Copy
Learn about the latest and greatest related to complex systems research. More at http://comdig.unam.mx
Complexity Digest


Success in books: predicting book sales before publication

Reading remains a preferred leisure activity fueling an exceptionally competitive publishing market: among more than three million books published each year, only a tiny fraction are read widely. It is largely unpredictable, however, which book will that be, and how many copies it will sell. Here we aim to unveil the features that affect the success of books by predicting a book’s sales prior to its publication. We do so by employing the Learning to Place machine learning approach, that can predicts sales for both fiction and nonfiction books as well as explaining the predictions by comparing and contrasting each book with similar ones. We analyze features contributing to the success of a book by feature importance analysis, finding that a strong driving factor of book sales across all genres is the publishing house. We also uncover differences between genres: for thrillers and mystery, the publishing history of an author (as measured by previous book sales) is highly important, while in literary fiction and religion, the author’s visibility plays a more central role. These observations provide insights into the driving forces behind success within the current publishing industry, as well as how individuals choose what books to read.

 

Success in books: predicting book sales before publication
Authors
Authors and affiliations
Xindi Wang, Burcu Yucesoy, Onur Varol, Tina Eliassi-Rad & Albert-László Barabási

EPJ Data Science
December 2019, 8:31

Source: link.springer.com


share on Twitter Like Success in books: predicting book sales before publication on Facebook


A Power Law Keeps the Brain’s Perceptions Balanced

Researchers have discovered a surprising mathematical relationship in the brain’s representations of sensory information, with possible applications to AI research.

Source: www.quantamagazine.org


share on Twitter Like A Power Law Keeps the Brain’s Perceptions Balanced on Facebook


Systematic comparison between methods for the detection of influential spreaders in complex networks

Influence maximization is the problem of finding the set of nodes of a network that maximizes the size of the outbreak of a spreading process occurring on the network. Solutions to this problem are important for strategic decisions in marketing and political campaigns. The typical setting consists in the identification of small sets of initial spreaders in very large networks. This setting makes the optimization problem computationally infeasible for standard greedy optimization algorithms that account simultaneously for information about network topology and spreading dynamics, leaving space only to heuristic methods based on the drastic approximation of relying on the geometry of the network alone. The literature on the subject is plenty of purely topological methods for the identification of influential spreaders in networks. However, it is unclear how far these methods are from being optimal. Here, we perform a systematic test of the performance of a multitude of heuristic methods for the identification of influential spreaders. We quantify the performance of the various methods on a corpus of 100 real-world networks; the corpus consists of networks small enough for the application of greedy optimization so that results from this algorithm are used as the baseline needed for the analysis of the performance of the other methods on the same corpus of networks. We find that relatively simple network metrics, such as adaptive degree or closeness centralities, are able to achieve performances very close to the baseline value, thus providing good support for the use of these metrics in large-scale problem settings. Also, we show that a further 2–5% improvement towards the baseline performance is achievable by hybrid algorithms that combine two or more topological metrics together. This final result is validated on a small collection of large graphs where greedy optimization is not applicable.

 

Systematic comparison between methods for the detection of influential spreaders in complex networks
Şirag Erkol, Claudio Castellano & Filippo Radicchi 
Scientific Reports volume 9, Article number: 15095 (2019)

Source: www.nature.com


share on Twitter Like Systematic comparison between methods for the detection of influential spreaders in complex networks on Facebook


Braess’s paradox and programmable behaviour in microfluidic networks

Microfluidic systems are now being designed with precision as miniaturized fluid manipulation devices that can execute increasingly complex tasks. However, their operation often requires numerous external control devices owing to the typically linear nature of microscale flows, which has hampered the development of integrated control mechanisms. Here we address this difficulty by designing microfluidic networks that exhibit a nonlinear relation between the applied pressure and the flow rate, which can be harnessed to switch the direction of internal flows solely by manipulating the input and/or output pressures. We show that these networks— implemented using rigid polymer channels carrying water—exhibit an experimentally supported fluid analogue of Braess’s paradox, in which closing an intermediate channel results in a higher, rather than lower, total flow rate. The harnessed behaviour is scalable and can be used to implement flow routing with multiple switches. These findings have the potential to advance the development of built-in control mechanisms in microfluidic networks, thereby facilitating the creation of portable systems and enabling novel applications in areas ranging from wearable healthcare technologies to deployable space systems.

