Structural Induction Principles for Functional Programmers

User defined recursive types are a fundamental feature of modern functional programming languages like Haskell, Clean, and the ML family of languages. Properties of programs defined by recursion on the structure of recursive types are generally proved by structural induction on the type. It is well known in the theorem proving community how to generate structural induction principles from data type declarations. These methods deserve to be better know in the functional programming community. Existing functional programming textbooks gloss over this material. And yet, if functional programmers do not know how to write down the structural induction principle for a new type - how are they supposed to reason about it? In this paper we describe an algorithm to generate structural induction principles from data type declarations. We also discuss how these methods are taught in the functional programming course at the University of Wyoming. A Haskell implementation of the algorithm is included in an appendix.Comment: In Proceedings TFPIE 2013, arXiv:1312.221

Teaching Computation in Neuroscience: Notes on the 2019 Society for Neuroscience Professional Development Workshop on Teaching

The 2019 Society for Neuroscience Professional 1Development Workshop on Teaching reviewed current tools, approaches, and examples for teaching computation in neuroscience. Robert Kass described the statistical foundations that students need to properly analyze data. Pascal Wallisch compared MATLAB and Python as programming languages for teaching students. Adrienne Fairhall discussed computational methods, training opportunities, and curricular considerations. Walt Babiec provided a view from the trenches on practical aspects of teaching computational neuroscience. Mathew Abrams concluded the session with an overview of resources for teaching and learning computational modeling in neuroscience

Providing appropriate social support to prevention of depression for high-anxious sufferers

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordNational Natural Science Foundation of ChinaFundamental Research Funds for the Central Universities, ChinaChina Postdoctoral Science FoundationFund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shaanxi Provinc

Using Embedded Xinu and the Raspberry Pi 3 to Teach Operating Systems

Multicore processors have become the standard in modern computing platforms. Such complex hardware enables faster execution of the programs it runs, but this is only true if its programmer has the knowledge and ability to make it so. Thus, there is a great need to prepare computing students by establishing robust educational tools. Existing tools often include abstract learning environments such as a virtual machine. While such platforms are widely available and convenient, they are unable to expose students to concurrency on real hardware.This paper presents multicore Embedded Xinu, an educational operating system used to teach concurrency concepts at the university level. The latest port of Embedded Xinu to the four-core, ARM-based Raspberry Pi 3 B+ enabled an operating systems curriculum in which students build their own concurrency-oriented kernel and execute it on a real machine. Assignments that have been run in the course include concepts of synchronization, scheduling, and memory allocation on a multicore platform. Upon completing the course, students are capable of solving problems commonly found in the field of parallel computing

A Project-based Undergraduate Aerospace Sequence, with Embedded Computational Intelligence

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90648/1/AIAA-2011-1581-293.pd