Positions for Postdoctoral Researchers

The Research, Innovation and Dissemination Center for Neuromathematics (NeuroMat), hosted by the University of Sao Paulo, Brazil, and funded by FAPESP (Sao Paulo Research Foundation), is offering several postdoctoral fellowships for recent PhDs with outstanding research potential.

Meeting: Numeracy in Brazil: diagnoses and perspectives

The Research, Innovation and Dissemination Center for Neuromathematics (RIDC NeuroMat) has organized the meeting "Numeracy in Brazil: diagnoses and perspectives," to be held on May 16. This event has the support of the São Paulo Research Foundation (FAPESP) and the University of São Paulo School of Education (FEUSP). (In portuguese)

Wikipedia is an effective tool for education

During her visit at NeuroMat, LiAnna Davis, director of the Wiki Edu Foundation, talked on how Wikimedia projects may have an impact in fostering scientific dissemination. Thais Paiva, Carta Capital (Carta Educação), 3/15/2017. (In Portuguese)

Potential Well Spectrum and Hitting Time in Renewal Processes

Miguel Abadi, Liliam Cardeño and Sandro Gallo

The potential well of a state can be interpreted physically as the energy that a stationary process needs to leave the state. We prove that for discrete time renewal processes, the potential well is the right scaling for the hitting and return time distributions of the state. We further detail the potential well spectrum of these processes by giving a complete classification of the states according to their potential well.

Estimating Parameters Associated with Monotone Properties

Carlos Hoppen, Yoshiharu Kohayakawa, Richard Lang, Hanno Lefmann and Henrique Stagni

There has been substantial interest in estimating the value of a graph parameter, i.e., of a real function defined on the set of finite graphs, by sampling a randomly chosen substructure whose size is independent of the size of the input. Graph parameters that may be successfully estimated in this way are said to be testable or estimable, and the sample complexity qz = qz(ε) of an estimable parameter z is the size of the random sample required to ensure that the value of z(G) may be estimated within error ε with probability at least 2/3. In this paper, we study the sample complexity of estimating two graph parameters associated with a monotone graph property, improving previously known results. To obtain our results, we prove that the vertex set of any graph that satisfies a monotone property P may be partitioned equitably into a constant number of classes in such a way that the cluster graph induced by the partition is not far from satisfying a natural weighted graph generalization of P. Properties for which this holds are said to be recoverable, and the study of recoverable properties may be of independent interest




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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