Author
James Taylor
Other affiliations: Institut national de la recherche agronomique, European Spallation Source, Children's Hospital of Philadelphia ...read more
Bio: James Taylor is an academic researcher from Newcastle University. The author has contributed to research in topics: Laser & Fiber laser. The author has an hindex of 95, co-authored 1161 publications receiving 39945 citations. Previous affiliations of James Taylor include Institut national de la recherche agronomique & European Spallation Source.
Topics: Laser, Fiber laser, Optical fiber, Picosecond, Photonic-crystal fiber
Papers published on a yearly basis
Papers
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TL;DR: In this paper, the authors describe a highly flexible ultrashort optical pulse source capable of generating duration-tunable pulses at single or multiple wavelengths, which is based on the filtering of the broad spectrum of femtosecond solitons which are generated using an electroabsorption modulator combined with nonlinear compression.
Abstract: We describe a highly flexible ultrashort optical pulse source capable of generating duration-tunable pulses at single or multiple wavelengths. The technique we report is based on the filtering of the broad spectrum of femtosecond solitons which are generated using an electroabsorption modulator combined with nonlinear compression. The simplicity and robust nature of this source make it highly stable and we demonstrate the generation of pulses at a repetition rate of 10 GHz with durations in the femtosecond and picosecond regimes and the simultaneous generation of eight 10-GHz channels of picosecond pulses with a channel spacing of 3.5 nm.
19 citations
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TL;DR: In this paper, the potential ability of fully polarimetric synthetic aperture radar (SAR) data in estimating above-ground biomass of oilseed rape was investigated, and the results indicated that when full polarization SAR data is available, a simpler model, higher saturation point and better accuracy can be achieved in biomass estimation, which highlights the importance and value of polarimetry information in quantitative crop monitoring.
Abstract: Accurate estimation of crop biophysical and biochemical parameters during crop growing seasons is essential for improving site-specific management and yield estimation. The potential ability of fully polarimetric synthetic aperture radar (SAR) data in estimating above-ground biomass of oilseed rape was investigated in this study. The temporal profile of different scattering intensity and polarimetric features during the entire growing season was identified with ground measurements. A polarimetric feature, relying on the polarimetric decomposition method, was put forward to estimate the biomass of oilseed rape. Validation results revealed great potential with a determination coefficient (R2) of 0.85, root mean squared error (RMSE) of 41.6 g/m2, and relative error (RE) of 28.5% for dry biomass, and an R2 of 0.76, RMSE of 527.4 g/m2 and RE of 28.6% for fresh biomass. Moreover, the use of full polarization SAR data was compared with single and dual polarization SAR data. The results suggest that when full polarization SAR data is available, a simpler model, higher saturation point and better accuracy can be achieved in biomass estimation of oilseed rape, which highlights the importance and value of polarimetry information in quantitative crop monitoring. This study provides guidelines for in-season monitoring of crop growth parameters with SAR data, which further improves crop monitoring capability in adverse weather conditions.
19 citations
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TL;DR: In this paper , the authors review the reasons why practitioners decide to spatialize crop models and the main methods they have used to do this, which questions the best place of the spatialization process in the modelling framework.
Abstract: Abstract Crop models are useful tools because they can help understand many complex processes by simulating them. They are mainly designed at a specific spatial scale, the field. But with the new spatial data being made available in modern agriculture, they are being more and more applied at multiple and changing scales. These applications range from typically at broader scales, to perform regional or national studies, or at finer scales to develop modern site-specific management approaches. These new approaches to the application of crop models raise new questions concerning the evaluation of their performance, particularly for downscaled applications. This article first reviews the reasons why practitioners decide to spatialize crop models and the main methods they have used to do this, which questions the best place of the spatialization process in the modelling framework. A strong focus is then given to the evaluation of these spatialized crop models. Evaluation metrics, including the consideration of dedicated sensitivity indices are reviewed from the published studies. Using a simple example of a spatialized crop model being used to define management zones in precision viticulture, it is shown that classical model evaluation involving aspatial indices (e.g. the RMSE) is not sufficient to characterize the model performance in this context. A focus is made at the end of the review on potentialities that a complementary evaluation could bring in a precision agriculture context.
19 citations
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TL;DR: In this article, the authors summarize the sensitivity achieved by the LIGO and Virgo gravitational wave detectors for compact binary coalescence (CBC) searches during the fifth science run and the first science run.
Abstract: We summarize the sensitivity achieved by the LIGO and Virgo gravitational wave detectors for compact binary coalescence (CBC) searches during LIGO's fifth science run and Virgo's first science run. We present noise spectral density curves for each of the four detectors that operated during these science runs which are representative of the typical performance achieved by the detectors for CBC searches. These spectra are intended for release to the public as a summary of detector performance for CBC searches during these science runs.
