Suchergebnisse - (( (statne OR stateeeeeninet) python code analysis ) OR ( stat python code analysis ))*

Py Code for Stats and Vis in Isotopic Evidence for Interseasonal Bioaccumulation of Perfluoroalkyl Substances (PFASs) in Icelandic seabirds

Autoren: Shen, Rui Larsen, Thomas

Relation: https://zenodo.org/records/10120463; oai:zenodo.org:10120463; https://doi.org/10.5281/zenodo.10120463

Verfügbarkeit: https://doi.org/10.5281/zenodo.10120463
https://zenodo.org/records/10120463

Primary data for BJC Carbonic anhydrase IX is a pH-stat that sets an acidic tumour extracellular pH in vivo

Autoren: Griffiths, JR Lee, Shen-Han McIntyre, Dominick John et al.

Schlagwörter: Carbonic anhydrase IX, Extracellular pH, Intracellular pH, ISUCA, Magnetic Resonance Spectroscopy, MRS, pH-stat, Tumour pH

Dateibeschreibung: jMRUI http://www.jmrui.eu/ python www.python.org Excel; application/vnd.openxmlformats-officedocument.spreadsheetml.sheet; application/octet-stream

Relation: https://www.repository.cam.ac.uk/handle/1810/361916; https://doi.org/10.17863/CAM.26029

Verfügbarkeit: https://www.repository.cam.ac.uk/handle/1810/361916
https://doi.org/10.17863/CAM.26029

Testing Gaussian process with applications to super-resolution

Autoren: Jean-Marc Azaïs Yohann De Castro Stéphane Mourareau et al.

Weitere Verfasser: Jean-Marc Azaïs Yohann De Castro Stéphane Mourareau et al.

Quelle: Applied and Computational Harmonic Analysis. 48:445-481

Schlagwörter: FOS: Computer and information sciences, [MATH.MATH-PR] Mathematics [math]/Probability [math.PR], Computer Science - Information Theory, Mathematics - Statistics Theory, Kac-Rice formula, Statistics Theory (math.ST), 01 natural sciences, 94A08, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], 62J05, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], FOS: Mathematics, 62F03, 0101 mathematics, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], 62H15, secondary 60E05, Super-Resolution, Information Theory (cs.IT), Probability (math.PR), MSC 2010 subject classifications: Primary 62E15, Hypothesis Testing, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], [MATH.MATH-PR]Mathematics [math]/Probability [math.PR], [INFO.INFO-IT]Computer Science [cs]/Information Theory [cs.IT], 60G15, 62E15, 62F03, 60G15, 62H10, 62H15 (Primary) 60E05, 60G10, 62J05, 94A08 (secondary), [INFO.INFO-IT] Computer Science [cs]/Information Theory [cs.IT], Gaussian Process, 62H10, 60G10, Mathematics - Probability

Dateibeschreibung: application/pdf

Zugangs-URL: https://www.sciencedirect.com/science/article/am/pii/S1063520318301556
http://arxiv.org/abs/1706.00679
https://inria.hal.science/hal-01686434v1/document
https://inria.hal.science/hal-01686434v1
https://www.sciencedirect.com/science/article/pii/S1063520318301556
https://hal-upec-upem.archives-ouvertes.fr/hal-01686434v1
http://dblp.uni-trier.de/db/journals/corr/corr1706.html#AzaisCM17
https://hal.inria.fr/hal-01686434/document
https://arxiv.org/pdf/1706.00679.pdf
https://dblp.uni-trier.de/db/journals/corr/corr1706.html#AzaisCM17
https://hal.science/hal-04955851v1
https://doi.org/10.1016/j.acha.2018.07.001

GPT-4.1 Sets the Standard in Automated Experiment Design Using Novel Python Libraries.

Autoren: Fachada, Nuno Fernandes, Daniel Fernandes, Carlos M. et al.

Quelle: Future Internet; Sep2025, Vol. 17 Issue 9, p412, 28p

Schlagwörter: LANGUAGE models, EXPERIMENTAL design, PYTHON programming language, GENERATIVE pre-trained transformers, QUANTITATIVE research, BENCHMARK problems (Computer science), CODE generators

Generation of Scale-Free Assortative Networks via Newman Rewiring for Simulation of Diffusion Phenomena

Autoren: Laura Di Lucchio Giovanni Modanese

Quelle: Stats, Vol 7, Iss 1, Pp 220-234 (2024)

Schlagwörter: scale-free networks, assortative networks, network rewiring, agent-based models, Bass innovation diffusion model, Statistics, HA1-4737

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2571-905X/7/1/14; https://doaj.org/toc/2571-905X

Zugangs-URL: https://doaj.org/article/1ed58599ad2b446e8fba7bbd67fd64e7

A statistical approach to automated analysis of the low‐contrast object detectability test for the large ACR MRI phantom.

Autoren: Golestani, Ali M. Gee, Julia M.

