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Corea, Carl · Wessling, Neal

2.0 MB

Gow, David

65 KB

Raw MEG/EEG data, defaced T1-weighted structural MRIs, behavioral data, processing/support vector machine (SVM) code

Hersam, Mark · Sangwan, Vinod · Trivedi, Amit · et al.

49 KB

Artificial intelligence (AI) algorithms are currently executed using silicon-based hardware, resulting in excessively high energy demand for data centers. Edge computing AI for healthcare, robotics, and autonomous vehicles presents even stricter constraints on power and latency, which are currently unmet by incumbent computing architectures. Efficient computation can be derived from the key properties of biological neurons, including memory-logic colocation, asynchronous parallelism, and spike-triggered computation. Here, we draw inspiration from the biological cerebellum to demonstrate an asymmetric-contact-gated MoS 2 memtransistor that exhibits bias-polarity-dependent excitatory/inhibitory short-term plasticity. A memtransistor-based neural network realizes a changing interplay of excitatory/inhibitory responses, emulating the emergent synaptic differentiation of the cerebellum, enabling rapid identification of novel events. When applied to electrocardiogram data, arrhythmias are detected on the time scale of a single heartbeat with 10,000-fold fewer operations than existing silicon-based approaches. In this manner, cerebellum-inspired neuromorphic hardware provides a pathway to low-computation, high-speed novelty detection for edge intelligence.

Liu, Yanlan · Kumar, Mukesh · Bisht, Gautam

8.0 MB

Data archive supporting the manuscript " Multilayer canopy model outperforms big-leaf model for evapotranspiration predictions under high water and heat stress conditions " (Authors: Raghav, Liu, Kumar, Bisht) This archive provides, for each of 28 eddy-covariance sites, the hourly time series of modeled and observed evapotranspiration (ET) together with the meteorological drivers used to force the models. ET is reported as latent heat flux (LE), in W m⁻² , the native model and tower-measurement unit (to convert to a water flux, divide by the latent heat of vaporization &lambda; ≈ 2.45 × 10⁶ J kg⁻&sup1;: 1 W m⁻² ≈ 0.00147 mm h⁻&sup1;). The two model configurations are run from the same CLM-ml code and differ only in the number of within-canopy layers; in this archive both configurations use identical below-ground (root) and soil parameters , so that differences reflect the above-ground canopy representation alone. Contents File Description <SITE>_ET_hourly.csv` Per-site hourly time series (28 files; columns below). all_sites_ET_hourly.csv All 28 sites concatenated (same columns, plus `Site`). sites_metadata.csv Site list with latitude, longitude, number of hours, and date range. model_forcing_netcdf/<SITE>_forcing.nc Complete model forcing for each site (all driver variables and the gap-filled/closure-corrected flux products; see below) Sites (28) CA-Cbo, CA-Gro, CA-TP3, CA-TPD, CH-Lae, CZ-Lnz, CZ-RAJ, CZ-Stn, DE-Hai, FR-Bil, FR-Hes, IT-Cp2, IT-SR2, US-Bar, US-Me2, US-Me6, US-NC1, US-NR1, US-Oho, US-UMB, US-UMd, US-xAB, US-xBR, US-xDL, US-xHA, US-xJE, US-xTA, US-xTR. Coordinates and record lengths are in sites_metadata.csv . Columns in <SITE>_ET_hourly.csv Column Definition Units TIMESTAMP Time at the start of the hour, as provided in the model forcing (site local-standard-time convention) YYYY-MM-DD HH:MM:SS Site Site identifier - ET_obs_LE_gapfilled_Wm2 Observed latent heat flux, gap-filled by the marginal-distribution-sampling (MDS) method (FLUXNET/ONEFlux `LE_F_MDS`). W m⁻² ET_obs_LE_corrected_Wm2 Observed latent heat flux after energy-balance-closure correction (Bowen-ratio-preserving). This is the target used to evaluate the models. W m⁻² ET_MLCAN_Wm2 Modeled latent heat flux from the multilayer canopy (MLCAN) configuration. W m⁻² ET_1L_Wm2 Modeled latent heat flux from the single-layer / big-leaf (1L) configuration. W m⁻² H_obs_gapfilled_Wm2 Observed sensible heat flux, MDS gap-filled (`H_F_MDS`). W m⁻² H_obs_corrected_Wm2 Observed sensible heat flux after energy-balance-closure correction. W m⁻² SW_IN_Wm2 Incoming shortwave radiation (model driver, `FSDS`). W m⁻² TA_degC Air temperature (model driver, `TBOT`, converted from K). °C VPD_kPa Vapor pressure deficit, computed from observed relative humidity and air temperature. kPa SWC_m3m3 Volumetric soil water content (same across all soil layers). m³ m⁻³ LAI_m2m2 Effective leaf area index used to drive the models (`ELAI`). m² m⁻² Note: Missing values are written as empty fields. The fraction of finite, energy-balance-corrected observed ET per site is given in `sites_metadata.csv` (`ET_obs_corrected_pct_finite`). Model forcing NetCDF files (`model_forcing_netcdf/`) Each `<SITE>_forcing.nc` contains the complete set of driver variables used to run both configurations and the full observed-flux products, at the same temporal resolution. Key variables (units as stored): - Meteorology: `TBOT` (K), `RH` (%), `WIND` (m s⁻&sup1;), `FSDS` (incoming shortwave, W m⁻²), `FLDS` (incoming longwave, W m⁻²), `PSRF` (surface pressure, Pa), `PRECTmms` (precipitation, mm s⁻&sup1;), `CO2MF` (CO₂ mole fraction), `ZBOT` (reference height, m). - Vegetation / soil: `ELAI`, `ESAI` (effective leaf/stem area index), `SWC` (volumetric soil water), `H2OSOI`, `TSOI` (soil-profile moisture and temperature). - Observed fluxes: `LE_F_MDS`, `H_F_MDS` (MDS gap-filled), `LE_c`, `H_c` (energy-balance corrected), `GPP_DT`, `GPP_NT` (daytime/nighttime partitioned gross primary productivity). - Temporal coverage: each site's record covers 1 July - 31 December of each year (the study's analysis window). Methods (brief) Eddy-covariance processing . Half-hourly fluxes were computed with EddyPro and quality-controlled; latent and sensible heat were gap-filled using the MDS algorithm and then corrected for the surface-energy-balance closure gap (Bowen-ratio-preserving). Models. CLM-ml (Bonan et al., 2021; https://doi.org/10.1016/j.agrformet.2021.108435) was run in two configurations viz. multilayer (MLCAN) and single-layer (1L) that share identical code, leaf-level formulations, and soil/root parameters and differ only in the number of within-canopy layers. Each configuration was independently calibrated to the energy-balance-corrected observed ET. Provenance, license, and citation The **original** half-hourly eddy-covariance observations for each site are distributed by AmeriFlux, the ICOS Drought-2018 and Warm-Winter-2020 collections, and NEON; the per-site dataset DOIs are listed in the Supplementary Information file of the associated manuscript. The non-gap-filled (raw) observed LE can be obtained from those original datasets. The modeled ET, the processed (gap-filled and corrected) observed ET, and the assembled forcing in this archive are the data generated by this study . - License: Creative Commons Attribution 4.0 (CC-BY-4.0).

