Oct 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
Added Power-law noise model (#25): ntpstats noise and ntpstats.noisefit fit h₂…h₋₂ and, optionally, a linear drift to OADEV/MDEV/HDEV curves and the spectrum. The expected estimator values are exact for the discrete Kasdin–Walter model. Model selection uses BIC. Intervals come from a parametric bootstrap. Monte Carlo coverage is ≥ 90 % per coefficient except at the edge of detection. Corner τ values are reported. --scenario writes a simulator clock ([clock.h_alpha]), and simulate --scenario runs it. The UI can overlay the fitted model on the Stability chart. Spectra (#25): ntpstats spectrum and ntpstats.spectrum compute S_x(f) and S_y(f) with Welch or sine multitaper, per gap-free stretch, log-binned with degrees of freedom. L(f) in dBc/Hz for a carrier. New Spectrum tab in the UI. N-cornered hat (#27): ntpstats hat and ntpstats.hat estimate each source's stability from pairwise differences: Groslambert covariance, three-cornered hat and N-cornered hat by least squares, with intervals. Negative variances are flagged. Sources are aligned from separate logs. Holdover prediction (#26): ntpstats holdover and ntpstats.holdover predict TIE(t) with an envelope and the time to violate limits. The 95 % envelope holds 95 % ± 3 % with a known noise model, and 94–100 % with a fitted one. The variance is computed exactly for the fitted noise, including the error of the fitted frequency and drift. The uncertainty of the noise model is mixed into the envelope. --backtest checks calibration on the log itself; --min-holdover exits with code 3 when the limit is not held long enough. Phase can also be integrated from a frequency column (chrony). New Holdover tab in the UI. Research data (#34): new formats cggtts (V2E, line checksums), rinex-clock (IGS .clk), circular-t (BIPM, UTC − UTC(k)), ripe-atlas (NTP results) and ntppool (monitor logs). ntpstats cv computes GNSS common-view and all-in-view time transfer, and research.group_summary gives per-server or per-probe distributions. Gzipped inputs are read directly. HTOT (#33): Hadamard total deviation (-k htot), bias-corrected per noise type, matching the NIST SP 1065 tables within 0.3 %. compat.allantools.htotdev returns the raw value. Docs: Metrology and Research data pages; synthetic examples of every new format in examples/data/.
This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.
P. Abrahamsson, O. Salo, Jussi Ronkainen et al.· arXiv.org· 727 citations· ⚡54
The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.
M. Pikkarainen, Jukka Haikara, O. Salo et al.· Empirical Software Engineeri...· 401 citations· ⚡48
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al.· Information and Software Tec...· 394 citations· ⚡54
The perception of the impact of agile methods is predominantly positive, and several challenge areas were discovered, but based on this study, agile methods are here to stay.
M. Laanti, O. Salo, P. Abrahamsson· Information and Software Tec...· 260 citations· ⚡20
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MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
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