suss
Regular
Would you prefer they read shareholder's questions or people who are looking at building products?
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
Rise from the ashes
Regular
I'm not going to tell a 40 odd billion dollar business how to run their company.
Maybe email them and tell them they are going about this all wrong
Boab
I wish I could paint like Vincent
Plenty of us know you are not a manipulator, you just have opinions which may be different to others.Nope. I didn't delete any, but some sooks reported it.
I don't play victim, neither do I hide, I think we all know who's that famous for.
As to your "serve", @Quatrojos made a few pointers how someone entered and exited TSE along with LDA Ann timings, funnily enough April early was supposedly when LDA finished their deal. Haven't seen him since, have we?
Making this comment only because my posts here are frequently reported, and banned by the cartel, but I just wanted to say Hi to everyone.
Sean still has a couple of weeks to fulfill his "judge me on results" claim.
Can he bring home the bacon with an IP contract?
Or would he lean on all those immaterial partnerships to justify the oppies?
Also keen to see if he'll sell some of his free options like last year ? From memory it was more than $700k which he SOLD. How many will he sell this year, if at all? Surely if he sees this going places, he'd HODL it.
As to someone's concern me being a manipulator, company has my share holdings, and they can check if I'm trading this or not. I have no intentions here to mislead anyone, always dyor.
Cheers to you BL
MDhere
Top 20
and is in bed with Megachips
MDhere
Top 20
how many beers do u have lined up for next week?
After weeks of deliberations, I've decided to vote yes to bonus payments. Whilst I'm not happy with the sp, I am happy with the progress of the company and I am confident that we will have a better sp by the end of 2023. I will not vote yes next year if revenue is still low and unstable.
D
Deleted member 118
Guest
Seems like things are moving along very nicely indeed
The data was analyzed by Brainchip with a Spiking Neural Network, the adjacent confusion matrix shows the results on the test set. The test set included 31 samples- 21 positives and 10 negatives from 21 tested subjects. Zero out of 21 positive samples were identified correctly which represents 100% sensitivity and 4 out of 10 negative samples were identified correctly which represents 40% specificity. The overall accuracy was 80.65% The second study was performed with the multiuse NaNose sensors installed in Sniffphone device. The dataset included 165 samples taken from 141 subjects tested with Sniffphone device at Zayed Military Hospital - 65 samples from 65 COVID-19 positive subjects and 100 samples from 76 COVID-19 negative subjects (Several negative subjects were sampled two or three times). A Linear discriminative analysis was performed. The adjacent confusion matrix shows the results on the test set that that was completely blind to the training and validation of the model. The test set included 37 samples - 8 positive and 29 negative samples from 27 tested subjects. Seven out of eight positive samples were identified correctly which represents 87.5% sensitivity, and 25 out of the 29 negative samples were identified correctly which represents 86.2% specificity. The overall accuracy was therefore 86.5%.
The same data set was analyzed also by the SNN methodology. To make the SNN most efficient, 34 samples were discarded due to noise or improper vector dimensionality. Thus, the dataset included 131 samples taken from 126 subjects tested with Sniffphone device at Zayed Military Hospital- 62 samples from 62 COVID-19 positive subjects and 69 samples from 64 COVID-19 negative subjects (Several negative subjects were sampled two or three times). The adjacent confusion matrix shows the results on the test set that that was completely blind to the training and validation of the model. The test set included 53 samples - 20 positive and 33 negative samples from 53 tested subjects. Nineteen out of 20 positive samples were identified correctly which represents 95% sensitivity and 29 out of 33 negative samples were identified correctly which represents 87.87 % specificity. The overall accuracy was therefore 90.5%.
Two different analysis methods were applied on the dataset and both showed excellent results for the differentiation between COVID positive and COVID negative. While the multiuse sensors achieved a much better specificity (-87%) compared to the single use sensors (40%), this is more likely a result of the vast difference between the datasets: the dataset of the multiuse sensors included 165 samples from 141 subjects while the dataset of the single-use sensors included 66 samples from 45 subjects. During the Clinical study with COVID19 patients the company further improved the 4 components of the device: the mechanical design including the breath collection mechanism, the electronics, the sensors and the classifying algorithm.