Gow, David

303 KB

Raw MEG/EEG data, T1-MRI data, experiment stimuli, behavioral data, trigger codes, and code used for decoding analysis

Richard Asiamah

23 KB

This repository contains both data for the input parameters of the neural network and the resulting approximations made by the neural network.

Octaviany Virgin · Jimmy Sapoetra

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This dataset contains raw data for a systematic literature review (SLR) on AI Interventions for Primary Dyslexic Learners Reading Comprehension. The dataset contains 64 data points retrieved from Scopus and Web of Science (WoS) using a search string. The data in this Excel sheet is intact and unfiltered. This dataset is uploaded as is to demonstrate transparency and to provide a track record of the initial stages of this literature data collection.

Untan, Sultan

19 KB

Raw qPCR quantification summaries (per-well Cq values and run metadata) for HOXA-10, HOXA-11, HOXA-13, and ACTB, exported directly from the BioRad CFX-96 / CFX Maestro software. These are the raw amplification run records (runs of 13-14 May 2024) underlying the relative-expression (RQ) values reported in the associated study. Each target is provided as a separate sheet (Well, Fluor, Target, Content, Sample, Cq), with a README sheet describing the contents. Standard-curve efficiency values were not generated as permanent records for these runs.

Gorski, Christopher

28 rows × 7 cols · 1.1 KB

6 numeric · 1 categorical

Agreement Between Large Language Models and Humans in Research Proposal Review - Data and Code This repository contains the data and code required to reproduce the analyses, statistical results, and figures presented in the associated manuscript. Files are organized by function and described below. All research proposals are anonymized and labeled with non-identifying identifiers (A, B, C, ...). Reviewer identities were never provided to the authors. To prevent inadvertent disclosure, all free-text review content from both human reviewers and large language models (LLMs) has been removed; only the numerical evaluation data required to reproduce the reported analyses are included. Data files Human_raw_scores.csv Individual numerical scores assigned by human reviewers, one row per reviewer × proposal × criterion. Used to compute panel-level summary statistics and the inter-reviewer reliability metrics reported in the manuscript. Human.csv Proposal-level human reference scores (one row per proposal) used as the human benchmark against which LLM scores and rankings are compared. LLM_data_combined_clean_filtered.csv All numerical scores generated by the evaluated LLMs. Preprocessed to remove evaluations in which a model failed to return one or more required numerical scores (see Data provenance and known limitations for counts). review_criteria.txt The evaluation criteria and rating scales. See the note under Data provenance regarding the 2023 vs. 2024 criterion naming. Data dictionary Human_raw_scores.csv Column Description Applicant Anonymized proposal identifier (A, B, C, ...). Cycle Review cycle the proposal belongs to (2023 or 2024). Label Scoring criterion: Intellectual merit , Potential for impact , Collaborative Potential , or Overall ranking . Reviewer_Seq Reviewer index within a proposal (1, 2, 3, ...). Identities are unknown; this index only links a single reviewer's ratings across criteria for the same proposal, in source-file order. It is not consistent across proposals (reviewer 1 for proposal A is not reviewer 1 for proposal B). Rating Numerical score. Criterion ratings use a 1-5 scale; Overall ranking uses a 1-3 scale (3 = fund, 2 = fund with modifications, 1 = do not fund). Human.csv Column Description Name Anonymized proposal identifier (matches Applicant above). Cycle Review cycle (2023 or 2024). IM , Impact , Collab , Overall Panel-mean scores for the four criteria. Score Weighted composite panel score, computed as 0.4·IM + 0.3·Impact + 0.3·Collab , matching the weighting applied to the LLM composite scores. LLM_data_combined_clean_filtered.csv Column Description Name Anonymized proposal identifier (matches Human.csv ). Type Input given to the model: Abstract or Full_Proposal . Model Model identifier. For models with controllable reasoning depth, the tier is appended as a suffix ( _low , _medium , _high ). The portion before the first underscore is the root model. Prompt Prompting strategy ( OneShot or CoT ). Seed Requested random seed. For models that did not support seed specification at the time of execution (the reasoning