This volume serves as the actionable "how" and "what's next" of the Brain Interpreter series, focusing on translating high-dimensional cognition into daily reality. It integrates **Somatic-Alchemical Practice** (e.g., "Cyber Natural Therapy: hacking body hardware with brain software", "Identify-Pause-Switch trilogy"), **Relationship Aesthetics** (e.g., "Intimate relationships as the harshest mirror", "Six script killers", "Love translation guide"), and **Paradox Navigation** (e.g., "Happiness paradox: running harder gets you further", "Correct answers are counterfeits of awakening"). The core methodology involves shifting from "theoretical understanding" to "manual override", from "controlling outcomes" to "cultivating causes", and from "seeking external validation" to "becoming your own new mother". Through real-life field tests (e.g., "Bus survival breakthrough", "40-minute parent-child call", "Yellow greasy tongue coating inner drama"), emotional dictionaries, and survivor reports, the work offers a field-tested roadmap for those ready to live their awakening. It invites readers to stop polishing the mirror and start being the light.
CAN CHEN· Zenodo (CERN European Organi...· 0 citations
The forthcoming data release from the Gaia space telescope expected to yield tens of thousands of newly detected exoplanets, as well as detection and 3D orbital constraints on binary stars and black holes. Many of these systems will warrant in-depth analyses of the astrometric data and radial velocity follow-up. This will require dedicated tools to exploit this wealth of data. We provide open-source software to analyse epoch astrometric data from Gaia, both independently and jointly with radial velocities. We add two models to the open-source orbit-fitting codebase įma and test these on both real and simulated data. This code uses diffusive nested sampling to explore the parameter space, calculate evidence for model comparison, and perform parameter estimation. We show that the results are consistent with published and expected values, validating the use of įma for the analysis of Gaia data. We explore various attributes of įma's Gaia model, including its potential to distinguish genuine orbital signals from scan-angle-dependent signals. s are thousands to
Thomas A. Baycroft, João Faria, J.-B. Delisle· Astronomy and Astrophysics· 0 citations
Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.
Yonatan Ayalew Mekonnen, Oscar Ospina, Vanessa Rubio et al.· bioRxiv (Cold Spring Harbor...· 0 citations
In 2010, in recognition of the continuing failings and overruns of government directed IT programmes, which included the NHS National Programme for Information Technology (NPfIT), the Government Digital Service was created to bring coherence to IT strategy in Government Departments and to its internet projection. This initiative created Gov.UK around a series of 25 exemplar projects, and a series of powerful design principles which focussed upon in house development, agile and iterative development, and a relentless focus and testing of the actual needs of the true end users of any system. This would make any such a system “Digital by Default” meaning so good that the user would choose no alternative way of working. In 2013, the then Secretary of State for Health Jeremy Hunt followed NPfIT with the “Go Paperless” strategy for the NHS, which was still heavily dependent upon paper records with their resulting costs and inefficiencies. The “Go paperless” mandate prompted an urgent plan at University Hospital Southampton (UHS) to undertake two major projects in parallel: the digitisation of archival paper records, and the acquisition of a commercial enterprise grade Electronic Document Management System (EDMS). The EDMS in turn was intended to serve as the primary EDMS for the Trust, and to replace the locally developed UHS Electronic Patient Record (EPR). This project proved to be more complex than intended, with modified outcomes. Old lessons were relearned about the evolution of complex digital software projects which are mandated by political imperatives rather than operational needs and technical maturity. In the previous essay in this series, I described the digital scanning programme at UHS from 2013 – 2018 and the attempts to create a workable classification scheme for the diverse documentation that comprises a typical medical record. In this essay, I describe and reflect upon the lessons and outcomes of the acquisition and implementation programme for the Hyland OnBase EDMS at UHS between 2014 and 2024.
