English Dataset and software · bilingual Bulgarian/English release The Cosmological Constructor is an audit-ready dataset and software release centred on append-only, typed scientific memory. The historical FITS files are never used as parents of the new chain and are never rewritten. Their contents are migrated into active semantic memory, while byte-exact snapshots remain available only as immutable provenance evidence. The source history contains 120 append-only FITS states with 274,180 table-row occurrences across 37 table schemas. The active migration stores 94,229 unique typed row values as standard SEMANTIC_OBJECTS. Identical typed values are stored once, but every original occurrence remains addressable by its exact [source sequence, HDU index, row index] position. ASCII, logical and integer cells are explicitly typed; floating-point cells retain their original IEEE-754 bit patterns. No averaging, sampling, binning or numerical rounding is introduced by the migration. The canonical FITS memory consists of nine SHA-256-linked commits. Commits 000002 and 000006 preserve the 120 historical files as byte-exact provenance snapshots. Commits 000007 and 000008 contain the active typed-value migration. The complete typed memory contains 47 action records, 218 layered-memory records, 94,385 semantic objects, 39 conclusions, 102 evidence paths, four cycle summaries and 449 lineage edges. Integrity and reproducibility all 120 source files pass FITS CHECKSUM/DATASUM and source-chain validation; all 274,180 source occurrences match the active typed representation; typed-value verdict: ALL_TYPED_VALUES_AND_OCCURRENCES_EXACT; file-chain verdict: CHAIN_INTEGRITY_OK; JSONL-to-FITS verdict: FULL_JSONL_FITS_OBJECT_EQUIVALENCE_OK; repeating either migration is idempotent and creates no additional commit; the focused FITS, universal-ingest, constant-frame-rate A/V and variable-frame-rate A/V tests pass; all eight runtime modules complete without exceptions, and repeated runtime evidence is byte-identical. Scientific and epistemic scope This revision changes storage, migration, consistency and provenance enforcement; it does not change the physical model, scientific equations, declared thresholds or numerical-precision rules. The machine contract remains fail-closed and additive. Results produced in the supplied runtime are labelled SELF_VALIDATED, INTERNAL_SELF_VALIDATED or INTERNAL_HELDOUT_SELF_VALIDATED. No result is presented as externally VALIDATED; that status requires a genuinely independent source and execution. Package contents 01_CURRENT_SOURCE/ — executable source, migration tools and focused tests; 02_DATA_INPUT/ — frozen, explicitly identified runtime input; 03_MEMORY/ — action ledger, layered semantic memory, cycle history and the nine-commit typed FITS chain; 04_RUNTIME_EVIDENCE/ — deterministic reports and auxiliary ledgers; 09_PROVENANCE_EVIDENCE/ — integrity report and quarantined/superseded artifacts; the release manifest and SHA256SUMS.txt — canonical inventory, sizes and hashes. Verify the downloaded release with sha256sum -c SHA256SUMS.txt. Python requires NumPy, Astropy and mpmath; the audio/video focused tests additionally require FFmpeg. Source-specific provenance and licensing notes remain part of the release documentation. Български Данни и софтуер · двуезично издание на български и английски Космологичният конструктор е одитируемо издание на данни и софтуер, изградено около append-only типизирана научна памет. Историческите FITS файлове никога не са родители на новата верига и никога не се презаписват. Съдържанието им е мигрирано в активната семантична памет, а byte-exact snapshots са запазени единствено като неизменимо доказателство за произхода. Изходната история съдържа 120 append-only FITS състояния с 274 180 срещания на таблични редове в 37 таблични схеми. Активната миграция съхранява 94 229 уникални типизирани стойности на редове като стандартни SEMANTIC_OBJECTS. Еднаквите типизирани стойности се пазят веднъж, но всяко първоначално срещане остава адресируемо чрез точната позиция [пореден номер на източника, HDU индекс, индекс на реда]. ASCII, логическите и целочислените клетки са изрично типизирани; клетките с плаваща запетая пазят първоначалните си IEEE-754 битове. Миграцията не въвежда усредняване, sampling, binning или числово закръгляне. Каноничната FITS памет се състои от девет SHA-256-свързани commits. Commits 000002 и 000006 пазят 120-те исторически файла като byte-exact provenance snapshots. Commits 000007 и 000008 съдържат активната миграция на типизираните стойности. Пълната типизирана памет съдържа 47 action записа, 218 layered-memory записа, 94 385 семантични обекта, 39 заключения, 102 доказателствени пътя, четири cycle summaries и 449 lineage връзки. Цялост и възпроизводимост всичките 120 изходни файла преминават FITS CHECKSUM/DATASUM и проверката на изходната верига; всичките 274 180 изходни срещания съвпадат с активното типизирано представяне; присъда за типизираните стойности: ALL_TYPED_VALUES_AND_OCCURRENCES_EXACT; присъда за файловата верига: CHAIN_INTEGRITY_OK; присъда за JSONL-to-FITS: FULL_JSONL_FITS_OBJECT_EQUIVALENCE_OK; повторното изпълнение на всяка миграция е идемпотентно и не създава допълнителен commit; focused тестовете за FITS, universal ingest, constant-frame-rate A/V и variable-frame-rate A/V преминават; всичките осем runtime модула завършват без изключения, а повторните runtime evidence файлове са byte-identical. Научен и епистемичен обхват Тази ревизия променя съхранението, миграцията, консистентността и контрола на произхода; тя не променя физическия модел, научните уравнения, обявените прагове или правилата за числова точност. Машинният договор остава fail-closed и additive. Резултатите от приложения runtime са означени като SELF_VALIDATED, INTERNAL_SELF_VALIDATED или INTERNAL_HELDOUT_SELF_VALIDATED. Нито един резултат не е представен като външно VALIDATED; този статус изисква действително независим източник и независимо изпълнение. Съдържание на пакета 01_CURRENT_SOURCE/ — изпълним source, инструменти за миграция и focused тестове; 02_DATA_INPUT/ — замразен и еднозначно идентифициран runtime вход; 03_MEMORY/ — action ledger, layered semantic memory, cycle history и деветкомитова typed FITS верига; 04_RUNTIME_EVIDENCE/ — детерминистични отчети и помощни ledgers; 09_PROVENANCE_EVIDENCE/ — integrity report и карантинни/заменени артефакти; manifest файлът на изданието и SHA256SUMS.txt — каноничен опис, размери и хешове. Проверката на сваленото издание се изпълнява с sha256sum -c SHA256SUMS.txt. Python средата изисква NumPy, Astropy и mpmath; focused тестовете за аудио и видео изискват допълнително FFmpeg. Бележките за произхода и лицензите на отделните източници остават част от документацията на изданието.
