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1. Data from: Ecological diversification in an adaptive radiation of plants: the role of de novo mutation and introgression

   https://doi.org/10.6084/m9.figshare.24480499  

   Stone, Benjamin; Wessinger, Carolyn A. (January 2023, figshare)

   Data from: Stone and Wessinger 2023, "Ecological diversification in an adaptive radiation of plants: the role of de novo mutation and introgression"DOI: 10.1101/2023.11.01.565185The code used to conduct analyses from this study can be found here: https://github.com/benstemon/MBE-23-0936The raw sequencing reads generated from this study have been deposited on the SRA under Project number: PRJNA1057825This repository contains a README.md file, which contains information on all files included. 

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2. Automatically Reproducing Timing-Dependent Flaky-Test Failures

   https://doi.org/10.5281/zenodo.7041599  

   Shanto Rahman, Aaron Massey (January 2023, Zenodo)

   This artifact contains the source code for FlakeRake, a tool for automatically reproducing timing-dependent flaky-test failures. It also includes raw and processed results produced in the evaluation of FlakeRake   Contents:   Timing-related APIs that FlakeRake considers adding sleeps at: timing-related-apis Anonymized code for FlakeRake (not runnable in its anonymized state, but included for reference; we will publicly release the non-anonymized code under an open source license pending double-blind review): flakerake.tgz Failure messages extracted from the FlakeFlagger dataset: 10k_reruns_failures_by_test.csv.gz  Output from running isolated reruns on each flaky test in the FlakeFlager dataset: 10k_isolated_reruns_all_results.csv.gz (all test results summarized into a CSV), 10k_isolated_reruns_failures_by_test.csv.gz (CSV including just test failures, including failure messages), 10k_isolated_reruns_raw_results.tgz (includes all raw results from reruns, including the XML files output by maven) Output from running the FlakeFlagger replication study (non-isolated 10k reruns):flakeFlaggerReplResults.csv.gz (all test results summarized into a CSV), 10k_reruns_failures_by_test.csv.gz (CSV including just failures, including failure messages), flakeFlaggerRepl_raw_results.tgz (includes all raw results from reruns, including the XML files output by maven - this file is markedly larger than the 10k isolated reruns results because we ran *all* tests in this experiment, whereas the 10k isolated rerun experiment only re-ran the tests that were known to be flaky from the FlakeFlagger dataset). Output from running FlakeRake on each flaky test in the FlakeFlagger dataset: For bisection mode: results-bis.tgz For one-by-one mode: results-obo.tgz Scripts used to execute FlakeRake using an HPC cluster: execution-scripts.tgz Scripts used to execute rerun experiments using an HPC cluster: flakeFlaggerReplScripts.tgz Scripts used to parse the "raw" maven test result XML files in this artifact into the CSV files contained in this artifact: parseSurefireXMLs.tgz  Output from running FlakeRake in “reproduction” mode, attempting to reproduce each of the failures that matched the FlakeFlagger dataset (collected for bisection mode only): results-repro-bis.tgz Analysis of timing-dependent API calls in the failure inducing configurations that matched FlakeFlagger failures: bis-sleepyline.cause-to-matched-fail-configs-found.csv 

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3. Sequential change‐point detection: Computation versus statistical performance

   https://doi.org/10.1002/wics.1628  

   Wang, Haoyun; Xie, Yao (July 2023, WIREs Computational Statistics)

   Abstract Change‐point detection studies the problem of detecting the changes in the underlying distribution of the data stream as soon as possible after the change happens. Modern large‐scale, high‐dimensional, and complex streaming data call for computationally (memory) efficient sequential change‐point detection algorithms that are also statistically powerful. This gives rise to a computation versus statistical power trade‐off, an aspect less emphasized in the past in classic literature. This tutorial takes this new perspective and reviews several sequential change‐point detection procedures, ranging from classic sequential change‐point detection algorithms to more recent non‐parametric procedures that consider computation, memory efficiency, and model robustness in the algorithm design. Our survey also contains classic performance analysis, which provides useful techniques for analyzing new procedures. This article is categorized under:Statistical Models > Time Series ModelsAlgorithms and Computational Methods > AlgorithmsData: Types and Structure > Time Series, Stochastic Processes, and Functional Data 

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4. Syllable as a unit of information transfer in linguistic communication: The entropy syllable parsing model

   https://doi.org/10.1002/wcs.1518  

   Malaia, Evie A.; Wilbur, Ronnie B. (September 2019, WIREs Cognitive Science)

   Abstract To understand human language—both spoken and signed—the listener or viewer has to parse the continuous external signal into components. The question of what those components are (e.g., phrases, words, sounds, phonemes?) has been a subject of long‐standing debate. We re‐frame this question to ask: What properties of the incoming visual or auditory signal are indispensable to eliciting language comprehension? In this review, we assess the phenomenon of language parsing from modality‐independent viewpoint. We show that the interplay between dynamic changes in the entropy of the signal and between neural entrainment to the signal at syllable level (4–5 Hz range) is causally related to language comprehension in both speech and sign language. This modality‐independent Entropy Syllable Parsing model for the linguistic signal offers insight into the mechanisms of language processing, suggesting common neurocomputational bases for syllables in speech and sign language. This article is categorized under:Linguistics > Linguistic TheoryLinguistics > Language in Mind and BrainLinguistics > Computational Models of LanguagePsychology > Language 

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5. Excitonic effects in the optical absorption of gapless semiconductor α -tin near the direct bandgap

   https://doi.org/10.1116/6.0003278  

   Zollner, Stefan (March 2024, Journal of Vacuum Science & Technology B)

   Most cubic semiconductors have threefold degenerate p-bonding valence bands and nondegenerate s-antibonding conduction bands. This allows strong interband transitions from the valence to the conduction bands. On the other hand, intervalence band transitions within p-bonding orbitals in conventional p-type semiconductors are forbidden at k=0 and, therefore, weak, but observable. In gapless semiconductors, however, the s-antibonding band moves down between the split-off hole band and the valence band maximum due to the Darwin shift. This band arrangement makes them three-dimensional topological insulators. It also allows strong interband transitions from the s-antibonding valence band to the p-bonding bands, which have been observed in α-tin with Fourier-transform infrared spectroscopic ellipsometry [Carrasco et al., Appl. Phys. Lett. 113, 232104 (2018)]. This manuscript presents a theoretical description of such transitions applicable to many gapless semiconductors. This model is based on k→⋅p→ theory, degenerate carrier statistics, the excitonic Sommerfeld enhancement, and screening of the transitions by many-body effects. The impact of nonparabolic bands is approximated within Kane’s 8×8k→⋅p→-model by adjustments of the effective masses. This achieves agreement with experiments. 

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