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Russian brains behind closest ever AI attempt

Russian scientists are closer than they have ever been to creating artificial intelligence. The program called “Eugene” has almost passed the famous Turing test, which checks a machine’s ability to exhibit intelligent behavior.

 

The program-emulating a personality of a 13-year old boy was exhibited at an international science contest in the United Kingdom along with four other programs.

 

Even with the exacting criteria, “Eugene” has left all its competitors far behind.

 

The test was designed by mathematician and computer scientist, Alan Turing over 60 years ago. During the examination a human judge engages in a text conversation with a machine and an actual human being without seeing them. If the judge fails to tell the machine from the human in at least 30 percent of the answers, the program passes.

 

So far no program has managed to pass successfully but Russia’s “Eugene” has come strikingly close. It deceived human judges in 29,2 percent of the answers.

 

A total of 29 judges took part in the test with some 150 dialogues taking place.


Via Ashish Umre
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Computing in the net of possibilities

 Scientists at the Max Planck Institute for Dynamics and Self-Organization in Göttingen have developed an entirely new principle for information processing. The complex network computer now stands as an alternative to the other possibilities in data processing - such as the conventional computer or the quantum computer. The fundamental requirement is a system, for instance a laser, with oscillating elements that can interact with one another. The researchers were able to demonstrate that the characteristic dynamics of such a system can be cleverly harnessed to perform the full range of logical operations. The complex network computer can even perform some tasks, such as the coarse sorting of numbers, considerably faster than conventional computers. Furthermore, the researchers have managed to take a first step in programming a robot according to the new principle.

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Opportunities and challenges for a sustainable energy future

Access to clean, affordable and reliable energy has been a cornerstone of the world's increasing prosperity and economic growth since the beginning of the industrial revolution. Our use of energy in the twenty–first century must also be sustainable. Solar and water–based energy generation, and engineering of microbes to produce biofuels are a few examples of the alternatives. This Perspective puts these opportunities into a larger context by relating them to a number of aspects in the transportation and electricity generation sectors. It also provides a snapshot of the current energy landscape and discusses several research and development opportunities and pathways that could lead to a prosperous, sustainable and secure energy future for the world.

 

Opportunities and challenges for a sustainable energy future

Steven Chu & Arun Majumdar

Nature 488, 294–303 (16 August 2012) http://dx.doi.org/10.1038/nature11475

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A Whole-Cell Computational Model Predicts Phenotype from Genotype

Understanding how complex phenotypes arise from individual molecules and their interactions is a primary challenge in biology that computational approaches are poised to tackle. We report a whole-cell computational model of the life cycle of the human pathogen Mycoplasma genitalium that includes all of its molecular components and their interactions. An integrative approach to modeling that combines diverse mathematics enabled the simultaneous inclusion of fundamentally different cellular processes and experimental measurements. Our whole-cell model accounts for all annotated gene functions and was validated against a broad range of data. The model provides insights into many previously unobserved cellular behaviors, including in vivo rates of protein-DNA association and an inverse relationship between the durations of DNA replication initiation and replication. In addition, experimental analysis directed by model predictions identified previously undetected kinetic parameters and biological functions. We conclude that comprehensive whole-cell models can be used to facilitate biological discovery.

 

A Whole-Cell Computational Model Predicts Phenotype from Genotype
Jonathan R. Karr, Jayodita C. Sanghvi, Derek N. Macklin, Miriam V. Gutschow, Jared M. Jacobs, Benjamin Bolival Jr., Nacyra Assad-Garcia, John I. Glass, Markus W. Covert

Cell Volume 150, Issue 2, 20 July 2012, Pages 389–401

http://dx.doi.org/10.1016/j.cell.2012.05.044

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Software systems through complex networks science: Review, analysis and applications

Complex software systems are among most sophisticated human-made systems, yet only little is known about the actual structure of 'good' software. We here study different software systems developed in Java from the perspective of network science. The study reveals that network theory can provide a prominent set of techniques for the exploratory analysis of large complex software system. We further identify several applications in software engineering, and propose different network-based quality indicators that address software design, efficiency, reusability, vulnerability, controllability and other. We also highlight various interesting findings, e.g., software systems are highly vulnerable to processes like bug propagation, however, they are not easily controllable.