models listed in the Methods), the API ignored this value and it functions only as a replicate index; output is not reproducible from it for those models. Temp Sampling temperature (0.1, 0.5, 0.9). IM , Impact , Collab Criterion ratings (1-5 scale). Overall Overall recommendation (1-3 scale). See limitation note on out-of-scale values. Score Weighted composite, 0.4·IM + 0.3·Impact + 0.3·Collab . Category Reasoning/architecture category of the model. Year Evaluation wave in which the run was performed (proposals were re-evaluated as new model generations were released); this is not the proposal's submission cycle. Use Cycle in the human files for submission cycle. Data provenance and known limitations We document the following so that users can interpret the data accurately. Two review cycles, combined. The 28 proposals come from two internal seed grant cycles: 15 from 2023 and 13 from 2024 ( Cycle column). For 2024 proposals, proposal-level means in Human.csv are the official institute panel means; for 2023 proposals they are computed from the individual ratings in Human_raw_scores.csv . Reviewers per proposal. Panels ranged from 3 to 6 reviewers. Reviewer identities were never provided; the human inter-reviewer reliability is therefore estimated with a one-way random-effects model (ICC(1,1)), which is the appropriate model when each proposal is rated by a different, unidentified set of reviewers. Six 2024 reviews not present at the individual level. For six 2024 proposals (G, R, T, W, X, Z), one reviewer's scores were submitted without written comments and are not included in Human_raw_scores.csv . For these proposals, Human.csv carries the official institute panel means, so the panel mean in Human.csv and the mean recomputed from Human_raw_scores.csv differ slightly. The reproducibility check in 06_Human_data.ipynb confirms exact agreement for all proposals with complete individual records and reports the expected small differences for these six. Out-of-scale LLM Overall ratings. A small number of responses (≈1.2% of reviews, almost entirely from gpt-3.5-turbo) rated the overall recommendation on a 1-5 scale rather than the requested 1-3 scale, in a format the parser could not distinguish. The analysis code masks values outside the valid range before computing any Overall -based result; the composite Score does not use Overall and is unaffected. Excluded LLM evaluations. Evaluations in which a model failed to return one or more required numerical scores were removed prior to analysis. The file provided here is the post-exclusion (analyzed) dataset. Code notebooks Two groups of notebooks are provided: (i) the LLM evaluation pipeline and (ii) statistical analysis and figure generation. LLM evaluation pipeline (Notebooks 1-4) Documentation of the methodology used to generate the LLM evaluations. These use synthetic examples and contain no confidential data, API credentials, or real proposal text. 01_pipeline_overview.ipynb - architecture, configuration, criteria, output format, evaluation matrix. 02_prompting_strategies.ipynb - one-shot and chain-of-thought prompting; example selection; text vs. vision input. 03_response_parsing.ipynb - regex extraction of ratings and comments; error handling; decimal ratings. 04_example_evaluation.ipynb - end-to-end workflow on synthetic data. Statistical analysis and figures (Notebooks 5-6) 05_Data_Processing.ipynb - ANOVA and effect sizes; empirical absolute score differences; ICC(2,1) and Spearman correlations vs. the human panel; Monte Carlo comparisons; scatter, slope, and bias figures. 06_Human_data.ipynb - ICC(1,1)/ICC(1,k) for human reviewers with bootstrap confidence intervals; Monte Carlo single-reviewer vs. leave-one-out panel rank agreement; consistency check of Human.csv against Human_raw_scores.csv . Reproducibility Running 05_Data_Processing.ipynb and 06_Human_data.ipynb against the included data reproduces the statistical results and figures in the manuscript. Notebooks 1-4 document the evaluation pipeline using synthetic examples. Requirements pandas numpy scipy statsmodels scikit-learn matplotlib The LLM evaluation pipeline notebooks (1-4) additionally use the packages listed in requirements.txt . Citation If you use this data or code, please cite the associated manuscript and this archive: Gorski, C., Leo, N., Gayah V. Agreement Between Large Language Models and Humans in Research Proposal Review - Data and Scripts. 2026. Zenodo. https://doi.org/10.5281/zenodo.18187034 License Data are released under CC BY 4.0; code is released under the MIT License.

