435 research outputs found

    Porting the NetBeans Java 8 Enhanced For Loop Lambda Expression Refactoring to Eclipse

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    Java 8 is one of the largest upgrades to the popular language and framework in over a decade. However, the Eclipse IDE is missing several key refactorings that could help developers take advantage of new features in Java 8 more easily. In this paper, we discuss our ongoing work in porting the enhanced for loop to lambda expression refactoring from the NetBeans IDE to Eclipse. We also discuss future plans for new Java 8 refactorings not found in any current IDE

    The Immitigable Nature of Assembly Bias: The Impact of Halo Definition on Assembly Bias

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    Dark matter halo clustering depends not only on halo mass, but also on other properties such as concentration and shape. This phenomenon is known broadly as assembly bias. We explore the dependence of assembly bias on halo definition, parametrized by spherical overdensity parameter, Δ\Delta. We summarize the strength of concentration-, shape-, and spin-dependent halo clustering as a function of halo mass and halo definition. Concentration-dependent clustering depends strongly on mass at all Δ\Delta. For conventional halo definitions (Δ∼200m−600m\Delta \sim 200\mathrm{m}-600\mathrm{m}), concentration-dependent clustering at low mass is driven by a population of haloes that is altered through interactions with neighbouring haloes. Concentration-dependent clustering can be greatly reduced through a mass-dependent halo definition with Δ∼20m−40m\Delta \sim 20\mathrm{m}-40\mathrm{m} for haloes with M200m≲1012 h−1M⊙M_{200\mathrm{m}} \lesssim 10^{12}\, h^{-1}\mathrm{M}_{\odot}. Smaller Δ\Delta implies larger radii and mitigates assembly bias at low mass by subsuming altered, so-called backsplash haloes into now larger host haloes. At higher masses (M200m≳1013 h−1M⊙M_{200\mathrm{m}} \gtrsim 10^{13}\, h^{-1}\mathrm{M}_{\odot}) larger overdensities, Δ≳600m\Delta \gtrsim 600\mathrm{m}, are necessary. Shape- and spin-dependent clustering are significant for all halo definitions that we explore and exhibit a relatively weaker mass dependence. Generally, both the strength and the sense of assembly bias depend on halo definition, varying significantly even among common definitions. We identify no halo definition that mitigates all manifestations of assembly bias. A halo definition that mitigates assembly bias based on one halo property (e.g., concentration) must be mass dependent. The halo definitions that best mitigate concentration-dependent halo clustering do not coincide with the expected average splashback radii at fixed halo mass.Comment: 19 pages, 13 figures. Updated to published version. Main result summarized in Figure 1

    Beautiful, Battered Lands: Making Peace with Place from the Rust Belt to Appalachia

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    FlashProfile: A Framework for Synthesizing Data Profiles

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    We address the problem of learning a syntactic profile for a collection of strings, i.e. a set of regex-like patterns that succinctly describe the syntactic variations in the strings. Real-world datasets, typically curated from multiple sources, often contain data in various syntactic formats. Thus, any data processing task is preceded by the critical step of data format identification. However, manual inspection of data to identify the different formats is infeasible in standard big-data scenarios. Prior techniques are restricted to a small set of pre-defined patterns (e.g. digits, letters, words, etc.), and provide no control over granularity of profiles. We define syntactic profiling as a problem of clustering strings based on syntactic similarity, followed by identifying patterns that succinctly describe each cluster. We present a technique for synthesizing such profiles over a given language of patterns, that also allows for interactive refinement by requesting a desired number of clusters. Using a state-of-the-art inductive synthesis framework, PROSE, we have implemented our technique as FlashProfile. Across 153153 tasks over 7575 large real datasets, we observe a median profiling time of only ∼ 0.7 \sim\,0.7\,s. Furthermore, we show that access to syntactic profiles may allow for more accurate synthesis of programs, i.e. using fewer examples, in programming-by-example (PBE) workflows such as FlashFill.Comment: 28 pages, SPLASH (OOPSLA) 201
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