Our Work

This dataset contains the complete measured data and the full analysis code for the paper "Direction and scale in secondary-path mismatch: a measurable decomposition of the FxLMS update on real acoust…

This dataset contains the complete measured data and the full analysis code for the paper "Direction and scale in secondary-path mismatch: a measurable decomposition of the FxLMS update on real acoustic paths". It comprises (i) measured secondary-path impulse responses and recorded reference and disturbance waveforms from a physical active-noise-control rig, acquired under a nominal condition and four propagation conditions in two re-installation campaigns; (ii) the complete Python analysis pipeline, including descriptor computation, sweep and baseline experiments, spectral diagnostics and figure generation; and (iii) verify_numbers.py, which re-checks every numerical value quoted in the paper against the raw data (335 automated checks). Running "python anc_stage2/verify_numbers.py" from the repository root reproduces the full numerical audit. The archive is restricted during peer review and will be made openly accessible upon acceptance of the paper.  Version 2.0 is the structural revision following the final pre-submission review round: the title reads "measured acoustic paths"; the abstract, highlights and contributions are rewritten around the exact batch one-step descent threshold mu_crit ~ rho/(kappa*lambda_eff) and the positioning of the decomposition as attribution rather than replacement of classical phase diagnostics; the ranking-disagreement counts are reported with double denominators (18/40 cross-condition pairs, equivalently 18/24 among pairs admitting a positive budget; campaign 1: 20/40 = 20/24); the mismatch-sweep table moves to the supplementary material (with the growth-diagnostic column) and a classical per-condition summary table is added to the main text; standard deviations are unified to the sample convention (N-1); the supplementary tables label mu_crit as the algebraic value 2*rho/(n*lambda_eff*kappa). No new experiments; all measured data unchanged. The automated audit is extended to 456 checks. Version 2.1 removes the LaTeX manuscript sources from the archive; data, results and analysis code are unchanged, and the automated audit skips the manuscript-source checks automatically when the sources are absent.

OrthoLaneMark contains 1,391 annotated orthographic pavement intensity images and 1,391 paired range-image exports from six surveyed road sections. All images are provided in PNG format. Annotations i…

OrthoLaneMark contains 1,391 annotated orthographic pavement intensity images and 1,391 paired range-image exports from six surveyed road sections. All images are provided in PNG format. Annotations identify the presence and lane-facing inner boundaries of the left and right lane markings, supporting research on lane-marking detection and pavement-region delineation. The dataset includes fixed training, validation, and test splits of 835, 192, and 294 images, respectively. An additional 70 images excluded by spatial buffering are retained in the archive. Splits comprise spatially separated blocks within the same six road sections. Polynomial annotations, annotation points, benchmark-compatible labels, image manifests, checksums, and validation utilities are included. The associated benchmark uses intensity images only. Range exports are supplied as supplementary data for future exploration. This dataset accompanies “Presence-Aware Benchmarking of Lane-Marking Detection Methods for Pavement Distress Measurement,” under review at the ASCE Journal of Computing in Civil Engineering. Dataset materials are licensed under CC BY 4.0. Contact: Haolin Wang, hlwang98@gatech.edu; or Zhongyu Yang, zyang398@gatech.edu. No funding supported this release. The authors thank Raghu Veerareddy for assistance with data preparation.

Zippy Lankin entfaltet in dieser Lerneinheit eine rabbinische Lesart von Genesis 4.Ausgangspunkt ist Bereshit Rabbah 22,7: Kain und Abel teilen die Welt in Grundbesitzund bewegliches Eigentum. Gerade …

