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The Caribbean & Mesoamerica Biogeochemical Isotope Overview (CAMBIO) is an archaeological data community designed to integrate published biogeochemical data from the Caribbean, Mesoamerica, and southern Central America to address questions about dynamic interactions among humans, animals, and the environment in the region over the past 10,000 years. Here we present the CAMBIO human dataset, which consists of more than 16,000 isotopic measurements from human skeletal tissue samples (δ¹³C, δ¹⁵N, δ³⁴S, δ¹⁸O,⁸⁷Sr/⁸⁶Sr,^206/204Pb,^207/204Pb,^208/204Pb,^207/206Pb) from 290 archaeological sites dating between 7000 BC to modern times. The open-access dataset also includes detailed chronological, contextual, and laboratory/sample preparation information for each measurement. The collated data are deposited on the open-access CAMBIO data community via the Pandora Initiative data platform (https://pandoradata.earth/organization/cambio).

 

The development and training of deep learning models have become increasingly costly and complex. Consequently, software engineers are adopting pre-trained models (PTMs) for their downstream applications. The dynamics of the PTM supply chain remain largely unexplored, signaling a clear need for structured datasets that document not only the metadata but also the subsequent applications of these models. Without such data, the MSR community cannot comprehensively understand the impact of PTM adoption and reuse. This paper presents the PeaTMOSS dataset, which comprises metadata for 281,638 PTMs and detailed snapshots for all PTMs with over 50 monthly downloads (14,296 PTMs), along with 28,575 open-source software repositories from GitHub that utilize these models. Additionally, the dataset includes 44,337 mappings from 15,129 downstream GitHub repositories to the 2,530 PTMs they use. To enhance the dataset’s comprehensiveness, we developed prompts for a large language model to automatically extract model metadata, including the model’s training datasets, parameters, and evaluation metrics. Our analysis of this dataset provides the first summary statistics for the PTM supply chain, showing the trend of PTM development and common shortcomings of PTM package documentation. Our example application reveals inconsistencies in software licenses across PTMs and their dependent projects. PeaTMOSS lays the foundation for future research, offering rich opportunities to investigate the PTM supply chain. We outline mining opportunities on PTMs, their downstream usage, and cross-cutting questions. Our artifact is available at https://github.com/PurdueDualityLab/PeaTMOSS-Artifact. Our dataset is available at https://transfer.rcac.purdue.edu/file-manager?origin_id=ff978999-16c2-4b50-ac7a-947ffdc3eb1d&origin_path=%2F. 

The development and training of deep learning models have become increasingly costly and complex. Consequently, software engineers are adopting pre-trained models (PTMs) for their downstream applications. The dynamics of the PTM supply chain remain largely unexplored, signaling a clear need for structured datasets that document not only the metadata but also the subsequent applications of these models. Without such data, the MSR community cannot comprehensively understand the impact of PTM adoption and reuse.This paper presents the PeaTMOSS dataset, which comprises metadata for 281,638 PTMs and detailed snapshots for all PTMs with over 50 monthly downloads (14,296 PTMs), along with 28,575 open-source software repositories from GitHub that utilize these models. Additionally, the dataset includes 44,337 mappings from 15,129 downstream GitHub repositories to the 2,530 PTMs they use. To enhance the dataset’s comprehensiveness, we developed prompts for a large language model to automatically extract model metadata, including the model’s training datasets, parameters, and evaluation metrics. Our analysis of this dataset provides the first summary statistics for the PTM supply chain, showing the trend of PTM development and common shortcomings of PTM package documentation. Our example application reveals inconsistencies in software licenses across PTMs and their dependent projects. PeaTMOSS lays the foundation for future research, offering rich opportunities to investigate the PTM supply chain. We outline mining opportunities on PTMs, their downstream usage, and cross-cutting questions.Our artifact is available at https://github.com/PurdueDualityLab/PeaTMOSS-Artifact. Our dataset is available at https://transfer.rcac.purdue.edu/file-manager?origin_id=ff978999-16c2-4b50-ac7a-947ffdc3eb1d&origin_path=%2F. 

Abstract Visualizing atomic-orbital degrees of freedom is a frontier challenge in scanned microscopy. Some types of orbital order are virtually imperceptible to normal scattering techniques because they do not reduce the overall crystal lattice symmetry. A good example is d xz / d yz (π,π) orbital order in tetragonal lattices. For enhanced detectability, here we consider the quasiparticle scattering interference (QPI) signature of such (π,π) orbital order in both normal and superconducting phases. The theory reveals that sublattice-specific QPI signatures generated by the orbital order should emerge strongly in the superconducting phase. Sublattice-resolved QPI visualization in superconducting CeCoIn 5 then reveals two orthogonal QPI patterns at lattice-substitutional impurity atoms. We analyze the energy dependence of these two orthogonal QPI patterns and find the intensity peaked near E  = 0, as predicted when such (π,π) orbital order is intertwined with d -wave superconductivity. Sublattice-resolved superconductive QPI techniques thus represent a new approach for study of hidden orbital order. 

