empirical studies

It’s the Season 10 finale of the Elixir Wizards podcast! José Valim, Guillaume Duboc, and Giuseppe Castagna join Wizards Owen Bickford and Dan Ivovich to dive into the prospect of types in the Elixir programming language! They break down their research on set-theoretical typing and highlight their goal of creating a type system that supports as many Elixir idioms as possible while balancing simplicity and pragmatism. José, Guillaume, and Giuseppe talk about what initially sparked this project, the challenges in bringing types to Elixir, and the benefits that the Elixir community can expect from this exciting work. Guillaume's formalization and Giuseppe's "cutting-edge research" balance José's pragmatism and "Guardian of Orthodoxy" role. Decades of theory meet the needs of a living language, with open challenges like multi-process typing ahead. They come together with a shared joy of problem-solving that will accelerate Elixir's continued growth. Key Topics Discussed in this Episode: Adding type safety to Elixir through set theoretical typing How the team chose a type system that supports as many Elixir idioms as possible Balancing simplicity and pragmatism in type system design Addressing challenges like typing maps, pattern matching, and guards The tradeoffs between Dialyzer and making types part of the core language Advantages of typing for catching bugs, documentation, and tooling The differences between typing in the Gleam programming language vs. Elixir The possibility of type inference in a set-theoretic type system The history and development of set-theoretic types over 20 years Gradual typing techniques for integrating typed and untyped code How José and Giuseppe initially connected through research papers Using types as a form of "mechanized documentation" The risks and tradeoffs of choosing syntax Cheers to another decade of Elixir! A big thanks to this season’s guests and all the listeners! Links and Resources Mentioned in this Episode: Bringing Types to Elixir | Guillaume Duboc & Giuseppe Castagna | ElixirConf EU 2023 (https://youtu.be/gJJH7a2J9O8) Keynote: Celebrating the 10 Years of Elixir | José Valim | ElixirConf EU 2022 (https://youtu.be/Jf5Hsa1KOc8) OCaml industrial-strength functional programming https://ocaml.org/ ℂDuce: a language for transformation of XML documents http://www.cduce.org/ Ballerina coding language https://ballerina.io/ Luau coding language https://luau-lang.org/ Gleam type language https://gleam.run/ "The Design Principles of the Elixir Type System" (https://www.irif.fr/_media/users/gduboc/elixir-types.pdf) by G. Castagna, G. Duboc, and J. Valim "A Gradual Type System for Elixir" (https://dlnext.acm.org/doi/abs/10.1145/3427081.3427084) by M. Cassola, A. Talagorria, A. Pardo, and M. Viera "Programming with union, intersection, and negation types" (https://www.irif.fr/~gc/papers/set-theoretic-types-2022.pdf), by Giuseppe Castagna "Covariance and Contravariance: a fresh look at an old issue (a primer in advanced type systems for learning functional programmers)" (https://www.irif.fr/~gc/papers/covcon-again.pdf) by Giuseppe Castagna "A reckless introduction to Hindley-Milner type inference" (https://www.lesswrong.com/posts/vTS8K4NBSi9iyCrPo/a-reckless-introduction-to-hindley-milner-type-inference) Special Guests: Giuseppe Castagna, Guillaume Duboc, and José Valim.

Noah Smith of Stony Brook University and writer at Bloomberg View talks with EconTalk host Russ Roberts about whether economics is a science in some sense of that word. How reliable are experiments in economics? What about the statistical analysis that underlies much of the empirical work in modern economics? Additional topics include the reliability of the empirical analysis of the minimum wage, the state of macroeconomics, and the role of prejudice or prior beliefs in the interpretation of data and evidence.

In this work, we will examine how mobile device could predict users’ wishes regarding the push services like incoming mobile phone call. We conducted some experiments in order to get contextual data and then did analysis, a limited number of sensors were tagged to the user which were meant to detect certain characteristics of the environment the users were in, such as light sensor, temperature sensor, surrounding audio/noise level. Our results show that machine learning algorithms were able to classify the instances correctly with a high accuracy rate.