🎯 http://www.popsci.com/this-book-is-a-complete-guide-to-the-science-of-snowflakes?con=TrueAnthem&dom=tw&src=SOC&utm_campaign=&utm_content=5869d74db8a9fe0007a16152&utm_medium=&utm_source=

🎯 2017 : WHAT SCIENTIFIC TERM OR CONCEPT OUGHT TO BE MORE WIDELY KNOWN?

https://www.edge.org/response-detail/27036

#networks

🎯 My answer to the Edge 2017 annual question: What scientific concept should be better known?

#Criticality

In physics we say a system is in a critical state when it is ripe for a phase transition. Consider water turning into ice, or a cloud that is pregnant with rain. Both of these are examples of physical systems in a critical state.

The dynamics of criticality, however, are not very intuitive. Consider the abruptness of freezing water. For an outside observer, there is no difference between cold water and water that is just about to freeze. This is because water that is just about to freeze is still liquid. Yet, microscopically, cold water and water that is about to freeze are not the same.

When close to freezing, water is populated by gazillions of tiny ice crystals, crystals that are so small that water remains liquid. But this is water in a critical state, a state in which any additional freezing will result in these crystals touching each other, generating the solid mesh we know as ice. Yet, the ice crystals that formed during the transition are infinitesimal. They are just the last straw. So, freezing cannot be considered the result of these last crystals. They only represent the instability needed to trigger the transition; the real cause of the transition is the criticality of the state.

But why should anyone outside statistical physics care about criticality?

The reason is that history is full of individual narratives that maybe should be interpreted in terms of critical phenomena.

Did Rosa Parks start the civil rights movement? Or was the movement already running in the minds of those who had been promised equality and were instead handed discrimination? Was the collapse of Lehman Brothers an essential trigger for the Great Recession? Or was the financial system so critical that any disturbance could have made the trick?

As humans, we love individual narratives. We evolved to learn from stories and communicate almost exclusively in terms of them. But as Richard Feynman said repeatedly: The imagination of nature is often larger than that of man. So, maybe our obsession with individual narratives is nothing but a reflection of our limited imagination. Going forward we need to remember that systems often make individuals irrelevant. Just like none of your cells can claim to control your body, society also works in systemic ways.

So, the next time the house of cards collapses, remember to focus on why we were building a house of cards in the first place, instead of focusing on whether the last card was the queen of diamonds or a two of clubs.

🔗 https://facebook.com/story.php?story_fbid=10154355446216693&id=727621692

📄 The Unfolding and Control of Network Cascades

Adilson E. Motter, Yang Yang

🔗 https://arxiv.org/pdf/1701.00578v1

📌 ABSTRACT
A characteristic property of networks is their ability to propagate influences, such as infectious diseases, behavioral changes, and failures. An especially important class of such contagious dynamics is that of cascading processes. These processes include, for example, cascading failures in infrastructure systems, extinctions cascades in ecological networks, and information cascades in social systems. In this review, we discuss recent progress and challenges associated with the modeling, prediction, detection, and control of cascades in networks.

🎯 https://www.quantamagazine.org/20170103-fractal-dynamics-from-3d-julia-sets/

🌀 Neural Networks for Machine Learning

About this course: Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well. This course contains the same content presented on Coursera beginning in 2013. It is not a continuation or update of the original course. It has been adapted for the new platform. Please be advised that the course is suited for an intermediate level learner - comfortable with calculus and with experience programming (Python).

🔗 https://www.coursera.org/learn/neural-networks?utm_medium=email&utm_source=marketing&utm_campaign=5Er1QNLaEeatnG9kVehcuw

🌀 Complex adaptive systems (cas), including ecosystems, governments, biological cells, and markets, are characterized by intricate hierarchical arrangements of boundaries and signals. In ecosystems, for example, niches act as semi-permeable boundaries, and smells and visual patterns serve as signals; governments have departmental hierarchies with memoranda acting as signals; and so it is with other cas. Despite a wealth of data and descriptions concerning different cas, there remain many unanswered questions about "steering" these systems. In Signals and Boundaries, John Holland argues that understanding the origin of the intricate signal/border hierarchies of these systems is the key to answering such questions. He develops an overarching framework for comparing and steering cas through the mechanisms that generate their signal/boundary hierarchies.

