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1. Whose Baseline Compiler is it Anyway?

   https://doi.org/10.1109/CGO57630.2024.10444855  

   Titzer, Ben L. (March 2024, 2024 IEEE/ACM International Symposium on Code Generation and Optimization (CGO))

   Compilers face an intrinsic tradeoff between compilation speed and code quality. The tradeoff is particularly stark in a dynamic setting where JIT compilation time contributes to application runtime. Many systems now employ multiple compilation tiers, where one tier offers fast compile speed while another has much slower compile speed but produces higher quality code. With proper heuristics on when to use each, the overall performance is better than using either compiler in isolation. At the introduction of WebAssembly into the Web platform in 2017, most engines employed optimizing compilers and pre-compiled entire modules before execution. Yet since that time, all Web engines have introduced new “baseline” compiler tiers for Wasm to improve startup time. Further, many new non-web engines have appeared, some of which also employ simple compilers. In this paper, we demystify single-pass compilers for Wasm, explaining their internal algorithms and tradeoffs, as well as providing a detailed empirical study of those employed in production. We show the design of a new single-pass compiler for a research Wasm engine that integrates with an in-place interpreter and host garbage collector using value tags, while also supporting flexible instrumentation. In experiments, we measure the effectiveness of optimizations targeting value tags and find, somewhat surprisingly, that the runtime overhead can be reduced to near zero. We also assess the relative compile speed and execution time of six baseline compilers and place these baseline compilers in a two-dimensional tradeoff space with other execution tiers for Wasm. 

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2. Magnetic flux rope: What is it?

   https://doi.org/10.1088/1742-6596/2544/1/012002  

   Hu, Qiang (July 2023, Journal of Physics: Conference Series)

   Abstract Magnetic flux rope, a type of magnetic field structure in space plasmas, has been studied for decades through both observational and theoretical means. We provide a brief report on our recent modeling study of its magnetic field configuration based on in-situ spacecraft measurements, focusing on those made for large-scale flux ropes in the interplanetary space. We illustrate the complexity in its field-line topology by presenting two event studies employing a unique analysis method. In particular, we demonstrate the feasibility and challenges for the approach to use two or more in-situ spacecraft datasets. We discuss the implications of our results and offer some thoughts on further advancing the investigation of the nature of the magnetic flux rope. 

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3. The Internet of Animals: what it is, what it could be

   https://doi.org/10.1016/j.tree.2023.04.007  

   Kays, Roland; Wikelski, Martin (May 2023, Trends in Ecology & Evolution)
4. KV-SSD: What Is It Good For?

   https://doi.org/10.1109/DAC18074.2021.9586111  

   Saha, Manoj P.; Maruf, Adnan; Kim, Bryan S.; Bhimani, Janki (December 2021, 2021 58th ACM/IEEE Design Automation Conference (DAC))
5. Is it Fair? Automated Open Response Grading.

   Erickson, J.A.; Botelho, A.; Peng, Z.; Huang, R; Kasal, M.V.; Heffernan, N. (January 2021, Educational Data Mining)

   Online education technologies, such as intelligent tutoring systems, have garnered popularity for their automation. Whether it be automated support systems for teachers (grading, feedback, summary statistics, etc.) or support systems for students (hints, common wrong answer messages, scaffolding), these systems have built a well rounded support system for both students and teachers alike. The automation of these online educational technologies, such as intelligent tutoring systems, have often been limited to questions with well structured answers such as multiple choice or fill in the blank. Recently, these systems have begun adopting support for a more diverse set of question types. More specifically, open response questions. A common tool for developing automated open response tools, such as automated grading or automated feedback, are pre-trained word embeddings. Recent studies have shown that there is an underlying bias within the text these were trained on. This research aims to identify what level of unfairness may lie within machine learned algorithms which utilize pre-trained word embeddings. We attempt to identify if our ability to predict scores for open response questions vary for different groups of student answers. For instance, whether a student who uses fractions as opposed to decimals. By performing a simulated study, we are able to identify the potential unfairness within our machine learned models with pre-trained word embeddings. 

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