Spatial+: a novel approach to spatial confounding

In spatial regression models, collinearity between covariates and spatial effects can lead to significant bias in effect estimates. This problem, known as spatial confounding, is encountered modelling forestry data to assess the effect of temperature on tree health. Reliable inference is difficult as results depend on whether or not spatial effects are included in the model. The mechanism behind spatial confounding is poorly understood and methods for dealing with it are limited. We propose a novel approach, spatial+, in which collinearity is reduced by replacing the covariates in the spatial model by their residuals after spatial dependence has been regressed away. Using a thin plate spline model formulation, we recognise spatial confounding as a smoothing-induced bias identified by Rice (1986), and through asymptotic analysis of the effect estimates, we show that spatial+ avoids the bias problems of the spatial model. This is also demonstrated in a simulation study. Spatial+ is straight-forward to implement using existing software and, as the response variable is the same as that of the spatial model, standard model selection criteria can be used for comparisons. A major advantage of the method is also that it extends to models with non-Gaussian response distributions. Finally, while our results are derived in a thin plate spline setting, the spatial+ methodology transfers easily to other spatial model formulations

Generalized Additive Models for Gigadata:Modeling the U.K. Black Smoke Network Daily Data

<p>We develop scalable methods for fitting penalized regression spline based generalized additive models with of the order of 10⁴ coefficients to up to 10⁸ data. Computational feasibility rests on: (i) a new iteration scheme for estimation of model coefficients and smoothing parameters, avoiding poorly scaling matrix operations; (ii) parallelization of the iteration’s pivoted block Cholesky and basic matrix operations; (iii) the marginal discretization of model covariates to reduce memory footprint, with efficient scalable methods for computing required crossproducts directly from the discrete representation. Marginal discretization enables much finer discretization than joint discretization would permit. We were motivated by the need to model four decades worth of daily particulate data from the U.K. Black Smoke and Sulphur Dioxide Monitoring Network. Although reduced in size recently, over 2000 stations have at some time been part of the network, resulting in some 10 million measurements. Modeling at a daily scale is desirable for accurate trend estimation and mapping, and to provide daily exposure estimates for epidemiological cohort studies. Because of the dataset size, previous work has focused on modeling time or space averaged pollution levels, but this is unsatisfactory from a health perspective, since it is often acute exposure locally and on the time scale of days that is of most importance in driving adverse health outcomes. If computed by conventional means our black smoke model would require a half terabyte of storage just for the model matrix, whereas we are able to compute with it on a desktop workstation. The best previously available reduced memory footprint method would have required three orders of magnitude more computing time than our new method. Supplementary materials for this article are available online.</p

Next Generation Sequencing Assay for Detection of Circulating HPV DNA (cHPV-DNA) in Patients Undergoing Radical (Chemo)Radiotherapy in Anal Squamous Cell Carcinoma (ASCC).

Background: Following chemo-radiotherapy (CRT) for human papilloma virus positive (HPV+) anal squamous cell carcinoma (ASCC), detection of residual/recurrent disease is challenging. Patients frequently undergo unnecessary repeated biopsies for abnormal MRI/clinical findings. In a pilot study we assessed the role of circulating HPV-DNA in identifying "true" residual disease. Methods: We prospectively collected plasma samples at baseline (n = 21) and 12 weeks post-CRT (n = 17). Circulating HPV-DNA (cHPV DNA) was measured using a novel next generation sequencing (NGS) assay, panHPV-detect, comprising of two primer pools covering distinct regions of eight high-risk HPV genomes (16, 18, 31, 33, 35, 45, 52, and 58) to detect circulating HPV-DNA (cHPV DNA). cHPV-DNA levels post-CRT were correlated to disease response. Results: In pre-CRT samples, panHPV-detect demonstrated 100% sensitivity and specificity for HPV associated ASCC. PanHPV-detect was able to demonstrate cHPV-DNA in 100% (9/9) patients with T1/T2N0 cancers. cHPV-DNA was detectable 12 weeks post CRT in just 2/17 patients, both of whom relapsed. 1/16 patients who had a clinical complete response (CR) at 3 months post-CRT but relapsed at 9 months and 1/1 patient with a partial response (PR). PanHPV-detect demonstrated 100% sensitivity and specificity in predicting response to CRT. Conclusion: We demonstrate that panHPV-detect, an NSG assay is a highly sensitive and specific test for the identification of cHPV-DNA in plasma at diagnosis. cHPV-DNA post-treatment may predict clinical response to CRT

Security and Privacy Preservation over Interconnected Networks

Security is a key concern in a wide spread network. Preserving private information is to be given due importance by all communication devices and search engines, since there is a threat of unauthorized users accessing secure information by trapping the network devices. Existing wide spread network of computers, mobile and other electronic devices does not define proper protocols neither based on users location nor based on the end users requirements in connecting to the network. Our proposed solution provides the most better and promising solution for a good network of plug and play Networks along with high level of authentication and authorization solutions. The proposal uses Flexi- Negotiable Security solutions that takes into account the cost and crude for such implementations along with best interoperability among the connected devices. Set of authorization policies are generated by a network manager using XACML based on the based on the available resources and the number of connected devices thus proving a reliable and secure network of devices.In this project, we are trying to incorporate a control point which will take care of controlling the devices access points. Each individual user needs to get authentication and authorization to access the resources in the network. Control point will take care of validating the request by the users. Once the users holds the authentication/authorization to access the resource in the network. They are permitted or else, no option to access the resources and they will be restricted. The authentication will be verified by the control points through a secure SOAP based web services. Our proposed system involves the above said techniques and it associated with attribute based authentication. So that, higher designated people will be provided with more access options

Irradiation study of a fully monolithic HV-CMOS pixel sensor design in AMS 180 nm

High-Voltage Monolithic Active Pixel Sensors (HV-MAPS) based on the 180 nm HV-CMOS process have been proposed to realize thin, fast and highly integrated pixel sensors. The MuPix7 prototype, fabricated in the commercial AMS H18 process, features a fully integrated on-chip readout, i.e. hit-digitization, zero suppression and data serialization. It is the first fully monolithic HV-CMOS pixel sensor that has been tested for the use in high irradiation environments like HL-LHC. We present results from laboratory and test beam measurements of MuPix7 prototypes irradiated with neutrons (up to $5.0\cdot10^{15}{\,\rm{n}_{\rm{eq}}/cm^2}$) and protons (up to $7.8\cdot 10^{15} \,\rm{protons}/cm^2$) and compare the performance with non-irradiated sensors. Efficiencies well above 90 % at noise rates below 200 Hz per pixel are measured. A time resolution better than 22 ns is measured for all tested settings and sensors, even at the highest irradiation fluences. The data transmission at 1.25 Gbit/s and the on-chip PLL remain fully functional

Open-domain topic identification of out-of-domain utterances using Wikipedia

Users of spoken dialogue systems (SDS) expect high quality interactions across a wide range of diverse topics. However, the implementation of SDS capable of responding to every conceivable user utterance in an informative way is a challenging problem. Multi-domain SDS must necessarily identify and deal with out-of-domain (OOD) utterances to generate appropriate responses as users do not always know in advance what domains the SDS can handle. To address this problem, we extend the current state-of-the-art in multi-domain SDS by estimating the topic of OOD utterances using external knowledge representation from Wikipedia. Experimental results on real human-to-human dialogues showed that our approach does not degrade domain prediction performance when compared to the base model. But more significantly, our joint training achieves more accurate predictions of the nearest Wikipedia article by up to about 30% when compared to the benchmarks