 

Braess’s paradox and programmable behaviour in microfluidic networks
Daniel J. Case, Yifan Liu, István Z. Kiss, Jean-Régis Angilella & Adilson E. Motter 
Nature (2019)

Source: www.nature.com


share on Twitter Like Braess’s paradox and programmable behaviour in microfluidic networks on Facebook


Quantum computing takes flight

A programmable quantum computer has been reported to outperform the most powerful conventional computers in a specific task — a milestone in computing comparable in importance to the Wright brothers’ first flights.

Source: www.nature.com


share on Twitter Like Quantum computing takes flight on Facebook


Segregation and polarization in urban areas

Social behaviours emerge from the exchange of information among individuals—constrained by and reciprocally influencing the structure of information flows. The Internet radically transformed communication by democratizing broadcast capabilities and enabling easy and borderless formation of new acquaintances. However, actual information flows are heterogeneous and confined to self-organized echo-chambers. Of central importance to the future of society is understanding how existing physical segregation affects online social fragmentation. Here, we show that the virtual space is a reflection of the geographical space where physical interactions and proximity-based social learning are the main transmitters of ideas. We show that online interactions are segregated by income just as physical interactions are, and that physical separation reflects polarized behaviours beyond culture or politics. Our analysis is consistent with theoretical concepts suggesting polarization is associated with social exposure that reinforces within-group homogenization and between-group differentiation, and they together promote social fragmentation in mirrored physical and virtual spaces.

 

Segregation and polarization in urban areas
Alfredo J. Morales, Xiaowen Dong, Yaneer Bar-Yam and Alex ‘Sandy’ Pentland

Royal Society Open Science

Source: royalsocietypublishing.org


share on Twitter Like Segregation and polarization in urban areas on Facebook


Probing complexity: thermodynamics and computational mechanics approaches to origins studies

This paper proposes new avenues for origins research that apply modern concepts from stochastic thermodynamics, information thermodynamics and complexity science. Most approaches to the emergence of life prioritize certain compounds, reaction pathways, environments or phenomena. What they all have in common is the objective of reaching a state that is recognizably alive, usually positing the need for an evolutionary process. As with life itself, this correlates with a growth in the complexity of the system over time. Complexity often takes the form of an intuition or a proxy for a phenomenon that defies complete understanding. However, recent progress in several theoretical fields allows the rigorous computation of complexity. We thus propose that measurement and control of the complexity and information content of origins-relevant systems can provide novel insights that are absent in other approaches. Since we have no guarantee that the earliest forms of life (or alien life) used the same materials and processes as extant life, an appeal to complexity and information processing provides a more objective and agnostic approach to the search for life’s beginnings. This paper gives an accessible overview of the three relevant branches of modern thermodynamics. These frameworks are not commonly applied in origins studies, but are ideally suited to the analysis of such non-equilibrium systems. We present proposals for the application of these concepts in both theoretical and experimental origins settings.

 

Probing complexity: thermodynamics and computational mechanics approaches to origins studies
Stuart J. Bartlett and Patrick Beckett

Interface Focus

Source: royalsocietypublishing.org


share on Twitter Like Probing complexity: thermodynamics and computational mechanics approaches to origins studies on Facebook


Large scale and information effects on cooperation in public good games

The problem of public good provision is central in economics and touches upon many challenging societal issues, ranging from climate change mitigation to vaccination schemes. However, results which are supposed to be applied to a societal scale have only been obtained with small groups of people, with a maximum group size of 100 being reported in the literature. This work takes this research to a new level by carrying out and analysing experiments on public good games with up to 1000 simultaneous players. The experiments are carried out via an online protocol involving daily decisions for extended periods. Our results show that within those limits, participants’ behaviour and collective outcomes in very large groups are qualitatively like those in smaller ones. On the other hand, large groups imply the difficulty of conveying information on others’ choices to the participants. We thus consider different information conditions and show that they have a drastic effect on subjects’ contributions. We also classify the individual decisions and find that they can be described by a moderate number of types. Our findings allow to extend the conclusions of smaller experiments to larger settings and are therefore a relevant step forward towards the understanding of human behaviour and the organisation of our society.

