Mechanical engineers have found a way to dramatically increase the sensitivity of a light-based plasmon sensor. They say it could potentially be used to detect incredibly minute and hard-to-detect explosives popular among terrorists.
Machine learning algorithms find patterns in big data sets. This talk presents quantum machine learning algorithms that give exponential speed-ups over their best existing classical counterparts. The algorithms work by mapping the data set into a quantum state (big quantum data) that contains the data in quantum superposition. Quantum coherence is then used to reveal patterns in the data. The quantum algorithms scale as the logarithm of the size of the database.
Seth Lloyd visited the Quantum AI Lab at Google LA to give a tech talk on "Quantum Machine Learning." This talk took place on January 29, 2014.
Modern portfolio theory (MPT) is a theory of finance that attempts to maximize portfolio expected return for a given amount of portfolio risk, or equivalently minimize risk for a given level of expected return, by carefully choosing the proportions of various assets. Although MPT is widely used in practice in the financial industry and several of its creators won a Nobel memorial prize for the theory, in recent years the basic assumptions of MPT have been widely challenged by fields such as behavioral economics.
MPT is a mathematical formulation of the concept of diversification in investing, with the aim of selecting a collection of investment assets that has collectively lower risk than any individual asset. This is possible, intuitively speaking, because different types of assets often change in value in opposite ways. For example, to the extent prices in the stock market move differently from prices in the bond market, a collection of both types of assets can in theory face lower overall risk than either individually. But diversification lowers risk even if assets' returns are not negatively correlated—indeed, even if they are positively correlated.
It's called the "Flynn effect" -- the fact that each generation scores higher on an IQ test than the generation before it. Are we actually getting smarter, or just thinking differently? In this fast-paced spin through the cognitive history of the 20th century, moral philosopher James Flynn suggests that changes in the way we think have had surprising (and not always positive) consequences.
"In this book, I suggest that to understand cities we must view them not simply as places in space but as systems of networks and flows. To understand space, we must understand flows, and to understand flows, we must understand networks—the relations between objects that comprise the system of the city. Drawing on the complexity sciences, social physics, urban economics, transportation theory, regional science, and urban geography, , I introduce theories and methods that reveal the deep structure of how cities function. (...)" Michael Batty
Sex is ubiquitous. The vast majority of animals and plants reproduce sexually at least some of the time. Some, such as humans, can reproduce no other way. Figuring out why sex is so common, though, has been a longstanding challenge for evolutionary biologists. The problem is that, as a reproductive strategy, sex seems wasteful. The mere fact that you have survived to adulthood means that you are reasonably well adapted to your environment, and it is not at all clear that reshuffling your genes with those of someone else will lead to anything as good, let alone better. Furthermore, a female who reproduces asexually by making diploid eggs passes roughly twice as much of her genetic material on to the next generation as does one who reproduces sexually. Overall, cloning yourself would seem to be the way to go.
In a wide ranging radio interview, SFI Distinguished Fellow Murray Gell-Mann discusses what it means to think like a scientist, the value of rejecting orthodoxy, beauty and simplicity, reductionism vs. interdisciplinarity, complex systems science and theory, and intelligent life on other planets, among other topics.
Real world network datasets often contain a wealth of complex topological information. In the face of these data, researchers often employ methods to extract reduced networks containing the most important structures or pathways, sometimes known as `skeletons' or `backbones'. Numerous such methods have been developed. Yet data are often noisy or incomplete, with unknown numbers of missing or spurious links. Relatively little effort has gone into understanding how salient network extraction methods perform in the face of noisy or incomplete networks. We study this problem by comparing how the salient features extracted by two popular methods change when networks are perturbed, either by deleting nodes or links, or by randomly rewiring links. Our results indicate that simple, global statistics for skeletons can be accurately inferred even for noisy and incomplete network data, but it is crucial to have complete, reliable data to use the exact topologies of skeletons or backbones. These results also help us understand how skeletons respond to damage to the network itself, as in an attack scenario.
Robustness of skeletons and salient features in networks Louis M. Shekhtman, James P. Bagrow, Dirk Brockmann
(This article was first published on Revolutions, and kindly contributed to R-bloggers)If you're thinking about starting a project (for example, a report or paper) using the R language for analysis, the Nice R code blog has some great advice.