Quelle: Journal of Applied Clinical Medical Physics; Jul2025, Vol. 26 Issue 7, p1-9, 9p

Schlagwörter: LINEAR statistical models, IMAGING phantoms, QUALITY control, PYTHON programming language, MAGNETIC resonance imaging

The state of the late Quaternary megafauna extinction debate: a systematic review and analysis.

Autoren: Stewart, Mathew Peters, Carli Ziegler, Michael J. et al.

Quelle: Frontiers in Mammal Science. 2025, p1-27. 27p.

Genome-wide analyses reveal intricate genetic mechanisms underlying egg production efficiency in chickens.

Autoren: Tan, Lizhi Cai, Xinyu Kong, Yuan et al.

Quelle: Journal of Animal Science & Biotechnology. 8/11/2025, Vol. 16 Issue 1, p1-21. 21p.

Regularized Maximum Likelihood Estimation for the Random Coefficients Model in Python.

Autoren: Dunker, Fabian Mendoza, Emil Reale, Marco

Quelle: Mathematics (2227-7390); Dec2025, Vol. 13 Issue 23, p3764, 29p

Schlagwörter: PYTHON programming language, MAXIMUM likelihood statistics, CONSUMPTION (Economics), MATHEMATICAL programming, MULTILEVEL models, SCIENTIFIC computing

Machine learning for prioritizing security code-based Vulnerability discovery

Autoren: Ballestas, Rafael García Bedoya, Olmer

Weitere Verfasser: Ballestas, Rafael García Bedoya, Olmer

Quelle: instname:Universidad de Bogotá Jorge Tadeo Lozano ; reponame:Expeditio Repositorio Institucional UJTL

Schlagwörter: Ingeniería, Máquinas, Automatización, Auditorías, Seguridad, Security auditing

Geographisches Schlagwort: Colombia

Dateibeschreibung: 58 páginas; application/pdf

Relation: Alon, U., Zilberstein, M., Levy, O., and Yahav, E. (2019). code2vec: learning distributed representations of code. Proc. ACM Program. Lang., 3(POPL):1– 29.; Antunes, N. and Vieira, M. (2009). Comparing the Effectiveness of Penetration Testing and Static Code Analysis on the Detection of SQL Injection Vulnerabilities in Web Services. In 2009 15th IEEE Pacific Rim International Symposium on Dependable Computing, pages 301–306, Shanghai, China. IEEE.; Chang, Y., Liu, B., Cong, L., Deng, H., Li, J., and Chen, Y. (2019). Vulnerability Parser: A Static Vulnerability Analysis System for Android Applications. J. Phys.: Conf. Ser., 1288:012053.; Chong, S., Guttman, J., Datta, A., Myers, A., Pierce, B., Schaumont, P., Sherwood, T., and Zeldovich, N. (2016). Report on the NSF Workshop on Formal Methods for Security. arXiv:1608.00678 [cs]. arXiv: 1608.00678.; Dauber, E., Caliskan, A., Harang, R., Shearer, G., Weisman, M., Nelson, F., and Greenstadt, R. (2019). Git Blame Who?: Stylistic Authorship Attribution of Small, Incomplete Source Code Fragments. Proceedings on Privacy Enhancing Technologies, 2019(3):389–408. arXiv: 1701.05681; Ferreira, A. M. and Kleppe, H. (2011). Effectiveness of Automated Application Penetration Testing Tools. Technical report, OS3 University of Amsterdam.; FluidAttacks (2020). Integrates.; Free Software Foundation (2020). GNU diffutils; Ghaffarian, S. M. and Shahriari, H. R. (2017). Software Vulnerability Analysis and Discovery Using Machine-Learning and Data-Mining Techniques: A Survey. ACM Computing Surveys, 50(4):1–36.; Li, Z., Zou, D., Xu, S., Jin, H., Zhu, Y., and Chen, Z. (2018a). SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities. arXiv:1807.06756 [cs, stat]. arXiv: 1807.06756.; Li, Z., Zou, D., Xu, S., Ou, X., Jin, H., Wang, S., Deng, Z., and Zhong, Y. (2018b). VulDeePecker: A Deep Learning-Based System for Vulnerability Detection. Proceedings 2018 Network and Distributed System Security Symposium. arXiv: 1801.01681.; Moor, O. d., Verbaere, M., Hajiyev, E., Avgustinov, P., Ekman, T., Ongkingco, N., Sereni, D., and Tibble, J. (2007). Keynote Address: .QL for Source Code Analysis. In Seventh IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM 2007), pages 3–16, Paris, France. IEEE; Ng, A. (2016). What Artificial Intelligence Can and Can’t Do Right Now. Harvard Business Review. Section: Analytics.; Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.; Rice, H. G. (1953). Classes of recursively enumerable sets and their decision problems. Trans. Amer. Math. Soc., 74(2):358–366.; Schwartz, E. J., Avgerinos, T., and Brumley, D. (2010). All You Ever Wanted to Know about Dynamic Taint Analysis and Forward Symbolic Execution (but Might Have Been Afraid to Ask). In 2010 IEEE Symposium on Security and Privacy, pages 317–331, Oakland, CA, USA. IEEE.; Sommer, R. and Paxson, V. (2010). Outside the Closed World: On Using Machine Learning for Network Intrusion Detection. In 2010 IEEE Symposium on Security and Privacy, pages 305–316, Oakland, CA, USA. IEEE.; Stefinko, Y., Piskozub, A., and Banakh, R. (2016). Manual and automated penetration testing. Benefits and drawbacks. Modern tendency. In 2016 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET), pages 488–491, Lviv, Ukraine. IEEE.; Yamaguchi, F., Wressnegger, C., Gascon, H., and Rieck, K. (2013). Chucky: exposing missing checks in source code for vulnerability discovery. In Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security - CCS ’13, pages 499–510, Berlin, Germany. ACM Press.; http://hdl.handle.net/20.500.12010/17241; http://expeditio.utadeo.edu.co

