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We present a comprehensive R software ivmodel for analyzing instrumental variables with one endogenous variable. The package implements a general class of estimators called k- class estimators and two confidence intervals that are fully robust to weak instruments. The package also provides power formulas for various test statistics in instrumental variables. Finally, the package contains methods for sensitivity analysis to examine the sensitivity of the inference to instrumental variables assumptions. We demonstrate the software on the data set from Card (1995), looking at the causal effect of levels of education on log earnings where the instrument is proximity to a four-year college., Comment: 24 pages, 2 figures, 3 tables

Funder: Expanding Excellence in England (E3); Id: http://dx.doi.org/10.13039/100006428, Funder: Issac Newton Trust, Two-sample summary data Mendelian randomization is a popular method for assessing causality in epidemiology, by using genetic variants as instrumental variables. If genes exert pleiotropic effects on the outcome not entirely through the exposure of interest, this can lead to heterogeneous and (potentially) biased estimates of causal effect. We investigate the use of Bayesian model averaging to preferentially search the space of models with the highest posterior likelihood. We develop a Metropolis-Hasting algorithm to perform the search using the recently developed MR-RAPS as the basis for defining a posterior distribution that efficiently accounts for pleiotropic and weak instrument bias. We demonstrate how our general modeling approach can be extended from a standard one-component causal model to a two-component model, which allows a large proportion of SNPs to violate the InSIDE assumption. We use Monte Carlo simulations to illustrate our methods and compare it to several related approaches. We finish by applying our approach to investigate the causal role of cholesterol on the development age-related macular degeneration.

A key assumption in Mendelian randomisation is that the relationship between the genetic instruments and the outcome is fully mediated by the exposure, known as the exclusion restriction assumption. However, in epidemiological studies, the exposure is often a coarsened approximation to some latent continuous trait. For example, latent liability to schizophrenia can be thought of as underlying the binary diagnosis measure. Genetically-driven variation in the outcome can exist within categories of the exposure measurement, thus violating this assumption. We propose a framework to clarify this violation, deriving a simple expression for the resulting bias and showing that it may inflate or deflate effect estimatesbut will not reverse their sign. We then characterise a set of assumptions and a straight-forward method for estimating the effect of standard deviation increases in the latent exposure. Our method relies on a sensitivity parameter which can be interpreted as the genetic variance of the latent exposure. We show that this method can be applied in both the one-sample and two-sample settings. We conclude by demonstrating our method in an applied example and re-analysing two papers which are likely to suffer from this type of bias, allowing meaningful interpretation of their effect sizes.

Background Instrumental variable (IV) methods are becoming an increasingly important tool in health services research as they can provide consistent estimates of causal effects in the presence of unobserved confounding. However, investigators must provide justifications that the IV is independent with any unmeasured confounder and its effect on the outcome occurs only through receipt of the exposure. These assumptions, while plausible in some contexts, cannot be verified from the data. Methods Falsification tests can be applied to provide evidence for the key IV assumptions. A falsification test cannot prove the assumptions hold, but can provide decisive evidence when the assumption fails. We provide a general overview of falsification tests for IV designs. We highlight a falsification test that utilizes a subpopulation of the data where an overwhelming proportion of units are treated or untreated. If the IV assumptions hold, we should find the intention-to-treat effect is zero within these subpopulations. Results We demonstrate the usage of falsification tests for IV designs using an IV known as tendency to operate from health services research. We show that the falsification test provides no evidence against the IV assumptions in this application.

As one of the most critical research contents in robotics, forward and inverse kinematics has an important influence on the system control and off-line programming accuracy of robot arm. And it is also the foundation of studying the motion simulation of robot arm. In this paper, the kinematic characteristics of six-axis joint robot arm are considered. As well, the forward and inverse kinematic parameters are derived based on exponential product method. The simulation model of robot arm is established by using MATLAB/Simulink, and the Graphical User Interface (GUI) operation Interface is built to simulate and control the robot arm. The simulation platform has friendly interface and simple operation. It can control the forward and inverse kinematics of robot arm and display the current posture of the robot arm in real time. By comparing the theoretical data and simulation results, the correctness of robot arm model and the accuracy of forward and inverse kinematics formula are verified.

The fields of machine learning and causal inference have developed many concepts, tools, and theory that are potentially useful for each other. Through exploring the possibility of extracting causal interpretations from black-box machine-trained models, we briefly review the languages and concepts in causal inference that may be interesting to machine learning researchers. We start with the curious observation that Friedman’s partial dependence plot has exactly the same formula as Pearl’s back-door adjustment and discuss three requirements to make causal interpretations: a model with good predictive performance, some domain knowledge in the form of a causal diagram and suitable visualization tools. We provide several illustrative examples and find some interesting and potentially causal relations using visualization tools for black-box models.