D. Rew· ePrints Soton (University of...· 0 citations
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Revised and tested implementation of MIND, replacing the initial 0.0.1 code on main. The previous implementation remains available at v1.0.0. First semver release. Addresses the SMARTbiomed/software-review checklist (Issue SMARTbiomed/software-review#2). All public API entry points from 0.0.1 are preserved. Added MIND.data.make_toy_dataset — pure-numpy synthetic generator that requires no network access; used in tests and the new quickstart tutorial. examples/reproduce_results.py — single-script reproduction CLI (python reproduce_results.py --dataset {synthetic,CCMA,CCLE,TCGA}). examples/quickstart_toy.ipynb — fast tutorial notebook driven by make_toy_dataset, geared at users applying MIND to their own data. MIND._validation — input-validation helpers; surfaced via MIND.MIND.__init__. Full pytest suite covering the model, data loaders, and validation. .github/workflows/ci.yml — automated CI on Linux + macOS, Python 3.10–3.12. docs/ — Sphinx skeleton (autodoc + napoleon) plus a written user guide (docs/user_guide.md) covering input format and hyperparameter tuning. .readthedocs.yaml, pyproject.toml, CITATION.cff, AUTHORS.md, CHANGELOG.md. download_if_missing flag on every load_* helper — allows fail-fast behaviour for users who don't want network calls. Changed Package layout: MIND_model.py → MIND/model.py + MIND/layers.py + MIND/_train.py. MIND_data.py → MIND/data.py. The public re-exports in MIND/__init__.py are unchanged, so from MIND import MIND and the existing from MIND import get_*, load_* imports keep working. Type hints added to every public function, method and attribute. NumPy-style docstrings written for every public symbol. MIND.data.get_data now uses os.path.join (was string concatenation that assumed a trailing /). README rewritten: jargon-free summary, fixed typos ("Sofrware", "User needs ot provide"), added "Citing" section, comparison-to-alternatives table, and a network-free quickstart. Fixed Stale device = 'cuda' if torch.cuda.is_available() else 'cpu' line in MIND_model.py that ran before import torch. Toy-dataset construction no longer leaves any patient missing from every modality (previously caused a divide-by-zero in the appearance denominator).
Hanwen Xing· Zenodo (CERN European Organi...· 0 citations
Reproducibility Package This repository contains the data and code required to reproduce all figures in the manuscript“Antarctic Ozone Depletion Shapes Surface Cooling Pattern and Climate Sensitivity.” Code Environment All analyses are implemented in Python using Jupyter notebooks. The code has been tested with the following software versions: Python 3.12.4 notebook 7.2.1 matplotlib 3.8.4 xarray 2023.6.0 numpy 1.26.4 scipy 1.13.1 netCDF4 1.7.2 pandas 2.2.2 cartopy 0.22.0 cmocean 4.0.3 tqdm 4.66.5 Data and Code Setup Download all data files and unzip them into a single directory. Download the code archive and unzip it into the same directory as the data. The directory structure is designed so that the notebooks can be executed without modifying any file paths. Usage Each Jupyter notebook is named according to the figure it reproduces in the manuscript. Running a notebook will generate the corresponding figure directly.
Peidong Wang· Zenodo (CERN European Organi...· 0 citations
This study introduces an innovative dynamic charging solution, defined as a platoon-based vehicle-to-vehicle charging (PV2VC). technology. A fleet of electricity suppliers (ESs) can be deployed to transfer power to other electric vehicles, defined as electricity requests (ERs), while moving in platoon to avoid the detour and delay at a charging station. We mathematically formulate a mixed integer linear programming (MILP) model for the PV2VC problem, along with two fundamental and benchmark operation scenarios with minor simplifications, the electric vehicle routing problem (EVRP) and electric vehicle platooning problem (EVPP). The objective is to minimize the total energy consumption and travel time for ERs, including the charging time at CS, platoon formation delay, and wait time for the PV2VC service. A set of numerical experiments with five scenarios are tested and the computational performance between the commercial software applied to the MILP model and the proposed genetic algorithm are compared on a modified Sioux Falls network. By comparison with the optimal benchmark scenario, the results show that the PV2VC technology can save up to 11.07% in energy consumption, 11.65% in travel time, and 11.26% in total cost. For the PV2VC operational scenario, it would be more beneficial for long-distance vehicle routes with low initial state of charge, sparse charging facilities, and where travel time is perceived to be higher than energy consumption costs.
