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📄 pyclustering 0.8.1

Quicklook:
Novikov, Andrei et al. (2018) · Zenodo
Reads: 0 · Citations: 1
DOI: 10.5281/zenodo.1254845

🔗 https://ui.adsabs.harvard.edu/abs/2018zndo...1254845N/abstract

#Astronomy #Astrophysics #AstroAI #ClusteringDataminingClusteranalysisAiMachinelearningOscillatoryn…

pyclustering 0.8.1

pyclustering 0.8.1 library is collection of clustering algorithms, oscillatory networks, neural networks, etc. GENERAL CHANGES: Implemented feature to use specific metric for distance calculation in K-Means algorithm (pyclustering.cluster.kmeans, ccore.clst.kmeans). See: https://github.com/annoviko/pyclustering/issues/434 Implemented BANG-clustering algorithm with result visualizer (pyclustering.cluster.bang). See: https://github.com/annoviko/pyclustering/issues/424 Implemented feature to use specific metric for distance calculation in K-Medians algorithm (pyclustering.cluster.kmedians, ccore.clst.kmedians). See: https://github.com/annoviko/pyclustering/issues/429 Supported new type of input data for K-Medoids - distance matrix (pyclustering.cluster.kmedoids, ccore.clst.kmedoids). See: https://github.com/annoviko/pyclustering/issues/418 Implemented TTSAS algorithm (pyclustering.cluster.ttsas, ccore.clst.ttsas). See: https://github.com/annoviko/pyclustering/issues/398 Implemented MBSAS algorithm (pyclustering.cluster.mbsas, ccore.clst.mbsas). See: https://github.com/annoviko/pyclustering/issues/398 Implemented BSAS algorithm (pyclustering.cluster.bsas, ccore.clst.bsas). See: https://github.com/annoviko/pyclustering/issues/398 Implemented feature to use specific metric for distance calculation in K-Medoids algorithm (pyclustering.cluster.kmedoids, ccore.clst.kmedoids). See: https://github.com/annoviko/pyclustering/issues/417 Implemented distance metric collection (pyclustering.utils.metric, ccore.utils.metric). See: no reference. Supported new type of input data for OPTICS - distance matrix (pyclustering.cluster.optics, ccore.clst.optics). See: https://github.com/annoviko/pyclustering/issues/412 Supported new type of input data for DBSCAN - distance matrix (pyclustering.cluster.dbscan, ccore.clst.dbscan). See: no reference. Implemented K-Means observer and visualizer to visualize and animate clustering results (pyclustering.cluster.kmeans, ccore.clst.kmeans). See: no reference. CORRECTED MAJOR BUGS: Bug with out of range in K-Medians (pyclustering.cluster.kmedians, ccore.clst.kmedians). See: https://github.com/annoviko/pyclustering/issues/428 Bug with fast linking in PCNN (python implementation only) that wasn't used despite the corresponding option (pyclustering.nnet.pcnn). See: https://github.com/annoviko/pyclustering/issues/419

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📄 Code for the Fanoos Multi-Resolution, Multi-Strength, Interactive XAI…

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Bayani, David et al. (2021) · Zenodo
Reads: 0 · Citations: 2
DOI: 10.5281/zenodo.5513079

🔗 https://ui.adsabs.harvard.edu/abs/2021zndo...5513079B/abstract

#Astronomy #Astrophysics #AstroAI #NeuralNetworkVerification #NeuralNetworkCertification

Code for the Fanoos Multi-Resolution, Multi-Strength, Interactive XAI System

This upload contains a tarred and compressed copy of the code and git-history available at https://github.com/DBay-ani/Fanoos as of hour 3 day 16 month 5 year 2021 UTC. See the following paper for a description:     @inproceedings{DBLP:conf/vmcai/BayaniM22,       author    = {David Bayani and Stefan Mitsch},       editor    = {Bernd Finkbeiner and                    Thomas Wies},       title     = {Fanoos: Multi-resolution, Multi-strength, Interactive Explanations for Learned Systems},       booktitle = {Verification, Model Checking, and Abstract Interpretation - 23rd International Conference, {VMCAI} 2022, Philadelphia, PA, USA, January 16-18, 2022, Proceedings},       series    = {Lecture Notes in Computer Science},       volume    = {13182},       pages     = {43--68},       publisher = {Springer},       year      = {2022},       url       = {https://doi.org/10.1007/978-3-030-94583-1\_3},       doi       = {10.1007/978-3-030-94583-1\_3},       timestamp = {Fri, 21 Jan 2022 22:02:46 +0100},       biburl    = {https://dblp.org/rec/conf/vmcai/BayaniM22.bib},       bibsource = {dblp computer science bibliography, https://dblp.org}     } or see the extended write-up at:     @article{bayani2020fanoos,       title={Fanoos: Multi-Resolution, Multi-Strength, Interactive Explanations for Learned Systems},       author={Bayani, David and Mitsch, Stefan},       journal={arXiv preprint arXiv:2006.12453},       year={2020},       url={https://arxiv.org/abs/2006.12453}     } This upload is related to the additional written materials available on the following Zenodo item:     @misc{david_bayani_2022_6069468,       author       = {David Bayani},       title        = {{Further Materials (Additional Slides, Write-ups, Results, etc.) for Fanoos: Multi-Resolution, Multi-Strength, Interactive XAI System}},       month        = feb,       year         = 2022,       publisher    = {Zenodo},       doi          = {10.5281/zenodo.6069468},       url          = {https://doi.org/10.5281/zenodo.6069468}     } We note that this upload ("Code for Fanoos [...]"), in contrast to the material cited immediately above, contains source related to Fanoos, as opposed to additional write-up, slides, etc.

