Well, we’re between coup attempts and attempts to throw out a seditionist president, so I’m going to take the opportunity to describe this research that was recently accepted in @SERestoration for the grassland ecology SF.

Led by @Blackburn_RC and in collaboration with @barberecology and others not on the Twitters, this work was from Ryan’s MSc research @NIULive. I say research and not thesis, dear reader, bc it didn’t make it into his thesis.
Truth be told, this was an epic battle of computer vs researcher and many times, it felt like computer won. I encourage students in my lab pick a risky part and less risky part of their research. Drone imagery was Ryan’s risky part.
As sometimes happens w the risky part, this one didn't go in the thesis, but Ryan persevered to get it published nonetheless.
More times than not, in fact, it felt like the drone work went like this:
Ask @Blackburn_RC for the tale of the battle because today, I’m just going to stick to the findings. We wanted to see whether drone imagery could accurately predict the fine-scale ecological data we were gathering @nachusa, a tallgrass prairie #restoration.
We used a fixed wing @Parrot DiscoProAg drone paired w Sequoia sensor, which collects green, red, red-edge and near-infrared bands of wavelength.

Parrot provided this drone to us via a climate change grant (thanks, Parrot!).

Here is how takeoffs are supposed to go, btw.
Ryan flew this puppy over all of Nachusa (how many flights, Ryan?), and we compared indices arrived f the sensor (e.g. NDVI and many others) to the plant/soil data we’d collected at the local scale.
Our broad question was: Could we use indices collected by drone imagery to predict soil nutrients, plant biomass, plant composition or functional traits?
A lot of research has shown in monocrops, drone imagery can be used to approximate leaf N content, biomass, etc. But what about in one of the most diverse grasslands?
Of the 66 responses we measured (b/c we looked at max/min/mean/variance of various responses, response numbers blew up quickly), four worked when predicting the data values from the training sets we used to generate predictions.
Specifically, mean graminoid (grass-like stuff) cover, dead aboveground biomass, dry biomass, and mean soil K were predicted with multispectral drone imagery! This is cool bc multi spec sensors are cost effective compared to hyper spectral sensors which are super expensive.
Another note is that bc we chose which sites to use for the test data randomly, our only remnant in the study and a woodier site ended up in the test data set.
That meant we were using drone indices f restoration sites that were much different, to try to predict soil, plant, and biomass dynamics of pretty different sites ecologically.
What’s that mean? I think that if we had better paired sites ecologically, perhaps even more biodiversity indices could have been predicted, even in the super heterogenous landscape pictured.
So, take home: Drones w relatively cheap multispectral sensors might be a good tool to predict landscape-scale changed in ecological data, that it’s too time and cost-intensive to measure over such large scales.
Another take home: Drone image processing, stitching, etc. can be a huge pain in the butt.
Lastly, we used Ridge Regression to analyze the tons of independent variables we had (24 indices and all their mins/maxes, variances, means medians, etc - more than the dependent variables), and that tool is underutilized in drone studies, but we found it to be effective.
So...any of y'all looking for the riskier sides of projects, give drone imagery a try...it will give you endless data, punctuated with a few headaches, but also lots of fun new stats and questions to ask. /end
And...just in case you all missed it, in typical Ryan fashion, he did better at me in conveying the drone launch fail: https://t.co/gIC5gLJ4vn

More from Tech

(1) Some haters of #Cardano are not only bag holders but also imperative developers.

If you are an imperative programmers you know that Plutus is not the most intuitive -> (https://t.co/m3fzq7rJYb)

It is, however, intuitive for people with IT financial background, e.g. banks

(2)

IELE + k framework will be a real game changer because there will be DSLs (Domain Specific Languages) in any programming language supported by K framework. The only issue is that we need to wait for all this

(3) Good news is that the moment we get IELE integrated into Cardano, we get some popular langs. To my knowledge we should get from day one: Solidity and Rust, maybe others as well?

List of langs:
https://t.co/0uj1eBfrYj, some commits from many years ago..

@rv_inc ?

#Cardano

(a) Last but not least, marketing to people with Haskell, functional programming with experience and decision makers in banks is a tricky one, how do you market but not tell them you want to replace them. In the end one strategy is to pitch new markets, e.g. developing world

(b) As banks realize what is happening they maybe more inclined to join - not because they would like to but because they will have to - in such cases some development talent maybe re-routed to Plutus / Cardano / Algorand / Tezos

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Master Thread of all my threads!

