arXiv - CS - Programming Languages最新文献

{"title":"A Comprehensive Guide to Combining R and Python code for Data Science, Machine Learning and Reinforcement Learning","authors":"Alejandro L. García Navarro, Nataliia Koneva, Alfonso Sánchez-Macián, José Alberto Hernández","doi":"arxiv-2407.14695","DOIUrl":"https://doi.org/arxiv-2407.14695","url":null,"abstract":"Python has gained widespread popularity in the fields of machine learning,\u0000artificial intelligence, and data engineering due to its effectiveness and\u0000extensive libraries. R, on its side, remains a dominant language for\u0000statistical analysis and visualization. However, certain libraries have become\u0000outdated, limiting their functionality and performance. Users can use Python's\u0000advanced machine learning and AI capabilities alongside R's robust statistical\u0000packages by combining these two programming languages. This paper explores\u0000using R's reticulate package to call Python from R, providing practical\u0000examples and highlighting scenarios where this integration enhances\u0000productivity and analytical capabilities. With a few hello-world code snippets,\u0000we demonstrate how to run Python's scikit-learn, pytorch and OpenAI gym\u0000libraries for building Machine Learning, Deep Learning, and Reinforcement\u0000Learning projects easily.","PeriodicalId":501197,"journal":{"name":"arXiv - CS - Programming Languages","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141775837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reactive graphs in action (extended version)","authors":"David Tinoco, Alexandre Madeira, Manuel A. Martins, José Proença","doi":"arxiv-2407.14705","DOIUrl":"https://doi.org/arxiv-2407.14705","url":null,"abstract":"Reactive graphs are transition structures whereas edges become active and\u0000inactive during its evolution, that were introduced by Dov Gabbay from a\u0000mathematical's perspective. This paper presents Marge\u0000(https://fm-dcc.github.io/MARGe), a web-based tool to visualise and analyse\u0000reactive graphs enriched with labels. Marge animates the operational semantics\u0000of reactive graphs and offers different graphical views to provide insights\u0000over concrete systems. We motivate the applicability of reactive graphs for\u0000adaptive systems and for featured transition systems, using Marge to tighten\u0000the gap between the existing theoretical models and their usage to analyse\u0000concrete systems.","PeriodicalId":501197,"journal":{"name":"arXiv - CS - Programming Languages","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141775949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fusing Gathers with Integer Linear Programming","authors":"David van Balen, Gabriele Keller, Ivo Gabede Wolff, Trevor L. McDonell","doi":"arxiv-2407.13585","DOIUrl":"https://doi.org/arxiv-2407.13585","url":null,"abstract":"We present an Integer Linear Programming based approach to finding the\u0000optimal fusion strategy for combinator-based parallel programs. While\u0000combinator-based languages or libraries provide a convenient interface for\u0000programming parallel hardware, fusing combinators to more complex operations is\u0000essential to achieve the desired performance. Our approach is not only suitable\u0000for languages with the usual map, fold, scan, indexing and scatter operations,\u0000but also gather operations, which access arrays in arbitrary order, and\u0000therefore goes beyond the traditional producer-consumer fusion. It can be\u0000parametrised with appropriate cost functions, and is fast enough to be suitable\u0000for just-in-time compilation.","PeriodicalId":501197,"journal":{"name":"arXiv - CS - Programming Languages","volume":"172 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Compressing Structured Tensor Algebra","authors":"Mahdi Ghorbani, Emilien Bauer, Tobias Grosser, Amir Shaikhha","doi":"arxiv-2407.13726","DOIUrl":"https://doi.org/arxiv-2407.13726","url":null,"abstract":"Tensor algebra is a crucial component for data-intensive workloads such as\u0000machine learning and scientific computing. As the complexity of data grows,\u0000scientists often encounter a dilemma between the highly specialized dense\u0000tensor algebra and efficient structure-aware algorithms provided by sparse\u0000tensor algebra. In this paper, we introduce DASTAC, a framework to propagate\u0000the tensors's captured high-level structure down to low-level code generation\u0000by incorporating techniques such as automatic data layout compression,\u0000polyhedral analysis, and affine code generation. Our methodology reduces memory\u0000footprint by automatically detecting the best data layout, heavily benefits\u0000from polyhedral optimizations, leverages further optimizations, and enables\u0000parallelization through MLIR. Through extensive experimentation, we show that\u0000DASTAC achieves 1 to 2 orders of magnitude speedup over TACO, a\u0000state-of-the-art sparse tensor compiler, and StructTensor, a state-of-the-art\u0000structured tensor algebra compiler, with a significantly