A Churn for the Better: Localizing Censorship using Network-level Path Churn and Network Tomography

Recent years have seen the Internet become a key vehicle for citizens around the globe to express political opinions and organize protests. This fact has not gone unnoticed, with countries around the world repurposing network management tools (e.g., URL filtering products) and protocols (e.g., BGP, DNS) for censorship. However, repurposing these products can have unintended international impact, which we refer to as "censorship leakage". While there have been anecdotal reports of censorship leakage, there has yet to be a systematic study of censorship leakage at a global scale. In this paper, we combine a global censorship measurement platform (ICLab) with a general-purpose technique -- boolean network tomography -- to identify which AS on a network path is performing censorship. At a high-level, our approach exploits BGP churn to narrow down the set of potential censoring ASes by over 95%. We exactly identify 65 censoring ASes and find that the anomalies introduced by 24 of the 65 censoring ASes have an impact on users located in regions outside the jurisdiction of the censoring AS, resulting in the leaking of regional censorship policies

Extending density surface models to include multiple and double-observer survey data

David L. Miller was funded by OPNAV N45 and the SURTASS LFA Settlement Agreement, being managed by the U.S. Navy’s Living Marine Resources program under Contract No. N39430-17-C-1982, collaboration between Douglas B. Sigourney and David L. Miller was also facilitated by the DenMod working group (https://synergy.st-andrews.ac.uk/denmod/) funded under the same agreement. The survey that the fin whale data originate from was funded through two inter-agency agreements with the National Marine Fisheries Service: inter-agency agreement number M14PG00005 with the US Department of the Interior, Bureau of Ocean Energy Management, Environmental Studies Program, Washington, DC and inter-agency agreement number NEC-16-011-01-FY18 with the US Navy. The survey that the fulmar data originate from was funded by the UK Natural Environmental Research Council (NERC) grant NE/M017990/1.Spatial models of density and abundance are widely used in both ecological research (e.g., to study habitat use) and wildlife management (e.g., for population monitoring and environmental impact assessment). Increasingly, modellers are tasked with integrating data from multiple sources, collected via different observation processes. Distance sampling is an efficient and widely used survey and analysis technique. Within this framework, observation processes are modelled via detection functions. We seek to take multiple data sources and fit them in a single spatial model. Density surface models (DSMs) are a two-stage approach: first accounting for detectability via distance sampling methods, then modelling distribution via a generalized additive model. However, current software and theory does not address the issue of multiple data sources. We extend the DSM approach to accommodate data from multiple surveys, collected via conventional distance sampling, double-observer distance sampling (used to account for incomplete detection at zero distance) and strip transects. Variance propagation ensures that uncertainty is correctly accounted for in final estimates of abundance. Methods described here are implemented in the dsm R package. We briefly analyse two datasets to illustrate these new developments. Our new methodology enables data from multiple distance sampling surveys of different types to be treated in a single spatial model, enabling more robust abundance estimation, potentially over wider geographical or temporal domains.Publisher PDFPeer reviewe

Production of 21 Ne in depth-profiled olivine from a 54 Ma basalt sequence, Eastern Highlands (37° S), Australia

In this study we investigate the cosmogenic neon component in olivine samples from a vertical profile in order to quantify muogenic 21Ne production in this mineral. Samples were collected from an 11 m thick Eocene basalt profile in the Eastern Highlands of southeastern Australia. An eruption age of 54.15 ± 0.36 Ma (2σ) was determined from 40Ar/39Ar step-heating experiments (n = 6) on three whole-rock samples. A 36Cl profile on the section indicated an apparent steady state erosion rate of 4.7 ± 0.5 m Ma−1. The eruption age was used to calculate in situ produced radiogenic 4He and nucleogenic 3He and 21Ne concentrations in olivine. Olivine mineral separates (n = 4), extracted from the upper two metres of the studied profile, reveal cosmogenic 21Ne concentrations that attenuate exponentially with depth. However, olivine (Fo68) extracted from below 2 m does not contain discernible 21Ne aside from magmatic and nucleogenic components, with the exception of one sample that apparently contained equal proportions of nucleogenic and muogenic neon. Modelling results suggest a muogenic neon sea-level high-latitude production rate of 0.02 ± 0.04 to 0.9 ± 1.3 atoms g−1 a−1 (1σ), or <2.5% of spallogenic cosmogenic 21Ne production at Earth’s surface. These data support a key implicit assumption in the literature that accumulation of muogenic 21Ne in olivine in surface samples is likely to be negligible/minimal compared to spallogenic 21Ne

The relationship between South Asian stock returns and macroeconomic variables

This article investigates whether economic variables have explanatory power for share returns in South Asian stock markets. In particular, using data for four South Asian emerging stock markets over the period 1998 – 2012, the article examines the influence of a selection of local, regional and global economic variables in explaining equity returns; most previous studies that have examined this issue have tended to focus on only local and/or global factors. Important factors are identified by distilling the macroeconomic variables into principal components. Economic activities, real interest rates, real exchange rates and the trade balance represent local factors. Regional factors are represented by inter-regional trade and regional economic activity while global factors are represented by world financial asset returns and world economic activity. The Vector Autoregression results suggest that the South Asian markets examined are not efficient. Both local and regional factors can directly and indirectly explain Bangladeshi, Pakistani and Sri Lankan stock returns while the lagged returns of the Pakistani stock market and world economic activity can explain Indian stock returns