Станислав Панайотов· Zenodo (CERN European Organi...· 0 citations
The trace-gamma distribution: a three-cumulant calibrated reference for quadratic-form tests, with Welch's F and Pearson's χ² as special cases William J. Dwyer, MD, MPH, FAAP — Department of Mathematics and Statistics, University of Massachusetts Lowell. ORCID 0009-0004-0855-7222. Concept DOI (always resolves to the latest version): 10.5281/zenodo.22060095. Published v1.0.13:10.5281/zenodo.22143061. What this is The reproducibility deposit for the trace-gamma paper. Many everyday tests are quadratic forms in approximately-normal estimates — Welch's heteroscedastic F, Pearson's χ² of independence, score and Wald statistics — and their exact null is a generalized chi-square (a weighted mix of χ²'s) that software almost always replaces with a one- or two-moment approximation. Those approximations miscalibrate exactly where it matters: few groups, unequal variances, sparse or skewed data. The paper introduces the trace-gamma distribution, a three-cumulant calibrated reference built from the quadratic form's own mean, variance, and third cumulant via the Lancaster cumulant-trace identity, and shows that Welch's F and Pearson's χ² fall out as special cases. It matches the exact Imhof (1961) inversion across a wide design grid, carries an estimable non-Gaussian cumulant correction, and — deployed as a test reference — is a deflation of the naive two-moment call (its rejection region is a strict subset, so a significant result can only be withdrawn, never manufactured; no reverse flip is possible). What the deposit contains Manuscript (author + anonymized; built .docx/.pdf), the novelty / prior-art companion, and the long-form derivations companion (D1–D8, proof-complete: the generalized-chi-square null, the Lancaster cumulant-trace identity, the trace-gamma parameterization, and the arguments behind Propositions 1–6). Reproducibility apparatus — the distribution object mseries_qgamma.py (TraceGamma, constructors from cumulants), the four cross-checks validate_qgamma.py, the base experiments qgamma_experiments.py (V1–V3), the estimable cumulant correction, the exact Imhof-inversion cross-check qgamma_imhof.py, the wider design grid (group size × imbalance × input shape), and the deployed stress grid — each with its locked CSV. Every reported number regenerates from these deterministically-seeded scripts. Figures — the accuracy panels, the cumulant-correction figure, the design-grid and stress-grid figures, and the Imhof cross-check. Interactive demonstrator honest_tracegamma.html — computes the two-cumulant-vs-trace-gamma verdict flip and its population incidence in the browser, reproduces the deposited Python, and carries the house flip-interpretation standard (deflation marker, "how to read a flip" beat, incidence panel, assertNoReverse). Deep-dive record and the submission apparatus. All evaluation is simulation-based with an exact-inversion cross-check. Code is released under the MIT License; text, figures, and data under CC BY 4.0. How to cite Please cite this deposit if you use the package or the method. Citing the concept DOI references the work in general and always resolves to the latest version; cite a specific version DOI to point at an exact snapshot. Dwyer, W. J. (2026). The trace-gamma distribution: a three-cumulant calibrated reference for quadratic-form tests — reproducibility deposit [Software]. Zenodo. https://doi.org/10.5281/zenodo.22060095 BibTeX: bibtex @software{dwyer_tracegamma_2026, author = {Dwyer, William J.}, title = {The trace-gamma distribution: a three-cumulant calibrated reference for quadratic-form tests --- reproducibility deposit}, year = {2026}, publisher = {Zenodo}, doi = {10.5281/zenodo.22060095}, url = {https://doi.org/10.5281/zenodo.22060095}, orcid = {0009-0004-0855-7222} } The DOI above is the concept DOI (resolves to the latest version); to cite a specific release use that version's DOI in place of it (e.g. 10.5281/zenodo.22143061 for v1.0.13). When the accompanying journal article appears, please cite it as the primary reference for the method and this deposit as the reproducibility archive. Version history v1.0.14 — ✅ 10.5281/zenodo.22187379 (2026-08-31) staged (pending upload): rendering-only refresh — the manuscript and Derivations Companion docx/PDF rebuilt through the current math-typography builder so nested-paren radicals draw as true Office-math radicals; the deposit now also carries the current shared house tooling (the bundled mseries_deposit.py includes the require_all deposit guard). No number, figure, table, or claim changed; the 71 in-text cross-reference links are intact. New version on concept 10.5281/zenodo.22060095 (tracegamma_reproducibility_v1.0.14.zip, md5 6f6a0e5c1e29d6cd3fa34008458666f0, 9,937,855 B, 59 files). v1.0.13 — ✅ 10.5281/zenodo.22143061 (2026-08-28). v1.0.12 — ✅ 10.5281/zenodo.22142142 (2026-08-28): two companion documents added — the novelty/prior-art review and the full-detail D1–D8 derivations ladder; §6 now points to the derivations companion; body unchanged. v1.0.11 — ✅ 10.5281/zenodo.22132594 (2026-08-27): cover letter brought to the house standard; first fully deterministic (byte-reproducible) deposit. v1.0.10 — ✅ 10.5281/zenodo.22125099 (2026-08-27): restored the in-text cross-reference hyperlinks in the Word/PDF. v1.0.0 — first deposit (10.5281/zenodo.22060096; concept 10.5281/zenodo.22060095): the trace-gamma distribution object, the Imhof cross-check, the design grid, and the honest_tracegamma.html demonstrator. Provenance: every number traces to a named, deterministically-seeded script and is cross-checked against the exact Imhof (1961) inversion; the demonstrator reproduces the deposited Python. Related records: the T_root methodology 10.5281/zenodo.21522471; the ANOVA sibling m01A 10.5281/zenodo.21908169.