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📄 Deep Residual Learning for Image Recognition

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He, Kaiming et al. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Reads: 45017 · Citations: 27779
DOI: 10.1109/CVPR.2016.90

🔗 https://ui.adsabs.harvard.edu/abs/2016cvpr.confE...1H/abstract

#Astronomy #Astrophysics #AstroAI #ComputerScienceComputerVisionAndPatternRecognition

adsdailyDec 29

📄 Random Forests.

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Breiman, Leo et al. (2001) · Machine Learning
Reads: 14 · Citations: 31413
DOI: 10.1023/A:1010933404324

🔗 https://ui.adsabs.harvard.edu/abs/2001MachL..45....5B/abstract

#Astronomy #Astrophysics #AstroAI #MachineLearning

Random Forests.

Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148-156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.

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📄 Rapid inversion method for parameters of contact binaries based on in…

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Zeng, Xiangyun et al. (2026) · New Astronomy
Reads: 0 · Citations: 0
DOI: 10.1016/j.newast.2025.102511

#Astronomy #Astrophysics #AstroAI #NeuralNetwork #DifferentialEvolution

More Fun Making ItOct 24

AstroAI DT132A Digital Multimeter unbox and first thoughts

https://makertube.net/w/93puqWs7xxvi2wfcFjot8E

AstroAI DT132A Digital Multimeter unbox and first thoughts

PeerTube
Howard Smith MD, AMJul 14, 2025
AstroAI 4-Liter/6-Can Minifridges have electrical switches that short circuit leading to fires and burns. The affected units are model number LY0204A. #astroai #inifridge #overheating #fires #burns #recall
https://www.instagram.com/p/DME454-pBKD/
Howard G. Smith MD, AM on Instagram: "AstroAI Minifridges Overheat AstroAI 4-Liter/6-Can Minifridges have electrical switches that short circuit leading to fires and burns. The affected units are model number LY0204A with serial numbers starting with 19, 20, 21, 2201, 2202, or 2203. About 249,100 of these fridges were sold nationwide through Amazon.com and AstroAI.com from June 2019 through June 2022. Turn off these minifridges immediately. Contact AstroAI for a free replacement by writing the word “Recalled” on the fridge in permanent marker and sending a photo showing both the model and serial number to recall@astroai.com or upload it at astroai.com/product-recall. Then discard the unit at your local recycling center. For additional information, call AstroAI at 1-877-278-7624. https://www.cpsc.gov/Recalls/2025/AstroAI-Recalls-Minifridges-Due-to-Fire-and-Burn-Hazards-Two-Fires-Resulted-in-More-Than-360000-in-Reported-Property-Damages #astroai #inifridge #overheating #fires #burns #recall"

0 likes, 0 comments - drhowardsmithreports on July 13, 2025: "AstroAI Minifridges Overheat AstroAI 4-Liter/6-Can Minifridges have electrical switches that short circuit leading to fires and burns. The affected units are model number LY0204A with serial numbers starting with 19, 20, 21, 2201, 2202, or 2203. About 249,100 of these fridges were sold nationwide through Amazon.com and AstroAI.com from June 2019 through June 2022. Turn off these minifridges immediately. Contact AstroAI for a free replacement by writing the word “Recalled” on the fridge in permanent marker and sending a photo showing both the model and serial number to recall@astroai.com or upload it at astroai.com/product-recall. Then discard the unit at your local recycling center. For additional information, call AstroAI at 1-877-278-7624. https://www.cpsc.gov/Recalls/2025/AstroAI-Recalls-Minifridges-Due-to-Fire-and-Burn-Hazards-Two-Fires-Resulted-in-More-Than-360000-in-Reported-Property-Damages #astroai #inifridge #overheating #fires #burns #recall".

Instagram
theclydeMar 18, 2024
Bought an #AstroAI multimeter. I am having not enough experience to judge if it really is good, but it seems great to me... nice packaging, came with batteries plus a set of batteries installed, has a flashlight, bright backlight, feels nice and rugged, and unexpectedly super fast shipping.
Esra BulbulApr 28, 2023
Super proud to see #AstroIA alive! Congrats @cecigarraffo @juramaga
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RT @juramaga
Check out the website of #AstroAI, a new center at the @CenterForAstro to enable the next generation of discovery in astrophysics, using state-of-the art tools of Artificial Intelligence and Machine Learning! Let us know if you want to be a partner! http://astroai.cfa.harvard.edu/
https://twitter.com/juramaga/status/1651774019308249089
AstroAI

Using Artificial Intelligence to Solve the Mysteries of the Universe

AstroAI
Stefan Dec 24, 2022
@Nathan wow didn’t know these devices are now also AI powered 😂 #AstroAI