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10
Date Added: Dec 2, 2021
Date Added: Dec 2, 2021
Count data is prevalent in many different areas of linguistics, such as when counting words, syntactic constructions, discourse particles, case markers, or speech errors. The Poisson distribution is the canonical distribution for characterising count data with no or unknown upper bound. Given the prevalence of count data in linguistics, Poisson regression has wide utility no matter what subfield of linguistics is considered. However, in contrast to logistic regression, Poisson regression is surprisingly little known. Here, we make a case for why linguists need to consider Poisson regression, and give recommendations for when Poisson regression is more appropriate compared to logistic regression. This tutorial introduces readers to foundational concepts needed to understand the basics of Poisson regression, followed by a hands-on tutorial using the R package brms. We discuss a dataset where Catalan and Korean speakers change the frequency of their co-speech gestures as a function of politeness contexts. This dataset also involves exposure variables (the incorporation of time to deal with unequal intervals) and overdispersion (excess variance). Altogether, we hope that more linguists will consider Poisson regression for the analysis of count data.
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6
Date Added: Apr 14, 2021
Date Added: Apr 14, 2021
Building high-performance virtual machines is a complex and expensive undertaking; many popular languages still have low-performance implementations. We describe a new approach to virtual machine (VM) construction that amortizes much of the effort in initial construction by allowing new languages to be implemented with modest additional effort. The approach relies on abstract syntax tree (AST) interpretation where a node can rewrite itself to a more specialized or more general node, together with an optimizing compiler that exploits the structure of the interpreter. The compiler uses speculative assumptions and deoptimization in order to produce efficient machine code. Our initial experience suggests that high performance is attainable while preserving a modular and layered architecture, and that new high-performance language implementations can be obtained by writing little more than a stylized interpreter.
5
Date Added: Apr 14, 2021
Date Added: Apr 14, 2021
We present VOQC, the first fully verified optimizer for quantum circuits, written using the Coq proof assistant. Quantum circuits are expressed as programs in a simple, low-level language called SQIR, a simple quantum intermediate representation, which is deeply embedded in Coq. Optimizations and other transformations are expressed as Coq functions, which are proved correct with respect to a semantics of SQIR programs. SQIR uses a semantics of matrices of complex numbers, which is the standard for quantum computation, but treats matrices symbolically in order to reason about programs that use an arbitrary number of quantum bits. SQIR's careful design and our provided automation make it possible to write and verify a broad range of optimizations in VOQC, including full-circuit transformations from cutting-edge optimizers.
0
Date Added: Dec 4, 2020
Date Added: Dec 4, 2020
Paper