We present an exact mathematical framework able to describe site-percolation transitions in real multiplex networks. Specifically, we consider the average percolation diagram valid over an infinite number of random configurations where nodes are present in the system with given probability. The approach relies on the locally treelike ansatz, so that it is expected to accurately reproduce the true percolation diagram of sparse multiplex networks with negligible number of short loops. The performance of our theory is tested in social, biological, and transportation multiplex graphs. When compared against previously introduced methods, we observe improvements in the prediction of the percolation diagrams in all networks analyzed. Results from our method confirm previous claims about the robustness of real multiplex networks, in the sense that the average connectedness of the system does not exhibit any significant abrupt change as its individual components are randomly destroyed.

 

Percolation in real multiplex networks

Ginestra Bianconi, Filippo Radicchi

Source: arxiv.org

When encountering novel object, humans are able to infer a wide range of physical properties such as mass, friction and deformability by interacting with them in a goal driven way. This process of active interaction is in the same spirit of a scientist performing an experiment to discover hidden facts. Recent advances in artificial intelligence have yielded machines that can achieve superhuman performance in Go, Atari, natural language processing, and complex control problems, but it is not clear that these systems can rival the scientific intuition of even a young child. In this work we introduce a basic set of tasks that require agents to estimate hidden properties such as mass and cohesion of objects in an interactive simulated environment where they can manipulate the objects and observe the consequences. We found that state of art deep reinforcement learning methods can learn to perform the experiments necessary to discover such hidden properties. By systematically manipulating the problem difficulty and the cost incurred by the agent for performing experiments, we found that agents learn different strategies that balance the cost of gathering information against the cost of making mistakes in different situations.

 

Learning to Perform Physics Experiments via Deep Reinforcement Learning

Misha Denil, Pulkit Agrawal, Tejas D Kulkarni, Tom Erez, Peter Battaglia, Nando de Freitas

Source: arxiv.org

Transfer entropy has been used to quantify the directed flow of information between source and target variables in many complex systems. Originally formulated in discrete time, we provide a framework for considering transfer entropy in continuous time systems. By appealing to a measure theoretic formulation we generalise transfer entropy, describing it in terms of Radon-Nikodym derivatives between measures of complete path realisations. The resulting formalism introduces and emphasises the idea that transfer entropy is an expectation of an individually fluctuating quantity along a path, in the same way we consider the expectation of physical quantities such as work and heat. We recognise that transfer entropy is a quantity accumulated over a finite time interval, whilst permitting an associated instantaneous transfer entropy rate. We use this approach to produce an explicit form for the transfer entropy for pure jump processes, and highlight the simplified form in the specific case of point processes (frequently used in neuroscience to model neural spike trains). We contrast our approach with previous attempts to formulate information flow between continuous time point processes within a discrete time framework, which incur issues that our continuous time approach naturally avoids. Finally, we present two synthetic spiking neuron model examples to exhibit the pertinent features of our formalism, namely that the information flow for point processes consists of discontinuous jump contributions (at spikes in the target) interrupting a continuously varying contribution (relating to waiting times between target spikes).

 

Transfer entropy in continuous time, with applications to jump and neural spiking processes

Richard E. Spinney, Mikhail Prokopenko, Joseph T. Lizier

Source: arxiv.org

Understanding the factors that facilitate the emergence of cooperation among organisms is central to the study of social evolution. Spotted hyenas Crocuta crocuta frequently cooperate to mob lions Panthera leo, approaching the lions as a tightknit group while vocalizing loudly in an attempt to overwhelm them and drive them away. Whereas cooperative mobbing behavior has been well documented in birds and some mammals, to our knowledge it has never been described during interactions between 2 apex predators. Using a 27-year dataset, we characterize lion–hyena encounters, assess rates of mobbing behavior observed during these interactions, and inquire whether mobbing results in successful acquisition of food. Lions and hyenas interacted most often at fresh kills, especially as prey size and the number of hyenas present increased. Possession of food at the beginning of an interaction positively affected retention of that food by each predator species. The presence of male lions increased the probability of an interspecific interaction but decreased the likelihood of hyenas obtaining or retaining possession of the food. Hyena mobbing rates were highest at fresh kills, but lower when adult male lions were present. The occurrence of mobbing was predicted by an increase in the number of hyenas present. Whether or not mobbing resulted in acquisition of food from lions was predicted by an increase in the number of mobs formed by the hyenas present, suggesting that cooperation among hyenas enhances their fitness.

 

Lions, hyenas and mobs (oh my!)
Kenna D.S. Lehmann, Tracy M. Montgomery, Sarah M. MacLachlan, Jenna M. Parker, Olivia S. Spagnuolo, Kelsey J. VandeWetering, Patrick S. Bills, Kay E. Holekamp
Current Zoology

http://dx.doi.org/10.1093/cz/zow073

 

Source: cz.oxfordjournals.org