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 …

I and i

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Using Multivariate Statistics

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

The Design and Analysis of Experiments

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.).

Machine learning

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.

/pdf/linear-matrix-inequalities-in-system-and-control-theory-2q5cf0pqm2.pdf

Linear Matrix Inequalities in System and Control Theory

https://jamanetwork.com/journals/jamapediatrics/articlepdf/2673716/jamapediatrics_taylor_2018_ed_170055.pdf

Initiating Phototherapy at Subthreshold Levels: The Slippery Slope.

Adaptations in crop rotation with the inclusion of temporary grass-clover leys and organic amendments, have been promoted as effective ways to improve soil carbon (C) sequestration and mitigate climate change in agricultural systems. However, there are still a lot of uncertainties related to i) the combined effects of different crop rotations and different fertilisation sources, e.g., organic amendments, on soil C stocks; and ii) their potential effect on C stabilisation. The objective of this study was to evaluate the effect of different arable crop rotations with varying degrees of diversity in crop type and lengths of grass-clover ley periods and fertilisation sources on soil C stocks and C stabilisation down to 0.60 m soil depth. This was investigated in a long-term factorial field experiment-combining different crop rotation (cereal-intensive conventional vs. diversified legume-intensive organic) with different lengths of grass-clover ley periods (2 vs. 3 years), fertilisation sources (mineral vs. compost), and years (samples taken at the beginning and at the last year of one complete cycle of rotation; 8 years apart)-to explore their individual and interactive effect on soil C stock and C stabilisation at two soil depths (0–0.30 and 0.30–0.60 m). Soil C stabilisation was assessed using a unique combination of three different techniques: physical fractionation for separation of C associated to organic and mineral fractions, thermal analysis combined with differential scanning calorimetry and a quadrupole mass spectrometry (TG-DSC-QMS) for physical-chemical aspects, and pyrolysis coupled with gas chromatography-mass spectrometry (Py-GC/MS) for molecular structural information. The findings showed higher soil C stocks under the diversified organic rotation with 3 years of grass-clover ley period at both soil depths, regardless of the fertilisation source or sampling year. However, the organic rotation seemed to deliver stable soil C stocks only in the subsoil layer. Compost fertilisation, in turn, increased topsoil C stocks between the two sample dates under both rotations, and it appears to be stable. These results suggested that combining a diversified organic rotation with 3 years grass-clover ley with compost fertilisation could be one way for agricultural systems to deliver stable soil C sequestration.

/pdf/diversified-crop-rotations-and-organic-amendments-as-rm4pwha5.pdf

Diversified crop rotations and organic amendments as strategies for increasing soil carbon storage and stabilisation in UK arable systems

/pdf/alignments-to-identify-functional-elements-esperr-learning-47j013weq6.pdf

alignments to identify functional elements ESPERR: Learning strong and weak signals in genomic sequence

Up-to 15 W power, CW Raman-soliton continua were generated in conventional highly-nonlinear-fibres in the 1.48-2.05 /spl mu/m range with spectral brightness up-to 30 mW/nm and flatness <1 dB. All-fibre-integrated pumping with 20 W 1480 nm Raman and 10 W Yb/Er sources is employed.

Multi-watt, 1.48-2.05 /spl mu/m range CW Raman-soliton continuum generation in highly-nonlinear fibres

Dissolution characteristics of a positive chemically amplified photoresist, AZ-PF514 from Hoechst Celanese, have been studied. Development rate monitor data for different developer concentrations are analyzed. The experimental dissolution rates of the X-ray-exposed photoresist are examined in view of the dissolution model proposed earlier by Reiser. Parameters of Reiser's model associated with the diffusion of an aqueous base through a thin penetration layer and solubility of a phenolate complex in an aqueous base solution are derived for different X-ray exposure conditions. Reiser's dissolution model was used to simulate the developed photoresist pattern profile. An example of 0.25 µm pattern development simulation using a dose-dependent coefficient for diffusion of a developer base into polymer is demonstrated.

James Taylor

Papers

Initiating Phototherapy at Subthreshold Levels: The Slippery Slope.

Diversified crop rotations and organic amendments as strategies for increasing soil carbon storage and stabilisation in UK arable systems

alignments to identify functional elements ESPERR: Learning strong and weak signals in genomic sequence

Multi-watt, 1.48-2.05 /spl mu/m range CW Raman-soliton continuum generation in highly-nonlinear fibres

Dissolution Study of a Novolak-Based Photoresist Based on a Developer Diffusion Model.