lower memory\u0000footprint.","PeriodicalId":501197,"journal":{"name":"arXiv - CS - Programming Languages","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsafe Impedance: Safe Languages and Safe by Design Software","authors":"Lee Barney, Adolfo Neto","doi":"arxiv-2407.13046","DOIUrl":"https://doi.org/arxiv-2407.13046","url":null,"abstract":"In December 2023, security agencies from five countries in North America,\u0000Europe, and the south Pacific produced a document encouraging senior executives\u0000in all software producing organizations to take responsibility for and\u0000oversight of the security of the software their organizations produce. In\u0000February 2024, the White House released a cybersecurity outline, highlighting\u0000the December document. In this work we review the safe languages listed in\u0000these documents, and compare the safety of those languages with Erlang and\u0000Elixir, two BEAM languages. These security agencies' declaration of some languages as safe is necessary\u0000but insufficient to make wise decisions regarding what language to use when\u0000creating code. We propose an additional way of looking at languages and the\u0000ease with which unsafe code can be written and used. We call this new\u0000perspective em{unsafe impedance}. We then go on to use unsafe impedance to\u0000examine nine languages that are considered to be safe. Finally, we suggest that\u0000business processes include what we refer to as an Unsafe Acceptance Process.\u0000This Unsafe Acceptance Process can be used as part of the memory safe roadmaps\u0000suggested by these agencies. Unsafe Acceptance Processes can aid organizations\u0000in their production of safe by design software.","PeriodicalId":501197,"journal":{"name":"arXiv - CS - Programming Languages","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141737169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PyTond: Efficient Python Data Science on the Shoulders of Databases","authors":"Hesam Shahrokhi, Amirali Kaboli, Mahdi Ghorbani, Amir Shaikhha","doi":"arxiv-2407.11616","DOIUrl":"https://doi.org/arxiv-2407.11616","url":null,"abstract":"Python data science libraries such as Pandas and NumPy have recently gained\u0000immense popularity. Although these libraries are feature-rich and easy to use,\u0000their scalability limitations require more robust computational resources. In\u0000this paper, we present PyTond, an efficient approach to push the processing of\u0000data science workloads down into the database engines that are already known\u0000for their big data handling capabilities. Compared to the previous work, by\u0000introducing TondIR, our approach can capture a more comprehensive set of\u0000workloads and data layouts. Moreover, by doing IR-level optimizations, we\u0000generate better SQL code that improves the query processing by the underlying\u0000database engine. Our evaluation results show promising performance improvement\u0000compared to Python and other alternatives for diverse data science workloads.","PeriodicalId":501197,"journal":{"name":"arXiv - CS - Programming Languages","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141718890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Haskelite: A Tracing Interpreter Based on a Pattern-Matching Calculus","authors":"Pedro Vasconcelos, Rodrigo Marques","doi":"arxiv-2407.11831","DOIUrl":"https://doi.org/arxiv-2407.11831","url":null,"abstract":"Many Haskell textbooks explain the evaluation of pure functional programs as\u0000a process of stepwise rewriting using equations. However, usual implementation\u0000techniques perform program transformations that make producing the\u0000corresponding tracing evaluations difficult. This paper presents a tracing\u0000interpreter for a subset of Haskell based on the pattern matching calculus of\u0000Kahl. We start from a big-step semantics in the style of Launchbury and develop\u0000a small-step semantics in the style of Sestoft's machines. This machine is used\u0000in the implementation of a step-by-step educational interpreter. We also\u0000discuss some implementation decisions and present illustrative examples.","PeriodicalId":501197,"journal":{"name":"arXiv - CS - Programming Languages","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141722116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modal Effect Types","authors":"Wenhao Tang, Leo White, Stephen Dolan, Daniel Hillerström, Sam Lindley, Anton Lorenzen","doi":"arxiv-2407.11816","DOIUrl":"https://doi.org/arxiv-2407.11816","url":null,"abstract":"We propose a novel type system for effects and handlers using modal types.