https://axon.cs.byu.edu/~martinez/classes/678/Presentations/Yao.pdf

Extreme learning machine: Theory and applications

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word "reinforcement." The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.

/pdf/reinforcement-learning-a-survey-38lmyylg2y.pdf

Reinforcement learning: a survey

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.

Reinforcement Learning: A Survey

The problem of safe microburst wind shear encounter during the approach and climb-out flight phases is addressed using flight path optimization. The purpose is to investigate the physical limits of safe penetration and to determine control strategies that take full advantage of those limits. Optimal trajectories for both jet transport and general aviation aircraft were computed for encounters with idealized and actual microburst profiles. The results demonstrate that limits on control system design rather than on the aircraft's physical performance may be the deciding factor in an aircraft's capability for safe passage through a wide class of microbursts. The best control strategies involved responding to airspeed loss in an unconventional manner by raising the nose to maintain lift.

Optimal Flight Paths Through Microburst Wind Profiles

Previous studies have shown that among populations with a high rate of consanguinity, there is a significant increase in the prevalence of cancer Single nucleotide polymorphism (SNP) array data (Affymetrix, 50K XbaI) analysis revealed long regions of homozygosity in genomic DNAs taken from tumor and matched normal tissues of colorectal cancer (CRC) patients The presence of these regions in the genome may indicate levels of consanguinity in the individual's family lineage We refer to these autozygous regions as identity-by-descent (IBD) segments In this study, we compared IBD segments in 74 mostly Caucasian CRC patients (mean age of 66 years) to two control data sets: (a) 146 Caucasian individuals (mean age of 80 years) who participated in an age-related macular degeneration (AMD) study and (b) 118 cancer-free Caucasian individuals from the Framingham Heart Study (mean age of 67 years) Our results show that the percentage of CRC patients with IBD segments (>or=4 Mb length and 50 SNPs probed) in the genome is at least twice as high as the AMD or Framingham control groups Also, the average length of these IBD regions in the CRC patients is more than twice the length of the two control data sets Compared with control groups, IBD segments are found to be more common among individuals of Jewish background We believe that these IBD segments within CRC patients are likely to harbor important CRC-related genes with low-penetrance SNPs and/or mutations, and, indeed, two recently identified CRC predisposition SNPs in the 8q24 region were confirmed to be homozygous in one particular patient carrying an IBD segment covering the region

https://cancerres.aacrjournals.org/content/canres/68/8/2610.full.pdf

The Signatures of Autozygosity among Patients with Colorectal Cancer

The robustness of 10 solutions to a benchmark control design problem presented at the 1990 American Control Conference has been evaluated. The 10 controllers have second-to-eighth-order transfer functions and have been designed using several different methods, including H-infinity optimization, loop-transfer recovery, imaginary-axis shifting, constrained optimization, structured covariance, game theory, and the internal model principle. Stochastic robustness analysis quantifies the controllers' stability and performance robustness with structured uncertainties in up to six system parameters. The analysis provides insights into system response that are not readily derived from other robustness criteria and provides a common ground for judging controllers produced by alternative methods. One important conclusion is that gain and phase margins are not reliable indicators of the probability of instability. Furthermore, parameter variations actually may improve the likelihood of achieving selected performance metrics, as demonstrated by results for the probability of settling-time exceedance.

Robustness of solutions to a benchmark control problem

Guaranteeing robustness has long been an important design objective of control system analysis. Stochastic robustness is a simple numerical procedure that can be used to measure and gain insight into robustness properties associated with linear control systems. In the realm of aircraft control systems, problems such as the effects of flight condition perturbations and model-order uncertainties on robustness are easily and effectively analyzed using stochastic robustness. The concept of stochastic robustness is reviewed and examples are presented demonstrating its use in flight control system analysis.

/pdf/application-of-stochastic-robustness-to-aircraft-control-57ujmjrr55.pdf

Robert F. Stengel

Papers

Optimal Flight Paths Through Microburst Wind Profiles

The Signatures of Autozygosity among Patients with Colorectal Cancer

Robustness of solutions to a benchmark control problem

Application of stochastic robustness to aircraft control systems

Determination of nonlinear aerodynamic coefficients using the estimation-before-modeling method