Zhexi Fu, Joseph Y. J. Chow· Transportation Research Reco...· 0 citations
This record contains the compact numerical data and plotting code supporting the quantitative figures in: T. M. R. Wolf, T. Xie, C. Jin, and A. H. MacDonald, “Exciton-based sensing of remote electron correlations in 2D heterostructures,” https://doi.org/10.1103/246f-2p98 Preprint: https://doi.org/10.48550/arXiv.2510.21522 The archive includes: • Numerical arrays and tabular data for Figs. 2 and 3 and Supplemental Figs. S2–S6.• The plotting-code snapshots used to generate the accepted figures.• The TikZ source for the schematic in Fig. 1.• The accepted figure PDFs for reference and automated comparison.• A figure-to-data map, data dictionary, software-environment specifications, provenance metadata, and SHA-256 checksums.• A one-command workflow (“make verify”) that regenerates the data-driven figures and compares them with the accepted figure PDFs. The archive starts from compact, postprocessed plotting data. Large mean-field checkpoints, raw susceptibility tensors, and other intermediate simulation files are not required to reproduce the published figures and are therefore not included. The numerical arrays and original generation script for the contextual noninteracting band-structure figure, Fig. S1, were not retained. Its exact accepted PDF is included for completeness, but this record does not claim numerical reproducibility for that figure. Version 2.0 replaces the original large internal-data bundle with a compact, documented, and independently tested figure-reproduction package aligned with the published article.
Wolf, Tobias· Zenodo (CERN European Organi...· 0 citations
The rapidly developing research field of brain-body neuroscience faces methodological challenges, as analysts continue to develop new analysis strategies for robust statistics in the absence of established best practices. This quest for robust statistics is further complicated by the (naturally) circular data involved in the study of phase-locked effects, e.g. in respiration-brain coupling. Circularity of respiratory data particularly affects the problem of multiple comparisons in phase-related inferential statistics. In this tutorial, we propose a robust pipeline for respiration-related analyses based on a circular extension of cluster-based permutation testing we developed. We highlight and offer guidance on critical parameters in the analysis, systematically compare various approaches being used in the field today, and provide open-access software code for flexible use and future development of our proposed pipeline.
Teresa Berther, Elio Balestrieri, Martina Saltafossi et al.· PLoS Computational Biology· 0 citations
Topology-aware machine-learning software and a preregistered graph-classification research program spanning 14 hypotheses (H001-H011b) and 53 falsifiable sub-predictions. Under the tested matched-capacity configurations, no unique L_0 Hodge advantage is supported once the external-residual architecture is matched. A narrow NCI1 positive difference over the matched MLP remains; the H009-R corrective replication did not detect a learned-sheaf improvement over fixed Hodge at the tested power, and H011b remains unresolved. The repository includes persistent-homology features, differentiable topology losses, simplicial and Hodge operators, a CI-gated test suite with 100% line and branch coverage on the library package, BCa bootstrap intervals, paired Wilcoxon tests with Benjamini-Hochberg correction, and explicit retention of null, invalidated, and inconclusive results.
Santiago Maniches· Zenodo (CERN European Organi...· 0 citations
Topology-aware machine-learning software and a preregistered graph-classification research program spanning 14 hypotheses (H001-H011b) and 53 falsifiable sub-predictions. Under the tested matched-capacity configurations, no unique L_0 Hodge advantage is supported once the external-residual architecture is matched. A narrow NCI1 positive difference over the matched MLP remains; the H009-R corrective replication did not detect a learned-sheaf improvement over fixed Hodge at the tested power, and H011b remains unresolved. The repository includes persistent-homology features, differentiable topology losses, simplicial and Hodge operators, a CI-gated test suite with 100% line and branch coverage on the library package, BCa bootstrap intervals, paired Wilcoxon tests with Benjamini-Hochberg correction, and explicit retention of null, invalidated, and inconclusive results.
