Electronic Footprints in the Sand: Technologies for Assisting Domestic Violence Survivors

With the rapid growth and spread of Internet-based social support systems, the impact that these systems can make to society – be it good or bad – has become more significant and can make a real difference to people’s lives. As such, various aspects of these systems need to be carefully investigated and analysed, including their security/privacy issues. In this paper, we present our work in designing and implementing various technological features that can be used to assist domestic violence survivors in obtaining help without leaving traces which might lead to further violence from their abuser. This case study serves as the core of our paper, in which we outline our approach, various de- sign considerations – including difficulties in keeping browsing history private, our currently implemented solutions (single use URL, targeted history sanitita- tion agent, and secret graphical gateway), as well as novel ideas for future work (including location-based service advertising and deployment in the wild)

A neural network-based estimate of the seasonal to inter-annual variability of the Atlantic Ocean carbon sink

The Atlantic Ocean is one of the most important sinks for atmospheric carbon dioxide (CO2), but this sink has been shown to vary substantially in time. Here we use surface ocean CO2 observations to estimate this sink and the temporal variability from 1998 through 2007 in the Atlantic Ocean. We benefit from (i) a continuous improvement of the observations, i.e. the Surface Ocean CO2 Atlas (SOCAT) v1.5 database and (ii) a newly developed technique to interpolate the observations in space and time. In particular, we use a two-step neural network approach to reconstruct basin-wide monthly maps of the sea surface partial pressure of CO2 (pCO2) at a resolution of 1° × 1°. From those, we compute the air–sea CO2 flux maps using a standard gas exchange parameterization and high-resolution wind speeds. The neural networks fit the observed pCO2 data with a root mean square error (RMSE) of about 10 μatm and with almost no bias. A check against independent time-series data and new data from SOCAT v2 reveals a larger RMSE of 22.8 μatm for the entire Atlantic Ocean, which decreases to 16.3 μatm for data south of 40° N. We estimate a decadal mean uptake flux of −0.45 ± 0.15 Pg C yr−1 for the Atlantic between 44° S and 79° N, representing the sum of a strong uptake north of 18° N (−0.39 ± 0.10 Pg C yr−1), outgassing in the tropics (18° S–18° N, 0.11 ± 0.07 Pg C yr−1), and uptake in the subtropical/temperate South Atlantic south of 18° S (−0.16 ± 0.06 Pg C yr−1), consistent with recent studies. The strongest seasonal variability of the CO2 flux occurs in the temperature-driven subtropical North Atlantic, with uptake in winter and outgassing in summer. The seasonal cycle is antiphased in the subpolar latitudes relative to the subtropics largely as a result of the biologically driven winter-to-summer drawdown of CO2. Over the 10 yr analysis period (1998 through 2007), sea surface pCO2 increased faster than that of the atmosphere in large areas poleward of 40° N, while in other regions of the North Atlantic the sea surface pCO2 increased at a slower rate, resulting in a barely changing Atlantic carbon sink north of the Equator (−0.01 ± 0.02 Pg C yr−1 decade−1). Surface ocean pCO2 increased at a slower rate relative to atmospheric CO2 over most of the Atlantic south of the Equator, leading to a substantial trend toward a stronger CO2 sink for the entire South Atlantic (−0.14 ± 0.02 Pg C yr−1 decade−1). In contrast to the 10 yr trends, the Atlantic Ocean carbon sink varies relatively little on inter-annual timescales (±0.04 Pg C yr−1; 1 σ)

Data-based estimates of the ocean carbon sink variability – First results of the Surface Ocean pCO2 Mapping intercomparison (SOCOM)

Using measurements of the surface-ocean CO2 partial pressure (pCO2) and 14 different pCO2 mapping methods recently collated by the Surface Ocean pCO2 Mapping intercomparison (SOCOM) initiative, variations in regional and global sea–air CO2 fluxes are investigated. Though the available mapping methods use widely different approaches, we find relatively consistent estimates of regional pCO2 seasonality, in line with previous estimates. In terms of interannual variability (IAV), all mapping methods estimate the largest variations to occur in the eastern equatorial Pacific. Despite considerable spread in the detailed variations, mapping methods that fit the data more closely also tend to agree more closely with each other in regional averages. Encouragingly, this includes mapping methods belonging to complementary types – taking variability either directly from the pCO2 data or indirectly from driver data via regression. From a weighted ensemble average, we find an IAV amplitude of the global sea–air CO2 flux of 0.31 PgC yr−1 (standard deviation over 1992–2009), which is larger than simulated by biogeochemical process models. From a decadal perspective, the global ocean CO2 uptake is estimated to have gradually increased since about 2000, with little decadal change prior to that. The weighted mean net global ocean CO2 sink estimated by the SOCOM ensemble is −1.75 PgC yr−1 (1992–2009), consistent within uncertainties with estimates from ocean-interior carbon data or atmospheric oxygen trend

Chemistry and toxicology of quinoxaline, organotin, organofluorine, and formamidine acaricides.

