, choosing thicker or shorter wires can arbitrarily reduce this voltage drop further; the same conclusion holds even according to the equations of Scheuer and Yariv.Comment: The older long response and the newer brief response (in press, PLA) with modelling data are fuse

Cartographic Analysis of Earth-Sun Relationships in Ancient Amazonia

The alignments of ancient man-made earthworks across the Amazon Basin, known as geoglyphs, have recently been discovered to predate early societal dates. Although much research indicated that the Amazon was uninhabitable until the last 1000 years (Meggers 1971), new evidence suggests this is not the case. The application of advanced cartographic and GIS technologies were implemented to link solar ‘marker’ days (e.g. solstices, equinoxes) with the alignment of geoglyphs, megaliths, stone architecture, and broader city forms to discover and analyze previously unknown Earth-Sun relationships across the Amazon Basin to conceivably sophisticated urban and architectural plans. The study of these geoglyphs and other man-made structures has revealed comparable solar linkages and alignments; it is hoped that similar alignments will be found, leading to a shift in our understanding of the level of complexity used by ancient Amazonian tribes and civilizations. The implications of these findings are significant, not only for understanding the Amazonian peoples\u27 history and culture but also for challenging the long-held Western-centric view of civilization and development. By recognizing the advanced knowledge and practices of ancient Amazonian societies, we can gain a more comprehensive and nuanced understanding of the diversity of human ability throughout history

Cartographic Analysis of Earth-Sun Relationships in Ancient Amazonia

The alignments of ancient man-made earthworks across the Amazon Basin, known as geoglyphs, have recently been discovered to predate early societal dates. Although much research indicated that the Amazon was uninhabitable until the last 1000 years (Meggers 1971), new evidence suggests this is not the case. The application of advanced cartographic and GIS technologies were implemented to link solar ‘marker’ days (e.g. solstices, equinoxes) with the alignment of geoglyphs, megaliths, stone architecture, and broader city forms to discover and analyze previously unknown Earth-Sun relationships across the Amazon Basin to conceivably sophisticated urban and architectural plans. The study of these geoglyphs and other man-made structures has revealed comparable solar linkages and alignments; it is hoped that similar alignments will be found, leading to a shift in our understanding of the level of complexity used by ancient Amazonian tribes and civilizations. The implications of these findings are significant, not only for understanding the Amazonian peoples\u27 history and culture but also for challenging the long-held Western-centric view of civilization and development. By recognizing the advanced knowledge and practices of ancient Amazonian societies, we can gain a more comprehensive and nuanced understanding of the diversity of human ability throughout history

Making common ground with strangers at Furnace Park

In this article we seek to widen the debate about the sites and processes of encounter with strangers by examining the ways in which ‘strangeness’ necessarily fades within the familiarisation processes at play in any sustained and situated place-making. Our analysis draws upon our experiences of encountering strangers – and of our familiarisation with them – in the initial, year-long, site acquisition and preparation phase of a project to create Furnace Park, an experimental urban space in a run-down backwater of central Sheffield. We show the tensions between a project commitment to the formation of a loose, open place and the pressures (which arose from our encounters with the urban development system) to render both the project and the site certain, bounded and less-than-strange. Furthermore, at Furnace Park the site itself presented to us as a non-human stranger, which we were urged to render familiar but which kept eluding that capture. We therefore show how the geographies of strange encounters could productively be widened to embrace both recent scholarship on the material-affective strangeness of ground itself, and a greater attentiveness to the familiarisation effects born of the intersection of diverse communities of practices within place-making projects

Optical Communication Noise Rejection Using Correlated Photons

This paper describes a completely new way to perform noise rejection using a two-photon sensitive detector and taking advantage of the properties of correlated photons to improve an optical communications link in the presence of uncorrelated noise. In particular, a detailed analysis is made of the case where a classical link would be saturated by an intense background, such as when a satellite is in front of the sun,and identifies a regime where the quantum correlating system has superior performance.Comment: 12 pages, 1 figure, 1 tabl

Twitter and Disasters: A Social Resilience Fingerprint

Understanding the resilience of a community facing a crisis event is critical to improving its adaptive capacity. Community resilience has been conceptualized as a function of the resilience of components of a community such as ecological, infrastructure, economic, and social systems, etc. In this paper, we introduce the concept of a “resilience fingerprint” and propose a multi-dimensional method for analyzing components of community resilience by leveraging existing definitions of community resilience with data from the social network Twitter. Twitter data from 14 events are analyzed and their resulting resilience fingerprints computed. We compare the fingerprints between events and show that major disasters such as hurricanes and earthquakes have a unique resilience fingerprint which is consistent between different events of the same type. Specifically, hurricanes have a distinct fingerprint which differentiates them from other major events. We analyze the components underlying the similarity among hurricanes and find that ecological, infrastructure and economic components of community resilience are the primary drivers of the difference between the community resilience of hurricanes and other major events

Mapping climate discourse to climate opinion: An approach for augmenting surveys with social media to enhance understandings of climate opinion in the United States