 

Software systems through complex networks science: Review, analysis and applications

Lovro Šubelj, Marko Bajec

http://arxiv.org/abs/1208.2518

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Why, when, and how fast innovations are adopted

When the full stock of a new product is quickly sold in a few days or weeks, one has the impression that new technologies develop and conquer the market in a very easy way. This may be true for some new technologies, for example the cell phone, but not for others, like the blue-ray. Novelty, usefulness, advertising, price, and fashion are the driving forces behind the adoption of a new product. But, what are the key factors that lead to adopt a new technology? In this paper we propose and investigate a simple model for the adoption of an innovation which depends mainly on three elements: the appeal of the novelty, the inertia or resistance to adopt it, and the interaction with other agents. Social interactions are taken into account in two ways: by imitation and by differentiation, i.e., some agents will be inclined to adopt an innovation if many people do the same, but other will act in the opposite direction, trying to differentiate from the "herd". We determine the conditions for a successful implantation of the new technology, by considering the strength of advertising and the effect of social interactions. We find a balance between the advertising and the number of anti-herding agents that may block the adoption of a new product. We also compare the effect of social interactions, when agents take into account the behavior of the whole society or just a part of it. In a nutshell, the present model reproduces qualitatively the available data on adoption of innovation.

 

Why, when, and how fast innovations are adopted

Sebastian Goncalves, M. F. Laguna, J. R. Iglesias

http://arxiv.org/abs/1208.2589

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Self-replicating loops: a survey

(2012). Self-replicating loops: a survey. International Journal of General Systems: Vol. 41, Cellular Automata (SICA), pp. 633-643.
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Guided self-organization: perception–action loops of embodied systems

Guided self-organization: perception–action loops of embodied systems | Papers | Scoop.it

Guided self-organization: perception–action loops of embodied systems

Special Issue, Theory in Biosciences, Volume 131, Issue 3 - Springer

http://link.springer.com/journal/12064/131/3/page/1

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Surprising finding: Tree's leaves genetically different from its roots

Surprising finding: Tree's leaves genetically different from its roots | Papers | Scoop.it

Black cottonwood trees (Populus trichocarpa) can clone themselves to produce offspring that are connected to their parents by the same root system. Now, after the first genome-wide analysis of a tree, it turns out that the connected clones have many genetic differences, even between tissues from the top and bottom of a single tree. The variation within a tree is as great as the variation across unrelated trees. Such somatic mutations — those that occur in cells other than sperm or eggs — are familiar to horticulturalists, who have long bred new plant varieties by grafting mutant branches onto ‘normal’ stocks. But until now, no one has catalogued the total number of somatic mutations in an individual plant.

 

In one tree, the top buds of the parent and offspring were genetically closer to each other than to their respective roots or lower branches. In another tree, the top bud was closer to the reference cottonwood genome than to any of the other tissues from the same individual.The tissue-specific mutations affected mainly genes involved in cell death, immune responses, metabolism, DNA binding and cell communication. Olds think that this may be because many of the mutations are harmful, and the tree reacts by destroying the mutated tissues or altering its metabolic pathways and the way it controls its genes, which leads to further mutations.

 

The findings have parallels to cancer studies, which have recently shown that separate parts of the same tumor can evolve independently and build up distinct genetic mutations, meaning that single biopsies give only a narrow view of the tumor’s diversity.


Via Dr. Stefan Gruenwald
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Disentangling nestedness from models of ecological complexity

Complex networks of interactions are ubiquitous1 and are particularly important in ecological communities, in which large numbers of species exhibit negative (for example, competition or predation) and positive (for example, mutualism) interactions with one another. Nestedness in mutualistic ecological networks is the tendency for ecological specialists to interact with a subset of species that also interact with more generalist species2. Recent mathematical and computational analysis has suggested that such nestedness increases species richness

 

Disentangling nestedness from models of ecological complexity

Alex James, Jonathan W. Pitchford & Michael J. Plank

Nature 487, 227–230 (12 July 2012) http://dx.doi.org/10.1038/nature11214

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Science for Designers: Complex Adaptive Systems

Science for Designers: Complex Adaptive Systems | Papers | Scoop.it
Today the world of design is in a position to benefit enormously from advances in sciences, mathematics and particularly, geometry—probably not in a way that many designers think.