Ke, Zhang · HAN, YAOYAO · Yuan, Jin

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General Information This repository contains the comprehensive collection of empirical lacustrine time series, sedimentary core records, standardized data matrices, and execution scripts required to fully replicate the figures, network topologies, and statistical null models presented in the associated manuscript. I. Software and Environment Requirements Python (v3.8+): Required libraries include numpy , pandas , matplotlib , scipy , statsmodels , and pymannkendall . R (v4.2+): Required libraries include wsyn , igraph , zoo , parallel , pbapply , ggplot2 , and cowplot . Geospatial Platform: Esri ArcGIS Desktop (v10.8) or ArcGIS Pro (for cartographic rendering and vector layer manipulation). Computation Note: To mitigate boundary artifacts and edge effects inherent in chronological sliding-window operations, the final 12-24 data points of the generated time series are systematically excluded from the final trend evaluation. II. File Inventory and Component Descriptions 1. Empirical and Core Datasets ( .csv ) Global_Lake_Chlorophyll_a_Time_Series.csv Description: Long-term, multi-decadal monthly gridded chlorophyll- a concentration time series across global limnological cohorts, featuring unique lake identification codes as columns and sequential temporal intervals as rows. Global_Lake_Water_Color_Time_Series.csv Description: Normalized global lake water color time series quantified via the Forel-Ule Index (FUI) framework, structured identically to the chlorophyll dataset for multi-proxy comparison. Global_Lake_Sediment_Pigments.csv Description: Stratigraphic sedimentary core pigment records providing long-term retrospective evidence of historical limnological synchronization and baseline shifts. Figures_data.CSV Description: Consolidated and curated data matrices containing the exact values, coordinates, and regional groupings utilized to plot the core text figures. 2. Statistical Analysis and Mathematical Scripts ( .py & .R ) Pairwise correlation -based synchrony.py Description: Computes macro-scale spatial synchrony trends across lacustrine nodes using the standard pairwise correlation matrix stream following frequency-domain decomposition. Loreau &phi; Metric for lake synchrony.py Description: Execution script utilizing the classic Loreau-de Mazancourt &phi; metric to calculate multi-lake population-level synchrony across global and latitudinal cohorts. Sliding window sensitivity.py Description: Explores scale dependency and temporal robustness by executing the analytical data stream across varying sliding temporal windows (e.g., 2, 5, 8, 10, 15 time steps). Network and modularity analysis.R Description: Implements the wsyn continuous signed-power soft-thresholding paradigm and leverages igraph to partition similarity networks into topological communities, calculating decadal modularity . Permutation-based significance of synchrony trends.py Description: Performs Mann-Kendall trend tests on sliding-window synchrony series and runs empirical hypothesis testing to extract true directional shifts. Permutation-based significance of synchrony trends-Null_Model_Generator.py Description: Harnesses multi-core parallel processing to shuffle network weights 1,000 times, constructing empirical null distributions to validate the significance of observed network community dissolution. 3. Geospatial Visualization & Documentation ( .rar & .txt ) Figure 1.rar Description: Compressed archive containing all raw geospatial project databases, vector layer shapefiles ( .shp ), metadata tables, and cartographic layout definitions ( .mxd ) used to generate the global geographic distribution map of sample lakes (Figure 1). Compiled within Esri ArcGIS 10.8. Data_Sources_and_References.txt Description: A comprehensive standalone text file documenting the complete bibliographic literature sources, historical baselines, and corresponding DOI attributions compiled within the empirical datasets. III. Execution and Replication Workflow Data Cleaning & Detrending: Feed raw time series through Python/R scripts to execute STL harmonic regression models and filter low-frequency background signals. Synchrony Calculations: Execute the pairwise and Loreau metric scripts to plot continuous synchrony variations over time. Network Configurations: Run the R network script to output the high-resolution PCA community plots and decadal modularity comparisons. Significance Evaluation: Launch the null model generator to confirm that network configuration shifts significantly exceed random stochastic expectations ( P < 0.001 ). Spatial Reconstruction: Extract Figure 1.rar into your local GIS directory to access, modify, or re-export the multi-layered baseline global sampling maps.