Verfügbarkeit: https://hdl.handle.net/20.500.12010/17241
http://expeditio.utadeo.edu.co

Strong correlation of gene counts and differentially expressed genes between a 3′ RNA-Seq and an RNA hybridization platform in transcriptome analyses from canine archival tissues.

Autoren: Haake, Alexander F. H. Loriani Fard, Alina K. Jovanovic, Vladimir M. et al.

Quelle: Frontiers in Veterinary Science. 2025, p1-13. 13p.

Data-Driven Early Warning Approach for Antimicrobial Resistance Prediction–Anomaly Detection Based on High-Level Indicators.

Autoren: Csorba, Szilveszter Vribék, Krisztián Farkas, Máté et al.

Quelle: Veterinary Sciences. Oct2025, Vol. 12 Issue 10, p935. 17p.

Survival Modelling Using Machine Learning and Immune–Nutritional Profiles in Advanced Gastric Cancer on Home Parenteral Nutrition.

Autoren: Matysiak, Konrad Hojdis, Aleksandra Szewczuk, Magdalena

Quelle: Nutrients. Aug2025, Vol. 17 Issue 15, p2414. 16p.

Unsupervised Voting for Detecting the Algorithmic Solving Strategy in Competitive Programming Solutions.

Autoren: Stoica, Alexandru Stefan Babiceanu, Daniel Mihaescu, Marian Cristian et al.

Quelle: Mathematics (2227-7390); Nov2025, Vol. 13 Issue 22, p3589, 19p

Schlagwörter: MACHINE learning, CLUSTERING algorithms, COMPUTER science, ALGORITHMS, SOURCE code

Sex-Specific Dietary Predictors of Blood Glucose Identified Through Decision Tree Modeling in Adults.

Autoren: Gautney, Joanna Aguilar, Christina Chan, Julian et al.

Quelle: Nutrients. Oct2025, Vol. 17 Issue 19, p3119. 12p.

Inferring leader-follower dynamics in three shark species using acoustic telemetry data.

Autoren: Kreuter, Nils Fernández-Gracia, Juan Eguíluz, Víctor M. et al.

Quelle: Movement Ecology. 8/28/2025, Vol. 13 Issue 1, p1-13. 13p.

Anti-Inflammatory Diet and Probiotic Supplementation as Strategies to Modulate Immune Dysregulation in Autism Spectrum Disorder.

Autoren: Naranjo-Galvis, Carlos Andrés Trejos-Gallego, Diana María Correa-Salazar, Cristina et al.

Quelle: Nutrients. Aug2025, Vol. 17 Issue 16, p2664. 21p.

GE-IA-NAM: gene–environment interaction analysis via imaging-assisted neural additive model.

Autoren: Li, Jingmao Xu, Yaqing Ma, Shuangge et al.

Quelle: Bioinformatics; Sep2025, Vol. 41 Issue 9, p1-9, 9p

Schlagwörter: CANCER research, GENOTYPE-environment interaction, DIAGNOSTIC imaging, DEEP learning, EMPIRICAL research, COMPUTER simulation

Official Replication Package of the EMSE Journal Article: 'Does Microservice Adoption Impact the Velocity? A Cohort Study'

Autoren: Saarimäki, Nyyti orcid:0000-0001-5538- Robredo, Mikel et al.

Schlagwörter: Empirical Software Engineering, Cohort Studies, Microservices Architecture, Development velocity, Velocity

Relation: https://zenodo.org/records/14876472; oai:zenodo.org:14876472; https://doi.org/10.5281/zenodo.14876472

Verfügbarkeit: https://doi.org/10.5281/zenodo.14876472
https://zenodo.org/records/14876472

Comparative virulence study of locally circulating peste des petits ruminants virus (PPRV) strains in indigenous Ethiopian goats.

Autoren: Aklilu, Fasil Ashenafi, Hagos Kassa, Tesfu et al.

Quelle: BMC Veterinary Research. 10/6/2025, Vol. 21 Issue 1, p1-11. 11p.

Schlagworte: Peste des petits ruminants, Microbial virulence, Symptoms, Goats, Comparative studies, Seroconversion, Communicable disease control

Geografische Kategorien: Ethiopia