Driven by technological progress, human life expectancy has increased greatly since the nineteenth century. Demographic evidence has revealed an ongoing reduction in old-age mortality and a rise of the maximum age at death, which may gradually extend human longevity. Together with observations that lifespan in various animal species is flexible and can be increased by genetic or pharmaceutical intervention, these results have led to suggestions that longevity may not be subject to strict, species-specific genetic constraints. Here, by analysing global demographic data, we show that improvements in survival with age tend to decline after age 100, and that the age at death of the world’s oldest person has not increased since the 1990s. Our results strongly suggest that the maximum lifespan of humans is fixed and subject to natural constraints.
Almost 40 years ago, Leonard Hayflick discovered that cultured normal human cells have limited capacity to divide, after which they become senescent — a phenomenon now known as the ‘Hayflick limit’. Hayflick's findings were strongly challenged at the time, and continue to be questioned in a few circles, but his achievements have enabled others to make considerable progress towards understanding and manipulating the molecular mechanisms of ageing.
Biomolecular systems like molecular motors or pumps, transcription and translation machinery, and other enzymatic reactions, can be described as Markov processes on a suitable network. We show quite generally that, in a steady state, the dispersion of observables, like the number of consumed or produced molecules or the number of steps of a motor, is constrained by the thermodynamic cost of generating it. An uncertainty ε requires at least a cost of 2k_B T/ε^2 independent of the time required to generate the output.
Recent advances in fields ranging from cosmology to computer science have hinted at a possible deep connection between intelligence and entropy maximization, but no formal physical relationship between them has yet been established. Here, we explicitly propose a first step toward such a relationship in the form of a causal generalization of entropic forces that we find can cause two defining behaviors of the human “cognitive niche”—tool use and social cooperation—to spontaneously emerge in simple physical systems. Our results suggest a potentially general thermodynamic model of adaptive behavior as a nonequilibrium process in open systems.
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Life was long thought to obey its own set of rules. But as simple systems show signs of lifelike behavior, scientists are arguing about whether this apparent complexity is all a consequence of thermodynamics.
This is a nice little general interest article by Philip Ball that does a relatively good job of covering several of my favorite topics (information theory, biology, complexity) for the layperson. While it stays relatively basic, it links to a handful of really great references, many of which I’ve already read, though several appear to be new to me. 
While Ball has a broad area of interests and coverage in his work, he’s certainly one of the best journalists working in this subarea of interests today. I highly recommend his work to those who find this area interesting.
We discuss properties of the "beamsplitter addition" operation, which provides a non-standard scaled convolution of random variables supported on the non-negative integers. We give a simple expression for the action of beamsplitter addition using generating functions. We use this to give a self-contained and purely classical proof of a heat equation and de Bruijn identity, satisfied when one of the variables is geometric.
Sage makes you a better developer. Modern build tooling, live reloading, modern PHP & requirements, DRY templates with template inheritance and more.
Modern front-end workflow
If Underscores is a “1,000 hour head start”, Sage is a 10,000 hour head start.
The Web Cryptography API is a now W3C Recommendation https://t.co/hz97mKXuYH
— The New Stack (@thenewstack) January 31, 2017
The first generation of the digital revolution brought us the Internet of information. The second generation—powered by blockchain technology—is bringing us the Internet of value: a new, distributed platform that can help us reshape the world of business and transform the old order of human affairs for the better.
Blockchain is the ingeniously simple, revolutionary protocol that allows transactions to be simultaneously anonymous and secure by maintaining a tamperproof public ledger of value. Though it’s the technology that drives bitcoin and other digital currencies, the underlying framework has the potential to go far beyond these and record virtually everything of value to humankind, from birth and death certificates to insurance claims and even votes.
Perhaps not necessarily this particular book which appears to be on the overview side, but sometime this year I’d like to delve more deeply into the concept of blockchain and the tech behind it.
Anyone have recommendations of books they liked?
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The World Wide Web has been around for long enough now that we can begin to evaluate the twists and turns of its evolution. I wrote this book to highlight some of the approaches to web design that have proven to be resilient. I didn’t do this purely out of historical interest (although I am fascinated by the already rich history of our young industry). In learning from the past, I believe we can better prepare for the future.
You won’t find any code in here to help you build better websites. But you will find ideas and approaches. Ideas are more resilient than code. I’ve tried to combine the most resilient ideas from the history of web design into an approach for building the websites of the future.
I hope you will join me in building a web that lasts; a web that’s resilient.
NIMBioS will host an Tutorial on Uncertainty Quantification for Biological Models
Uncertainty Quantification for Biological Models
Meeting dates: June 26-28, 2017
Location: NIMBioS at the University of Tennessee, Knoxville
Marisa Eisenberg, School of Public Health, Univ. of Michigan
Ben Fitzpatrick, Mathematics, Loyola Marymount Univ.