Via Spaceweaver
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Temporal dynamics and network analysis

1. Network analysis is widely used in diverse fields and can be a powerful framework for studying the structure of biological systems. Temporal dynamics are a key issue for many ecological and evolutionary questions. These dynamics include both changes in network topology and flow on the network. Network analyses that ignore or do not adequately account for the temporal dynamics can result in inappropriate inferences.

2. We suggest that existing methods are currently under-utilized in many ecological and evolutionary network analyses and that the broader incorporation of these methods will considerably advance the current field. Our goal is to introduce ecologists and evolutionary biologists interested in studying network dynamics to extant ideas and methodological approaches, at a level appropriate for those new to the field.

3. We present an overview of time-ordered networks, which provide a framework for analysing network dynamics that addresses multiple inferential issues and permits novel types of temporally informed network analyses. We review available methods and software, discuss the utility and considerations of different approaches, provide a worked example analysis and highlight new research opportunities in ecology and evolutionary biology.

 

Temporal dynamics and network analysis
Benjamin Blonder, Tina W. Wey, Anna Dornhaus, Richard James, Andrew Sih

Methods in Ecology and Evolution
Early View

http://dx.doi.org/10.1111/j.2041-210X.2012.00236.x

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First map of the human brain reveals a simple, grid-like structure between neurons

First map of the human brain reveals a simple, grid-like structure between neurons | Papers | Scoop.it

"It turns out that the pathways in your brain — the connections between neurons — are almost perfectly grid-like. It’s rather weird: If you’ve ever seen a computer ribbon cable — a flat, 2D ribbon of wires stuck together, such as an IDE hard drive cable — the brain is basically just a huge collection of these ribbons, traveling parallel or perpendicular to each other. There are almost zero diagonals, nor single neurons that stray from the neuronal highways. The human brain is just one big grid of neurons — a lot like the streets of Manhattan, minus Broadway, and then projected into three dimensions. (...)

 

“Before, we had just driving directions. Now, we have a map showing how all the highways and byways are interconnected,” says Van Wedeen, a member of the Human Connectome Project. (...)

 

Brain wiring is not like the wiring in your basement, where it just needs to connect the right endpoints. Rather, the grid is the language of the brain and wiring and re-wiring work by modifying it.” Curiously, it seems like this network of highways and byways is laid out when we’re still an early fetus. At a very early stage, our brains form three “primal pathways” that traverse our brains horizontally, vertically, and transversely. The NIH scientists now think that those early connections act as markers, forcing the continued growth of an orderly, grid-like structure. Apparently such a setup is more amenable to evolutionary adaptation, too."


Via Amira
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Differences between game based learning and educational games

Differences between game based learning and educational games | Papers | Scoop.it
Games Based Learning is then much more than using an educational game to teach a subject. It's an approach to allowing the ebb and flow of play to fuel imagination.

Via Viktor Markowski
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Next-Generation Digital Information Storage in DNA

Digital information is accumulating at an astounding rate, straining our ability to store and archive it. DNA is among the most dense and stable information media known. The development of new technologies in both DNA synthesis and sequencing make DNA an increasingly feasible digital storage medium. Here, we develop a strategy to encode arbitrary digital information in DNA, write a 5.27-megabit book using DNA microchips, and read the book using next-generation DNA sequencing.

 

Next-Generation Digital Information Storage in DNA
George M. Church, Yuan Gao, Sriram Kosuri

Science http://dx.doi.org/10.1126/science.1226355

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Camouflage and Display for Soft Machines

Synthetic systems cannot easily mimic the color-changing abilities of animals such as cephalopods. Soft machines—machines fabricated from soft polymers and flexible reinforcing sheets—are rapidly increasing in functionality. This manuscript describes simple microfluidic networks that can change the color, contrast, pattern, apparent shape, luminescence, and surface temperature of soft machines for camouflage and display. The color of these microfluidic networks can be changed simultaneously in the visible and infrared—a capability that organisms do not have. These strategies begin to imitate the functions, although not the anatomies, of color-changing animals.

