The manufacturing trade deficit is up significantly in the first two years of the Trump Presidency. Pretty clear that stimulus and a strong dollar has had a bigger effect than his trade protectionism.

TILAK:AN AGE OLD PRACTICE OF HINDUS

The beautiful Matribhumi of Bharat is the land of many religions &cultures but its Hinduism with which the world identifies this great Nation. Hinduism is the religion with science at its heart &its also a religion of many rituals &traditions.

Today we will talk about a very prominent and noticeable Mark on the foreheads of #Hindus called 'Tilak'. Tilaks are of different designs and colors. It symbolizes different meanings for different people such as religious expression, spiritual symbol, marital status etc.

Tilak is a very alluring design that is made in between the eyebrows. Vermilion, Chandan, Turmeric, Kumkum, Rice and Rice water, Vibhuti etc can all be used as Tilaks.
Why this particular spot is used for Tilak? Bcoz, it is mentioned as 'Ajna Chakra' in terms of yoga.

This chakra is the sixth & most important chakra of the human body. It's the junction where the highest number of neurons meet & is described as most important and sensitive spot in the human body.Different #Hindu traditions use different shapes and ingredients to make the Tilak.

SPIRITUAL SIGNIFICANCE

Various Hindu Scriptures mention the importance of Tilak in Sanatan Dharma. It is believed that energy, strength and divinity comes to those who apply Tilak. It is also regarded as a welcoming expression and to honour the guests when they arrive.

कुंडली में 12 भाव होते हैं। कैसे ज्योतिष द्वारा रोग के आंकलन करते समय कुंडली के विभिन्न भावों से गणना करते हैं आज इस पर चर्चा करेंगे।
कुण्डली को कालपुरुष की संज्ञा देकर इसमें शरीर के अंगों को स्थापित कर उनसे रोग, रोगेश, रोग को बढ़ाने घटाने वाले ग्रह

रोग की स्थिति में उत्प्रेरक का कार्य करने वाले ग्रह, आयुर्वेदिक/ऐलोपैथी/होमियोपैथी में से कौन कारगर होगा इसका आँकलन, रक्त विकार, रक्त और आपरेशन की स्थिति, कौन सा आंतरिक या बाहरी अंग प्रभावित होगा इत्यादि गणना करने में कुंडली का प्रयोग किया जाता है।

मेडिकल ज्योतिष में आज के समय में Dr. K. S. Charak का नाम निर्विवाद रूप से प्रथम स्थान रखता है। उनकी लिखी कई पुस्तकें आज इस क्षेत्र में नए ज्योतिषों का मार्गदर्शन कर रही हैं।
प्रथम भाव -
इस भाव से हम व्यक्ति की रोगप्रतिरोधक क्षमता, सिर, मष्तिस्क का विचार करते हैं।

द्वितीय भाव-
दाहिना नेत्र, मुख, वाणी, नाक, गर्दन व गले के ऊपरी भाग का विचार होता है।
तृतीय भाव-
अस्थि, गला,कान, हाथ, कंधे व छाती के आंतरिक अंगों का शुरुआती भाग इत्यादि।

चतुर्थ भाव- छाती व इसके आंतरिक अंग, जातक की मानसिक स्थिति/प्रकृति, स्तन आदि की गणना की जाती है

पंचम भाव-
जातक की बुद्धि व उसकी तीव्रता,पीठ, पसलियां,पेट, हृदय की स्थिति आंकलन में प्रयोग होता है।

षष्ठ भाव-
रोग भाव कहा जाता है। कुंडली मे इसके तत्कालिक भाव स्वामी, कालपुरुष कुंडली के स्वामी, दृष्टि संबंध, रोगेश की स्थिति, रोगेश के नक्षत्र औऱ रोगेश व भाव की डिग्री इत्यादि।

The 2018 State of JavaScript survey is out. They got 20,000 responses and have some delicious, delicious data. I'mma thread in some highlights:

First up: JavaScript flavors. There's TypeScript at 47% adoption, a tad higher than npm's own survey results (which said 46%). npm's survey is coming up again soon and will ask about TypeScript in a lot more detail.

I really, *really* like SoJ's "would not use again" question, which lets people who've abandoned a tech self-identify. This is noticeable in the graph above with Flow users -- 41% of people who've used Flow say they wouldn't use it again.

Now JavaScript frameworks. It's been a full year since npm's survey, so these numbers differ from ours in the directions I expected:
React 65% (vs. 60%)
Vue 29% (vs. 24%)
Ember 5% (vs 4%, I was expecting a bigger rise)

But there's a shocker in here: Angular.

npm's survey had Angular at 40% last year and SoJ has it at either:
- 58% (if you include those who don't want to use it again)
- 24% (if you count only those who like it)
Since npm's question didn't ask if they intend to *continue* using it I think that might explain this.

Important paper from Google on large batch optimization. They do impressively careful experiments measuring # iterations needed to achieve target validation error at various batch sizes. The main "surprise" is the lack of surprises. [thread]

https://t.co/7QIx5CFdfJ

The paper is a good example of lots of elements of good experimental design. They validate their metric by showing lots of variants give consistent results. They tune hyperparamters separately for each condition, check that optimum isn't at the endpoints, and measure sensitivity.

They have separate experiments where the hold fixed # iterations and # epochs, which (as they explain) measure very different things. They avoid confounds, such as batch norm's artificial dependence between batch size and regularization strength.

When the experiments are done carefully enough, the results are remarkably consistent between different datasets and architectures. Qualitatively, MNIST behaves just like ImageNet.

Importantly, they don't find any evidence for a "sharp/flat optima" effect whereby better optimization leads to worse final results. They have a good discussion of experimental artifacts/confounds in past papers where such effects were reported.