Sampling correctors

In many situations, sample data is obtained from a noisy or imperfect source. In order to address such corruptions, this paper introduces the concept of a sampling corrector. Such algorithms use structure that the distribution is purported to have, in order to allow one to make "on-the-fly" corrections to samples drawn from probability distributions. These algorithms then act as filters between the noisy data and the end user. We show connections between sampling correctors, distribution learning algorithms, and distribution property testing algorithms. We show that these connections can be utilized to expand the applicability of known distribution learning and property testing algorithms as well as to achieve improved algorithms for those tasks. As a first step, we show how to design sampling correctors using proper learning algorithms. We then focus on the question of whether algorithms for sampling correctors can be more efficient in terms of sample complexity than learning algorithms for the analogous families of distributions. When correcting monotonicity, we show that this is indeed the case when also granted query access to the cumulative distribution function. We also obtain sampling correctors for monotonicity without this stronger type of access, provided that the distribution be originally very close to monotone (namely, at a distance O(1/log2 n)). In addition to that, we consider a restricted error model that aims at capturing "missing data" corruptions. In this model, we show that distributions that are close to monotone have sampling correctors that are significantly more efficient than achievable by the learning approach. We then consider the question of whether an additional source of independent random bits is required by sampling correctors to implement the correction process. We show that for correcting close-to-uniform distributions and close-to-monotone distributions, no additional source of random bits is required, as the samples from the input source itself can be used to produce this randomness

Automatic Classification of Roof Shapes for Multicopter Emergency Landing Site Selection

Geographic information systems (GIS) now provide accurate maps of terrain, roads, waterways, and building footprints and heights. Aircraft, particularly small unmanned aircraft systems, can exploit additional information such as building roof structure to improve navigation accuracy and safety particularly in urban regions. This paper proposes a method to automatically label building roof shape types. Satellite imagery and LIDAR data from Witten, Germany are fed to convolutional neural networks (CNN) to extract salient feature vectors. Supervised training sets are automatically generated from pre-labeled buildings contained in the OpenStreetMap database. Multiple CNN architectures are trained and tested, with the best performing networks providing a condensed feature set for support vector machine and decision tree classifiers. Satellite and LIDAR data fusion is shown to provide greater classification accuracy than through use of either data type individually

Pollution control can help mitigate future climate change impact on European grayling in the UK

Aim: We compare the performance of habitat suitability models using climate data only or climate data together with water chemistry, land cover and predation pressure data to model the distribution of European grayling (Thymallus thymallus). From these models, we (a) investigate the relationship between habitat suitability and genetic diversity; (b) project the distribution of grayling under future climate change; and (c) model the effects of habitat mitigation on future distributions. Location: United Kingdom. Methods: Maxent species distribution modelling was implemented using a Simple model (only climate parameters) or a Full model (climate, water chemistry, land use and predation pressure parameters). Areas of high and low habitat suitability were designated. Associations between habitat suitability and genetic diversity for both neutral and adaptive markers were examined. Distribution under minimal and maximal future climate change scenarios was modelled for 2050, incorporating projections of future flow scenarios obtained from the Centre for Ecology and Hydrology. To examine potential mitigation effects within habitats, models were run with manipulation of orthophosphate, nitrite and copper concentrations. Results: We mapped suitable habitat for grayling in the present and the future. The Full model achieved substantially higher discriminative power than the Simple model. For low suitability habitat, higher levels of inbreeding were observed for adaptive, but not neutral, loci. Future projections predict a significant contraction of highly suitable areas. Under habitat mitigation, modelling suggests that recovery of suitable habitat of up to 10% is possible. Main conclusions: Extending the climate-only model improves estimates of habitat suitability. Significantly higher inbreeding coefficients were found at immune genes, but not neutral markers in low suitability habitat, indicating a possible impact of environmental stress on evolutionary potential. The potential for habitat mitigation to alleviate distributional changes under future climate change is demonstrated, and specific recommendations are made for habitat recovery on a regional basis