William Dwyer· Zenodo (CERN European Organi...· 0 citations
A guaranteed-level repair for Welch's two-sample t, which silently over-rejects under skew, heteroscedasticity, and small samples William J. Dwyer, MD, MPH, FAAP — Department of Mathematics and Statistics, University of Massachusetts Lowell. ORCID 0009-0004-0855-7222. Concept DOI (always resolves to the latest version): 10.5281/zenodo.22036361. Published v1.4.3:10.5281/zenodo.22104110. What this is The reproducibility deposit for the m01t paper — the two-sample companion to the guaranteed-level ANOVA work (m01A/m01x). Welch's two-sample t is the field default for comparing two means under unequal variances, but at the corner where the data are skewed and heteroscedastic and the samples are small it runs liberal: it rejects the null more often than its nominal level allows. A scan of 515 real public-data comparisons finds that about one in three borderline-significant Welch results do not survive a level-guaranteed test — a concrete, real-data measure of the over-rejection. m01t specializes the companion procedure's Berger-Boos deflation to two groups: it deflates the known-variance quadratic by a closed-form radius built from each group's kurtosis-widened variance instability and refers the result to a chi-square. The test holds worst-case size at or below nominal exactly where Welch is liberal; its raw conservatism is the honest cost of the guarantee, and size-adjusted it matches Welch to within about 0.02 in power. The deposit ships the browser demonstrator honest_ttest.html, which computes Welch beside the guaranteed T_BB on your own data and prints the honest receipt — a deflation mechanism (the guaranteed rejection region is a strict subset of Welch's, so a significant call can only ever be withdrawn, never manufactured; no reverse flip is possible). What the deposit contains Manuscript (author + anonymized; built .docx/.pdf), the novelty / prior-art companion, and the derivationscompanion (the two-group Berger-Boos radius, the kurtosis-widening bound, the size proof, and the one-sided variant). Reproducibility apparatus — the simulation runners (the shared-grid scoreboard, the Fleishman skew×kurtosis decomposition grid, the competitor and gate-ablation runners) and the locked result CSVs. Every reported number regenerates from these deterministically-seeded scripts. Figures — the headline, the Welch over-rejection heat map, the validity-versus-power frontier, the method × stress-regime worst-size heat map, the per-method failure map, the kurtosis-axis curve, and the orthogonal-axis decomposition of Welch's realized size. Interactive demonstrator honest_ttest.html — reproduces the deposited Python exactly and carries the house flip-interpretation standard (deflation marker, "how to read a flip" beat, real-data incidence panel, and an assertNoReverseload guard). Deep-dive record (transfer/power-comparison, the 12-cell flip taxonomy with citations, the declarations and reference-block fixes, the decomposition study) and the submission apparatus. All evaluation is simulation-based. Code is released under the MIT License; text, figures, and data under CC BY 4.0. How to cite Please cite this deposit if you use the package or the method. Citing the concept DOI references the work in general and always resolves to the latest version; cite a specific version DOI to point at an exact snapshot. Dwyer, W. J. (2026). A guaranteed-level repair for Welch's two-sample t — reproducibility deposit[Software]. Zenodo. https://doi.org/10.5281/zenodo.22036361 BibTeX: bibtex @software{dwyer_m01t_2026, author = {Dwyer, William J.}, title = {A guaranteed-level repair for Welch's two-sample t --- reproducibility deposit}, year = {2026}, publisher = {Zenodo}, doi = {10.5281/zenodo.22036361}, url = {https://doi.org/10.5281/zenodo.22036361}, orcid = {0009-0004-0855-7222} } The DOI above is the concept DOI (resolves to the latest version); to cite a specific release use that version's DOI in place of it (e.g. 10.5281/zenodo.22104110 for v1.4.3). When the accompanying journal article appears, please cite it as the primary reference for the method and this deposit as the reproducibility archive. Version history v1.4.4 — ✅ 10.5281/zenodo.22187004 (2026-08-31) staged (pending upload): rendering-only refresh — the manuscript and Derivations docx/PDF rebuilt through the current math-typography builder so nested-paren radicals draw as true Office-math radicals; the deposit now also carries the current shared house tooling (the bundled mseries_deposit.py includes the require_all deposit guard). No number, figure, table, or claim changed. New version on concept 10.5281/zenodo.22036361 (m01t_reproducibility_v1.4.4.zip, md5 6e4e09e7dd1a5a747fa1493db94c922d, 3,561,269 B, 73 files). v1.4.3 — ✅ 10.5281/zenodo.22104110 (2026-08-24): arXiv source refreshed; declarations split into separate paragraphs; reference-block one-per-line fix. v1.4.2 — ✅ 10.5281/zenodo.22103410 · v1.4.1 — ✅ 10.5281/zenodo.22102558 · v1.4.0 (2026-08-23): new Figure 8, the orthogonal skew/kurtosis decomposition of Welch's realized size (Fleishman power method; kurtosis alone does not inflate the two-sided size, T_BB holds throughout). v1.3.0 — ✅ 10.5281/zenodo.22054950 (2026-08-22): deposit-scale shared-grid contest fold (40,000 reps, B=699); Table 6 becomes a five-method property scorecard; four contest figures added. v1.1.0 — ✅ 10.5281/zenodo.22036735 (2026-08-21): reviewer- hardening leads folded (held-out-family validity, Monte-Carlo CI on the worst-case size, studentized bootstrap-t and permutation benchmarks, the one-sided T_BB). v1.0.0 — first deposit (concept 10.5281/zenodo.22036361): the guaranteed-level two-group repair, the 515-comparison public-data flip scan, and the honest_ttest.html demonstrator. Provenance: every number traces to a named, deterministically-seeded runner; the demonstrator reproduces the deposited Python (parity self-checked on load). Related records: the one-way ANOVA sibling m01A 10.5281/zenodo.21908169; the moderated-Welch record m01x; the T_root methodology 10.5281/zenodo.21522471.
