The mix of products that countries export is a good predictor of income distribution, study finds.
DHS says agents are in the right to ask for passwords, decryption help.
Days before taking office, President-elect Donald Trump made two surprise calls to the Air Force general managing the Pentagon’s largest weapons program, the Lockheed Martin Corp. F-35 jet.
Some news programs have said they will no longer interview Kellyanne Conway because she isn't credible.
Last night Tucker Hottes, Den Temple and I held the first Homebrew Website Club at The Keys in Scranton, PA. I really appreciate that HWC will force me to set aside some time to work on my personal site since it is often neglected for more pressing projects.
Nota bene: Colin is dogfooding his IndieWeb friendly WordPress theme on Github! It’s a beautiful, simple, and very clean theme for a personal website/blog.
Colin, do you mind if we provide a link to your theme on https://indieweb.org/WordPress/Themes for others to potentially use and/or improve upon? (See also discussion at https://indieweb.org/WordPress/Development#Themes.)Syndicated copies to:
YouTube canceled its top star’s show on Tuesday over his anti-Semitic jokes, complicating its efforts to court television advertisers while also retaining its edgy video stars.
The interplay between structural connections and emerging information flow in the human brain remains an open research problem. A recent study observed global patterns of directional information flow in empirical data using the measure of transfer entropy. For higher frequency bands, the overall direction of information flow was from posterior to anterior regions whereas an anterior-to-posterior pattern was observed in lower frequency bands. In this study, we applied a simple Susceptible-Infected-Susceptible (SIS) epidemic spreading model on the human connectome with the aim to reveal the topological properties of the structural network that give rise to these global patterns. We found that direct structural connections induced higher transfer entropy between two brain regions and that transfer entropy decreased with increasing distance between nodes (in terms of hops in the structural network). Applying the SIS model, we were able to confirm the empirically observed opposite information flow patterns and posterior hubs in the structural network seem to play a dominant role in the network dynamics. For small time scales, when these hubs acted as strong receivers of information, the global pattern of information flow was in the posterior-to-anterior direction and in the opposite direction when they were strong senders. Our analysis suggests that these global patterns of directional information flow are the result of an unequal spatial distribution of the structural degree between posterior and anterior regions and their directions seem to be linked to different time scales of the spreading process.
One of America’s foremost philosophers offers a major new account of the origins of the conscious mind.
How did we come to have minds?
For centuries, this question has intrigued psychologists, physicists, poets, and philosophers, who have wondered how the human mind developed its unrivaled ability to create, imagine, and explain. Disciples of Darwin have long aspired to explain how consciousness, language, and culture could have appeared through natural selection, blazing promising trails that tend, however, to end in confusion and controversy. Even though our understanding of the inner workings of proteins, neurons, and DNA is deeper than ever before, the matter of how our minds came to be has largely remained a mystery.
That is now changing, says Daniel C. Dennett. In From Bacteria to Bach and Back, his most comprehensive exploration of evolutionary thinking yet, he builds on ideas from computer science and biology to show how a comprehending mind could in fact have arisen from a mindless process of natural selection. Part philosophical whodunit, part bold scientific conjecture, this landmark work enlarges themes that have sustained Dennett’s legendary career at the forefront of philosophical thought.
In his inimitable style―laced with wit and arresting thought experiments―Dennett explains that a crucial shift occurred when humans developed the ability to share memes, or ways of doing things not based in genetic instinct. Language, itself composed of memes, turbocharged this interplay. Competition among memes―a form of natural selection―produced thinking tools so well-designed that they gave us the power to design our own memes. The result, a mind that not only perceives and controls but can create and comprehend, was thus largely shaped by the process of cultural evolution.
An agenda-setting book for a new generation of philosophers, scientists, and thinkers, From Bacteria to Bach and Back will delight and entertain anyone eager to make sense of how the mind works and how it came about.
4 color, 18 black-and-white illustrations
Syndicated copies to:
Epigenetics refers to information transmitted during cell division other than the DNA sequence per se, and it is the language that distinguishes stem cells from somatic cells, one organ from another, and even identical twins from each other. In contrast to the DNA sequence, the epigenome is relatively susceptible to modification by the environment as well as stochastic perturbations over time, adding to phenotypic diversity in the population. Despite its strong ties to the environment, epigenetics has never been well reconciled to evolutionary thinking, and in fact there is now strong evidence against the transmission of so-called “epi-alleles,” i.e. epigenetic modifications that pass through the germline.
However, genetic variants that regulate stochastic fluctuation of gene expression and phenotypes in the offspring appear to be transmitted as an epigenetic or even Lamarckian trait. Furthermore, even the normal process of cellular differentiation from a single cell to a complex organism is not understood well from a mathematical point of view. There is increasingly strong evidence that stem cells are highly heterogeneous and in fact stochasticity is necessary for pluripotency. This process appears to be tightly regulated through the epigenome in development. Moreover, in these biological contexts, “stochasticity” is hardly synonymous with “noise”, which often refers to variation which obscures a “true signal” (e.g., measurement error) or which is structural, as in physics (e.g., quantum noise). In contrast, “stochastic regulation” refers to purposeful, programmed variation; the fluctuations are random but there is no true signal to mask.
This workshop will serve as a forum for scientists and engineers with an interest in computational biology to explore the role of stochasticity in regulation, development and evolution, and its epigenetic basis. Just as thinking about stochasticity was transformative in physics and in some areas of biology, it promises to fundamentally transform modern genetics and help to explain phase transitions such as differentiation and cancer.