 

Large scale and information effects on cooperation in public good games
María Pereda, Ignacio Tamarit, Alberto Antonioni, Jose A. Cuesta, Penélope Hernández & Angel Sánchez
Scientific Reports volume 9, Article number: 15023 (2019)

Source: www.nature.com


share on Twitter Like Large scale and information effects on cooperation in public good games on Facebook


Science and Technology Advance through Surprise

Breakthrough discoveries and inventions involve unexpected combinations of contents including problems, methods, and natural entities, and also diverse contexts such as journals, subfields, and conferences. Drawing on data from tens of millions of research papers, patents, and researchers, we construct models that predict more than 95% of next year’s content and context combinations with embeddings constructed from high-dimensional stochastic block models, where the improbability of new combinations itself predicts up to half of the likelihood that they will gain outsized citations and major awards. Most of these breakthroughs occur when problems in one field are unexpectedly solved by researchers from a distant other. These findings demonstrate the critical role of surprise in advance, and enable evaluation of scientific institutions ranging from education and peer review to awards in supporting it.

 

Science and Technology Advance through Surprise
Feng Shi, James Evans

Source: arxiv.org


share on Twitter Like Science and Technology Advance through Surprise on Facebook


Faculty Position in Statistical Physics of Complex Systems @EPFL

The School of Basic Sciences (Physics, Chemistry and Mathematics) at EPFL seeks to appoint a Professor in Statistical Physics of Complex Systems. This includes statistical physics of inference and learning, soft matter theory and theoretical biophysics. The appointment is offered at the Tenure Track Assistant Professor or tenured Associate Professor levels. We expect candidates to establish leadership and strengthen the EPFL endeavor in Statistical Physics of Complex Systems. Priority will be given to the overall originality and promise of the candidate’s work over any particular specialization area. Candidates should hold a PhD and have an excellent record of scientific accomplishments in the field. In addition, commitment to teaching at the undergraduate, master and doctoral levels is expected. Proficiency in French teaching is not required, but willingness to learn the language expected. EPFL, with its main campus located in Lausanne, Switzerland, on the shores of lake Geneva, is a dynamically growing and well-funded institution fostering excellence and diversity. It has a highly international campus with first-class infrastructure, including high performance computing As a technical university covering essentially the entire palette of engineering and science, EPFL offers a fertile environment for research cooperation between different disciplines. The EPFL environment is multi-lingual and multi-cultural, with English often serving as a common interface. Applications should include a cover letter, a CV with a list of publications, a concise statement of research (maximum 3 pages) and teaching interests (one page), and the names and addresses (including e-mail) of at least three references for a junior position or five references for a senior position. Applications should be uploaded (as PDFs) by November 15th, 2019 to https://facultyrecruiting.epfl.ch/position/18186240 Enquiries may be addressed to: Prof. Jan Hesthaven Dean of the School of Basic Sciences E-mail: fsbdean@epfl.ch Prof. Harald Brune Director of the Institute of Physics E-mail: IPHYSDirector@epfl.ch For additional information, please consult www.epfl.ch, sb.epfl.ch, iphys.epfl.ch EPFL is an equal opportunity employer and family friendly university. It is committed to increasing the diversity of its faculty. It strongly encourages women to Apply.

Source: www.epfl.ch


share on Twitter Like Faculty Position in Statistical Physics of Complex Systems @EPFL on Facebook
Email Marketing Powered by Mailchimp







This email was sent to <<Email Address>>
why did I get this?    unsubscribe from this list    update subscription preferences
Complexity Digest · Universidad Nacional Autónoma de México · Ciudad Universitaria · Mexico City, DF 01000 · Mexico