A NeuroMat network to empower persons with Parkinson's disease in shaping therapeutic strategies

FAPESP’s Research, Innovation and Dissemination Center for Neuromathematics (RIDC NeuroMat) has launched in September a network to promote the collaboration of patients, families and professionals health to face clinical and research challenges associated with Parkinson’s disease. This initiative is called Amparo in Portuguese and is coordinated by the NeuroMat investigator Maria Elisa Pimentel Piemonte, a physical therapist and professor at the University of Sao Paulo.

With assistance from NeuroMat the Museum of Veterinary Anatomy adheres to open scientific dissemination

The Museum of Veterinary Anatomy, at the University of São Paulo, is currently relicensing all images so they can be uploaded to the open educational repository Wikimedia Commons. These images have had in August several hundred thousands views. This initiative was organized and supported by the NeuroMat scientific dissemination team. Roberta Minhoto, CFMV Sala de Imprensa, 9/22/2016. (In Portuguese.)

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.




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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