Muanenda, Mitiku

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3 text · 3 categorical

# FEE Synonym Checklist v1.0 This dataset maps accepted plant names (WFO/POWO) to synonyms published in the *Flora of Ethiopia and Eritrea* (FEE, 1995-2010). **Key statistics:** - 602 synonym records - 455 unique accepted names - 83 plant families - Geographic coverage: Ethiopia and Eritrea **Data fields:** - `acceptedNameUsage` - Current accepted name (WFO/POWO) - `scientificName` - Synonym as published in FEE - `family` - Plant family - references(FEE-volumes) - Page numbers **Purpose:** Bridge historical FEE taxonomy with current global standards for herbarium digitization, data cleaning, and floristic research. **License:** CC BY 4.0 **Source:** Hedberg, I. et al. (eds.) 1995-2010. Flora of Ethiopia and Eritrea. Vols 1-7.

Zhao, Huang

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This record provides a governance-compatible reproducibility and transparency package accompanying the manuscript "Risk-adapted surveillance for lung and bone metastasis in hepatocellular carcinoma: international evaluation and prospective two-wave pathway implementation". The package includes fixed LM/BM threshold definitions, predictor and endpoint dictionaries, denominator maps, missing-data rules, pathway-assignment code, reviewer-oriented analysis-code skeletons, synthetic example data, table-generation workflows, software-environment files, validation scripts, model-card documentation, source-document materials, and a public-calculator manifest. No identifiable patient-level data are included. Controlled-access analytic extracts are governed by institutional approvals, data-use agreements, and confidentiality agreements.

Hemming, Sidney · Simões Pereira, Patric

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This dataset contains 40Ar/39Ar single-grain ages from two downcore sediment cores, PC493 and PS58/254, from the Amundsen Sea, used in support of the study "Reduced West Antarctic Ice Sheet size during prominent Quaternary interglacials constrained by iceberg-rafted debris provenance in the Amundsen Sea" . The data include biotite grains from PC493 and both biotite and hornblende grains from PS58/254. These age distributions are used to trace changes in sediment provenance linked to iceberg-rafted debris (IRD) delivery and variations in West Antarctic Ice Sheet behaviour during Quaternary interglacials. All grains were irradiated in 1.9 cm aluminium disks alongside Fish Canyon sanidine as a neutron flux monitor at the USGS reactor in Denver, Colorado. 40Ar/39Ar ages were obtained by single-step CO2 laser fusion at the AGES Laboratory (Lamont-Doherty Earth Observatory, USA) using a VG5400 mass spectrometer.

Ren, Junming

150 rows × 6 cols · 15 KB

4 numeric · 2 text

This dataset contains processed ERC-8004 agent records collected from Ethereum. It includes agent identity records, reputation feedback, agent wallet transactions, derived agent-level statistics. The dataset supports analysis of agent activity, reputation formation, feedback networks, and bulk-registered or inactive agents in the ERC-8004 early ecosystem. The data cover blocks 24,339,925 to 25,277,687, spanning the period from ERC-8004 deployment on Jan 29, 2026, to the end of the observation period on Jun 09, 2026.

