SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.

We report the formation of thin anisotropic phase gratings in a nematic liquid-crystalline film by use of lateral (fringing) electric fields induced by transparent interdigitated electrodes. These gratings yield high diffraction efficiency (>30%) with a strong dependence on the readout beam incidence angle. In addition, the formation of a defect wall is observed that has a significant effect on the diffraction properties of the phase grating.

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How can we close the gap between animals and robots when it comes to intelligently interacting with the environment? On our quest for answers, we have investigated the problem of physically exploring complex mechanical puzzles, called lockboxes. Biologists have discovered that cockatoos are intrinsically motivated to explore and solve such problems through physical explorative behavior. In this work, we study how different strategies shape the robots' exploration, given basic perception-action skills. Our evaluation highlights the influence of different statistical priors on the performance of the exploration strategies, showing that not only a range of computational methods, but also a range of priors could explain different exploration behaviors. We carry out our study of exploration strategies both in simulation and on two robot platforms. This first step towards a fully integrated real-world system allowed us to identify and remove limitations of our prior theoretical work on cross-entropy-based exploration when applied to complex realistic scenarios. In this paper we propose novel variants of this strategy and our experiments verify that the cross-entropy method performs well on a physical lockbox analogue of the cockatoo apparatus, and can generalize to lockboxes of different properties.

SummaryIn order to achieve an optimum performance of a given application on a given computer platform, a program developer or compiler must be aware of computer architecture parameters, including those related to branch predictors. Although dynamic branch predictors are designed with the aim to automatically adapt to changes in branch behavior during program execution, code optimizations based on the information about predictor structure can greatly increase overall program performance. Yet, exact predictor implementations are seldom made public, even though processor manuals provide valuable optimization hints. This paper presents an experiment flow with a series of microbenchmarks that determine the organization and size of a branch predictor using on-chip performance monitoring registers. Such knowledge can be used either for manual code optimization or for design of new, more architecture-aware compilers. Three examples illustrate how insight into exact branch predictor organization can be directly applied to code optimization. The proposed experiment flow is illustrated with microbenchmarks tuned for Intel Pentium III and Pentium 4 processors, although they can easily be adapted for other architectures. The described approach can also be used during processor design for performance evaluation of various branch predictor organizations and for testing and validation during implementation.