 

Camouflage and Display for Soft Machines
Stephen A. Morin, Robert F. Shepherd, Sen Wai Kwok, Adam A. Stokes, Alex Nemiroski, George M. Whitesides

Science 17 August 2012:
Vol. 337 no. 6096 pp. 828-832
http://dx.doi.org/10.1126/science.1222149

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Communication activity in a social network: relation between long-term correlations and inter-event clustering

Human communication in social networks is dominated by emergent statistical laws such as non-trivial correlations and temporal clustering. Recently, we found long-term correlations in the user's activity in social communities. Here, we extend this work to study the collective behavior of the whole community with the goal of understanding the origin of clustering and long-term persistence. At the individual level, we find that the correlations in activity are a byproduct of the clustering expressed in the power-law distribution of inter-event times of single users, i.e. short periods of many events are separated by long periods of no events. On the contrary, the activity of the whole community presents long-term correlations that are a true emergent property of the system, i.e. they are not related to the distribution of inter-event times. This result suggests the existence of collective behavior, possibly arising from nontrivial communication patterns through the embedding social network.

 

Communication activity in a social network: relation between long-term correlations and inter-event clustering

Diego Rybski, Sergey V. Buldyrev, Shlomo Havlin, Fredrik Liljeros & Hernán A. Makse

Scientific Reports 2, Article number: 560 http://dx.doi.org/10.1038/srep00560

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Wolfram's Classification and Computation in Cellular Automata Classes III and IV

We conduct a brief survey on Wolfram's classification, in particular related to the computing capabilities of Cellular Automata (CA) in Wolfram's classes III and IV. We formulate and shed light on the question of whether Class III systems are capable of Turing universality or may turn out to be "too hot" in practice to be controlled and programmed. We show that systems in Class III are indeed capable of computation and that there is no reason to believe that they are unable, in principle, to reach Turing-completness.

 

Wolfram's Classification and Computation in Cellular Automata Classes III and IV

Genaro J. Martinez, J. C. Seck-Touh-Mora, Hector Zenil

http://arxiv.org/abs/1208.2456

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Network structure, topology, and dynamics in generalized models of synchronization

Network structure is a product of both its topology and interactions between its nodes. We explore this claim using the paradigm of distributed synchronization in a network of coupled oscillators. As the network evolves to a global steady state, nodes synchronize in stages, revealing the network's underlying community structure. Traditional models of synchronization assume that interactions between nodes are mediated by a conservative process similar to diffusion. However, social and biological processes are often nonconservative. We propose a model of synchronization in a network of oscillators coupled via nonconservative processes. We study the dynamics of synchronization of a synthetic and real-world networks and show that the traditional and nonconservative models of synchronization reveal different structures within the same network.

 

"Network structure, topology, and dynamics in generalized models of synchronization"

Kristina Lerman and Rumi Ghosh

Phys. Rev. E 86, 026108 (2012)

http://link.aps.org/doi/10.1103/PhysRevE.86.026108

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Information storage, loop motifs, and clustered structure in complex networks

We use a standard discrete-time linear Gaussian model to analyze the information storage capability of individual nodes in complex networks, given the network structure and link weights. In particular, we investigate the role of two- and three-node motifs in contributing to local information storage. We show analytically that directed feedback and feedforward loop motifs are the dominant contributors to information storage capability, with their weighted motif counts locally positively correlated to storage capability. We also reveal the direct local relationship between clustering coefficient(s) and information storage. These results explain the dynamical importance of clustered structure and offer an explanation for the prevalence of these motifs in biological and artificial networks.

 

Information storage, loop motifs, and clustered structure in complex networks

Joseph T. Lizier, Fatihcan M. Atay and Jürgen Jost

Phys. Rev. E 86, 026110 (2012)

http://dx.doi.org/10.1103/PhysRevE.86.026110

 

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Computationally Tractable Pairwise Complexity Profile

Quantifying the complexity of systems consisting of many interacting parts has been an important challenge in the field of complex systems in both abstract and applied contexts. One approach, the complexity profile, is a measure of the information to describe a system as a function of the scale at which it is observed. We present a new formulation of the complexity profile, which expands its possible application to high-dimensional real-world and mathematically defined systems. The new method is constructed from the pairwise dependencies between components of the system. The pairwise approach may serve as both a formulation in its own right and a computationally feasible approximation to the original complexity profile. We compare it to the original complexity profile by giving cases where they are equivalent, proving properties common to both methods, and demonstrating where they differ. Both formulations satisfy linear superposition for unrelated systems and conservation of total degrees of freedom (sum rule). The new pairwise formulation is also a monotonically non-increasing function of scale. Furthermore, we show that the new formulation defines a class of related complexity profile functions for a given system, demonstrating the generality of the formalism.