Kim, Youngsam · burke, kieron · Sim, Eunji

65 MB

This record contains raw output files and Python scripts used to reproduce the figures and tables in the manuscript "Density-Corrected Density Functional Theory for Solids." Each numbered top-level directory corresponds to one dataset or analysis section. Detailed information on the directory structure, system labels, energy definitions, included methods, and special notes is provided in the README file inside each directory.

Garrett, Adair

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This study involved conducting over 40 interviews to explore impediments and enablers associated with rail transportation adaptation and resilience. In accordance with the study's IRB protocol, raw audio recordings and transcripts will not be made available. The primary products of this research include a finalized thematic codebook, a series of spreadsheets (crosstabulations), and general metadata for interviewees. Excel files include aggregated thematic summaries from the coding process, such as impediments and strategies categorized by agency type and region. Additionally, a thematic hierarchy chart is provided in PDF format to illustrate the relationship between research concepts. The semi-structured interview guide used in this research is also available in PDF format.

Clouâtre, Hugues

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Annotated corpus and classifier evaluation results for a benchmark comparing three classifier designs on their ability to distinguish reversible from irreversible agentic actions. Contains 60 synthetic scenarios independently annotated by two simulated SRE agents (Cohen's kappa 0.89 reversibility, 0.92 risk tier), classifier outputs from 540 verdict files (3 classifiers x 3 runs x 60 scenarios at temperature 0.3), aggregate metrics (halt rate, miss rate, false-positive rate, Fisher p, Holm-corrected p), and all scoring and figure-generation scripts. Classifier C (combined reversibility gate and multi-factor risk label) achieves zero misses on irreversible scenarios at a 71.0% false-positive rate; Fisher p=0.002 (Holm-corrected p=0.006) for the low-risk irreversible cell (exploratory at n=5). Pre-registered protocol locked before annotation; methodology document records the executed procedure.

Li, Jiaguang · Ganti, Vamsi · Liu, Jiawei

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The dataset comprises a comprehensive 15-year spatiotemporal record of the unvegetated Sulengguole River, integrating high-resolution satellite imagery, field surveys, and derived morphodynamic metrics to document a complete post-avulsion cascade. High-resolution optical imagery and in-situ photographs visually capture the multi-stage morphological evolution, detailing the 2019 annexational flow capture, subsequent rapid channel widening, and the critical emergence of alternate bars. These visual observations are directly substantiated by extensive quantitative spatial analyses, which track the downstream kinematic propagation of both the channel-widening waves and the critical half-width-to-depth ratio ( β ) instability fronts. Ultimately, this combination of planform imagery and extracted geometric data explicitly links the macroscopic avulsion-driven reorganization to the extreme, width-dependent acceleration of local meander migration.

