Search Results - "Python A"

Author Correction: An Open Source Python Library for Anonymizing Sensitive Data

Authors: Judith Sáinz-Pardo Díaz Álvaro López García

Source: Sci Data
Scientific Data, Vol 11, Iss 1, Pp 1-1 (2024)
Scientific Data

Subject Terms: Science, Author Correction

Access URL: https://doaj.org/article/9f2e25f506b54ec39c094deb50cee2f0

ABCal: a Python package for Author Bias Computation and Scientometric Plotting for Reviews and Meta-Analyses

Authors: Louis-Stéphane Le Clercq

Source: Scientometrics. 129:581-600

Subject Terms: 0301 basic medicine, 0303 health sciences, 03 medical and health sciences

An Accessible Python based Author Identification Process

Authors: Anthony Breitzman

Source: Proceedings of the Python in Science Conference. :24-31

Author response: A Python Toolbox for Representational Similarity Analysis

Authors: Jasper JF van den Bosch Tal Golan Benjamin Peters et al.

Author response for 'nDTomo: A Modular Python Toolkit for X-ray Chemical Imaging and Tomography'

Authors: null Antonis Vamvakeros null Evangelos Papoutsellis null Hongyang Dong et al.

A Validated Transformation Matrix Linking Cosmic and Neural Complexity: An Evolution of the Unified Recursive Emergence Theory Version: 2.0 Publication Date: June 7, 2025 Authors: Allen Wagner, Gemini Research Group Abstract The Unified Recursive Emergence Theory (URET) posits that a common mathematical framework governs the emergence of complexity across quantum, neural, and cosmic scales. The original formulation of this theory presented a tentative link but was hampered by methodological limitations. This paper presents a significant evolution of the theory by demonstrating a specific, non-linear transformation that successfully predicts the complexity signature of neural systems from cosmic data with remarkable accuracy. Through a large-scale analysis of real-world datasets from EEG recordings and Cosmic Microwave Background (CMB) observations, we validate a 'Power-of-Two Transformation Hypothesis.' We show that a transformation matrix of [~1, 2, 4, ~1] applied to a vector of four key complexity metrics (Lyapunov exponent, correlation dimension, sample entropy, and Hurst exponent) consistently maps the cosmic signature to the neural signature with mean prediction errors as low as 2.52%. This finding moves URET from a speculative concept to a predictive, falsifiable model and suggests the fundamental processes governing complexity at different universal scales may be linked by a profound and precise mathematical relationship. 1. Introduction: The Original URET Hypothesis The study of complex systems has often revealed tantalizing similarities in patterns observed at vastly different scales. The Unified Recursive Emergence Theory (URET) was proposed as a framework to formalize this connection, suggesting that a single, recursive mathematical dynamic could describe the emergence of complexity in domains as disparate as quantum mechanics, neural networks, and the large-scale structure of the cosmos. The initial computational study (Zenodo #15347469) provided preliminary support for a link between the quantum and cosmic domains. However, its analysis of the neural domain was rendered inconclusive due to severe numerical instability in the chaotic models used. This paper outlines the successful resolution of these methodological issues and the subsequent discovery and validation of a far more precise and powerful formulation of the theory. 2. Methodology: From Instability to a Testable Framework Our research progressed through a multi-stage protocol designed to build a robust foundation for testing the URET hypothesis. 2.1. Solving Numerical Instability: The primary flaw in the original work was its reliance on standard 64-bit floating-point arithmetic, which is insufficient for the iterative calculations central to chaos theory. We first demonstrated that using high-precision (256-bit) numerical libraries solves this problem, producing stable and meaningful complexity signatures from chaotic models. 2.2. A Multi-Metric Approach: To capture a richer 'fingerprint' of a system's dynamics, we moved beyond a single parameter. We defined a 'complexity vector' composed of four distinct metrics calculated using the nolds Python library: Largest Lyapunov Exponent (LLE): A measure of the rate of divergence of nearby trajectories, indicating the presence and degree of chaos. Correlation Dimension: A measure of the fractal dimension and geometric complexity of the system's attractor. Sample Entropy: A measure of the system's unpredictability and regularity. Hurst Exponent: A measure of the long-term memory or persistence within the time series. 2.3. Large-Scale Data Library: We established a library of real-world, public time-series data from two primary domains: Neural Domain: Four distinct EEG datasets from multiple subjects and tasks, sourced from PhysioNet. Cosmic Domain: Two distinct Cosmic Microwave Background (CMB) temperature anisotropy datasets from the Planck and WMAP satellite missions. 3. Results: Discovery and Validation of the Transformation Matrix Our analysis led to a breakthrough discovery that reshaped the original theory. 3.1. Rejection of Simple Scaling: An initial comparison of the mean complexity vectors from the Neural and Cosmic domains revealed they were not identical. Furthermore, we found no single scaling factor that could relate them; the ratio between corresponding metrics was inconsistent. 3.2. The Power-of-Two Transformation Hypothesis: A deeper analysis of the scaling ratios revealed a striking pattern, approximating a power-of-two series: [~1, 2, 4, ~1]. This led to a new, more specific hypothesis: the complexity vector of the Neural domain can be predicted by applying this transformation matrix to the Cosmic domain's vector. 3.3. Large-Scale Validation: We tested this hypothesis by using the transformation rule to predict the complexity of our four neural datasets from our two cosmic datasets, resulting in eight unique comparisons. The aggregated results are shown below. Table 1: Mean Prediction Error of the Power-of-Two Transformation | Metric | Mean Prediction Error (%) | | :--- | :--- | | Correlation Dimension | 2.52% | | Sample Entropy | 5.66% | | Lyapunov Exponent | 9.36% | | Hurst Exponent | 15.28% | As shown in Table 1, the transformation predicted the geometric complexity (Correlation Dimension) and unpredictability (Sample Entropy) of neural systems with excellent accuracy. The prediction for the degree of chaos (LLE) was also strong. Only the Hurst Exponent fell outside our success criterion, suggesting it follows a different or more complex scaling law. 4. Discussion and Conclusion The results of our large-scale validation provide strong, compelling evidence for a revised and far more powerful Unified Recursive Emergence Theory. We have successfully demonstrated that a structured, non-linear transformation matrix ([~1, 2, 4, ~1]) connects the complexity signatures of cosmic and neural systems. This finding is significant. The 'missing scaled element' sought by the original theory appears not to be a single number but a specific, predictable transformation. This suggests that as complexity unfolds from one universal scale to the next, different facets of that complexity—its geometry, its predictability, its chaotic nature—are amplified by distinct, near-integer factors. We have moved URET from a speculative idea to a predictive, falsifiable model that holds up to rigorous testing against real-world data. 5. Future Work This breakthrough opens several avenues for future research: Refining the Hurst Exponent: Investigating the specific scaling law that governs the long-term memory component of these systems. Expanding Domains: Applying the transformation hypothesis to other complex systems, such as geophysical data, biological evolution, or socio-economic networks, to test its universality. Theoretical Origins: Exploring the potential theoretical foundations for this power-of-two relationship. Does it derive from principles in information theory, thermodynamics, or a yet-undiscovered aspect of fundamental physics? This work establishes a new foundation for exploring the profound and surprisingly precise mathematical relationships that may link all complex structures in our universe

