🌀 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
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 Systems Studies
https://www.amazon.com/Signals-Boundaries-Building-Complex-Adaptive/dp/0262525933/
🌀 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.
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.
Complex Systems Studies
https://www.amazon.com/Signals-Boundaries-Building-Complex-Adaptive/dp/0262525933/
A short, 2 page, review of the work by Chris Adami can be found here:
🔗 http://adamilab.msu.edu/wp-content/uploads/Adami2012c.pdf
🔗 http://adamilab.msu.edu/wp-content/uploads/Adami2012c.pdf
Forwarded from کتابخانه حسینیه ارشاد
برنامه هشتمين دوره سلسه نشست هاي علم اطلاعات و دانش شناسي در فصل زمستان 95
Complex Systems Studies
https://www.elsevier.com/books/the-synchronized-dynamics-of-complex-systems/boccaletti/978-0-444-52743-1
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
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.
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.
🗞 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.
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.
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.
Nature
Trade-offs between driving nodes and time-to-control in complex networks
Scientific Reports - Trade-offs between driving nodes and time-to-control in complex 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.
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.
My reasons for thinking that this concept ought to be more widely known is that equipoise carries with it aspects of science that remain sorely needed these days. It connotes judgment—for it asks what problems are worthy of consideration. It connotes humility—for we do not know what lies ahead. It connotes open vistas—because it looks out at the unknown. It connotes discovery—because, whatever way forward we choose, we will learn something. And it connotes risk—because it is sometimes dangerous to embark on such a journey.
Equipoise is a state of hopeful ignorance, the quiet before the storm of discovery.
Equipoise is a state of hopeful ignorance, the quiet before the storm of discovery.
🗞 Information theory, predictability, and the emergence of complex life
Luís F Seoane, Ricard Solé
🔗 https://arxiv.org/pdf/1701.02389v1
📌ABSTRACT
Despite the obvious advantage of simple life forms capable of fast replication, different levels of cognitive complexity have been achieved by living systems in terms of their potential to cope with environmental uncertainty. Against the inevitable cost associated to detecting environmental cues and responding to them in adaptive ways, we conjecture that the potential for predicting the environment can overcome the expenses associated to maintaining costly, complex structures. We present a minimal formal model grounded in information theory and selection, in which successive generations of agents are mapped into transmitters and receivers of a coded message. Our agents are guessing machines and their capacity to deal with environments of different complexity defines the conditions to sustain more complex agents.
Luís F Seoane, Ricard Solé
🔗 https://arxiv.org/pdf/1701.02389v1
📌ABSTRACT
Despite the obvious advantage of simple life forms capable of fast replication, different levels of cognitive complexity have been achieved by living systems in terms of their potential to cope with environmental uncertainty. Against the inevitable cost associated to detecting environmental cues and responding to them in adaptive ways, we conjecture that the potential for predicting the environment can overcome the expenses associated to maintaining costly, complex structures. We present a minimal formal model grounded in information theory and selection, in which successive generations of agents are mapped into transmitters and receivers of a coded message. Our agents are guessing machines and their capacity to deal with environments of different complexity defines the conditions to sustain more complex agents.
🗞 Equivalence between non-Markovian and Markovian dynamics in epidemic spreading processes
Michele Starnini, James P. Gleeson, Marián Boguñá
🔗 https://arxiv.org/pdf/1701.02805v1
📌 ABSTRACT
A general formalism is introduced to allow the steady state of non-Markovian processes on networks to be reduced to equivalent Markovian processes on the same substrates. The example of an epidemic spreading process is considered in detail, where all the non-Markovian aspects are shown to be captured within a single parameter, the effective infection rate. Remarkably, this result is independent of the topology of the underlying network, as demonstrated by numerical simulations on two-dimensional lattices and various types of random networks. Furthermore, an analytic approximation for the effective infection rate is introduced, which enables the calculation of the critical point and of the critical exponents for the non-Markovian dynamics.
Michele Starnini, James P. Gleeson, Marián Boguñá
🔗 https://arxiv.org/pdf/1701.02805v1
📌 ABSTRACT
A general formalism is introduced to allow the steady state of non-Markovian processes on networks to be reduced to equivalent Markovian processes on the same substrates. The example of an epidemic spreading process is considered in detail, where all the non-Markovian aspects are shown to be captured within a single parameter, the effective infection rate. Remarkably, this result is independent of the topology of the underlying network, as demonstrated by numerical simulations on two-dimensional lattices and various types of random networks. Furthermore, an analytic approximation for the effective infection rate is introduced, which enables the calculation of the critical point and of the critical exponents for the non-Markovian dynamics.
🗞 Types and Forms of Emergence
Jochen Fromm
🔗 https://arxiv.org/pdf/nlin/0506028v1.pdf
📌 ABSTRACT
The knowledge of the different types of emergence is essential if we want to understand and master complex systems in science and engineering, respectively. This paper specifies a universal taxonomy and comprehensive classification of the major types and forms of emergence in Multi-Agent Systems, from simple types of intentional and predictable emergence in machines to more complex forms of weak, multiple and strong emergence.
Jochen Fromm
🔗 https://arxiv.org/pdf/nlin/0506028v1.pdf
📌 ABSTRACT
The knowledge of the different types of emergence is essential if we want to understand and master complex systems in science and engineering, respectively. This paper specifies a universal taxonomy and comprehensive classification of the major types and forms of emergence in Multi-Agent Systems, from simple types of intentional and predictable emergence in machines to more complex forms of weak, multiple and strong emergence.