George G Vega Yon @gvegayon@qoto.org

Material for the R project sprint. Contribute to r-devel/r-project-sprint-2023 development by creating an account on GitHub.

We numerically simulated the spread of disease for a Susceptible-Infected-Recovered (SIR) model on contact networks drawn from a small-world ensemble. We investigated the impact of two types of vaccination strategies, namely random vaccination and high-degree heuristics, on the probability density function (pdf) of the cumulative number C of infected people over a large range of its support. To obtain the pdf even in the range of probabilities as small as 10−80, we applied a large-deviation approach, in particular the 1/t Wang-Landau algorithm. To study the size-dependence of the pdfs within the framework of large-deviation theory, we analyzed the empirical rate function. To find out how typical as well as extreme mild or extreme severe infection courses arise, we investigated the structures of the time series conditioned to the observed values of C.

Full time, continuing position. Located on the Camperdown Campus Exciting opportunity to join the Complex Systems Group in the School of Physics Base Salary, Level B/C $118,390- $167,221 p.a + 17% superannuation About the opportunity The School of Physics is seeking to appoint a Senior Lecturer or Lecturer in Complex Systems. This is a continuing 40% (teaching), 40% (research) and 20% (service) position. Complex Systems is a research strength of the School, having been a node of four Commonwealth-funded research centers and two University centers in the last decade, with strong cross-Faculty, national, and international links. We seek an appointee with strong theoretical, mathematical, and computational ability with a track record of applications to modeling and analyzing complex physical systems. It is anticipated that the appointee will grow their research program while contributing to the School’s undergraduate and postgraduate teaching and training in Physics, including especially its interdisciplinary links. We are seeking to recruit a mid-career academic to both strengthen and complement existing research strengths of the group, while expanding into new areas as appropriate. Areas of expertise that would be desirable include: statistical mechanics of complex systems critical phenomena field theory large-scale simulations networks. Application areas of particular interest would include: neurophysics and brain dynamics econophysics/sociophysics diagnostics and imaging of complex systems physics of deep learning biological physics. Your key responsibilities will be to: contribute to outstanding teaching and learning in Physics, including interdisciplinary aspects develop a competitively funded program of original research, either independently or as part of a team develop external research partnerships and engagement contribute to and help shape the strategic goals of School and Faculty contribute to engagement and service/administrative activities of the School of Physics and Faculty of Science. If you would like to find out more about the School of Physics leading research, click here About you The University values courage and creativity; openness and engagement; inclusion and diversity; and respect and integrity. As such, we see the importance of recruiting talent aligned to these values and are looking for a Senior Lecturer or Lecturer who has: a PhD or other research doctorate in a relevant field strong theoretical and mathematical abilities, focusing on areas relating to physical systems strong computational research ability and experience in relevant programming experience in university-level teaching, including lecturing level C: demonstrated experience and success in curriculum design and delivery; a track record of innovative pedagogy is desirable demonstrated progress toward your own independent research program evidenced by original, high-quality, research, as evidenced by refereed publications in particular; a track record of external funding success is an advantage, especially at Level C. demonstrated capacity to communicate effectively across a range of forums and to work collegially, collaboratively, and inclusively with fellow scholars and professional staff a developing network of relationships with key academic and professional institutions, and partners, including industry where appropriate evidence of developing capacity to contribute to governance, administration, and leadership in the higher education sector. 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Author summary The brain performs visual object recognition using much shallower hierarchical stages than artificial deep neural networks employ. However, the mechanism underlying this cost-efficient function is elusive. Here, we show that cortical long-range connections (LRC) may enable this parsimonious organization of circuits for balancing cost and performance. Using model network simulations based on data in tree shrews, we found that sparse LRCs, when added to local connections, organize a small-world network that dramatically enhances object recognition of shallow feedforward networks. We found that optimization of the ratio between LRCs and local connections maximizes the small-world coefficient and task performance of the network, by minimizing the total length of wiring needed for integration of the global information. We also found that the effect of LRCs varies by network size, which explains the existence of species-specific LRCs in mammalian visual cortex of various sizes. Our results demonstrate a biological strategy to achieve cost-efficient brain circuits.