William Dwyer· Zenodo (CERN European Organi...· 0 citations
Reproducible Nextflow DSL2 pipeline accompanying the manuscript "Pangenomic Evaluation of Host Adaptation and Recombination-Mediated Emergence in Capripoxviruses". Full workflow from read QC through the polarised McDonald-Kreitman test 13 process modules, 5 subworkflows, conda and container profiles, SLURM config Version-pinned software environments matching the published analysis Original analysis scripts preserved verbatim under bin/legacy/ Continuous integration verifies the workflow wiring on every commit Validation targets for a full reproduction run are listed in docs/scripts.md.
Raana Tabashiri, Sajad Rashidi Monfared, Ali Akbar Masoudi· Zenodo (CERN European Organi...· 0 citations
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This software release provides the computational workflow developed for lithology-specific reconstruction and evaluation of thermal-conductivity response surfaces in mineral soils. Thermal conductivity is represented as a function of gravimetric water content on a dry-mass basis and measured wet bulk density. These variables are treated as mathematically coupled but nonredundant descriptors of achieved material state. Four lithological groups are analysed separately: sand, silt, clayey soil, and clay. The software implements Support Vector Regression, Generalised Additive Models, Random Forests, and Extreme Gradient Boosting, together with the controlled BASE, SMOOTH, INTERMEDIATE, and TUNED candidate configurations. Measurements mapped to the same material state are aggregated to a single state-level target. Predictive reliability is evaluated using five-fold cross-validation grouped by parent-material group. For TUNED candidates, hyperparameters are selected exclusively within each outer-training subset using three-fold grouped inner cross-validation with RMSE as the optimisation criterion. All fit-dependent preprocessing is restricted to the applicable training folds. After grouped predictive validation, the candidate models are refitted to all available material states of the corresponding lithology for response-surface reconstruction. These full-data fits are used only for surface geometry, model-agreement diagnostics, and response-profile extraction; their predictions do not enter the grouped predictive metrics. Surface interpretation is restricted to the lithology-specific convex hull of the observed material-state domain. Response profiles are extracted as mathematical cross-sections of the fitted surfaces at state-level quartiles. A central component of the release is the Response-Surface Consistency Index (RSCI), a post-hoc, within-lithology relative diagnostic of response-surface geometry. It combines boundedness, smoothness, local extrema, gradient variation, and gradient-sign fragmentation. RSCI complements grouped predictive metrics—R², RMSE, MAE, and MBE—but is not a physical-validity score, an uncertainty measure, or a tuning objective. Its derivative-based components are defined under the adopted physical-coordinate convention and should be interpreted together with predictive performance, observational support, cross-family agreement, and geological–geotechnical reasoning. The archive includes deterministic synthetic demonstration data, input-schema documentation, automated tests, model and tuning configurations, aggregate reference outputs, selected response grids, response-profile data, and frozen publication figures. The final aggregate checkpoints correspond to 2,242 source observations, 703 unique material states, and 235 parent-material groups. Restricted record-level experimental and harmonised datasets, institutional identifiers, parent-material mappings, fold assignments, record-level group-withheld predictions, detailed inner-fold records, and manuscript-trained estimators are not distributed. The archive therefore supports inspection, testing, and execution of the documented workflow on schema-compliant data, but not exact retraining of the reported models without authorised access to the original analysis dataset. Version: 1.1.0Software author: Mateusz ŻeruńCopyright holder: Polish Geological Institute – National Research Institute (PGI-NRI)Licence: MIT
Mateusz Żeruń· Zenodo (CERN European Organi...· 0 citations
