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Oluwanishola Akeju (Whitehead)
@Fekazii8 5 years, 10 months ago 945 views

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Let The Daughter in-law live.

A friend got his girlfriend pregnant back then in school. The news got home.
Well, my friend was 'of age'. So his mom seek for the girl, but then rejected her because she is an Ijaw. she agreed to accept the baby.

My friend refused. The girl has no parents. Because of his refusal to break up with the girl; his mom got angry and he was denied from going home. And he never did for years.

Following, They graduated and both served same year. And as fate will have it; The girl was retained where she served, while my friend couldn't land a good job in time. With a good salary scale my friend's wife earned, she sponsored him so as to have his masters in the states.

He got a job there and worked on his stay. His wife and child then joined him.
On their third baby arrival last year May, he rang me after he'd missed my calls.
I was surprised when he told me, his mom was with them in the states for 'Omugo'

He told me, his mom and wife are now so close; like mother and daughter.
I wanted to ask questions but well..

Only if we don't discriminate..
Only if we see ourselves as one..
Only if our mothers could be more cordial and loving to our wives not minding the tribe or color; and seeing them as their own daughter.

Only if those husband's sisters could respect and let our wives enjoy their home and marriage.
Only if our husband's family knows that the secret of greatness of their son can also be as a result of a praying and an effortless wife.

Only if we can see the hidden power, strength of our wives; and the plans they've in making their husband a better man and king.
Only if mothers know that their prayers are not enough...but they needed that of their son's wife too.

A lot of men out there are great with the help of their wives; but they take no glory. They are Seated behind the scene, While the mothers in-law believed their sons made it all alone.
Only if they know. Only if.

And Maybe we should start treating our daughters in-law the way we would want our own daughters to be treated too.

Oluwanishola Akeju whitehead
@threadreaderapp unroll

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$Vibhu Vashisth \U0001f1ee\U0001f1f3$

Vibhu Vashisth 🇮🇳...
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Did you know?

Rani Bhabani was the Queen of Natore, Bengal or 'Natorer Rani' as she was famously called. She made great contributions in Tarapith & renovated Benaras.

In Varanasi, Durga Kund Mandir was constructed by Rani Bhabani in the 18th century.

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A woman as a zamindar was extremely rare in those days, but Rani Bhabani managed the vast Rajshahi zamindari most efficiently and effectively for over four decades.
Maharani Bhavani was very pious in her personal life. She passed her life in a strict disciplined way.

She conducted the administrative work skillfully. As a Maharani she was an impartial judge. Rani Bhavani had a very hard time during the fag end of her life. The Zamindari (estate) incurred serious loss due to the change of the revenue policy of the company.

In spite of being the owner of a large Zamindari (estate), she had to depend on the stipend of the government in the later part of her life. The amount of this stipend gradually decreased and became Rs. 1000/- only in the end.

JayneshKasliwal
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#stocks #StockMarket
1/n

Moving averages
EMA and SMA
1. Exponential Moving Average (EMA) is similar to Simple Moving Average (SMA), measuring trend direction over a period of time.
2. However, whereas SMA simply calculates an average of price data, EMA applies more weight to data that is more current
2/

21 EMA : SHORT TERM TREND
1. Calculates the Average Price of past 21 days
2. A stock above 21 ema is in strong momentum , which will give pullbacks to 21 ema again and again.
3. Stock above 21 ema should never be shorted so as to stay with the trend
3/n

3. A stock which moves 10-15% above 21 ema will be too extended and hence will generally pullback
Stocks take dynamic support on 21 ema
4. You can Short only if its too extended like 15 to 20% from 21 ema ( Not advised )
#ADANIPOWER:
#pnbhousing
4/n

1. Whenever 21 ema is sloping upwards its called uptrend
2. Whenever 21 ema is sloping downwards its called dowtrend
3. Whenever 21 ema is sideways its called consolidation
⏫Buy in uptrend
⏬Sell in downtrend
Examples:
5/n

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#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.

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How can we use language supervision to learn better visual representations for robotics?

Introducing Voltron: Language-Driven Representation Learning for Robotics!

Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z

🧵👇(1 / 12)

Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.

Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)

The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).

The secret is *balance* (3/12)

Starting with a masked autoencoder over frames from these video clips, make a choice:

1) Condition on language and improve our ability to reconstruct the scene.

2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)

By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.

Why is the ability to shape this balance important? (5/12)

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