\u0000Conventional effect systems attach effects to function types, which can lead to\u0000verbose effect-polymorphic types, especially for higher-order functions. Our\u0000modal effect system provides succinct types for higher-order first-class\u0000functions without losing modularity and reusability. The core idea is to\u0000decouple effects from function types and instead to track effects through\u0000relative and absolute modalities, which represent transformations on the\u0000ambient effects provided by the context. We formalise the idea of modal effect types in a multimodal System F-style\u0000core calculus Met with effects and handlers. Met supports modular effectful\u0000programming via modalities without relying on effect variables. We encode a\u0000practical fragment of a conventional row-based effect system with effect\u0000polymorphism, which captures most common use-cases, into Met in order to\u0000formally demonstrate the expressive power of modal effect types. To recover the\u0000full power of conventional effect systems beyond this fragment, we seamlessly\u0000extend Met to Mete with effect variables. We propose a surface language Metel\u0000for Mete with a sound and complete type inference algorithm inspired by\u0000FreezeML.","PeriodicalId":501197,"journal":{"name":"arXiv - CS - Programming Languages","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141718891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PutnamBench: Evaluating Neural Theorem-Provers on the Putnam Mathematical Competition","authors":"George Tsoukalas, Jasper Lee, John Jennings, Jimmy Xin, Michelle Ding, Michael Jennings, Amitayush Thakur, Swarat Chaudhuri","doi":"arxiv-2407.11214","DOIUrl":"https://doi.org/arxiv-2407.11214","url":null,"abstract":"We present PutnamBench, a new multilingual benchmark for evaluating the\u0000ability of neural theorem-provers to solve competition mathematics problems.\u0000PutnamBench consists of 1697 hand-constructed formalizations of 640 theorems\u0000sourced from the William Lowell Putnam Mathematical Competition, the premier\u0000undergraduate-level mathematics competition in North America. All the theorems\u0000have formalizations in Lean 4 and Isabelle; a substantial subset also has Coq\u0000formalizations. Proving the theorems requires significant problem-solving\u0000ability and proficiency in a broad range of topics taught in undergraduate\u0000mathematics courses. We use PutnamBench to evaluate several established neural\u0000and symbolic theorem-provers. These approaches can only solve a handful of the\u0000PutnamBench problems, establishing the benchmark as a difficult open challenge\u0000for research on neural theorem-proving. PutnamBench is available at\u0000https://github.com/trishullab/PutnamBench.","PeriodicalId":501197,"journal":{"name":"arXiv - CS - Programming Languages","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141718943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Compositional Symbolic Execution for Correctness and Incorrectness Reasoning (Extended Version)","authors":"Andreas Lööw, Daniele Nantes-Sobrinho, Sacha-Élie Ayoun, Caroline Cronjäger, Petar Maksimović, Philippa Gardner","doi":"arxiv-2407.10838","DOIUrl":"https://doi.org/arxiv-2407.10838","url":null,"abstract":"The introduction of separation logic has led to the development of\u0000symbolic-execution techniques and tools that are (functionally) compositional\u0000with function specifications that can be used in broader calling contexts. Many\u0000of the compositional symbolic-execution tools developed in academia and\u0000industry have been grounded on a formal foundation, but either the function\u0000specifications are not validated concerning the underlying separation logic of\u0000the theory, or there is a large gulf between the theory and the tool\u0000implementation. We introduce a formal compositional symbolic-execution engine which creates\u0000and uses function specifications from an underlying separation logic and\u0000provides a sound theoretical foundation partially inspired by the Gillian\u0000symbolic-execution platform. This is achieved by providing an axiomatic\u0000interface which describes the properties of the consume and produce operations\u0000used in the engine to compositionally update the symbolic state, including,\u0000when calling function specifications -- a technique used by VeriFast, Viper,\u0000and Gillian but not previously characterised independently of the tool. Our\u0000result consume and produce operations inspired by the Gillian implementation\u0000that satisfy the properties described by our axiomatic interface. A surprising\u0000property of our engine semantics is its ability to underpin both correctness\u0000and incorrectness reasoning, with the primary distinction being the choice\u0000between satisfiability and validity. We use this property to extend the Gillian\u0000platform, which previously only supported correctness reasoning, with\u0000incorrectness reasoning and automatic true bug-finding using incorrectness\u0000bi-abduction. We evaluate our new Gillian platform through instantiation to C.\u0000This instantiation is the first tool grounded on a common formal compositional\u0000symbolic-execution engine to support both correctness and incorrectness\u0000reasoning.","PeriodicalId":501197,"journal":{"name":"arXiv - CS - Programming Languages","volume":"54 44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141718895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}