I released the online version of my number puzzle game Summit today, free to play in browser. Aimed at mathsy types with a few minutes to kill!
https://t.co/qYOJTsHJAx
I have been developing a free mobile number puzzle game, and excitingly its now in Early Access on Google Play. If you own an Android, like number games (sudoku, nerdle etc.), and would like to test the game, please get in contact. See the video here for more details and a demo!
Summit is a new number puzzle game that will be releasing for free on Android/iPhone. Seeking Android Early Access testers. If you like number games and own an Android, watch here for more details
https://t.co/sSqy92zK16
The PROV ontology can be used to model provenance information for data generated from multiple entities and processes. But what if my workflow contains a feedback loop? I.e recursively generating and updating data in light of new information. Is there a way to model this?
@LeftHandedDave@grassmannian @SC_Griffith As you point out, ii) could equally be phrased as assuming that if it works for n=M then it must work for n=M+1. The point is, knowing that statement is true for n=1 and combining with ii) means its also true for n=2. Then combining with ii) again means itโs true for n=3, etc.
@LeftHandedDave@grassmannian @SC_Griffith This isnโt quite the logic of induction though. i) you show the statement holds in the case n=1. ii) you assume the statement holds for n=N, and show that if this is the case, then it must also hold for n=N+1. Combining i) and ii) proves the statement for all n > 0
@helenahhartmann@m_wall From what I can see, the code in the es_cpi_spm is computing confidence intervals for cohen's d rather than cohen's g. The bias correction factor that converts d to g (J in the script) is not used in the loops where the CI's are created. Happy to be corrected if i am mistaken!
These results highlight the importance of replication/reproduction and the need for further work towards pipeline harmonization.
Group-level maps for all workflows shared on Neurovault, scripts shared on Github/OSF. See the manuscript for full details and full results! 5/5
Isolating the Sources of Pipeline-Variability in Group-Level Task-fMRI results - now out in HBM! w/ @ten_photos & @cmaumet
https://t.co/hApyivWs0w
Reanalyzing 3 task-fMRI studies with workflows that interchange between AFNI, FSL and SPM at different stages of the pipeline. 1/5
But we also identify analysis step manipulations that cause minimal differences in the final results. E.g., group-level results were largely unaffected by changes in the software used to model the low-frequency fMRI drifts. 4/5
Today was my last day in academia. I'm very excited to be joining @smithinst as a mathematical consultant on 22nd November. As an RA, PhD student and postdoc I had so many good times I will remember, thank you to all friends and collaborators that have helped me along the way!