This workshop will include a poster session; a request for poster titles will be sent to registered participants in advance of the workshop.
Adam Arkin (Lawrence Berkeley Laboratory)
Gábor Balázsi (SUNY Stony Brook)
Domitilla Del Vecchio (Massachusetts Institute of Technology)
Michael Elowitz (California Institute of Technology)
Andrew Feinberg (Johns Hopkins University)
Don Geman (Johns Hopkins University)
Anita Göndör (Karolinska Institutet)
John Goutsias (Johns Hopkins University)
Garrett Jenkinson (Johns Hopkins University)
Andre Levchenko (Yale University)
Olgica Milenkovic (University of Illinois)
Johan Paulsson (Harvard University)
Leor Weinberger (University of California, San Francisco (UCSF))
The equations of gauge theory lie at the heart of our understanding of particle physics. The Standard Model, which describes the electromagnetic, weak, and strong forces, is based on the Yang-Mills equations. Starting with the work of Donaldson in the 1980s, gauge theory has also been successfully applied in other areas of pure mathematics, such as low dimensional topology, symplectic geometry, and algebraic geometry.
More recently, Witten proposed a gauge-theoretic interpretation of Khovanov homology, a knot invariant whose origins lie in representation theory. Khovanov homology is a “categorification” of the celebrated Jones polynomial, in the sense that its Euler characteristic recovers this polynomial. At the moment, Khovanov homology is only defined for knots in the three-sphere, but Witten’s proposal holds the promise of generalizations to other three-manifolds, and perhaps of producing new invariants of four-manifolds.
This workshop will bring together researchers from several different fields (theoretical physics, mathematical gauge theory, topology, analysis / PDE, representation theory, symplectic geometry, and algebraic geometry), and thus help facilitate connections between these areas. The common focus will be to understand Khovanov homology and related invariants through the lens of gauge theory.
This workshop will include a poster session; a request for posters will be sent to registered participants in advance of the workshop.
Edward Witten will be giving two public lectures as part of the Green Family Lecture series:
March 6, 2017
From Gauge Theory to Khovanov Homology Via Floer Theory
The goal of the lecture is to describe a gauge theory approach to Khovanov homology of knots, in particular, to motivate the relevant gauge theory equations in a way that does not require too much physics background. I will give a gauge theory perspective on the construction of singly-graded Khovanov homology by Abouzaid and Smith.
March 8, 2017
An Introduction to the SYK Model
The Sachdev-Ye model was originally a model of quantum spin liquids that was introduced in the mid-1990′s. In recent years, it has been reinterpreted by Kitaev as a model of quantum chaos and black holes. This lecture will be primarily a gentle introduction to the SYK model, though I will also describe a few more recent results.
Open for submission now
Deadline for manuscript submissions: 31 August 2017
Deadline for manuscript submissions: 31 August 2017
Whereas Bayesian inference has now achieved mainstream acceptance and is widely used throughout the sciences, associated ideas such as the principle of maximum entropy (implicit in the work of Gibbs, and developed further by Ed Jaynes and others) have not. There are strong arguments that the principle (and variations, such as maximum relative entropy) is of fundamental importance, but the literature also contains many misguided attempts at applying it, leading to much confusion.
This Special Issue will focus on Bayesian inference and MaxEnt. Some open questions that spring to mind are: Which proposed ways of using entropy (and its maximisation) in inference are legitimate, which are not, and why? Where can we obtain constraints on probability assignments, the input needed by the MaxEnt procedure?
More generally, papers exploring any interesting connections between probabilistic inference and information theory will be considered. Papers presenting high quality applications, or discussing computational methods in these areas, are also welcome.
Dr. Brendon J. Brewer
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed Open Access monthly journal published by MDPI.
No papers have been published in this special issue yet.
This book originated from a series of papers which were published in "Die Naturwissenschaften" in 1977178. Its division into three parts is the reflection of a logic structure, which may be abstracted in the form of three theses:
A. Hypercycles are a principle of natural self-organization allowing an integration and coherent evolution of a set of functionally coupled self-replicative entities.
B. Hypercycles are a novel class of nonlinear reaction networks with unique properties, amenable to a unified mathematical treatment.
C. Hypercycles are able to originate in the mutant distribution of a single Darwinian quasi-species through stabilization of its diverging mutant genes. Once nucleated hypercycles evolve to higher complexity by a process analogous to gene duplication and specialization. In order to outline the meaning of the first statement we may refer to another principle of material self organization, namely to Darwin's principle of natural selection. This principle as we see it today represents the only understood means for creating information, be it the blue print for a complex living organism which evolved from less complex ancestral forms, or be it a meaningful sequence of letters the selection of which can be simulated by evolutionary model games.
The intimate relation between biology and cognition can be formally examined through statistical models constrained by the asymptotic limit theorems of communication theory, augmented by methods from statistical mechanics and nonequilibrium thermodynamics. Cognition, often involving submodules that act as information sources, is ubiquitous across the living state. Less metabolic free energy is consumed by permitting crosstalk between biological information sources than by isolating them, leading to evolutionary exaptations that assemble shifting, tunable cognitive arrays at multiple scales, and levels of organization to meet dynamic patterns of threat and opportunity. Cognition is thus necessary for life, but it is not sufficient: An organism represents a highly patterned outcome of path-dependent, blind, variation, selection, interaction, and chance extinction in the context of an adequate flow of free energy and an environment fit for development. Complex, interacting cognitive processes within an organism both record and instantiate those evolutionary and developmental trajectories.