Code and frozen result records for rotcert, an angle-aware distribution-free conformal certification method for oriented (rotated) object detection in aerial imagery. rotcert scores oriented detections with a seam-continuous, square-safe Gaussian–Wasserstein distance (GWD) nonconformity measure and issues two certificates: G1, a Mondrian per-class GWD-ball localization certificate with a finite-sample marginal-coverage guarantee, validated out-of-sample over R=20 scene splits; and G2, a Learn-then-Test Hoeffding–Bentkus certified bound on the rotated-IoU false-negative rate that refuses below its power floor rather than emitting a number it cannot support. GWD is consumed as a nonconformity score, not proposed as a new detector loss. The contribution is a reliability-certification protocol for oriented detection, not a new detector. Evidence is frozen and scoped: ten G1 cells across three aerial datasets (DIOR-R, DOTA-v1.0, HRSC2016-MS) and five detector architectures. All 20 DIOR-R classes certify for G1. DOTA overlapping crops are a diagnosed exchangeability stress case, running roughly 1–1.2 points below nominal. Archive contents. The rotcert package and its test suite, the detector and orchestration configs, the DOTA and DIOR-R certificate and inference result trees, the coverage-matched and per-class ablations, the HRSC seed cells, and the Holm-8 analysis records. DATA_MANIFEST.md describes the datasets and their provenance; SECURITY-PATHS.md documents path hygiene. Reassembling the archive. The tarball is deposited in ten 5 MB parts to work around an upload size limit. Concatenate them in order and extract: cat rotcert_zenodo_v0.2.0.tar.gz.part* > rotcert_zenodo_v0.2.0.tar.gz sha256sum rotcert_zenodo_v0.2.0.tar.gz # expected: 7f361f415422bcb892bee5543a752b8aa6abfde335f556aa60d6d8568b629d7c tar -xzf rotcert_zenodo_v0.2.0.tar.gz Changes since 0.1.0. Internal planning and review documents, editorial process notes, and manuscript sources have been removed from the archive; it now contains only the software snapshot and the frozen result records that the manuscript's provenance comments cite. Workstation absolute paths in result provenance fields were replaced with a placeholder, and CITATION.cff is now included. No scientific result record was altered.
Zeyu Fu, Xin Pu, Kuan Yu et al.· Zenodo (CERN European Organi...· 0 citations
AlphaFold Y-Shape Studio 1.4.0 is a desktop application for screening AlphaFold 3 IgG1 antibody models and inspecting their structures with Matplotlib. It assigns chains and domains, measures hinge and Fab/Fc geometry, reports candidate disulfide bonds, and provides an interactive three-dimensional preview. Analysis runs locally. Changes in version 1.4.0 Accepts arbitrary, case-insensitive CIF filenames. Optional seed- and sample- metadata may be read from filenames or enclosing folders. Models are processed in numeric seed/sample order, and files without numbered metadata follow the numbered models. Reads the first coordinate model directly from mmCIF without an intermediate PDB file. Multi-character author chain IDs, insertion codes, and mmCIF coordinate precision are retained by the analysis reader. Reopens completed and partial runs in read-only mode. Version 1.4 previews are embedded in run_summary.json and remain available if the original CIF files are moved; version 1.3 tables also reopen, with previews rebuilt when their original CIF files remain accessible. The screening criteria and thresholds are unchanged from version 1.3.0. Downloads Windows: AlphaFold_Y_Shape_Studio_Windows_x86_64_v1.4.0.zip contains the ready-to-run executable and bundled runtime. Extract the entire ZIP, keep the _internal folder beside the executable, and double-click AlphaFold_Y_Shape_Studio.exe. Python is not required. The executable is unsigned; do not disable operating-system security protections. Linux and source: AlphaFold_Y_Shape_Studio_Source_v1.4.0.tar.gz contains the source, pinned requirements, setup and launch scripts, regression tests, build scripts, licenses, and sample. Python 3.10 with Tk, venv support, and a desktop display is recommended. Run ./scripts/setup_ui_env.sh once and then ./run_ui_from_source.sh. Later launches need only the second command. The first setup requires internet access. Both archives include the complete 15-model sample dataset with its AF3 JSON files, dependency notices, and the same README.md. The general README.md is also supplied separately. Validation and limitations Version 1.4.0 passed 86 regression tests on each tested platform: Windows 11 x64 with Python 3.10.4 and Ubuntu 22.04.2 under WSL2/WSLg with Python 3.10.12, using NumPy 2.2.6, Biopython 1.88, and Matplotlib 3.10.9. The bundled Windows executable was tested with Python absent from PATH. Default and AF3 JSON sample runs each analyzed all 15 structures with 3 passing and 0 failed inputs. Testing also covered numeric seed/sample ordering, arbitrary Unicode filenames, direct mmCIF reading, saved-run reopening, invalid inputs, cancellation, output preservation, UI interaction, and image export. This software is a geometric screening heuristic, not experimental validation of structure, function, or biological activity. Additional profiles are retained, but execution on the antibody sample does not establish their scientific classification accuracy. Other operating systems, Linux distributions, Python versions, and hardware configurations have not been exhaustively tested. No standalone Linux binary or tested container image is supplied.