Authors: Wagner, Allen

Author response for 'Teaching Python with team-based learning: using cloud-based notebooks for interactive coding education'

Authors: null Osorio, Nuno S. null Garma, Leonardo D.

Python Love

Authors: Shannon Arntfield

Resource Type: eBook.

Subjects: Gynecology, Medicine, Women authors, Canadian poetry, Caregivers, Grief, Electronic books

Categories: POETRY / Canadian, MEDICAL / Caregiving, MEDICAL / Gynecology & Obstetrics, PSYCHOLOGY / Psychopathology / Post-Traumatic Stress Disorder (PTSD), POETRY / Subjects & Themes / Death, Grief, Loss, POETRY / Women Authors, PSYCHOLOGY / Trauma Psychology

ABCal: a Python package for author bias computation and scientometric plotting for reviews and meta-analyses.

Authors: Le Clercq, Louis-Stéphane^1,2 (AUTHOR) leclercq.l.s@gmail.com

Source: Scientometrics. Jan2024, Vol. 129 Issue 1, p581-600. 20p.

Subject Terms: Python programming language, Software development tools, Publication bias, Research personnel

Author response for 'Morphological Variation in the Genitalia of the Burmese Python'

Authors: null Rachel Keeffe null Brandon P. Hedrick null Ian Bartoszek et al.

Author response for 'pyRheo: An open-source Python package for complex rheology'

Authors: null Isaac Yair Miranda-Valdez null Aaro Niinistö null Tero Mäkinen et al.

Author response: Heron, a Knowledge Graph editor for intuitive implementation of Python-based experimental pipelines

Authors: George Dimitriadis Ella Svahn Andrew MacAskill et al.