19 citations
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TL;DR: The primary outcome is the immunisation status of children of VHPs at 19 months, 0 day of age expressed as the percentage of days underimmunised from birth to 19 months for 22 doses of eight vaccines recommended during this interval.
Abstract: Introduction A key contributor to underimmunisation is parental refusal or delay of vaccines due to vaccine concerns. Many clinicians lack confidence in communicating with vaccine-hesitant parents (VHP) and perceive that their discussions will do little to change parents' minds. Improving clinician communication with VHPs is critical to increasing childhood vaccine uptake. Methods and analysis We describe the protocol for a cluster randomised controlled trial to test the impact of a novel, multifaceted clinician vaccine communication strategy on child immunisation status. The trial will be conducted in 24 primary care practices in two US states (Washington and Colorado). The strategy is called Presumptively Initiating Vaccines and Optimizing Talk with Motivational Interviewing (PIVOT with MI), and involves clinicians initiating the vaccine conversation with all parents of young children using the presumptive format, and among those parents who resist vaccines, pivoting to using MI. Our primary outcome is the immunisation status of children of VHPs at 19 months, 0 day of age expressed as the percentage of days underimmunised from birth to 19 months for 22 doses of eight vaccines recommended during this interval. Secondary outcomes include clinician experience communicating with VHPs, parent visit experience and clinician adherence to the PIVOT with MI communication strategy. Ethics and dissemination This study is approved by the following institutional review boards: Colorado Multiple Institutional Review Board, Washington State Institutional Review Board and Swedish Health Services Institutional Review Board. Results will be disseminated through peer-reviewed manuscripts and conference presentations. Trial registration number NCT03885232.
19 citations
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TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.
Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …
33,785 citations
01 Jan 2016
TL;DR: The using multivariate statistics is universally compatible with any devices to read, allowing you to get the most less latency time to download any of the authors' books like this one.
Abstract: Thank you for downloading using multivariate statistics. As you may know, people have look hundreds times for their favorite novels like this using multivariate statistics, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their laptop. using multivariate statistics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the using multivariate statistics is universally compatible with any devices to read.
14,604 citations
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TL;DR: This book by a teacher of statistics (as well as a consultant for "experimenters") is a comprehensive study of the philosophical background for the statistical design of experiment.
Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON
13,333 citations
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TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).
13,246 citations
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01 Jan 1994
TL;DR: In this paper, the authors present a brief history of LMIs in control theory and discuss some of the standard problems involved in LMIs, such as linear matrix inequalities, linear differential inequalities, and matrix problems with analytic solutions.
Abstract: Preface 1. Introduction Overview A Brief History of LMIs in Control Theory Notes on the Style of the Book Origin of the Book 2. Some Standard Problems Involving LMIs. Linear Matrix Inequalities Some Standard Problems Ellipsoid Algorithm Interior-Point Methods Strict and Nonstrict LMIs Miscellaneous Results on Matrix Inequalities Some LMI Problems with Analytic Solutions 3. Some Matrix Problems. Minimizing Condition Number by Scaling Minimizing Condition Number of a Positive-Definite Matrix Minimizing Norm by Scaling Rescaling a Matrix Positive-Definite Matrix Completion Problems Quadratic Approximation of a Polytopic Norm Ellipsoidal Approximation 4. Linear Differential Inclusions. Differential Inclusions Some Specific LDIs Nonlinear System Analysis via LDIs 5. Analysis of LDIs: State Properties. Quadratic Stability Invariant Ellipsoids 6. Analysis of LDIs: Input/Output Properties. Input-to-State Properties State-to-Output Properties Input-to-Output Properties 7. State-Feedback Synthesis for LDIs. Static State-Feedback Controllers State Properties Input-to-State Properties State-to-Output Properties Input-to-Output Properties Observer-Based Controllers for Nonlinear Systems 8. Lure and Multiplier Methods. Analysis of Lure Systems Integral Quadratic Constraints Multipliers for Systems with Unknown Parameters 9. Systems with Multiplicative Noise. Analysis of Systems with Multiplicative Noise State-Feedback Synthesis 10. Miscellaneous Problems. Optimization over an Affine Family of Linear Systems Analysis of Systems with LTI Perturbations Positive Orthant Stabilizability Linear Systems with Delays Interpolation Problems The Inverse Problem of Optimal Control System Realization Problems Multi-Criterion LQG Nonconvex Multi-Criterion Quadratic Problems Notation List of Acronyms Bibliography Index.
11,085 citations