Hongyun Zhao· Zenodo (CERN European Organi...· 0 citations
This v1.5 release accompanies the manuscript by Gerhard Rambold, “Full-sky CMB temperature topology as evidence for large-scale differential rotation in the early universe”. The analysis applies one frozen multiscale reconstructibility operator (AC*) separately to the Planck PR3 SMICA, NILC, Commander, and SEVEM temperature reconstructions. The protected analysis chain runs from the support-indexed AC* field through high-response morphology, reciprocal high-resolution lineages, a reconstructed spherical tangent field and blind topology to Focus A, its 3D-compatible persistence, differential-rotation profile, transverse physical realization, and a common regular-vector recombination state. A pre-specified sign-sensitive polarization operator then tests the corresponding second-channel relation. The opposite-sign relation is reproduced across all four temperature-derived physical source realizations and both Planck half missions. The four component-separation products are correlated reconstructions of the same CMB sky and are therefore used for cross-reconstruction robustness, not as four independent detections. The release does not claim an absolute material speed, physical angular velocity, vorticity amplitude, unique volumetric flow, unique Bianchi type, cosmological centre, or unique ultimate cosmological generator. The record contains exactly four public objects: the frozen 28-page manuscript; a portable interactive scientific viewer; an authority-bound Python analysis/replay package with explicit four-map branches; and a licences/manifests/checksums archive containing frozen SHA-256 bindings, observational-input and mask bindings, provenance, and freeze receipts. The canonical AC* implementation and response field remain frozen and reproducibly bound by SHA-256. The public package contains no raw Planck or WMAP FITS files. Original third-party observational products must be obtained from their public source archives under the providers’ applicable terms. Author-created scientific text, figures, documentation, manifests and derived presentation material are released under CC BY 4.0 to the extent rights are held; original author-created software is released under the MIT License.
Gerhard Rambold· Zenodo (CERN European Organi...· 0 citations
ملخص: تهدف هذه الدراسة إلى تحليل ومقارنة المتغيرات البيوميكانيكية المرتبطة بأداء مهارة القلبة الهوائية الأمامية المستقيمة مع لفتين على بساط الحركات الأرضية بين لاعبي المنتخبين العراقي والإيراني في الجمناستك الفني، حيث اعتمد الباحث على المنهج الوصفي بأسلوبه المسحي، وتكونت العينة من (6) لاعبين من المنتخب العراقي ولاعب واحد من المنتخب الإيراني تم اختياره عمديًا وفق تصنيفات الاتحاد المختص، حيث تم استخدام تقنيات التحليل الحركي من خلال برمجيات (Kinovea) و(Tracker)، إضافة إلى جهاز قياس القوة (Dynafoot)، بهدف استخراج مجموعة من المتغيرات الكينماتيكية والكينيتكية مثل زوايا المفاصل، السرعة الزاوية، ارتفاع مركز الثقل، زمن الطيران، وقوى الدفع والاصطدام. وتمت معالجة البيانات إحصائيًا باستخدام الوسط الحسابي والانحراف المعياري واختبار (t-test) لبيان الفروق بين المجموعتين. وقد أظهرت النتائج وجود فروق واضحة في عدد من المتغيرات البيوميكانيكية بين لاعبي المنتخبين، خاصة في قوة الدفع، وزوايا المفاصل، وارتفاع مركز الثقل، حيث أظهر اللاعب الإيراني كفاءة أعلى في التحكم الحركي وتوليد القوة. في المقابل، لم تظهر بعض المتغيرات فروقًا ذات دلالة إحصائية، مما يعكس تقارب الأداء في بعض مراحل المهارة، كما استنتجت الدراسة أن المتغيرات البيوميكانيكية، ولا سيما قوة الدفع، وزوايا المفاصل، وارتفاع مركز الثقل، تعد محددات أساسية لجودة الأداء الفني في هذه المهارة. وأوصت بضرورة اعتماد التحليل البيوميكانيكي في تصميم البرامج التدريبية، والعمل على تصحيح الأخطاء الفنية وتحسين الكفاءة الحركية للاعبين. الكلمات المفتاحية: الجمناستك الفني؛ التحليل البيوميكانيكي؛ القلبة الهوائية الأمامية؛ المتغيرات الكينماتيكية؛ الأداء الحركي. Abstract: This study aimed to analyze and compare the biomechanical variables associated with the performance of the straight front somersault with two twists on the floor exercise between Iraqi and Iranian artistic gymnastics national team athletes. The descriptive survey method was adopted, and the sample consisted of six Iraqi national team gymnasts and one Iranian gymnast who was intentionally selected based on federation rankings. Motion analysis techniques were applied using Kinovea and Tracker software, in addition to the Dynafoot force measurement system, to extract key kinematic and kinetic variables, including joint angles, angular velocity, center of mass height, flight time, take-off force, and impact force. Data were statistically analyzed using mean, standard deviation, and independent t-test to identify differences between the two groups. The results indicated clear differences between the two teams in several biomechanical variables, particularly take-off force, joint angles, and center of mass height, with the Iranian gymnast demonstrating superior biomechanical efficiency in force production and movement control. However, some variables showed no statistically significant differences, indicating similarity in certain phases of execution. The study concluded that biomechanical variables—especially take-off force, joint angles, and center of mass height—are critical determinants of performance quality in this skill. It recommends integrating biomechanical analysis into training programs to enhance performance efficiency and correct technical errors. Keywords: Artistic gymnastics; biomechanical analysis; front somersault; kinematic variables; motor performance.
This record contains the software, manuscript source, tests, and publication-ready derived artifacts for the study “Strict Trace Replay and Local Diagnosis for Distributed Accelerator Slack Recovery”. The archive includes the current manuscript, Python source code, unit tests, and the CSV, JSON, LaTeX-table, and figure artifacts used in the paper. Raw Alibaba and Microsoft Philly traces are not redistributed here; they should be obtained from the public sources cited in the manuscript.