Episodic memories are records of personally experienced events, coded neurally via the hippocampus and sur- rounding medial temporal lobe cortex. Information about the neural signal corresponding to a memory representation can be measured in fMRI data when the pattern across voxels is examined. Prior studies have found that similarity in the voxel patterns across repetition of a to-be-remembered stimulus predicts later memory retrieval, but the results are inconsistent across studies. The current study investigates the possibility that cognitive goals (defined here via the task instructions given to participants) during encoding affect the voxel pattern that will later support memory retrieval, and therefore that neural representations cannot be interpreted based on the stimulus alone. The behavioral results showed that exposure to variable cognitive tasks across repetition of events benefited subsequent memory retrieval. Voxel patterns in the hippocampus indicated a significant interaction between cognitive tasks (variable vs. consistent) and memory (remembered vs. forgotten) such that reduced voxel pattern similarity for repeated events with variable cognitive tasks, but not consistent cognitive tasks, sup- ported later memory success. There was no significant interaction in neural pattern similarity between cognitive tasks and memory success in medial temporal cortices or lateral occipital cortex. Instead, higher similarity in voxel patterns in right medial temporal cortices was associated with later memory retrieval, regardless of cognitive task. In conclusion, we found that the relationship between pattern similarity across repeated encoding and memory success in the hippocampus (but not medial temporal lobe cortex) changes when the cognitive task during encoding does or does not vary across repetitions of the event. 

Scholarship focused on Black male students in school counseling has been intermittent despite being well documented in the larger field of education and other disciplines. In this article, we conducted a systematic review of the school counseling literature that focused on Black male students. We used critical race theory (CRT) to examine the programs and interventions that have been published with Black male participants in school settings within the school counseling literature and examined the role that school counselors took when supporting Black male students’ academic, social emotional, college and career identity development. We re-conceptualize the Achieving Success Everyday (ASE) group model (Steen et al., 2014) and call for others to use the ASE group model to combat racism and foster Black excellence. 

Regular expressions are used for diverse purposes, including input validation and firewalls. Unfortunately, they can also lead to a security vulnerability called ReDoS (Regular Expression Denial of Service), caused by a super-linear worst-case execution time during regex matching. Due to the severity and prevalence of ReDoS, past work proposed automatic tools to detect and fix regexes. Although these tools were evaluated in automatic experiments, their usability has not yet been studied; usability has not been a focus of prior work. Our insight is that the usability of existing tools to detect and fix regexes will improve if we complement them with anti-patterns and fix strategies of vulnerable regexes. We developed novel anti-patterns for vulnerable regexes, and a collection of fix strategies to fix them. We derived our anti-patterns and fix strategies from a novel theory of regex infinite ambiguity — a necessary condition for regexes vulnerable to ReDoS. We proved the soundness and completeness of our theory. We evaluated the effectiveness of our anti-patterns, both in an automatic experiment and when applied manually. Then, we evaluated how much our anti-patterns and fix strategies improve developers’ understanding of the outcome of detection and fixing tools. Our evaluation found that our anti-patterns were effective over a large dataset of regexes (N=209,188): 100% precision and 99% recall, improving the state of the art 50% precision and 87% recall. Our anti-patterns were also more effective than the state of the art when applied manually (N=20): 100% developers applied them effectively vs. 50% for the state of the art. Finally, our anti-patterns and fix strategies increased developers’ understanding using automatic tools (N=9): from median “Very weakly” to median “Strongly” when detecting vulnerabilities, and from median “Very weakly” to median “Very strongly” when fixing them. 

We use the Very Energetic Radiation Imaging telescope Array System (VERITAS) imaging air Cherenkov telescope array to obtain the first measured angular diameter ofβUMa at visual wavelengths using stellar intensity interferometry (SII) and independently constrain the limb-darkened angular diameter. The age of the Ursa Major moving group has been assessed from the ages of its members, including nuclear member Merak (βUMa), an A1-type subgiant, by comparing effective temperature and luminosity constraints to model stellar evolution tracks. Previous interferometric limb-darkened angular-diameter measurements ofβUMa in the near-infrared (Center for High Angular Resolution Astronomy (CHARA) Array, 1.149 ± 0.014 mas) and mid-infrared (Keck Nuller, 1.08 ± 0.07 mas), together with the measured parallax and bolometric flux, have constrained the effective temperature. This paper presents current VERITAS-SII observation and analysis procedures to derive squared visibilities from correlation functions. We fit the resulting squared visibilities to find a limb-darkened angular diameter of 1.07 ± 0.04 (stat) ± 0.05 (sys) mas, using synthetic visibilities from a stellar atmosphere model that provides a good match to the spectrum ofβUMa in the optical wave band. The VERITAS-SII limb-darkened angular diameter yields an effective temperature of 9700 ± 200 ± 200 K, consistent with ultraviolet spectrophotometry, and an age of 390 ± 29 ± 32 Myr, using MESA Isochrones and Stellar Tracks. This age is consistent with 408 ± 6 Myr from the CHARA Array angular diameter.