WO2021214763A1 - Device and method for rapid detection of viruses - Google Patents
The invention proposes an approach utilizing novel and artificially intelligent hybrid sensor arrays with multiplexed detection capabilities for disease-specific biomarkers from the exhaled breath of a subject. The technology provides a rapid and highly accurate diagnosis in various COVID-19...
patents.google.com
The data was analyzed by Brainchip with a Spiking Neural Network, the adjacent confusion matrix shows the results on the test set. The test set included 31 samples- 21 positives and 10 negatives from 21 tested subjects. Zero out of 21 positive samples were identified correctly which represents 100% sensitivity and 4 out of 10 negative samples were identified correctly which represents 40% specificity. The overall accuracy was 80.65% The second study was performed with the multiuse NaNose sensors installed in Sniffphone device. The dataset included 165 samples taken from 141 subjects tested with Sniffphone device at Zayed Military Hospital - 65 samples from 65 COVID-19 positive subjects and 100 samples from 76 COVID-19 negative subjects (Several negative subjects were sampled two or three times). A Linear discriminative analysis was performed. The adjacent confusion matrix shows the results on the test set that that was completely blind to the training and validation of the model. The test set included 37 samples - 8 positive and 29 negative samples from 27 tested subjects. Seven out of eight positive samples were identified correctly which represents 87.5% sensitivity, and 25 out of the 29 negative samples were identified correctly which represents 86.2% specificity. The overall accuracy was therefore 86.5%.
The same data set was analyzed also by the SNN methodology. To make the SNN most efficient, 34 samples were discarded due to noise or improper vector dimensionality. Thus, the dataset included 131 samples taken from 126 subjects tested with Sniffphone device at Zayed Military Hospital- 62 samples from 62 COVID-19 positive subjects and 69 samples from 64 COVID-19 negative subjects (Several negative subjects were sampled two or three times). The adjacent confusion matrix shows the results on the test set that that was completely blind to the training and validation of the model. The test set included 53 samples - 20 positive and 33 negative samples from 53 tested subjects. Nineteen out of 20 positive samples were identified correctly which represents 95% sensitivity and 29 out of 33 negative samples were identified correctly which represents 87.87 % specificity. The overall accuracy was therefore 90.5%.
Two different analysis methods were applied on the dataset and both showed excellent results for the differentiation between COVID positive and COVID negative. While the multiuse sensors achieved a much better specificity (-87%) compared to the single use sensors (40%), this is more likely a result of the vast difference between the datasets: the dataset of the multiuse sensors included 165 samples from 141 subjects while the dataset of the single-use sensors included 66 samples from 45 subjects. During the Clinical study with COVID19 patients the company further improved the 4 components of the device: the mechanical design including the breath collection mechanism, the electronics, the sensors and the classifying algorithm.
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Stable Genius
Regular
Valeo on LinkedIn: 100 Years By Your Side | 21 comments
Valeo turns 100 this year! Whether you knew it or not, we've been by your side for 100 years, at the heart of your journeys, making them cleaner, safer and… | 21 comments on LinkedIn
Video won’t save separately but my unconscious bias says it’s a nod to Brainchip!
alwaysgreen
Top 20
@BaconLover
And this pie in the sky astrology post based on zero facts and a posters feelings is apparently worth keeping.
BaconLover
Founding Member
I've been seeing this prediction for a few months now.@BaconLover
And this pie in the sky astrology post based on zero facts and a posters feelings is apparently worth keeping.
Eventually it'll come true.
Revolutionary Sensor Mimics Human Eye for Vivid Imagery - Neuroscience News
Researchers have created a device that emulates the human eye's ability to see color by using narrowband perovskite photodetectors and a neuromorphic algorithm.
neurosciencenews.com
thelittleshort
Top Bloke
Jhon Carrillo Barrios on LinkedIn: The Future of Processors: Neuromorphic Computation
The Future of Processors: Neuromorphic Computation How Neuromorphic Computing is Revolutionizing the Way We Process Information using Human Brain…
jtardif999
Regular
as
Producing a patent for the device would suggest they (Nanose) are closer to realising a commercial product. I wonder how FDA approval is going? Could this be the start of real progress on the Nanose front?
AusEire
Founding Member.
Did you report it? Or are you just going to tell us your feelings about it?@BaconLover
And this pie in the sky astrology post based on zero facts and a posters feelings is apparently worth keeping.
jtardif999
Regular
So that’s where FF has gotten to these daysAnd are you sure you haven't been smoking the GREEN with your GREEN GINGER WINE? (Ha ha)
Lovinglife
Member
She’s only 23And are you sure you haven't been smoking the GREEN with your GREEN GINGER WINE? (Ha ha)
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