Liu, Kejun

283 KB

Reproduction Code and Data for "Exceptional Points as Manifestations of Analyticity Breakdown in the 't Hooft Model" Author: Kejun Liu (Soochow University) Manuscript: K. Liu, arXiv:2606.10141 (2026) Paper DOI: https://doi.org/10.48550/arXiv.2606.10141 Archive DOI: [pending - assigned by Zenodo upon deposit] Funding: This work was supported by the National High-Level Overseas Talent Program (KS21400126), the Suzhou Talent project (ZXP2025057), the Jiangsu Distinguished Professorship Fund (SR21400225), and the Research Start-up Fund (NH21400525). The numerical calculations were supported by a project funded by the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions. This archive contains the numerical code, raw data, and analytical derivations that support the results reported in the manuscript. Contents ├── README.md ← this file ├── LICENSE ← MIT (code) + CC-BY 4.0 (data) ├── requirements.txt ← Python dependencies ├── code/ │ ├── thooft_prl_numerics.py ← core library: operator construction, HPC campaigns A-C │ ├── higher_order_ep.py ← higher-order EP probe (cascade diagnostics) │ ├── si_skin_demo.py ← Supplemental Fig. S1 (skin envelope = e^{αx}) │ ├── skin_profile.py ← skin decay-rate analysis (exp vs power-law) │ ├── make_fig1.py ← main-text Fig. 1 (FSS, EP, γ_c scaling) │ ├── definitive_dq_test_v3.py ← D-Q conjecture test (Supplement S6.3) │ └── run_array.slurm ← SLURM batch script for HPC campaigns ├── data/ │ ├── campaignA_*.json ← Campaign A: exploratory (γ, m) parameter survey (not reported in paper) │ ├── campaignB_*.json ← Campaign B: finite-size scaling │ ├── campaignC_*.json ← Campaign C: NHSE + quench dynamics │ ├── ep_*.json ← EP scan data (chiral and massive) │ ├── nhse_*.json ← NHSE scan data │ └── ep_scan_*.json ← fine-mesh EP spectrum └── derivations/ └── route_C_theta_term_analysis.md ← analytical notes on the cos &theta; tridiagonal structure Computation Narrative The numerical programme followed a reconnaissance-to-production workflow: theory defines the observable, a coarse survey locates the interesting region, and high-resolution production runs characterise it in detail. Step 1 - Reconnaissance (Campaign A). A coarse 2D survey of the (γ, m) parameter plane-60 γ-values × 20 mass values at L = 100-mapped the PT-symmetry-breaking phase boundary γ_c(m). This survey established three facts that guided all subsequent computation: The phase boundary γ_c(m) is monotonically increasing : quark mass stabilises the PT-unbroken phase, pushing the EP to larger deformation strength. The chiral-limit threshold γ_c(m=0) ≈ 7.97 g²N_c was confirmed to agree with the Jacobi continued-fraction prediction (§S2) within the coarse mesh resolution. The EP disappears from the physical γ > 0 axis for m ≳ 0.3 (at g²N_c = 1), setting the upper bound for the massive-quark analysis. Campaign A is exploratory : its coarse mesh (γ step 0.17, L = 100) and two-digit precision are insufficient for publication-grade results. Its role was to identify where to look , not to produce final numbers. Step 2 - Production (Campaigns B, C, HOEP). Guided by the reconnaissance map, production runs targeted specific points with high resolution: Campaign B performed finite-size scaling at L = 200-2000, scanning 250 γ-points in a narrow window centred on γ c for each (g²N_c, m) combination. This yielded the &nu; = 1/2 exponent (Fig. 1a, Table S1), the γ c scaling (Fig. 1c), and the massive-quark threshold trend (§S6.1). Campaign C characterised the non-reciprocal deformation (NHSE, Fig. 3, Table S2) and the real-time quench dynamics (Fig. 2b). HOEP probed the higher-order EP cascade (Fig. 1d). Step 3 - Falsification test (D-Q conjecture). Independent of the HPC campaigns, a self-contained high-precision calculation (mpmath, 40 decimal digits) tested the FLZ D-Q conjecture at the chiral point (§S6.3). This emerged from a dialogue with the integrability community and is included here for transparency. The reconnaissance data (Campaign A) is preserved in this archive even though it does not appear in the paper, because it documents the exploratory reasoning that led to the production parameter choices. Quick Start pip install -r requirements.txt # Run the demo (takes ~30 s, no HPC needed) python code/thooft_prl_numerics.py --demo # Regenerate Supplemental Fig. S1 python code/si_skin_demo.py # Regenerate main-text Fig. 1 (requires HPC data in results_v2/) python code/make_fig1.py Code-Manuscript Mapping Main-text results Manuscript result Code Key data files §3: EP as branch point of G(z;γ); γ_c = 7.966 g²N_c thooft_prl_numerics.py → campaign_b_fss campaignB_*.json , ep_*.json §3, Eq. (4): Jacobi J-fraction threshold (two-pole → depth-5 convergence) thooft_prl_numerics.py → campaign_b_collect Table S1 data in campaignB_*.json §3: &nu; = 1/2 finite-size scaling (N up to 1999) thooft_prl_numerics.py → campaign_b_fss campaignB_L*_g1.00_m0.000.json §4: propagator norm