Hello!! 👋

• I have curated some of the best tweets from the best traders we know of.

• Making one master thread and will keep posting all my threads under this.

• Go through this for super learning/value totally free of cost! 😃

1. 7 FREE OPTION TRADING COURSES FOR


2. THE ABSOLUTE BEST 15 SCANNERS EXPERTS ARE USING

Got these scanners from the following accounts:

1. @Pathik_Trader
2. @sanjufunda
3. @sanstocktrader
4. @SouravSenguptaI
5. @Rishikesh_ADX


3. 12 TRADING SETUPS which experts are using.

These setups I found from the following 4 accounts:

1. @Pathik_Trader
2. @sourabhsiso19
3. @ITRADE191
4.


4. Curated tweets on HOW TO SELL STRADDLES.

Everything covered in this thread.
1. Management
2. How to initiate
3. When to exit straddles
4. Examples
5. Videos on
#தினம்_ஒரு_திருவாசகம்
தொல்லை இரும்பிறவிச் சூழும் தளை நீக்கி
அல்லல் அறுத்து ஆனந்தம் ஆக்கியதே – எல்லை
மருவா நெறியளிக்கும் வாதவூர் எங்கோன்
திருவாசகம் என்னும் தேன்

பொருள்:
1.எப்போது ஆரம்பித்தது என அறியப்படமுடியாத தொலை காலமாக (தொல்லை)

2. இருந்து வரும் (இரும்)


3.பிறவிப் பயணத்திலே ஆழ்த்துகின்ற (பிறவி சூழும்)

4.அறியாமையாகிய இடரை (தளை)

5.அகற்றி (நீக்கி),

6.அதன் விளைவால் சுகதுக்கமெனும் துயரங்கள் விலக (அல்லல் அறுத்து),

7.முழுநிறைவாய்த் தன்னுளே இறைவனை உணர்த்துவதே (ஆனந்த மாக்கியதே),

8.பிறந்து இறக்கும் காலவெளிகளில் (எல்லை)

9.பிணைக்காமல் (மருவா)

10.காக்கும் மெய்யறிவினைத் தருகின்ற (நெறியளிக்கும்),

11.என் தலைவனான மாணிக்க வாசகரின் (வாதவூரெங்கோன்)

12.திருவாசகம் எனும் தேன் (திருவா சகமென்னுந் தேன்)

முதல்வரி: பிறவி என்பது முன்வினை விதையால் முளைப்பதோர் பெருமரம். அந்த ‘முன்வினை’ எங்கு ஆரம்பித்தது எனச் சொல்ல இயலாது. ஆனால் ‘அறியாமை’ ஒன்றே ஆசைக்கும்,, அச்சத்துக்கும் காரணம் என்பதால், அவையே வினைகளை விளைவிப்பன என்பதால், தொடர்ந்து வரும் பிறவிகளுக்கு, ‘அறியாமையே’ காரணம்

அறியாமைக்கு ஆரம்பம் கிடையாது. நமக்கு ஒரு பொருளைப் பற்றிய அறிவு எப்போதிருந்து இல்லை? அதைச் சொல்ல முடியாது. அதனாலேதான் முதலடியில், ஆரம்பமில்லாத அஞ்ஞானத்தை பிறவிகளுக்குக் காரணமாகச் சொல்லியது. ஆனால் அறியாமை, அறிவின் எழுச்சியால், அப்போதே முடிந்து விடும்.
Recently, the @CNIL issued a decision regarding the GDPR compliance of an unknown French adtech company named "Vectaury". It may seem like small fry, but the decision has potential wide-ranging impacts for Google, the IAB framework, and today's adtech. It's thread time! 👇

It's all in French, but if you're up for it you can read:
• Their blog post (lacks the most interesting details):
https://t.co/PHkDcOT1hy
• Their high-level legal decision: https://t.co/hwpiEvjodt
• The full notification: https://t.co/QQB7rfynha

I've read it so you needn't!

Vectaury was collecting geolocation data in order to create profiles (eg. people who often go to this or that type of shop) so as to power ad targeting. They operate through embedded SDKs and ad bidding, making them invisible to users.

The @CNIL notes that profiling based off of geolocation presents particular risks since it reveals people's movements and habits. As risky, the processing requires consent — this will be the heart of their assessment.

Interesting point: they justify the decision in part because of how many people COULD be targeted in this way (rather than how many have — though they note that too). Because it's on a phone, and many have phones, it is considered large-scale processing no matter what.