Santiago Maniches· Zenodo (CERN European Organi...· 0 citations
Background. RewardBench 2 aggregates pairwise preference accuracy across heterogeneous task families (factuality, instruction following, safety, and others). A single leaderboard score can mask systematic subset specialization, yet standard benchmark reporting rarely tests whether accuracy is independent of task category. Methods. We applied CONFIRM, a chi-square test of independence with Cramér's V effect sizing and empirically anchored letter grades, to 174 publicly released reward models. For each model we constructed a 5 × 2 contingency table (subset × correct/incorrect) from published per-prompt scores (n = 1,763 prompts after excluding the non-binary Ties subset). The null hypothesis was that correct/incorrect outcomes are independent of subset category. Grades A–F reflect V magnitude (CONFIRM v2 thresholds); grade I denotes insufficient power. Non-significant results grade F only when power ≥ 0.80 to detect V = 0.10. Results. All 174 models rejected independence at α = 0.05 (all p < 0.02; median V = 0.272, range 0.082–0.467). No model received F or I. Grade distribution: A 36.8% (n = 64), B 50.6% (n = 88), C 11.5% (n = 20), D 1.1% (n = 2). Pooling across models, mean per-subset accuracy was lowest on Precise IF (36.3 per 100) and highest on Safety (75.7 per 100). In 93.1% of models the largest subset gap involved Precise IF as the weakest category; Safety was the strongest endpoint in 70.7% of cases. Conclusions. Published RewardBench 2 reward models exhibit statistically detectable subset heterogeneity at this sample size. Aggregate accuracy therefore understates structured performance imbalance, particularly weakness on precise instruction-following relative to safety-oriented subsets. CONFIRM provides a reproducible heterogeneity diagnostic to be read alongside ranking metrics. Plain-language summary. For all 174 reward models tested, the rate of correct judgments differed across the five task types by more than the prespecified statistical threshold. The direction of the difference was shared: 93.1% of models were weakest on precise instruction-following, and 70.7% were strongest on safety. Each model was tested on 1,763 prompts, enough sensitivity to detect even a small difference had one been present, so a model showing no difference would have been identifiable as such. None did. A single overall leaderboard score does not show this — two models with the same average can differ substantially underneath. This analysis measures whether a model's accuracy is uneven across task types, not which model is best overall, and it identifies a pattern without establishing its cause. Supplementary material. The deposited archive (rewardbench2_validation.zip) contains per-model results, subset breakdowns, contingency cell counts, validation flags, and step-by-step mathematical derivations for all 174 models. Competing interests. The author is affiliated with TraceSeis, Inc., which is developing CONFIRM as a commercial product. This constitutes a competing interest. All results are reproducible from the cited public data and the deposited analysis outputs. AI use disclosure. Generative AI tools were used during preparation of this work, in two distinct roles. For drafting and implementation: Anthropic Claude assisted with manuscript prose; the CONFIRM engine and analysis pipeline were implemented with AI coding tools (Cursor, Anthropic Claude) to the author's specification; and Google Gemini was consulted during writing and analysis runs. For review: Perplexity provided editorial review of a late draft, and xAI Grok was used as a general consistency check. This reflects the author's record of tool use and is not offered as an exhaustive log. Research design, statistical methodology, and interpretation are the author's. Because the analysis software was AI-implemented, every reported statistic was independently recomputed from observed cell counts and checked against pipeline output before reporting; per-model derivations are deposited as confirm_math.html and can be checked by hand. The author takes full responsibility for the contents of this record.
Alvaro Chaveste-Fernandez· Zenodo (CERN European Organi...· 0 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.
