Category: Papers

Advantage of Being Multicomponent and Spatial: Multipartite Viruses Colonize Structured Populations with Lower Thresholds

Multipartite viruses have a genome divided into different disconnected viral particles. A majority of multipartite viruses infect plants; very few target animals. To understand why, we use a simple, network-based susceptible-latent-infectious-recovered model. We show both analytically and numerically that, provided that the average degree of the contact network exceeds a critical value, even in the absence of an explicit microscopic advantage, multipartite viruses have a lower threshold to colonizing network-structured populations compared to a well-mixed population. We further corroborate this finding on two-dimensional lattice networks, which better represent the typical contact structures of plants.

Yi-Jiao Zhang, Zhi-Xi Wu, Petter Holme, and Kai-Cheng Yang
Phys. Rev. Lett. 123, 138101

Source: link.aps.org

Topological portraits of multiscale coordination dynamics

Living systems exhibit complex yet organized behavior on multiple spatiotemporal scales. To investigate the nature of multiscale coordination in living systems, one needs a meaningful and systematic way to quantify the complex dynamics, a challenge in both theoretical and empirical realms. The present work shows how integrating approaches from computational algebraic topology and dynamical systems may help us meet this challenge. In particular, we focus on the application of multiscale topological analysis to coordinated rhythmic processes. First, theoretical arguments are introduced as to why certain topological features and their scale-dependency are highly relevant to understanding complex collective dynamics. Second, we propose a method to capture such dynamically relevant topological information using persistent homology, which allows us to effectively construct a multiscale topological portrait of rhythmic coordination. Finally, the method is put to test in detecting transitions in real data from an experiment of rhythmic coordination in ensembles of interacting humans. The recurrence plots of topological portraits highlight collective transitions in coordination patterns that were elusive to more traditional methods. This sensitivity to collective transitions would be lost if the behavioral dynamics of individuals were treated as separate degrees of freedom instead of constituents of the topology that they collectively forge. Such multiscale topological portraits highlight collective aspects of coordination patterns that are irreducible to properties of individual parts. The present work demonstrates how the analysis of multiscale coordination dynamics can benefit from topological methods, thereby paving the way for further systematic quantification of complex, high-dimensional dynamics in living systems.

Topological portraits of multiscale coordination dynamics

Mengsen Zhang, William D. Kalies, J. A. Scott Kelso, Emmanuelle Tognoli

Source: arxiv.org

Northeast Journal of Complex Systems (NEJCS) | Vol 1 | No. 1

The Northeast Journal of Complex Systems (NEJCS) is launched by the community of Complex Systems researchers in the US Northeast region to provide a solution to the above challenge. As the official journal of the Complex Systems Society US Northeast Chapter, NEJCS serves as an open-access publication venue that is completely free for everyone, including authors, readers, libraries, and the public. Editors and reviewers also work “for free” as this journal is run by many volunteers. This is made possible by the editorial management and support provided by the Center for Collective Dynamics of Complex Systems and the Open Repository @ Binghamton (ORB) initiative at Binghamton University, State University of New York. Even though its name carries the word “Northeast”, this journal is open to everyone regardless of geographical locations. International contributions are more than welcome as well.

Sayama, Hiroki and Georgiev, Georgi (2019) "Editorial Introduction to the Northeast Journal of Complex Systems (NEJCS)," Northeast Journal of Complex Systems (NEJCS): Vol. 1 : No. 1 , Article 1.

Source: orb.binghamton.edu

The WIRED Guide to Artificial Intelligence

Supersmart algorithms won’t take all the jobs, But they are learning faster than ever, doing everything from medical diagnostics to serving up ads.

Even if progress on making artificial intelligence smarter stops tomorrow, don’t expect to stop hearing about how it’s changing the world. Big tech companies such as Google, Microsoft, and Amazon have amassed strong rosters of AI talent and impressive arrays of computers to bolster their core businesses of targeting ads or anticipating your next purchase.

They’ve also begun trying to make money by inviting others to run AI projects on their networks, which will help propel advances in areas such as health care or national security. Improvements to AI hardware, growth in training courses in machine learning, and open source machine-learning projects will also accelerate the spread of AI into other industries.

Artificial general intelligence

As yet nonexistent software that displays a humanlike ability to adapt to different environments and tasks, and transfer knowledge between them. Meanwhile, consumers can expect to be pitched more gadgets and services with AI-powered features. Google and Amazon in particular are betting that improvements in machine learning will make their virtual assistants and smart speakers more powerful. Amazon, for example, has devices with cameras to look at their ownersand the world around them.

The commercial possibilities make this a great time to be an AI researcher. Labs investigating how to make smarter machines are more numerous and better-funded than ever. And there’s plenty to work on: Despite the flurry of recent progress in AI and wild prognostications about its near future, there are still many things that machines can’t do, such as understanding the nuances of language, common-sense reasoning, and learning a new skill from just one or two examples. AI software will need to master tasks like these if it is to get close to the multifaceted, adaptable, and creative intelligence of humans. One deep-learning pioneer, Google’s Geoff Hinton, argues that making progress on that grand challenge will require rethinking some of the foundations of the field.

As AI systems grow more powerful, they will rightly invite more scrutiny. Government use of software in areas such as criminal justice is often flawed or secretive, and corporations like Facebook have begun confronting the downsides of their own life-shaping algorithms. More powerful AI has the potential to create worse problems, for example by perpetuating historical biases and stereotypes against women or black people. Civil-society groups and even the tech industry itself are now exploring rules and guidelines on the safety and ethics of AI. For us to truly reap the benefits of machines getting smarter, we’ll need to get smarter about machines.

Source: www.wired.com

Training-free measures based on algorithmic probability identify high nucleosome occupancy in DNA sequences

We introduce and study a set of training-free methods of an information-theoretic and algorithmic complexity nature that we apply to DNA sequences to identify their potential to identify nucleosomal binding sites. We test the measures on well-studied genomic sequences of different sizes drawn from different sources. The measures reveal the known in vivo versus in vitro predictive discrepancies and uncover their potential to pinpoint high and low nucleosome occupancy. We explore different possible signals within and beyond the nucleosome length and find that the complexity indices are informative of nucleosome occupancy. We found that, while it is clear that the gold standard Kaplan model is driven by GC content (by design) and by k-mer training; for high occupancy, entropy and complexity-based scores are also informative and can complement the Kaplan model.

Training-free measures based on algorithmic probability identify high nucleosome occupancy in DNA sequences
Hector Zenil, Peter Minary
Nucleic Acids Research, gkz750,

Source: academic.oup.com