Dorn Yuriy· Zenodo (CERN European Organi...· 0 citations
This is a major release: three changes break a straight git pull upgrade from v1.1.0 and require the manual steps in the Upgrade Tutorial below: migrating dependency management from pip to uv, adding Node/npm as a required build-time dependency on the server, and moving scheduled maintenance jobs onto a persistent Dagster daemon process. Changelog Breaking Changes Dependency management: pip → uv. requirements.txt is gone; dependencies are now declared in pyproject.toml and installed via uv sync. Existing venvs built with pip need to be rebuilt. Node.js + npm now required on the server. The map dashboard's frontend is a Vite app that must be built during deploy (npm ci && npm run build). v1.1.0 had no frontend build step at all. Maintenance jobs now require a running dagster-daemon process. The old subprocess.Popen-based trigger from the admin is gone; jobs are submitted via dagster job launch and need the daemon (plus a DAGSTER_HOME) to actually run. Existing deployments need a new supervised process (systemd unit or equivalent) — see Upgrade Tutorial. Python 3.13+ now required. Permission system rewritten. Group/permission definitions changed; a one-time --reset of the predefined groups is recommended after upgrading (applied automatically otherwise via post_migrate, but --reset guarantees a clean rebuild — see Upgrade Tutorial). Security Fixes Fixed an IDOR in project re-parenting in the admin layer (58bb604). Fixed a bbox filter bypass and hardened import_landforms against malformed input (ec3eeee). FieldPhoto.file is no longer served as a raw media URL — downloads now go through a project-scoped, permission-checked view (5c7cb4d). Fixed a fallback in ReferenceAdmin.has_delete_permission that could grant unintended delete access (007758a). Fixed a 500 error path in RasterSceneAdmin for non-superusers that could leak state (fb74c61). Broader admin-layer security review: fixed several access-control issues found during an internal architecture/security audit (7a044a1, 84906f0). CI hardened with a full bandit, vulture, xenon, basedpyright and mypy sweep, plus pylint, to catch this class of issue earlier (d0959f6). New Features Raster data app: import, admin, and metadata recomputation for raster scenes; corpus_path/file precedence fixed during recompute (5f2b101). Geodata app: extended API and location import; GPS accuracy tracking added to location capture. Map: Vite-based rebuild; Google Satellite basemap. Analysis: cosmogenic nuclide dating model and admin support. Admin/UX: sample-admin now summarizes all sample-related measurements in one place; luminescence, grainsize, and location admin interfaces revised. Internal data_quality flag added to LuminescenceDating and RadiocarbonDating, with test coverage. Permission system rewrite, covering project-, group- and object-level access. deploy management command for scripted, repeatable production deploys. Dagster orchestration for scheduled database maintenance, including per-table DuckDB export failure tracking (MaintenanceRun.log now populated for daemon-triggered runs). Bug Fixes & Reliability Fixed inconsistent on_delete behavior across Sample-related foreign keys, preventing orphaned or unexpectedly cascaded records (fa3fb7c). Fixed Meta.ordering inheritance and duplicate ordering on M2M relations; fixed eager validator binding (47b4517). Fixed process/notes property casing mismatch in landform import (82aa5a2). Added error handling around three previously unguarded map data loaders (23a0d66). Smaller fixes across UI, dashboard, datetime handling, and admin resources (campaign, sample, researcher, manufacturer). Infrastructure & Developer Experience CI pipeline added for tests and linting, with a throwaway local_settings.py so Django settings actually load in CI (9a0db28). Pre-commit hardened: full basedpyright coverage across all 8 apps, plus bandit, vulture, xenon, pylint duplicate-code detection. Dead code and leftover Dagster boilerplate removed. Test coverage extended (analysis, field_data, geodata, laboratory, raster_data, orchestration), including direct reachability tests for permission fallbacks and coverage for CosmogenicNuclideDating. Routine dependency updates via Dependabot (Pillow, GitPython, and others). Documentation Sphinx-based documentation site added (docs/), with a GitHub Actions workflow that builds it on every push (publishing to GitHub Pages is not yet enabled). CONTRIBUTING.md, CODE_OF_CONDUCT.md, and a revised security policy/reporting process added. Pull request template added. README overhauled: deploy workflow, Vite frontend, Dagster daemon migration, and full test structure documented. Upgrade Tutorial Audience: operators upgrading an existing v1.1.0 (or earlier) CGDB deployment to v2.0.0 "Arica". Steps 1–3 are one-time migration steps specific to this release; step 4 onward is the normal deploy flow. Before you start: Take a full database backup yourself, independent of the automatic pre-migrate backup manage.py deploy takes — this upgrade touches more than a single migration. Schedule a maintenance window. Steps 1–3 involve service restarts and are not zero-downtime. Confirm shell access with the same privileges your normal deploy uses (the app-owning user for most steps, sudo only for manage.py deploy itself). 1. Install prerequisites on the server Python 3.13+ — confirm with python3 --version; upgrade the interpreter first if it's older. uv — install if not already present. Node.js + npm — required from this release on, as a one-shot build tool only (nothing Node-based runs persistently). Any reasonably current LTS Node works; there's no pinned minimum version. Confirm with node --version / npm --version. 