Informative noncompliance in endpoint trials

Noncompliance with study medications is an important issue in the design of endpoint clinical trials. Including noncompliant patient data in an intention-to-treat analysis could seriously decrease study power. Standard methods for calculating sample size account for noncompliance, but all assume that noncompliance is noninformative, i.e., that the risk of discontinuation is independent of the risk of experiencing a study endpoint. Using data from several published clinical trials (OPTIMAAL, LIFE, RENAAL, SOLVD-Prevention and SOLVD-Treatment), we demonstrate that this assumption is often untrue, and we discuss the effect of informative noncompliance on power and sample size

Real-Time Dynamics of Ca2+, Caspase-3/7, and Morphological Changes in Retinal Ganglion Cell Apoptosis under Elevated Pressure

Quantitative information on the dynamics of multiple molecular processes in individual live cells under controlled stress is central to the understanding of the cell behavior of interest and the establishment of reliable models. Here, the dynamics of the apoptosis regulator intracellular Ca2+, apoptosis effector caspase-3/7, and morphological changes, as well as temporal correlation between them at the single cell level, are examined in retinal gangling cell line (differentiated RGC-5 cells) undergoing apoptosis at elevated hydrostatic pressure using a custom-designed imaging platform that allows long-term real-time simultaneous imaging of morphological and molecular-level physiological changes in large numbers of live cells (beyond the field-of-view of typical microscopy) under controlled hydrostatic pressure. This examination revealed intracellular Ca2+ elevation with transient single or multiple peaks of less than 0.5 hour duration appearing at the early stages (typically less than 5 hours after the onset of 100 mmHg pressure) followed by gradual caspase-3/7 activation at late stages (typically later than 5 hours). The data reveal a strong temporal correlation between the Ca2+ peak occurrence and morphological changes of neurite retraction and cell body shrinkage. This suggests that Ca2+ elevation, through its impact on ion channel activity and water efflux, is likely responsible for the onset of apoptotic morphological changes. Moreover, the data show a significant cell-to-cell variation in the onset of caspase-3/7 activation, an inevitable consequence of the stochastic nature of the underlying biochemical reactions not captured by conventional assays based on population-averaged cellular responses. This real-time imaging study provides, for the first time, statistically significant data on simultaneous multiple molecular level changes to enable refinements and testing of models of the dynamics of mitochondria-mediated apoptosis. Further, the platform developed and the approach has direct significance to the study of a variety of signaling pathway phenomena

Deconstructing Insight: EEG Correlates of Insightful Problem Solving

Background: Cognitive insight phenomenon lies at the core of numerous discoveries. Behavioral research indicates four salient features of insightful problem solving: (i) mental impasse, followed by (ii) restructuring of the problem representation, which leads to (iii) a deeper understanding of the problem, and finally culminates in (iv) an “Aha!” feeling of suddenness and obviousness of the solution. However, until now no efforts have been made to investigate the neural mechanisms of these constituent features of insight in a unified framework. Methodology/Principal Findings: In an electroencephalographic study using verbal remote associate problems, we identified neural correlates of these four features of insightful problem solving. Hints were provided for unsolved problems or after mental impasse. Subjective ratings of the restructuring process and the feeling of suddenness were obtained on trial-by-trial basis. A negative correlation was found between these two ratings indicating that sudden insightful solutions, where restructuring is a key feature, involve automatic, subconscious recombination of information. Electroencephalogram signals were analyzed in the space×time×frequency domain with a nonparametric cluster randomization test. First, we found strong gamma band responses at parieto-occipital regions which we interpreted as (i) an adjustment of selective attention (leading to a mental impasse or to a correct solution depending on the gamma band power level) and (ii) encoding and retrieval processes for the emergence of spontaneous new solutions. Secondly, we observed an increased upper alpha band response in right temporal regions (suggesting active suppression of weakly activated solution relevant information) for initially unsuccessful trials that after hint presentation led to a correct solution. Finally, for trials with high restructuring, decreased alpha power (suggesting greater cortical excitation) was observed in right prefrontal area. Conclusions/Significance: Our results provide a first account of cognitive insight by dissociating its constituent components and potential neural correlates

Unveiling patterns of international communities in a global city using mobile phone data

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