CCS'15 Satellite Meeting: Information Processing in Complex Systems (IPCS'15)
Abstracts due: June 20 Decision of admission: June 25 Satellite meeting: October 1
All systems in nature have one thing in common: they process information. Information is registered in the state of a system and its elements, implicitly and invisibly. As elements interact, information is transferred. Indeed, bits of information about the state of one element will travel – imperfectly – to the state of the other element, forming its new state. This storage and transfer of information, possibly between levels of a multi level system, is imperfect due to randomness or noise. From this viewpoint, a system can be formalized as a collection of bits that is organized according to its rules of dynamics and its topology of interactions. Mapping out exactly how these bits of information percolate through the system could reveal new fundamental insights in how the parts orchestrate to produce the properties of the system. A theory of information processing would be capable of defining a set of universal properties of dynamical multi level complex systems, which describe and compare the dynamics of diverse complex systems ranging from social interaction to brain networks, from financial markets to biomedicine. Each possible combination of rules of dynamics and topology of interactions, with disparate semantics, would reduce to a single language of information processing.
Leonard Mlodinow takes us on a passionate and inspiring tour through the exciting history of human progress and the key events in the development of science. In the process, he presents a fascinating new look at the unique characteristics of our species and our society that helped propel us from stone tools to written language and through the birth of chemistry, biology, and modern physics to today’s technological world.
Along the way he explores the cultural conditions that influenced scientific thought through the ages and the colorful personalities of some of the great philosophers, scientists, and thinkers: Galileo, who preferred painting and poetry to medicine and dropped out of university; Isaac Newton, who stuck needlelike bodkins into his eyes to better understand changes in light and color; and Antoine Lavoisier, who drank nothing but milk for two weeks to examine its effects on his body. Charles Darwin, Albert Einstein, Werner Heisenberg, and many lesser-known but equally brilliant minds also populate these pages, each of their stories showing how much of human achievement can be attributed to the stubborn pursuit of simple questions (why? how?), bravely asked.
The Upright Thinkers is a book for science lovers and for anyone interested in creative thinking and in our ongoing quest to understand our world. At once deeply informed, accessible, and infused with the author’s trademark wit, this insightful work is a stunning tribute to humanity’s intellectual curiosity.
Pessoas com poucas capacidades não conseguirão realmente assimilar com facilidade uma língua estrangeira: embora aprendam as suas palavras, empregam-nas apenas no significado do equivalente aproximad...
What do 24,000 ideas look like? Ecologist Eric Berlow and physicist Sean Gourley apply algorithms to the entire archive of TEDx Talks, taking us on a stimulating visual tour to show how ideas connect globally.
(...) complex systems are characterized by the interactions between their numerous elements. The word ‘complex’ comes from the Latin plexus which means entwined. In other words, it is difficult to correlate global properties of complex systems with the properties of the individual constituent components. This is primarily because the interactions between these individual elements partly determine the future states of the system (Gershenson 2013). If these interactions are not included in the developed models, the models would not be an accurate reflection of the modelled phenomenon.
Gershenson, C. & M. A. Niazi (2013). Multidisciplinary applications of complex networks modeling, simulation, visualization, and analysis. Complex Adaptive Systems Modeling 1:17 http://dx.doi.org/10.1186/2194-3206-1-17
Math is logical, functional and just ... awesome. Mathemagician Arthur Benjamin explores hidden properties of that weird and wonderful set of numbers, the Fibonacci series. (And reminds you that mathematics can be inspiring, too!
Accounting Today FASB: Final Revenue Recognition Standard Coming Soon Accountingweb.com On November 6, the Financial Accounting Standards Board (FASB) voted to move forward with preparing the final standard on revenue recognition, which is slated...
Government is not traditionally the domain of natural science. But a growing body of researchers think it should be. In their view, rather than being one damned thing after another, human history is just as much in thrall to natural laws as anthills or oceans. If so, the mathematics developed to understand such systems might also help explain how human societies work – and why they sometimes don't.
For example, some complexity theorists say that many civic institutions, built for a minimally networked world, are unfit for purpose. Our more populous and connected societies can't be governed via traditional hierarchies: these need to be displaced by more decentralised networks. In this regard, industry may be faster on the uptake, since many companies are already making that transition.
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