Goodreads helps you keep track of books you want to read. Want to Read saving…. Want to Read Currently Reading Read. Other editions. Enlarge cover. Error rating book. Refresh and try again. Open Preview See a Problem? Details if other :. Thanks for telling us about the problem. Return to Book Page. Preview — Quantifying Chaos by Henry Kirchhoff.
Quantifying Chaos: The Science of Neo-Anarchism details the composition of chaos and order in universal systems using basic economic and scientific principles. The purpose of the text is to help people understand how equilibrium between chaos and order can be sustained in society and the universe at large. Get A Copy. Paperback46 pages. Published February 12th by Psychoplasmic Pulp Publishing. More Details Friend Reviews. To see what your friends thought of this book, please sign up.
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Rating details. All Languages. More filters. Sort order. Mike Kleine marked it as to-read Oct 09, Blog IT Pro. Inside Azure Search: Chaos Engineering. A central truth in cloud computing is that failure is inevitable. As systems scale, we expect nodes to fail ungracefully in random and unexpected ways, networks to experience sudden partitions, and messages to be dropped at any time. Rather than fight this truth, we embrace it. We plan for failure and design our systems to be fault-tolerant, resilient, and self-stabilizing.
But once we've finished designing and building, how do we verify that our beautiful, fault-tolerant systems actually react to failures as they should? Functional tests can only do so much. Distributed systems are complex ecosystems of moving parts. Each component is subject to failure, and more than that, its interactions with other system components will also have their own unique failure modes.
We can sit around and armchair-theorize all we like about how these components will respond to imagined failures, but finding every possible combination of failure is just not feasible.
Even if we do manage to exhaustively account for every failure mode our system can encounter, it's not sustainable or practical to re-verify system responses in this way every time we make a change to its behavior. Chaos Engineering Azure Search uses chaos engineering to solve this problem. As coined by Netflix in a recent excellent blog post, chaos engineering is the practice of building infrastructure to enable controlled automated fault injection into a distributed system.Why Use Uncertainty Quantification?
To accomplish this, Netflix has created the Netflix Simian Army with a collection of tools dubbed "monkeys" that inject failures into customer services. The environment contains a search service that is continuously and randomly changing topology and state.
Service calls are made against this service on a regular basis to verify that it is fully operational. Even just setting up this target environment for the Search Chaos Monkey to play in has been incredibly helpful in smoking out issues with our provisioning and scaling workflows. When the Search Chaos Monkey is dormant, we expect the test service to operate smoothly.
Any errors coming from it can therefore be assumed to be caused by bugs in existing workflows or false positives from the alerting system. We've caught several bugs this way before they had a chance to escape into production. Quantifying Chaos After the test service was stabilized, we unleashed the Search Chaos Monkey and gave it some tools of destruction to have fun with.
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It runs continuously and will randomly choose an operation at regular intervals to run in its test environment.
Low chaos refers to failures that our system can recover from gracefully with minimal or no interruption to service availability. Accordingly, while the Search Chaos Monkey is set to run only low chaos operations, any alerts raised from the test service are considered to be bugs. Medium chaos failures can also be recovered from gracefully, but may result in degraded service performance or availability, raising low priority alerts to engineers on call.
High chaos failures are more catastrophic and will interrupt service availability. These will cause high priority alerts to be sent to on-call engineers and often require manual intervention to fix.
High chaos operations are important for ensuring that our system can fail in a graceful manner while maintaining the integrity of customer data. Along with medium chaos operations, they also function as negative tests that verify alerts are raised as expected, enabling engineers to respond to the problem.We need your help.
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Quantifying Chaos: The Science of Neo-Anarchism
Although the diverse work in Susan Murrell's massive show at galleryHOMELAND—which ranges from psychedelic gouache drawings to expansive multimedia installations—is difficult to classify, the concept of classification itself is at the heart of Instinctive Inquiry. Using the visual vocabulary of science and education in general, the Hood River-based artist communicates in a language that asserts the existence of absolutes and promises that even the world's most inscrutable phenomena will reveal their secrets to analysis.
But in the artist's unwieldy multimedia presentation, she essentially lampoons epistemology with work that refuses to obey any organizing logic. In "Archive," Murrell has arranged clusters of sculptures—pieces of porous green coral and translucent, fleshy seaweed—across a wall. Each tiny sculpture is identified with a numerical tag, which corresponds to a JPG image that inspired it.
For reference, Murrell has included a clinical white three-ring binder of all the digital source images of aquatic flora and fauna.
Of course, the sort of information you'd expect to find in the book—the names of the specimens, why Murrell chose them as subjects—is noticeably absent. According to "Archive," science can be interpreted as little more than an organizing device, in which the rigorous cataloging of information distracts from the abyss of non-knowledge that lurks beneath it. More to the point is Murrell's perverted bar graph, "Indication of Oversight.
Its bars are flaccid and spill onto the floor of the gallery in limp, plastic folds. Directional arrows have migrated across the space, grouped in bee-like swarms. And, again, Murrell withholds what is actually being quantified by the graph, using its forms as little more than visual shorthand for scientific analysis.
In "Indication of Oversight," it's as if the graph has become animated to acknowledge its own futile insistence on absolute knowledge. And as the bars and arrows refuse to cooperate, randomly malfunctioning, the only information Murrell's graph reveals is the reality of chaos. SuitePortland, OR Dear readers, We need your help.
