Dependent type theory is rich enough to express that a program satisfies an input/output relational specification, but it could be hard to construct the proof term. On the other hand, squiggolists know very well how to show that one relation is included in another by algebraic reasoning. We demonstrate how to encode functional and relational derivations in a dependently typed programming language. A program is coupled with an algebraic derivation from a specification, whose correctness is guaranteed by the type system. Code accompanying the paper has been developed into an Agda library AoPA.
In many problems that require extensive searching, the solution can be described as satisfying two competing constraints, where satisfying each independently does not pose a challenge. As an alternative to tree-based and stochastic searching, for these problems we propose using an iterated map built from the projections to the two constraint sets. Algorithms of this kind have been the method of choice in a large variety of signal-processing applications; we show here that the scope of these algorithms is surprisingly broad, with applications as diverse as protein folding and Sudoku.Our survey of applications shows the difference map algorithm often achieves results comparable to much more sophisticated, special purpose algorithms. Efficient implementations of constraint projections usually are easier than designing the linear program solver at the heart of many optimization algorithms.
Despite its powerful module system, ML has not yet evolved for the modern world of dynamic and open modular programming, to which more primitive languages have adapted better so far. We present the design and semantics of a simple yet expressive first-class component system for ML. It provides dynamic linking in a type-safe and type-flexible manner, and allows selective execution in sandboxes. The system is defined solely by reduction to higher-order modules plus an extension with simple module-level dynamics, which we call packages. To represent components outside processes we employ generic pickling. We give a module calculus formalising the semantics of packages and pickling.
A Tutorial Implementation of a Dependently Typed Lambda Calculus Andres Löh, Conor McBride and Wouter Swierstra We present the type rules for a dependently-typed core calculus together with a straightforward implementation in Haskell. We explicitly highlight the changes necessary to shift from a simply-typed lambda calculus to the dependently-typed lambda calculus. We also describe how to extend our core language with data types and write several small example programs. The paper is accompanied by an executable interpreter and example code that allows immediate experimentation with the system we describe. Download Draft Paper (submitted to FI) Haskell source code (executable Haskell file containing all the code from the paper plus the interpreter; automatically generated from the paper sources) prelude.lp (prelude for the LambdaPi interpreter, containing several example programs) Instructions (how to get started with the LambdaPi interpreter)
Scientific publishing as it stands is an inefficient way to do science on a global scale. A lot of time and money is being wasted by groups around the world duplicating research that has already been carried out. FigShare allows you to share all of your data, negative results and unpublished figures.
Hier erfährst du (fast) alles über Papierflieger (Faltflieger und Papierflugzeuge): Bauanleitungen, Papierflieger-Geschichte, verwendbares Papier, Papierflieger-Physik, Faltanleitungen, Termin von Wettbewerb oder Workshop, Papierflieger-Literatur usw.
Last week's post (The truth about vaccinations: Your physician knows more than the University of Google) sparked a very lively discussion, with comments from several people trying to persuade me (and the other readers) that their paper disproved everything that I'd been saying. While I encourage you to go read the comments and contribute your…
Proceedings of the 1st Annual Conference on Robot Learning on 13-15 November 2017 Published as Volume 78 by the Proceedings of Machine Learning Research on 18 October 2017. Volume Edited by: Sergey Levine Vincent Vanhoucke Ken Goldberg Series Editors: Neil D. Lawrence Mark Reid
Glioblastoma (GBM, WHO grade IV astrocytoma) is among the most common adult brain tumors and one that is invariably fatal. GBM is classified as either primary (de novo) or secondary in origin. Secondary GBMs are derived from previously lower grade (WHO grades II or III) gliomas. While secondary GBMs present with similar clinical characteristics as their primary counterparts, the molecular pathways involved in their pathogenesis distinguish the two and have functional consequences for their behavior. Although a large number of histologic markers are routinely utilized to distinguish primary from secondary GBM, advances in genomic and bioinformatics techniques have drastically improved classification of high-grade gliomas and our understanding of the molecular pathways that influence tumor behavior and response to treatment. The significant influence of molecular identity on tumor behavior has been recognized by the most recent WHO classification of CNS tumors, wherein specific molecular markers have been integrated as part of tumor subtype identification process, as a supplement to traditional histological analysis. Indeed, the change heralds a new era for neuro-oncology, one that is moving toward targeted therapeutics as part of the standard of care. Thus, a comprehensive grasp of this diverse landscape is necessary. In this chapter, we provide an overview of our latest understanding of the molecular diversity of GBM, specifically as it pertains to primary and secondary GBMs, and how it influences prognostication and therapeutic decision-making.
A. Greubel, J. Wenkmann, H. Siller, и M. Hennecke. Proceedings of the 20th International Conference on Cognition and Exploratory Learning in Digital Age (CELDA2023), (2023)
L. Ehrlinger, J. Schrott, и W. Wöß. Database and Expert Systems Applications - DEXA 2023 Workshops, стр. 3--10. Cham, Springer Nature Switzerland, (2023)