Zippy Lankin entfaltet in dieser Lerneinheit eine rabbinische Lesart von Genesis 4.Ausgangspunkt ist Bereshit Rabbah 22,7: Kain und Abel teilen die Welt in Grundbesitzund bewegliches Eigentum. Gerade weil die Welt „mehr als genug“ für beide ist,wird der Brudermord zur Erzählung über qin’ah, Neid, nicht über Knappheit. Vondort zieht Zippy einen Bogen durch die Urgeschichte: ta’avah, Begierde, bei Adamund Eva; qin’ah, Neid, bei Kain; kavod, Ehre, in der Generation des Enosch. PirkeiAvot 4,21 bündelt diese drei Kräfte in dem Satz: „Neid, Begierde und Ehre bringenden Menschen aus der Welt.“ Zippy Lankin develops a rabbinic reading of Genesis 4. Her point of departureis Bereshit Rabbah 22:7: Cain and Hevel divide the world into land and movableproperty. Precisely because the world is “more than enough” for both, the murderof the brother becomes a story about qin’ah, envy, rather than scarcity. From there,Zippy traces a line through primeval history: ta’avah, desire, with Adam and Eve;qin’ah, envy, with Cain; and kavod, honor, in the generation of Enosh. Pirkei Avot4:21 gathers these three forces in the saying: “Envy, lust, and honor remove a personfrom the world.”

The Amber plugin for AiiDA aims to enable the capture and sharing of the full provenance of data when parameterising and running molecular dynamics simulations. This plugin is being developed as part …

The Amber plugin for AiiDA aims to enable the capture and sharing of the full provenance of data when parameterising and running molecular dynamics simulations. This plugin is being developed as part of the Physical Sciences Data Infrastructure programme to improve the practices around data within the Physical Sciences remit area within the UK.

Code associated with the manuscript "Risk of unprecedented warm months controlled by warming rate, record margin and internal variability". Released on 2 October 2026.

Code accompanying the paper "Exploiting Adiabaticity for Variational Ground-States" (https://arxiv.org/abs/2602.06137). The repository contains the implementations and scripts used to obtain…

Code accompanying the paper "Exploiting Adiabaticity for Variational Ground-States" (https://arxiv.org/abs/2602.06137). The repository contains the implementations and scripts used to obtain the numerical results and generate the figures presented in the manuscript.

CODECHECK certificate documenting the successful reproduction of computational results from the paper "Constructing melanopic light exposure profiles from sparse daily-route measurements." by Tabandeh…

CODECHECK certificate documenting the successful reproduction of computational results from the paper "Constructing melanopic light exposure profiles from sparse daily-route measurements." by Tabandeh, N., Veitch, J. A., & Spitschan, M. (2026). This certificate confirms that the Jupyter notebooks, R Quarto documents and R function work as described and that all main figures and tables from the original paper can be independently reproduced. Original paper: https://doi.org/10.1038/s44323-026-00108-x  

Added "L23Net_Drug_Neuron9" folder for code updated to work in NEURON 9.0.2 and LFPy 2.3.5, including mod files (Modified 2026-09-24 by Valis Sowilo - github.com/ValisSowilo), circuit.py, an…

Added "L23Net_Drug_Neuron9" folder for code updated to work in NEURON 9.0.2 and LFPy 2.3.5, including mod files (Modified 2026-09-24 by Valis Sowilo - github.com/ValisSowilo), circuit.py, and circuit_functions.py.

Google Earth Engine (JavaScript) and R scripts, together with the derived data, used to study snow occurrence before and after the 2005 Lanjarón wildfire (Sierra Nevada, Spain) in plots where differen…

Google Earth Engine (JavaScript) and R scripts, together with the derived data, used to study snow occurrence before and after the 2005 Lanjarón wildfire (Sierra Nevada, Spain) in plots where different post-fire treatments were applied: salvage logging (SL), partial cut plus lopping (PCL) and non-intervention (NI).The workflow includes: (1) creation of a Landsat-pixel grid and assignment of treatments; (2) calculation of the Normalized Difference Snow Index (NDSI) from Landsat 5 and 7 Collection 2 surface reflectance for cloud-free scenes; (3) extraction of ancillary variables (elevation, slope, shadows, etc.); (4) pixel-level snow percentage calculation (NDSI threshold 0.4); and (5) statistical comparison between treatments before and after the fire.