three-regime law (bounded / linear / exponential) thooft_prl_numerics.py → campaign_c_quench campaignC_quench_*.json §5: EP cascade γ c^(k) ≈ k γ c^(1) higher_order_ep.py ep_scan_*.json §5: NHSE from V_α = e^{αX} V e^{-αX}; skin rate &kappa; = α si_skin_demo.py , skin_profile.py nhse_*.json , campaignC_nhse_*.json Fig. 1: EP exponent FSS, fine-mesh spectrum, γ_c scaling make_fig1.py campaignB_*.json Supplemental Material SM section Code Data S1: functional-analytic setting - (analytical) derivations/route_C_theta_term_analysis.md S2: J-fraction derivation of γ_c thooft_prl_numerics.py → campaign_b_fss Table S1 in campaignB_*.json S3: Jordan secular law + Wannier-Stark mapping thooft_prl_numerics.py → campaign_c_quench campaignC_quench_*.json S4: imaginary-gauge similarity; Fig. S1 si_skin_demo.py - S5: extended connections (Yang-Lee, Roberge-Weiss) - (analytical) - S6.1: massive-quark basis bias thooft_prl_numerics.py → campaign_b_fss (m>0) campaignB_L*_g1.00.json (m=0.1) S6.3: D-Q conjecture test at chiral point definitive_dq_test_v3.py (self-contained, no external data) S7: numerical tables - Tables S1, S2 derived from campaignB_*.json , nhse_*.json HPC Reproduction The main production runs used SLURM array jobs. To reproduce: cd code/ python thooft_prl_numerics.py --make-jobs # generates job_array.json (~200 jobs) # Update --array range in run_array.slurm to match job count sbatch run_array.slurm # submit to SLURM cluster python thooft_prl_numerics.py --collect-fss # FSS extrapolation after all jobs finish HPC Environment The production results were obtained on the Soochow University high-performance computing cluster (Slurm-based). Each compute node hosts 2 × AMD EPYC 7763 (64 cores) with 256 GB RAM; the GPU node hosts 2 × AMD EPYC 9654 (96 cores) with 384 GB RAM. All jobs ran on CPU (NumPy/SciPy dense linear algebra, no GPU acceleration required). Total compute: ~42 CPU-hours across 200+ completed SLURM array tasks (1 CPU, 4 GB RAM each), verified from cluster sacct records: Campaign Jobs CPU-hours Purpose B (v1) 58 9.7 FSS of EP exponent B (v2, large N) 61 19.8 FSS to N = 1999, large-N refinement B (individual, N = 74-79) 6 8.7 Gap-fill at N = 1200-2000 C (NHSE + quench) 83 2.5 Skin effect and real-time evolution HOEP (cascade probe) 3 1.5 Higher-order EP cascade Total (paper results) 211 42.2 A (exploratory) 32 19.6 Parameter survey, not reported in paper The data/ directory contains representative results at L = 100 (demo scale). Full-resolution results (L up to 2000) were produced on the HPC cluster; the largest individual jobs (dense complex diagonalization at N = 1999) took ~1 hour each. D-Q Conjecture Test (Supplement S6.3) The script definitive_dq_test_v3.py is self-contained and reproduces the falsification of the FLZ D-Q relation at the chiral point (α = -1). python code/definitive_dq_test_v3.py Result: The conjectural D-Q relation predicts c₁⁰ - c₃⁰ = 4.029; two independent numerical methods give 7.592 (PV-integral difference) and 7.583 (contour integration), disagreeing by ~88%. The exact chiral-limit spectral sum G⁻⁽&sup1;⁾ = 2 log 2 ≈ 1.386 is not recovered. See SM S6.3 for full discussion. Software Requirements Python ≥ 3.9 NumPy ≥ 1.21 SciPy ≥ 1.7 Matplotlib ≥ 3.5 mpmath ≥ 1.2 (for D-Q conjecture test only) Tested on Linux x86_64 with Python 3.11, NumPy 1.26, SciPy 1.12. Citation If you use this code or data, please cite: @misc{Liu2026thooft, author = {Kejun Liu}, title = {Exceptional Points as Manifestations of Analyticity Breakdown in the 't Hooft Model}, year = {2026}, eprint = {2606.10141}, archivePrefix= {arXiv}, primaryClass = {quant-ph}, doi = {10.48550/arXiv.2606.10141}, url = {https://doi.org/10.48550/arXiv.2606.10141}, note = {Reproduction code and data: zenodo.org/record/XXXXXXX} } Contact Kejun Liu - kjliu@suda.edu.cn Soochow University, Suzhou, China

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Dati derivati di una rassegna sistematica (di tipo PRISMA, con codifica assistita da modelli linguistici di grandi dimensioni sotto supervisione umana) sulla ricerca storico-educativa relativa al libro di testo, condotta sull'intera produzione digitalizzata di quattordici riviste internazionali di settore (1961-2026, sei lingue). Il deposito documenta sia la selezione (828 articoli inclusi + 853 esclusi con motivo = 1.681 record recuperati) sia la classificazione tematica in 24 cluster ricondotti a 5 famiglie. Oltre a rendere verificabile la rassegna, il dataset si offre come mappa bibliografica e tematica riutilizzabile della ricerca sul libro di testo nelle riviste internazionali di settore, base per ulteriori analisi bibliometriche, comparative e storiografiche indipendenti. Include: indice del corpus incluso con codifica, indice degli esclusi con motivo, codebook della tassonomia, tabella di distribuzione, termini di ricerca. NON include gli abstract integrali e i PDF (copyright di terzi). Versione anonima per revisione a doppio cieco.