James Hyman, Mathematics, Tulane Univ.
Ralph Smith, Mathematics, North Carolina State Univ.
Clayton Webster, Computational and Applied Mathematics (CAM), Oak Ridge National Laboratory; Mathematics, Univ. of Tennessee
Mathematical modeling and computer simulations are widely used to predict the behavior of complex biological phenomena. However, increased computational resources have allowed scientists to ask a deeper question, namely, “how do the uncertainties ubiquitous in all modeling efforts affect the output of such predictive simulations?” Examples include both epistemic (lack of knowledge) and aleatoric (intrinsic variability) uncertainties and encompass uncertainty coming from inaccurate physical measurements, bias in mathematical descriptions, as well as errors coming from numerical approximations of computational simulations. Because it is essential for dealing with realistic experimental data and assessing the reliability of predictions based on numerical simulations, research in uncertainty quantification (UQ) ultimately aims to address these challenges.
Uncertainty quantification (UQ) uses quantitative methods to characterize and reduce uncertainties in mathematical models, and techniques from sampling, numerical approximations, and sensitivity analysis can help to apportion the uncertainty from models to different variables. Critical to achieving validated predictive computations, both forward and inverse UQ analysis have become critical modeling components for a wide range of scientific applications. Techniques from these fields are rapidly evolving to keep pace with the increasing emphasis on models that require quantified uncertainties for large-scale applications. This tutorial will focus on the application of these methods and techniques to mathematical models in the life sciences and will provide researchers with the basic concepts, theory, and algorithms necessary to quantify input and response uncertainties and perform sensitivity analysis for simulation models. Concepts to be covered may include: probability and statistics, parameter selection techniques, frequentist and Bayesian model calibration, propagation of uncertainties, quantification of model discrepancy, adaptive surrogate model construction, high-dimensional approximation, random sampling and sparse grids, as well as local and global sensitivity analysis.
This tutorial is intended for graduate students, postdocs and researchers in mathematics, statistics, computer science and biology. A basic knowledge of probability, linear algebra, and differential equations is assumed.
Application deadline: March 1, 2017
To apply, you must complete an application on our online registration system:
- Click here to access the system
- Login or register
- Complete your user profile (if you haven’t already)
- Find this tutorial event under Current Events Open for Application and click on Apply
Participation in NIMBioS tutorials is by application only. Individuals with a strong interest in the topic are encouraged to apply, and successful applicants will be notified within two weeks after the application deadline. If needed, financial support for travel, meals, and lodging is available for tutorial attendees.
The application process is now closed.
Summary Report. TBA
Live Stream. The Tutorial will be streamed live. Note that NIMBioS Tutorials involve open discussion and not necessarily a succession of talks. In addition, the schedule as posted may change during the Workshop. To view the live stream, visit http://www.nimbios.org/videos/livestream. A live chat of the event will take place via Twitter using the hashtag #uncertaintyTT. The Twitter feed will be displayed to the right of the live stream. We encourage you to post questions/comments and engage in discussion with respect to our Social Media Guidelines.
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Prof. Walter B. Rudin presents the lecture, "Set Theory: An Offspring of Analysis." Prof. Jay Beder introduces Prof. Dattatraya J. Patil who introduces Prof....
MyScript MathPad is a mathematic expression demonstration that lets you handwrite your equations or mathematical expressions on your screen and have them rendered into their digital equivalent for easy sharing. Render complex mathematical expressions easily using your handwriting with no constraints. The result can be shared as an image or as a LaTeX* or MathML* string for integration in your documents.
This looks like something I could integrate into my workflow.Syndicated copies to:
“The three volumes of Green’s Dictionary of Slang demonstrate the sheer scope of a lifetime of research by Jonathon Green, the leading slang lexicographer of our time. A remarkable collection of this often reviled but endlessly fascinating area of the English language, it covers slang from the past five centuries right up to the present day, from all the different English-speaking countries and regions. Totaling 10.3 million words and over 53,000 entries, the collection provides the definitions of 100,000 words and over 413,000 citations. Every word and phrase is authenticated by genuine and fully-referenced citations of its use, giving the work a level of authority and scholarship unmatched by any other publication in this field.”
If you head over to Amazon.com, that’s how you will find Green’s Dictionary of Slang pitched to consumers. The dictionary is an attractive three-volume, hard-bound set. But it comes at a price. $264 for a used edition. $600 for a new one.
Now comes the good news. In October, Green’s Dictionary of Slang became available as a free website, giving you access to an even more updated version of the dictionary. Collectively, the website lets you trace the development of slang over the past 500 years. And, as Mental Floss notes, the site “allows lookups of word definitions and etymologies for free, and, for a well-worth-it subscription fee, it offers citations and more extensive search options.” If you’ve ever wondered about the meaning of words like kidlywink, gollier, and linthead, you now know where to begin.