 

Computationally Tractable Pairwise Complexity Profile

Yavni Bar-Yam, Dion Harmon, Yaneer Bar-Yam

http://arxiv.org/abs/1208.0823

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Programmable single-cell mammalian biocomputers

Synthetic biology has advanced the design of standardized control devices that program cellular functions and metabolic activities in living organisms. Rational interconnection of these synthetic switches resulted in increasingly complex designer networks that execute input-triggered genetic instructions with precision, robustness and computational logic reminiscent of electronic circuits (...) we have designed a set of synthetic transcription–translation control devices that could be rewired in a plug-and-play manner

 

Programmable single-cell mammalian biocomputers

Simon Ausländer, David Ausländer, Marius Müller, Markus Wieland & Martin Fussenegger

Nature 487, 123–127 (05 July 2012) http://dx.doi.org/10.1038/nature11149

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Dynamics of fashion: The case of given names

We analyze the social mechanisms that shape the popularity rise and fall of the names given to newborn babies. During the initial stage, popularity increases by imitation. As the people with the same name grow in number, however, its usage is inhibited and eventually decays. This process mirrors the dynamics of fashion fads. An activator-inhibitor dynamical model for the interplay of the population bearing a name and the expecting couples wishing to give it to their children provides a satisfactory explanation of historical data from the Canadian province of Quebec during the twentieth century.

 

Dynamics of fashion: The case of given names

Damian H. Zanette

http://arxiv.org/abs/1208.0576

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Coevolution of role preference and fairness in the ultimatum game

Traditional economics assumes that humans are rational. However, it is known that humans behave fairly in the ultimatum game (UG). There are various explanations for this apparent paradox, such as the “inequity aversion.” However, the role preference (proposer or responder) of humans in the UG is obscure. I conducted a UG scenario experiment where subjects were asked their role preference in addition to their decision in the game. The results showed that the subjects prefer to be proposers rather than responders. In particular, it was found that rational subjects had a high preference for the proposer role. On the basis of these results, I conducted evolutionary simulations of the UG, where each individual has role preference intensity. A role is allocated to the individual proportional to the preference intensity. The results showed coevolution of role preference and fairness. The preference for the proposer role evolved when rational strategy evolved, whereas this preference weakened as rationality decreased. This indicates that fairness has a strong link with role preference; in other words, human fairness is always threatened by the “power and position” of some particular individuals. Hence, its equal distribution among individuals may be effective in maintaining a high level of fairness.

 

Coevolution of role preference and fairness in the ultimatum game
Genki Ichinose

Complexity
Early View

http://dx.doi.org/10.1002/cplx.21413

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A measure of statistical complexity based on predictive information with application to finite spin systems

We propose the binding information as an information theoretic measure of complexity between multiple random variables, such as those found in the Ising or Potts models of interacting spins, and compare it with several previously proposed measures of statistical complexity, including excess entropy, Bialek et al.ʼs predictive information, and the multi-information. We discuss and prove some of the properties of binding information, particularly in relation to multi-information and entropy, and show that, in the case of binary random variables, the processes which maximise binding information are the ‘parity’ processes. The computation of binding information is demonstrated on Ising models of finite spin systems, showing that various upper and lower bounds are respected and also that there is a strong relationship between the introduction of high-order interactions and an increase of binding-information. Finally we discuss some of the implications this has for the use of the binding information as a measure of complexity.

 

A measure of statistical complexity based on predictive information with application to finite spin systems

Samer A. Abdallah, Mark D. Plumbley

Physics Letters A, Volume 376, Issue 4, 2012, pp. 275-281

http://dx.doi.org/10.1016/j.physleta.2011.10.066

 

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