Holland lays out a path for developing the framework that emphasizes agents, niches, theory, and mathematical models. He discusses, among other topics, theory construction; signal-processing agents; networks as representations of signal/boundary interaction; adaptation; recombination and reproduction; the use of tagged urn models (adapted from elementary probability theory) to represent boundary hierarchies; finitely generated systems as a way to tie the models examined into a single framework; the framework itself, illustrated by a simple finitely generated version of the development of a multi-celled organism; and Markov processes.

A short, 2 page, review of the work by Chris Adami can be found here:

🔗 http://adamilab.msu.edu/wp-content/uploads/Adami2012c.pdf

ثبت نام: www.evand.com/events/hershad-951025

برنامه هشتمين دوره سلسه نشست هاي علم اطلاعات و دانش شناسي در فصل زمستان 95

📽 https://www.youtube.com/shared?ci=2Yw2UAx6ys0

📽 https://www.youtube.com/shared?ci=I_x7EdjWSiI

Table of Contents
Chapter 1 – Preface Chapter 2 – Introduction Chapter 3 – Identical Systems Chapter 4 – Non identical Systems Chapter 5 – Structurally non equivalent Systems Chapter 6 – Effects of noise Chapter 7 – Distributed and Extended Systems Chapter 8 – Complex Networks

🗞 Community detection, link prediction and layer interdependence in multilayer networks

Caterina De Bacco, Eleanor A. Power, Daniel B. Larremore, Cristopher Moore

🔗 https://arxiv.org/pdf/1701.01369v1

📌 ABSTRACT
Complex systems are often characterized by distinct types of interactions between the same entities. These can be described as a multilayer network where each layer represents one type of interaction. These layers may be interdependent in complicated ways, revealing different kinds of structure in the network. In this work we present a generative model, and an efficient expectation-maximization algorithm, which allows us to perform inference tasks such as community detection and link prediction in this setting. Our model assumes overlapping communities that are common between the layers, while allowing these communities to affect each layer in a different way, including arbitrary mixtures of assortative, disassortative, or directed structure. It also gives us a mathematically principled way to define the interdependence between layers, by measuring how much information about one layer helps us predict links in another layer. In particular, this allows us to bundle layers together to compress redundant information, and identify small groups of layers which suffice to predict the remaining layers accurately. We illustrate these findings by analyzing synthetic data and two real multilayer networks, one representing social support relationships among villagers in South India and the other representing shared genetic substrings material between genes of the malaria parasite.

🔻 http://blog.algorithmia.com/introduction-to-deep-learning-2016/?utm_content=buffer31b71&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

🗞 Disease Localization in Multilayer Networks

Guilherme Ferraz de Arruda, Emanuele Cozzo, Tiago P. Peixoto, Francisco A. Rodrigues, Yamir Moreno

🔗 https://arxiv.org/pdf/1509.07054v3

📌 ABSTRACT
We present a continuous formulation of epidemic spreading on multilayer networks using a tensorial representation, extending the models of monoplex networks to this context. We derive analytical expressions for the epidemic threshold of the SIS and SIR dynamics, as well as upper and lower bounds for the disease prevalence in the steady state for the SIS scenario. Using the quasi-stationary state method we numerically show the existence of disease localization and the emergence of two or more susceptibility peaks, which are characterized analytically and numerically through the inverse participation ratio. Furthermore, when mapping the critical dynamics to an eigenvalue problem, we observe a characteristic transition in the eigenvalue spectra of the supra-contact tensor as a function of the ratio of two spreading rates: if the rate at which the disease spreads within a layer is comparable to the spreading rate across layers, the individual spectra of each layer merge with the coupling between layers. Finally, we verified the barrier effect, i.e., for three-layer configuration, when the layer with the largest eigenvalue is located at the center of the line, it can effectively act as a barrier to the disease. The formalism introduced here provides a unifying mathematical approach to disease contagion in multiplex systems opening new possibilities for the study of spreading processes.