Quinoxaline, organotin, organofluorine, and formamidine compounds are among the newer pesticide chemicals used for acarine control. Included in these four classes are some of the most selective synthetic organic toxicants currently in the acaricide/insecticide arsenal. Oxythioquinox, Plictran (tricyclohexylhydroxytin), Nissol [2-fluoro-N-methyl-N-(1-naphthyl)acetamide], and chlordimeform are examples of quinoxaline, organotin, organofluorine, and formamidine acaricides, respectively. The chemistry and toxicology of these and related compounds are discussed

Neural network-based integration of polygenic and clinical information: development and validation of a prediction model for 10-year risk of major adverse cardiac events in the UK Biobank cohort

Background: In primary cardiovascular disease prevention, early identification of high-risk individuals is crucial. Genetic information allows for the stratification of genetic predispositions and lifetime risk of cardiovascular disease. However, towards clinical application, the added value over clinical predictors later in life is crucial. Currently, this genotype–phenotype relationship and implications for overall cardiovascular risk are unclear. Methods: In this study, we developed and validated a neural network-based risk model (NeuralCVD) integrating polygenic and clinical predictors in 395 713 cardiovascular disease-free participants from the UK Biobank cohort. The primary outcome was the first record of a major adverse cardiac event (MACE) within 10 years. We compared the NeuralCVD model with both established clinical scores (SCORE, ASCVD, and QRISK3 recalibrated to the UK Biobank cohort) and a linear Cox-Model, assessing risk discrimination, net reclassification, and calibration over 22 spatially distinct recruitment centres. Findings: The NeuralCVD score was well calibrated and improved on the best clinical baseline, QRISK3 (ΔConcordance index [C-index] 0·01, 95% CI 0·009–0·011; net reclassification improvement (NRI) 0·0488, 95% CI 0·0442–0·0534) and a Cox model (ΔC-index 0·003, 95% CI 0·002–0·004; NRI 0·0469, 95% CI 0·0429–0·0511) in risk discrimination and net reclassification. After adding polygenic scores we found further improvements on population level (ΔC-index 0·006, 95% CI 0·005–0·007; NRI 0·0116, 95% CI 0·0066–0·0159). Additionally, we identified an interaction of genetic information with the pre-existing clinical phenotype, not captured by conventional models. Additional high polygenic risk increased overall risk most in individuals with low to intermediate clinical risk, and age younger than 50 years. Interpretation: Our results demonstrated that the NeuralCVD score can estimate cardiovascular risk trajectories for primary prevention. NeuralCVD learns the transition of predictive information from genotype to phenotype and identifies individuals with high genetic predisposition before developing a severe clinical phenotype. This finding could improve the reprioritisation of otherwise low-risk individuals with a high genetic cardiovascular predisposition for preventive interventions. Funding: Charité–Universitätsmedizin Berlin, Einstein Foundation Berlin, and the Medical Informatics Initiative

Measuring the shielding properties of flexible or rigid enclosures for portable electronics

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Heaviside, in volume 1 of Electromagnetic theory, considered shielding of conducting materials in the form of attenuation. This treatment is still significant in the understanding of shielding effectiveness. He also considered propagation of electromagnetic waves in free-space. What Heaviside (1850–1925) could never have imagined is that 125 years later, there would be devices we know as mobile phones (or cell phones, handies, etc.) with capabilities beyond the dreams of the great science fiction writers of the day like H. G. Wells (1866–1949) or Jules Verne (1828–1905). More than this, that there would be a need for law enforcement agencies, among others, to use electromagnetically shielded enclosures to protect electronic equipment from communicating with the ‘outside world’. Nevertheless, Heaviside’s work is still fundamental to the developments discussed here. This paper provides a review of Heaviside’s view of shielding and propagation provided in volume 1 of Electromagnetic theory and develops that to the design of new experiments to test the shielding of these portable enclosures in a mode-stirred reverberation chamber, a test environment that relies entirely on reflections from conducting surfaces for its operation

Adapting astronomical source detection software to help detect animals in thermal images obtained by unmanned aerial systems

In this paper we describe an unmanned aerial system equipped with a thermal-infrared camera and software pipeline that we have developed to monitor animal populations for conservation purposes. Taking a multi-disciplinary approach to tackle this problem, we use freely available astronomical source detection software and the associated expertise of astronomers, to efficiently and reliably detect humans and animals in aerial thermal-infrared footage. Combining this astronomical detection software with existing machine learning algorithms into a single, automated, end-to-end pipeline, we test the software using aerial video footage taken in a controlled, field-like environment. We demonstrate that the pipeline works reliably and describe how it can be used to estimate the completeness of different observational datasets to objects of a given type as a function of height, observing conditions etc. - a crucial step in converting video footage to scientifically useful information such as the spatial distribution and density of different animal species. Finally, having demonstrated the potential utility of the system, we describe the steps we are taking to adapt the system for work in the field, in particular systematic monitoring of endangered species at National Parks around the world

Bootstrapping Trust in Online Dating: Social Verification of Online Dating Profiles

Online dating is an increasingly thriving business which boasts billion-dollar revenues and attracts users in the tens of millions. Notwithstanding its popularity, online dating is not impervious to worrisome trust and privacy concerns raised by the disclosure of potentially sensitive data as well as the exposure to self-reported (and thus potentially misrepresented) information. Nonetheless, little research has, thus far, focused on how to enhance privacy and trustworthiness. In this paper, we report on a series of semi-structured interviews involving 20 participants, and show that users are significantly concerned with the veracity of online dating profiles. To address some of these concerns, we present the user-centered design of an interface, called Certifeye, which aims to bootstrap trust in online dating profiles using existing social network data. Certifeye verifies that the information users report on their online dating profile (e.g., age, relationship status, and/or photos) matches that displayed on their own Facebook profile. Finally, we present the results of a 161-user Mechanical Turk study assessing whether our veracity-enhancing interface successfully reduced concerns in online dating users and find a statistically significant trust increase.Comment: In Proceedings of Financial Cryptography and Data Security (FC) Workshop on Usable Security (USEC), 201