Attractive regular stochastic chains: perfect simulation and phase transition

Sandro Gallo and Daniel Y. Takahashi

We prove that uniqueness of the stationary chain, or equivalently, of the g-measure, compatible with an attractive regular probability kernel is equivalent to either one of the following two assertions for this chain: (1) it is a finitary coding of an independent and identically distributed (i.i.d.) process with countable alphabet; (2) the concentration of measure holds at exponential rate. We show in particular that if a stationary chain is uniquely defined by a kernel that is continuous and attractive, then this chain can be sampled using a coupling-from-the-past algorithm. For the original Bramson–Kalikow model we further prove that there exists a unique compatible chain if and only if the chain is a finitary coding of a finite alphabet i.i.d. process. Finally, we obtain some partial results on conditions for phase transition for general chains of infinite order.

An improved upper bound on the density of universal random graphs

Domingos Dellamonica Jr., Yoshiharu Kohayakawa, Vojtěch Rödl and Andrzej Ruciński

We give a polynomial time randomized algorithm that, on receiving as input a pair (H,G) of n-vertex graphs, searches for an embedding of H into G. If H has bounded maximum degree and G is suitably dense and pseudorandom, then the algorithm succeeds with high probability. Our algorithm proves that, for every integer d ≥ 3 and a large enough constant C = Cd, as n →∞, asymptotically almost all graphs with n vertices and at least Cn2−1/d log1/d n edges contain as subgraphs all graphs with n vertices and maximum degree at most d.

Opening for [Game Architecture and Development Scholarship]

The Research, Innovation and Dissemination Center on Neuromathematics (NeuroMat) is offering a scholarship for information technology professionals interested in being part of a breakthrough and innovative scientific project.

Video: Neuroscience Experiments System (NES)

At the 2016 INCF Neuroinformatics conference, held at the University of Reading, UK, Kelly Braghetto, NeuroMat researcher and University of São Paulo professor, presented the demo "NES: a free software to manage data from neuroscience experiments".

Observing Grasping Actions Directed to Emotion-Laden Objects: Effects upon Corticospinal Excitability

Anaelli A. Nogueira-Campos, Ghislain Saunier, Valeria Della-Maggiore, Laura A. S. de Oliveira, Erika C. Rodrigues and Claudia D. Vargas

The motor system is recruited whenever one executes an action as well as when one observes the same action being executed by others. Although it is well established that emotion modulates the motor system, the effect of observing other individuals acting in an emotional context is particularly elusive. The main aim of this study was to investigate the effect induced by the observation of grasping directed to emotion-laden objects upon corticospinal excitability (CSE). Participants classified video-clips depicting the right-hand of an actor grasping emotion-laden objects. Twenty video-clips differing in terms of valence but balanced in arousal level were selected. Motor evoked potentials (MEPs) were then recorded from the first dorsal interosseous using transcranial magnetic stimulation (TMS) while the participants observed the selected emotional video-clips. During the video-clip presentation, TMS pulses were randomly applied at one of two different time points of grasping: (1) maximum grip aperture, and (2) object contact time. CSE was higher during the observation of grasping directed to unpleasant objects compared to pleasant ones. These results indicate that when someone observes an action of grasping directed to emotion-laden objects, the effect of the object valence promotes a specific modulation over the motor system.

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The Research, Innovation and Dissemination Center for Neuromathematics is hosted by the University of São Paulo and funded by FAPESP (São Paulo Research Foundation).

 

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