🗞 Trade-offs between driving nodes and time-to-control in complex networks

S Pequito, VM Preciado, AL Barabási, GJ Pappas

🔗 http://www.nature.com/articles/srep39978


📌 ABSTRACT
Recent advances in control theory provide us with efficient tools to determine the minimum number of driving (or driven) nodes to steer a complex network towards a desired state. Furthermore, we often need to do it within a given time window, so it is of practical importance to understand the trade-offs between the minimum number of driving/driven nodes and the minimum time required to reach a desired state. Therefore, we introduce the notion of actuation spectrum to capture such trade-offs, which we used to find that in many complex networks only a small fraction of driving (or driven) nodes is required to steer the network to a desired state within a relatively small time window. Furthermore, our empirical studies reveal that, even though synthetic network models are designed to present structural properties similar to those observed in real networks, their actuation spectra can be dramatically different. Thus, it supports the need to develop new synthetic network models able to replicate controllability properties of real-world networks.

🌀 2017 : WHAT SCIENTIFIC TERM OR CONCEPT OUGHT TO BE MORE WIDELY KNOWN?

Nicholas A. Christakis
Physician and Social Scientist, Yale University; Co-author, Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives

👉🏻 Equipoise

There is an old word in our language, equipoise, which has been around since at least the 16th century—when it meant something like “an equal distribution of weight.” With respect to science, it means, roughly, standing at the foot of a valley and not knowing which way is best to proceed to get up high—poised between alternative theories and ideas about which, given current information, one is neutral.

Use of the word peaked around 1840, and declined roughly five-fold since then, according to Google Ngram, though it appears to be enjoying an incipient resurgence in the last decade. But attention to equipoise ought to be greater.

The concept found a new application in the 1980s, when ethicists were searching for deep justifications for the conduct of randomized clinical trials in medicine. A trial was only justified, they rightly argued, when the doctors and researchers doing the trial (and the medical knowledge they were relying on) saw the new drug or its alternative (a placebo, perhaps) as potentially equally good. If those doing the research felt otherwise, how could they justify the trial? Was it ethical to place patients at risk of harm to do research if doctors had reason to suppose that one course of action might be materially better than another?

So equipoise is a state of equilibrium, where a scientist cannot be sure which of the alternatives he or she is contemplating might be true.

In my view, it is related to that famous Popperian sine qua non of science itself: falsifiability. Something is not science if it is not capable of disproof. We cannot even imagine an experiment that would disprove the existence of God—so that is what makes a belief in God religion. When Einstein famously conjectured that matter and energy warp the fabric of space and time itself, experiments to test the claim were not possible, but they were at least imaginable, and the theory was capable of disproof. And, eventually, he was proven right, first based on astronomical observations regarding the orbit of Mercury, and most recently by the magnificent discovery at LIGO of gravitational waves from the collision of two black holes over a billion years ago. Yet, even if he had been wrong, his conjecture would still have been scientific.

If falsifiability solves the “problem of demarcation” that Popper identified between science and non-science, equipoise addresses the problem of origin: Where ought scientists start from? Thinking about where scientists do—and should—start from is often lacking. Too often, we simply begin from where we are.

In some ways, therefore, equipoise is an antecedent condition to falsifiability. It is a state we can be in before we hazard a guess that we might test. It is not quite a state of ignorance, but rather a state of quasi-neutrality, when glimmers of ideas enter our minds.

Scientific equipoise tends to characterize fields both early and late in their course, for different reasons. Early in a field or in a new area of research, it is often true that little is known about anything, so any direction can seem promising, and might actually be productive. An exciting neutrality prevails. Late in the exploration of a field, much is known, and so it might be hard to head towards new things, or the new things, even if true, might be rather small or unimportant. An oppressive neutrality can rule.