Author response for 'DOPtools: a Python platform for descriptor calculation and model optimization'

Authors: null said BYADI null Philippe Gantzer null Timur Gimadiev et al.

Author's Reply: Critical Limitations in Systematic Reviews of Large Language Models in Health Care

Authors: Andre Python HongYi Li Jun-Fen Fu

Source: Journal of Medical Internet Research, Vol 27, Pp e82729-e82729 (2025)

Subject Terms: Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270

File Description: electronic resource

Relation: https://www.jmir.org/2025/1/e82729; https://doaj.org/toc/1438-8871

Access URL: https://doaj.org/article/f86af40097c64ed8ad7a81ca0e653cf3

ManyTypes4Py: A Benchmark Python Dataset for Machine Learning-based Type Inference

Authors: Mir, S.A.M. (author) Latoskinas, Evaldas (author) Gousios, G. (author) et al.

Contributors: Mir, S.A.M. (author) Latoskinas, Evaldas (author) Gousios, G. (author) et al.

Source: IEEE/ACM 18th International Conference on Mining Software Repositories (MSR)

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Programming Languages, Static Analysis, Type Annotations, 02 engineering and technology, Type Inference, Machine Learning (cs.LG), Machine Learning, Software Engineering (cs.SE), Computer Science - Software Engineering, 0202 electrical engineering, electronic engineering, information engineering, Python, Programming Languages (cs.PL)

Access URL: https://repository.tudelft.nl/islandora/object/uuid%3A899fabe3-15fc-40b5-afb4-59444d6f05ea/datastream/OBJ/download
http://arxiv.org/abs/2104.04706
https://www.narcis.nl/publication/RecordID/oai%3Atudelft.nl%3Auuid%3A899fabe3-15fc-40b5-afb4-59444d6f05ea
https://ieeexplore.ieee.org/document/9463150/
https://dblp.uni-trier.de/db/journals/corr/corr2104.html#abs-2104-04706
https://zenodo.org/record/4719447
https://research.tudelft.nl/en/publications/manytypes4py-a-benchmark-python-dataset-for-machine-learning-base
https://doi.org/10.1109/MSR52588.2021.00079
http://resolver.tudelft.nl/uuid:899fabe3-15fc-40b5-afb4-59444d6f05ea

Surface Corrosion Detection for Ferrous-metal Parts: Application of Artificial Intelligence, Python and Microscopic Images.

Authors: WDOWIK, Roman BEŁZO, Artur BENDIKIENE, Regita

Source: Materials Science / Medziagotyra; 2025, Vol. 31 Issue 3, p431-438, 8p

Subject Terms: ARTIFICIAL intelligence, PYTHON programming language, CORROSION engineering, PRODUCT quality, MICROSCOPY, STEEL corrosion, IRON metallurgy, CONDITION-based maintenance

Author response for 'Automated processing of chromatograms: a comprehensive python package with a GUI for intelligent peak identification and deconvolution in chemical reaction analysis'

Authors: null Jan Obořil null Christian P. Haas null Maximilian Lübbesmeyer et al.

Author Correction: SciPy 1.0: fundamental algorithms for scientific computing in Python.

Authors: Virtanen, Pauli Gommers, Ralf Oliphant, Travis E et al.

Index Terms: SciPy 1.0 Contributors, Other

URL: https://www.repository.cam.ac.uk/handle/1810/318028

ABCal: a Python package for author bias computation and scientometric plotting for reviews and meta-analyses

Authors: Le Clercq, Louis-Stéphane University of the Free State South Africa (UFS) South African National Biodiversity Institute

Contributors: Le Clercq, Louis-Stéphane University of the Free State South Africa (UFS) South African National Biodiversity Institute

Source: ISSN: 0138-9130.

Subject Terms: Systematic, Reviews, Authorship, Bias detection, Author bias, Scientometric, Plots, [MATH]Mathematics [math], [INFO]Computer Science [cs]

Relation: hal-04308276; https://hal.science/hal-04308276; https://hal.science/hal-04308276/document; https://hal.science/hal-04308276/file/Le%20Clercq%20%282023%29%20-%20ABCal%20Article.pdf

Availability: https://hal.science/hal-04308276
https://hal.science/hal-04308276/document
https://hal.science/hal-04308276/file/Le%20Clercq%20%282023%29%20-%20ABCal%20Article.pdf
https://doi.org/10.1007/s11192-023-04880-6

Author response for 'FLApy: A Python package for evaluating the 3D light availability heterogeneity within forest communities'

Authors: null Bin Wang null Cameron Proctor null Zhiliang Yao et al.