Visual Art Oct 16, Thanks for helping us catch any problems with articles on DeepDyve. We'll do our best to fix them. Check all that apply - Please note that only the first page is available if you have not selected a reading option after clicking "Read Article". Include any more information that will help us locate the issue and fix it faster for you. In many applications, there is a desire to determine if the dynamics of interest are chaotic or not. Since positive Lyapunov exponents are a signature for chaos, they are often used to determine this.
Reliable estimates of Lyapunov exponents should demonstrate evidence of convergence; but literature abounds in which this evidence lacks. This Letter presents two maps through which it highlights the importance of providing evidence of convergence of Lyapunov exponent estimates. The results suggest cautious conclusions when confronted with real data. Moreover, the maps are interesting in their own right. Physics Letters A — Elsevier. Enjoy affordable access to over 18 million articles from more than 15, peer-reviewed journals.
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These methods include Lyapunov exponents, invariant probability measures, Kolmogorov—Sinai entropy, fractal dimensions, and correlation dimensions. Each method is developed mathematically and then explored using simple numerical examples. The strengths and weaknesses of each method are carefully treated, including their application to data results from measurements on real systems.
For systems exhibiting the period-doubling route to chaos, the Lyapunov exponent grows in a universal way as the systems enters the chaotic regime. If the attractor for a dissipative system has a noninteger dimension that is, a fractal dimensionthen we say that the system has a strange attractor.
Several methods of determining the fractal dimension of a geometric object, such as a state space attractor, are described in detail.
Cantor sets are used to explore various methods of determining dimensions. The Julia set and the Mandelbrot set are introduced as examples of fractal basin of attraction boundaries. Oxford Scholarship Online requires a subscription or purchase to access the full text of books within the service.
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Quantifying Chaos Quantifying Chaos Chapter: p. If you think you should have access to this title, please contact your librarian. All rights reserved. Powered by: Safari Books Online.Quantifying Chaos to Understand Liquids. October 12, For those readers in regions where autumn is quickly approaching, a pumpkin spice latte might be just the thing to help you relax. As scientists like Moupriya Das and Jason R.
Green from the University of Massachusetts Boston know, however, zoom in on this seasonal treat and the world is anything but relaxing. Their work could bring us closer to linking the chaos of the nanoscale to the properties of liquids as we experience them. Labels chaos Fluid dynamics.
Labels: chaos Fluid dynamics. Post a Comment. September 10, The answer is "a bad week for the casino"—but you'd never guess why. Read more. June 13, Our science teacher claims that the pain comes from a small electrical shock, but we believe that this is due to the absorption of light. Please help us resolve this dispute! June 24, Even though it's been a warm couple of months already, it's officially summer.
A delicious, science-filled way to beat the heat?
Part H: Quantifying Chaos
Making homemade ice cream. We've since updated this article to include the science behind vegan ice cream. But what kind of milk should you use to make ice cream? And do you really need to chill the ice cream base before making it? Why do ice cream recipes always call for salt on ice?
A snapshot showing a side view of one of the simulated systems. A snapshot showing five out of the sixteen sizes of systems the researchers simulated with molecular dynamics.Acoustic environments vary dramatically within the home setting. They can be a source of comfort and tranquility or chaos that can lead to less optimal cognitive development in children. Research to date has only subjectively measured household chaos.
These unsupervised techniques include hierarchical clustering using K-Means, clustering using self-organizing map SOMand deep learning.
We evaluated these techniques using data from 9 participants which is a total of hours. Results show that these techniques are promising to quantify household chaos. Infants experience a tremendous amount of positive and negative auditory stimulation.
Reducing the later and increasing the former can contribute to the healthy mental development of infants and proper acquisition of language [ 18 ].
Research evidence suggest that higher levels of chaotic home environment are associated with less optimal cognitive and social development in children [ 2 ]. Research to date has only subjectively measured household chaos [ 2 ] [ 17 ].
For this project, we propose to use three unsupervised machine learning techniques to classify the intensity of household chaos. Our goal is to predict the intensity of chaos for each 10 second segments of the audio samples. Low chaos refers to distant or soft sounds or sounds that have a tonal quality e.
We define these terms with respective to the infant and what could be positive or detrimental to the cognitive development of the infant [ 2 ]. Prior work shows that there is a connection between household chaos and child development [ 2 ] [ 13 ]. Higher levels of chaotic home environment are associated with less optimal cognitive and social development in children [ 2 ].
For example, music and speech patterns with low frequencies have shown to improve the neural development of infants [ 13 ]. Currently, household chaos is only measured qualitatively using surveys [ 2 ] [ 17 ]. A common measure decomposed household chaos into household instability and household disorganization [ 2 ]. Within the child development domain, objective measurement methods utilizing machine learning techniques have been explored.
For instance, Random Forest have been used to distinguish between cry of preterm and full-term newborns [ 14 ]. Support Vector Machines were used to generalize babies of varying ages and vocalization context [ 1 ]. Kaya De Barbaro. The data was collected for 26 infants aged six weeks to nine months for a continuous period of 24 hrs each total of hrs.
However, we choose to use a subset of hours for 9 participants 23 hours for 4 and 21 hours for 5 due to computational constraints and missing raw data. For pre-processing, we used version 2.