The GstLAL Search Analysis Methods for Compact Binary Mergers in Advanced LIGO's Second and Advanced Virgo's First Observing Runs

After their successful first observing run (September 12, 2015 - January 12, 2016), the Advanced LIGO detectors were upgraded to increase their sensitivity for the second observing run (November 30, 2016 - August 26, 2017). The Advanced Virgo detector joined the second observing run on August 1, 2017. We discuss the updates that happened during this period in the GstLAL-based inspiral pipeline, which is used to detect gravitational waves from the coalescence of compact binaries both in low latency and an offline configuration. These updates include deployment of a zero-latency whitening filter to reduce the over-all latency of the pipeline by up to 32 seconds, incorporation of the Virgo data stream in the analysis, introduction of a single-detector search to analyze data from the periods when only one of the detectors is running, addition of new parameters to the likelihood ratio ranking statistic, increase in the parameter space of the search, and introduction of a template mass-dependent glitch-excision thresholding method.Comment: 12 pages, 7 figures, to be submitted to Phys. Rev. D, comments welcom

EGFAFS:A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data

Toward Early-Warning Detection of Gravitational Waves from Compact Binary Coalescence

Rapid detection of compact binary coalescence (CBC) with a network of advanced gravitational-wave detectors will offer a unique opportunity for multi-messenger astronomy. Prompt detection alerts for the astronomical community might make it possible to observe the onset of electromagnetic emission from (CBC). We demonstrate a computationally practical filtering strategy that could produce early-warning triggers before gravitational radiation from the final merger has arrived at the detectors.Comment: 16 pages, 7 figures, published in ApJ. Reformatted preprint with emulateap

Improving Dynamics Estimations and Low Level Torque Control Through Inertial Sensing

In 1996, professors J. Edward Colgate and Michael Peshkin invented the cobots as robotic equipment safe enough for interacting with human workers. Twenty years later, collaborative robots are highly demanded in the packaging industry, and have already been massively adopted by companies facing issues for meeting customer demands. Meantime, cobots are still making they way into environments where value-added tasks require more complex interactions between robots and human operators. For other applications like a rescue mission in a disaster scenario, robots have to deal with highly dynamic environments and uneven terrains. All these applications require robust, fine and fast control of the interaction forces, specially in the case of locomotion on uneven terrains in an environment where unexpected events can occur. Such interaction forces can only be modulated through the control of joint internal torques in the case of under-actuated systems which is typically the case of mobile robots. For that purpose, an efficient low level joint torque control is one of the critical requirements, and motivated the research presented here. This thesis addresses a thorough model analysis of a typical low level joint actuation sub-system, powered by a Brushless DC motor and suitable for torque control. It then proposes procedure improvements in the identification of model parameters, particularly challenging in the case of coupled joints, in view of improving their control. Along with these procedures, it proposes novel methods for the calibration of inertial sensors, as well as the use of such sensors in the estimation of joint torques

Visual balance--the tightrope of computer generated layout

Thesis (M.S.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1995.Includes bibliographical references (leaves [55]-60).by Xiaoyang Yang.M.S

The GstLAL Search Analysis Methods for Compact Binary Mergers in Advanced LIGO's Second and Advanced Virgo's First Observing Runs

After their successful first observing run (September 12, 2015 - January 12, 2016), the Advanced LIGO detectors were upgraded to increase their sensitivity for the second observing run (November 30, 2016 - August 26, 2017). The Advanced Virgo detector joined the second observing run on August 1, 2017. We discuss the updates that happened during this period in the GstLAL-based inspiral pipeline, which is used to detect gravitational waves from the coalescence of compact binaries both in low latency and an offline configuration. These updates include deployment of a zero-latency whitening filter to reduce the over-all latency of the pipeline by up to 32 seconds, incorporation of the Virgo data stream in the analysis, introduction of a single-detector search to analyze data from the periods when only one of the detectors is running, addition of new parameters to the likelihood ratio ranking statistic, increase in the parameter space of the search, and introduction of a template mass-dependent glitch-excision thresholding method

Aplication Multi Vegetation Index to Mapping Magrove Distribution Coast Environtment Northeast Province of Aceh, Indonesia

The mangrove such forest very important at coastal ecosystem and environment. The purpose of the research to mapping mangrove distribution at the coast environment using multi vegetation index, comparison accuracy assessment to mapping mangrove area. The method of the research use by multi imagery transformation as NDVI, Infrared II, SAVI, EVI and Maximum Likelihood. Data on the research have using by Landsat OLI8, tools use by ENVI 5.0 and ArcGIS 10.1. Optimizing the used of data from Landsat satellite imagery for mapping mangrove found where sharper appearance mangrove area in the gray scale image of the results of the analysis of vegetation transformation NDVI, Infrared II, SAVI and EVI showing difference specification, but also found has founded difference objects of interpreted it was showing like shadows of cloud be the another object. To classification on mangrove object is seen from the results of density slicing of transformation value to classing vegetation. The percentage accuracy of image prove some dominant image transformation is able to indicate a more optimal mangrove and mangrove separating the object is not present, but the accuracy of the data analysis result has variations, refers to the number of samples use

Essays in Economic Growth and International Trade

The first chapter of this dissertation identifies the decline in mortality risk as an important trigger of demographic transition. To that end, we solve a precautionary demand for fertility model to fit the time series of total fertility rate and average years of schooling in the labor force for the 16 rich countries. Overall, the time series of fertility and average years of schooling for individuals in the labor force generated by our model closely match the actual observations. Furthermore, the out-of-sample prediction of output per worker are also highly correlated with the data. Using the model, we also identify a temporary decline in the price of housing space as the leading cause of baby booms across these countries. The second chapter employs machine learning techniques to capture heterogeneity in free trade agreements. The tools of machine learning allow us to quantify several features of trade agreements, including volume, comprehensiveness, and legal enforceability. Combining machine learning results with gravity analysis of trade, we find that more comprehensive agreements result in larger trade creation effects. In addition, we identify the specific trade policy provisions that tend to have the substantial effect in creating trade flows. In particular, legally binding provisions on anti-dumping, capital mobility, competition, customs harmonization, dispute settlement mechanism, e-commerce, environmental standards, export restrictions, freedom of transit, import restrictions, institutional arrangements, intellectual property rights, investment, labor standards, public procurement, sanitary and phytosanitary measures, services, subsidies and countervailing measures, technical barriers to trade, telecommunications, and transparency tend to have the largest trade creation effects