Surveys are commonly used to quantify public opinions of climate change and to inform sustainability policies. However, conducting large-scale population-based surveys is often a difficult task due to time and resource constraints. This paper outlines a machine learning framework—grounded in statistical learning theory and natural language processing—to augment climate change opinion surveys with social media data. The proposed framework maps social media discourse to climate opinion surveys, allowing for discerning the regionally distinct topics and themes that contribute to climate opinions. The analysis reveals significant regional variation in the emergent social media topics associated with climate opinions. Furthermore, significant correlation is identified between social media discourse and climate attitude. However, the dependencies between topic discussion and climate opinion are not always intuitive and often require augmenting the analysis with a topic’s most frequent n-grams and most representative tweets to effectively interpret the relationship. Finally, the paper concludes with a discussion of how these results can be used in the policy framing process to quickly and effectively understand constituents’ opinions on critical issues.publishedVersio

The challenges, uncertainties and opportunities of bioaerosol dispersion modelling from open composting facilities

Bioaerosols are ubiquitous organic particles that comprise viruses, bacteria and coarser fractions of organic matter. Known to adversely affect human health, the impact of bioaerosols on a population often manifests as outbreaks of illnesses such as Legionnaires Disease and Q fever, although the concentrations and environmental conditions in which these impacts occur are not well understood. Bioaerosol concentrations vary from source to source, but specific human activities such as water treatment, intensive agriculture and composting facilitate the generation of bioaerosol concentrations many times higher than natural background levels. Bioaerosols are not considered ‘traditional’ pollutants in the same way as PM10, PM2.5, and gases such as NO2, and consequently dispersion models do not include a bespoke method for their assessment. As identified in previous studies, priority areas for improving the robustness of these dispersion models include: 1) the development of bespoke monitoring studies designed to generate accurate modelling input data; 2) the publication of a robust emissions inventory; 3) a code of practice to provide guidelines for consistent bioaerosol modelling practices; and 4) a greater understanding of background bioaerosol emissions. The aim of this research project, funded by the Natural Environmental Research Council (NERC), is to address these key areas through a better understanding of the generation, concentration and potential dispersion of bioaerosols from intensive agricultural and biowaste facilities, using case studies developed at specific locations within the UK. The objective is to further refine existing bioaerosol monitoring and modelling guidelines to provide a more robust framework for regulating authorities and site operators. This contribution outlines the gaps that hinder robust dispersion modelling, and describes the on-site bioaerosol data collection methods used in the study, explaining how they might be used to close these gaps. Examples of bioaerosol dispersion modelled using ADMS 5 are presented and discussed

Towards improved bioaerosol model validation and verification

Bioaerosols, comprised of bacteria, fungi and viruses are ubiquitous in ambient air. Known to adversely affect human health, the impact of bioaerosols on a population often manifests as outbreaks of illnesses such as Legionnaires Disease and Q fever, although the concentrations and environmental conditions in which these impacts occur are not well understood. Bioaerosol concentrations vary from source to source, but specific industrialised human activities such as water treatment, intensive agriculture and open windrow composting facilitate the generation of bioaerosol concentrations many times higher than natural background levels. Bioaerosol sampling is currently undertaken according to the requirements of the Environment Agency’s regulatory framework, in which the collection of bioaerosols and not its long-term measurement is of most importance. As a consequence, sampling devices are often moved around site according to changing wind direction and sampling intervals are invariably short-term. The dispersion modelling of bioaerosols from composting facilities typically relies on proxy pollutant parameters. In addition, the use of short term emission data gathering strategies in which monitors are moved frequently with wind direction, do not provide a robust reliable and repeatable dataset by which to validate any modelling or to verify its performance. New sampling methods such as the Spectral Intensity Bioaerosol Sensor (SIBS) provide an opportunity to address several gaps in bioaerosol model validation and verification. In the context of model validation, this paper sets out the current weaknesses in bioaerosol monitoring from the perspective of robust modelling requirements

The expression of Toll-like receptor 4, 7 and co-receptors in neurochemical sub-populations of rat trigeminal ganglion sensory neurons.

The recent discovery that mammalian nociceptors express Toll-like receptors (TLRs) has raised the possibility that these cells directly detect and respond to pathogens with implications for either direct nociceptor activation or sensitization. A range of neuronal TLRs have been identified, however a detailed description regarding the distribution of expression of these receptors within sub-populations of sensory neurons is lacking. There is also some debate as to the composition of the TLR4 receptor complex on sensory neurons. Here we use a range of techniques to quantify the expression of TLR4, TLR7 and some associated molecules within neurochemically-identified sub-populations of trigeminal (TG) and dorsal root (DRG) ganglion sensory neurons. We also detail the pattern of expression and co-expression of two isoforms of lysophosphatidylcholine acyltransferase (LPCAT), a phospholipid remodeling enzyme previously shown to be involved in the lipopolysaccharide-dependent TLR4 response in monocytes, within sensory ganglia. Immunohistochemistry shows that both TLR4 and TLR7 preferentially co-localize with transient receptor potential vallinoid 1 (TRPV1) and purinergic receptor P2X ligand-gated ion channel 3 (P2X3), markers of nociceptor populations, within both TG and DRG. A gene expression profile shows that TG sensory neurons express a range of TLR-associated molecules. LPCAT1 is expressed by a proportion of both nociceptors and non-nociceptive neurons. LPCAT2 immunostaining is absent from neuronal profiles within both TG and DRG and is confined to non-neuronal cell types under naïve conditions. Together, our results show that nociceptors express the molecular machinery required to directly respond to pathogenic challenge independently from the innate immune system