2. Migrate dependency management from pip to uv Do this once, before the first uv sync on the server: # from the project root, with the OLD pip-based venv still in place deactivate 2>/dev/null || true rm -rf .venv # the old pip-managed venv — uv creates its own requirements.txt no longer exists in the new codebase; uv sync (run automatically by manage.py deploy, see step 4) reads pyproject.toml/uv.lock instead and creates a fresh .venv. If anything outside this repo activates the old venv by hard-coded path (a systemd unit, a cron job, a supervisor config), update those paths — uv sync still creates .venv in the same location, so most setups need no changes here, but double-check anything referencing .venv/bin/pip directly. 3. Migrate scheduled maintenance jobs to the Dagster daemon Skip this step if you never enabled the optional Dagster orchestration on this deployment. Older deployments triggered maintenance jobs via a detached subprocess.Popen from the admin, with no daemon and no dedicated Dagster run storage. This release requires a persistent dagster-daemon process instead: Deploy the new code first (step 4 below covers this) — uv sync pulls in dagster>=1.13.16, dagster-webserver>=1.13.11, dagster-postgres>=0.29.11. No Django migration is involved in this particular change; it's a config/behavior change, not a schema change. Make orchestration/dagster_home writable by the same user your web server process runs as (e.g. www-data on Debian/Ubuntu, apache on RHEL/CentOS — check whichever user your existing WSGIDaemonProcess, or equivalent, is configured with). Triggering a maintenance job from the admin runs dagster job launch synchronously in the web request (orchestration/admin.py, via subprocess.run) — not just the daemon. Both that in-request launch and the daemon itself need write access to DAGSTER_HOME (SQLite run storage creates a history/ subdirectory there on first use). Since the directory typically arrives owned by whoever ran git pull (your personal account) or root (from sudo manage.py deploy), fix ownership explicitly: sudo chown -R : /orchestration/dagster_home (Optional — only if you want PostgreSQL run storage instead of the SQLite default) create a dedicated Postgres database for Dagster's own run storage — reuse the same Postgres instance/host/credentials your app already runs on, just a separate database name (e.g. dagster): CREATE DATABASE dagster; GRANT ALL PRIVILEGES ON DATABASE dagster TO ; Then edit orchestration/dagster_home/dagster.yaml: comment out the sqlite storage block, uncomment the postgres block, and set: export DAGSTER_PG_USER=dagster export DAGSTER_PG_PASSWORD=... export DAGSTER_PG_HOST=localhost export DAGSTER_PG_DB=dagster The SQLite default needs none of this — just DAGSTER_HOME (next step). Add the daemon as its own supervised process. If served via Apache/mod_wsgi, add a new, separate systemd unit — Apache doesn't need to know about it. First check which user your existing WSGIDaemonProcess runs as (/etc/apache2/sites-available/*.conf, the user=/group= on that directive) so the daemon runs as the same user rather than a new one. Example /etc/systemd/system/cgdb-dagster-daemon.service: [Unit] Description=CGDB Dagster daemon After=network.target [Service] Type=simple User= Group= WorkingDirectory= Environment=DAGSTER_HOME= /orchestration/dagster_home # The four lines below are only needed if you switched dagster.yaml to # PostgreSQL storage (step 3 above) — omit them for the SQLite default. # Environment=DAGSTER_PG_USER=dagster # Environment=DAGSTER_PG_PASSWORD=... # Environment=DAGSTER_PG_HOST=localhost # Environment=DAGSTER_PG_DB=dagster ExecStart= /.venv/bin/dagster-daemon run Restart=on-failure RestartSec=5 [Install] WantedBy=multi-user.target sudo systemctl daemon-reload sudo systemctl enable --now cgdb-dagster-daemon 4. Deploy git pull --ff-only sudo python manage.py deploy This runs, in order: a clean-working-tree check, a pre-migrate database backup, uv sync (now against pyproject.toml, per step 2), np
Dennis Handy· Zenodo (CERN European Organi...· 0 citations
This dataset is the reproducibility and robustness evidence companion to the SCILLA v1.1.0 canonical research note (DOI: 10.5281/zenodo.22209746). It contains the synthetic evidence used to audit and reproduce the principal computational results of SCILLA, including matched run-level results, aggregate policy effects, paired-bootstrap confidence intervals, sign-flip tests, parameter sensitivity sweeps, prior-mismatch and other ablation experiments, independent trace-verifier results, canonical trace records, the frozen experimental protocol, assumptions, and seed registry. The evidence preserves both positive and negative results. In particular, the released experiments do not support universal adaptive superiority for raw discovery. The dataset instead supports more bounded findings concerning paired ascent/return reacquisition, the constructed priority-weighted Track Debt metric, and decision-weighted utility under the declared synthetic reference conditions. All performance data contained in this record are synthetic or recomputed from the released SCILLA research software. No physical SCILLA field measurement is included. The dataset does not establish operational detection range, field tracking performance, customer ROI, patent status, QPU execution, or quantum advantage. An independent trace verifier is included in the associated evidence architecture to recompute geometry, eligibility, scan constraints, observation identities, paired-state counts, and final metric identities without selecting the optimizer’s actions. This dataset supplements the canonical SCILLA v1.1.0 research publication: Michele Giletto, “SCILLA: Altitude-Coupled Transient Maritime Surface Observation — Geometry, Paired State Estimation, and Falsification of Adaptive Scan Allocation,” Giletto Systems Lab, v1.1.0, 2026. DOI: 10.5281/zenodo.22209746. The next validation gates are external historical sensor replay and a cooperative paired-observation field experiment using synchronized platform navigation and independent target ground truth.
Michele Giletto· Zenodo (CERN European Organi...· 2 citations
A USAF cadet and a Lincoln Laboratory researcher found AI chatbots can help nontechnical service members produce viable software applications for their unique problems.
