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Julia: A Fresh Approach to Numerical Computing
TLDR
The Julia programming language as discussed by the authors combines expertise from the diverse fields of computer science and computational science to create a new approach to numerical computing, which is designed to be easy and fast.Abstract:
Bridging cultures that have often been distant, Julia combines expertise from the diverse fields of computer science and computational science to create a new approach to numerical computing. Julia is designed to be easy and fast. Julia questions notions generally held as "laws of nature" by practitioners of numerical computing:
1. High-level dynamic programs have to be slow.
2. One must prototype in one language and then rewrite in another language for speed or deployment, and
3. There are parts of a system for the programmer, and other parts best left untouched as they are built by the experts.
We introduce the Julia programming language and its design --- a dance between specialization and abstraction. Specialization allows for custom treatment. Multiple dispatch, a technique from computer science, picks the right algorithm for the right circumstance. Abstraction, what good computation is really about, recognizes what remains the same after differences are stripped away. Abstractions in mathematics are captured as code through another technique from computer science, generic programming.
Julia shows that one can have machine performance without sacrificing human convenience.read more
Citations
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Journal ArticleDOI
JuMP: A Modeling Language for Mathematical Optimization
TL;DR: JuMP as mentioned in this paper is an open-source modeling language that allows users to express a wide range of optimization problems (linear, mixed-integer, quadratic, conic-quadratic, semidefinite, and nonlinear) in a high-level, algebraic syntax.
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Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures.
TL;DR: This work develops PIDC, a fast, efficient algorithm that uses partial information decomposition (PID) to identify regulatory relationships between genes and demonstrates that the higher-order information captured by PIDC allows it to outperform pairwise mutual information-based algorithms when recovering true relationships present in simulated data.
Journal ArticleDOI
The (black) art of runtime evaluation: Are we comparing algorithms or implementations?
TL;DR: This work substantiates its points with extensive experiments, using clustering and outlier detection methods with and without index acceleration, and discusses what one can learn from evaluations, whether experiments are properly designed, and what kind of conclusions one should avoid.
Journal ArticleDOI
Demonstration of quantum advantage in machine learning
Diego Ristè,Marcus P. da Silva,Colm A. Ryan,Andrew W. Cross,Antonio Corcoles,John A. Smolin,Jay M. Gambetta,Jerry M. Chow,Blake R. Johnson +8 more
TL;DR: In this article, a quantum algorithm for learning parity with noise on a five-qubit superconducting processor is presented, and the authors show that the quantum algorithm finds the solution much faster than by classical methods.
Posted ContentDOI
Network inference from single-cell data using multivariate information measures
TL;DR: PIDC, a fast, efficient algorithm that uses partial information decomposition (PID) to identify regulatory relationships between genes, is developed and demonstrated that the higher order information captured by PIDC allows it to outperform pairwise mutual information-based algorithms when recovering true relationships present in simulated data.
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