A key assumption in Mendelian randomisation is that the relationship between the genetic instruments and the outcome is fully mediated by the exposure measurement, known as the exclusion restriction assumption. However, in epidemiological studies, the exposure measurement is often a discrete marker for some latent continuous trait. For example, latent liability to schizophrenia can be thought of as underlying the binary diagnosis measure. Genetically-driven variation in the outcome can exist within categories of the exposure measurement, thus violating this assumption. We propose a framework to clarify this violation, deriving a simple expression for the resulting bias and showing that it may inflate or deflate effect estimates but will not reverse their sign. We then characterise a set of assumptions and a straight-forward method for estimating the effect of standard deviation increases in the latent exposure. Our method relies on a sensitivity parameter which can be interpreted as the genetic variance of the latent exposure. We show that this method can be applied in both the one-sample and two-sample settings. We conclude by demonstrating our method in an applied example and re-analysing two papers which are likely to suffer from this type of bias, allowing meaningful interpretation of their effect sizes.

In this paper, we applied the idea of deep learning to aircraft targets recognition based on time-frequency diagram. Firstly we introduced application of Convolutional Neural Network (CNN), and the methods of radar target recognition. Secondly, Short Time Fourier Transformation (STFT) was introduced. Thirdly, the structure of improved LeNet CNN was described, considering the character of radar echo wave. Fourthly, 4 kinds of aircraft targets were introduced. Then, the algorithm based on CNN and STFT was validated based on measured data, and was compared with Support Vector Machine (SVM). The accuracy rate could reaches up to 99.98%, 25% higher than SVM. Finally, we summarized advantages of the method proposed in this paper and give the suggestion in engineering application.

In this paper, we apply the idea of deep learning to radar interference recognition. Firstly we introduced history and concept of Convolutional Neural Network, and summarized the method of interference recognition. Secondly, the structure of improved LeNet CNN is described, considering the character of radar echo wave. Thirdly, 7 kinds of jamming are introduced. Fourthly, several important parameters of net structure such as kernel size and batch size are adjusted to achieve best performance, through measured interference radar echo. The accuracy rate reaches up to 99.7%. Finally, we summarize advantages of the method proposed in this paper.

First, we would like to congratulate the authors for successfully hosting the causal inference data competition (referred to as Competition henceforth) and contributing a unique and thought-provoking article to the literature. The authors have provided a comprehensive and timely platform to evaluate the ever-growing number of methods used for covariate adjustment in observational studies. In our comment, we don’t generally question the results of the competition, but we do wish to emphasize several other key elements about the role statistics plays in causal inference and observational studies.

In order to solve the problem of voltage sags in low-voltage power grid, the control strategy and application of Unified Power Quality Controller (UPQC) are given. Based on the main circuit of UPQC, the method of Second-Order Generalized Integral (SOGI) is used to detect the voltage drop quickly and accurately, and the direct control method is used to compensate the voltage drop. The method is simulated by PSCAD/EMTDC, and the theoretical results are verified. TMS320F28335 controller is used to realize the algorithm, and a physical platform is built. The test waveform verifies the feasibility of the method.

BackgroundTwo-sample summary data Mendelian randomization (MR) incorporating multiple genetic variants within a meta-analysis framework is a popular technique for assessing causality in epidemiology. If all genetic variants satisfy the instrumental variable (IV) and necessary modelling assumptions, then their individual ratio estimates of causal effect should be homogeneous. Observed heterogeneity signals that one or more of these assumptions could have been violated.MethodsCausal estimation and heterogeneity assessment in MR requires an approximation for the variance, or equivalently the inverse-variance weight, of each ratio estimate. We show that the most popular ‘1st order’ weights can lead to an inflation in the chances of detecting heterogeneity when in fact it is not present. Conversely, ostensibly more accurate ‘2nd order’ weights can dramatically increase the chances of failing to detect heterogeneity, when it is truly present. We derive modified weights to mitigate both of these adverse effects.ResultsUsing Monte Carlo simulations, we show that the modified weights outperform 1st and 2nd order weights in terms of heterogeneity quantification. Modified weights are also shown to remove the phenomenon of regression dilution bias in MR estimates obtained from weak instruments, unlike those obtained using 1st and 2nd order weights. However, with small numbers of weak instruments, this comes at the cost of a reduction in estimate precision and power to detect a causal effect compared to 1st order weighting. Moreover, 1st order weights always furnish unbiased estimates and preserve the type I error rate under the causal null. We illustrate the utility of the new method using data from a recent two-sample summary data MR analysis to assess the causal role of systolic blood pressure on coronary heart disease risk.ConclusionsWe propose the use of modified weights within two-sample summary data MR studies for accurately quantifying heterogeneity and detecting outliers in the presence of weak instruments. Modified weights also have an important role to play in terms of causal estimation (in tandem with 1st order weights) but further research is required to understand their strengths and weaknesses in specific settings.

Social networking websites allow users to create and share content. Big information cascades of post resharing can form as users of these sites reshare others' posts with their friends and followers. One of the central challenges in understanding such cascading behaviors is in forecasting information outbreaks, where a single post becomes widely popular by being reshared by many users. In this paper, we focus on predicting the final number of reshares of a given post. We build on the theory of self-exciting point processes to develop a statistical model that allows us to make accurate predictions. Our model requires no training or expensive feature engineering. It results in a simple and efficiently computable formula that allows us to answer questions, in real-time, such as: Given a post's resharing history so far, what is our current estimate of its final number of reshares? Is the post resharing cascade past the initial stage of explosive growth? And, which posts will be the most reshared in the future? We validate our model using one month of complete Twitter data and demonstrate a strong improvement in predictive accuracy over existing approaches. Our model gives only 15% relative error in predicting final size of an average information cascade after observing it for just one hour., 10 pages, published in KDD 2015

These years, WLAN-based positioning technology developed rapidly due to the limitation of GPS in ”city canyon”. Some people try to apply the indoor fingerprint positioning technology in the outdoor environment. Unfortunately, they ignore the difference between the indoor and outdoor environment and can't achieve preferable results. Considering more complex factors in outdoor environment, we propose a new approach called Common-APs(Access points)-Likelihood Algorithm, which uses the overlapping APs to measure the similarity among users and training points instead of the traditional Signal Strength (SS) information. Our experiment shows that our algorithm could improve the accuracy by 48.75% and reduce the time cost significantly. In addition, the proposed algorithm has strong robustness and could work much better in the busy downtown surrounding dense APs.

In WLAN-based fingerprint positioning systems, the data preprocessing before applying the matching algorithm is important for improving the accuracy. In this paper, an effective preprocessing scheme is proposed to improve selection methods of reference point (RP) and access point (AP). The spatial filter is used to select RPs, and APs are decided in the principle of minimal redundancy. The experiment is carried out on campus, whose results show that the proposed scheme could improve the accuracy of both the Kernel-based and KNN matching algorithm, especially the former, by about 25%. Moreover, the denser is the AP distribution, the better the proposed scheme performs.

Reliable and real-time location information is important for location-based services (LBS) of the mobile terminals. Existing location techniques have poor performance in urban environments due to severe multipath and NLOS propagation. Fingerprint localization based on wireless-LAN (WLAN) has been preferred for those types of environments. However, the most challenge in fingerprint positioning is the fluctuation of the received signal strength (RSS) and the online computational burden or latency for a metropolitan-scale radio map. To alleviate this problem, we propose a real-time algorithm including two key techniques: (1) The micro-cell-based radio map construction method exploiting spatial diversity is used to tackle the fluctuation of RSS and enhance positioning accuracy. (2) The RSS-based clustering of the radio map is presented to reduce the computational burden and improve the performance of the locating system. The practical implementation of positioning mobile terminals at different speeds in urban environments is described and conducted. Experimental results show that our algorithms can significantly improve the performance of WLAN-based positioning system.

Recently, fingerprint positioning systems for urban areas using the existing wireless-LAN (WLAN) play important role in ubiquitous applications. The most challenge in fingerprint positioning is how to reduce the number of training samples and high time consumption with no significant degradation in location accuracy. To deal with this problem, we propose a micro-cell-based radio map construction method based on spatial correlation of collected signal samples and spatial diversity. We present how to apply these properties to improve positioning accuracy and reduce time consumption of constructing a metropolitan-scale radio map. To further enhance the positioning accuracy and reduce latency for real-time positioning, we propose an unequal weighting fast matching algorithm to exploit temporal-spatial diversity and spatial continuity of the objects' movement trajectory. The practical implementation of positioning mobile device at different speeds in urban environments is described and conducted. Experimental results show that our algorithms can significantly improve the performance of WLAN-based fingerprint positioning system.