Comments URL: https://news.ycombinator.com/item?id=42949883

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ONDC is not an application, an intermediary, or software, but a set of specifications designed to foster open interchange and connections between shoppers, technology platforms, and retailers.[3] Technological self reliance, demand for level playing field mainly from small retailers, lower the barrier of entry and discovery online, adoption of open digital ecosystem across key sectors and fixing the non-competitive behavior of big ecommerce firms like Amazon and Flipkart to capture the US$810 billion domestic retail market led to its creation.[4] Designed to keep check on Big Tech companies from violating Consumer Protection (E-Commerce) (Amendment) Rules, 2021 due to concentration of market power by integrating them into an open-source decentralised network where data portability will break data silos while data interoperability will allow innovation.

Comments URL: https://news.ycombinator.com/item?id=29097734

Points: 4

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Comments URL: https://news.ycombinator.com/item?id=26647898

Points: 2

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Some of the usecase to build such graph is to get node embeddeding for fraud prevention or link prediction etc.

We are trying : https://www.thehansindia.com/posts/index/Young-Hans/2018-11-...

As far as I know, I think he might have sent it to journals and conferences. but the problem is for them to consider it as it is a very big problem in math they look for some initial validation from the community I may be wrong.

This is my attempt to understand how the hacker news community would validate it.

Dr. Kumar's response to the above: I certainly know that the lambda - sequence is fixed and unalterable because each lambda(n) is obtained by factorizing n into prime factors and then defining lambda(n)=+1 if there are even factors else, lambda(n) =-1 if n is a prime or has odd prime factors.

In the paper for very long sequences, the lambda-sequence is treated as one instance of a hypothetical random walk. If this analogy is true then the magnitude of L(N), which is the sum of the first N terms of lambda(n)’s, (where N is very large) can be likened to the expected distance travelled by a random walker in N steps which is given by C .N^(1/2) (see S. Chandrasekhar(1943)).

However, for this analogy to be really meaningful and accurate, one must prove the lambda(n), for large and arbitrary n, must satisfy the criteria: (i) equal probabilities of being +1 or -1 , (ii) the lambda-sequence has no cycle and (iii) unpredictability.

In the paper I provide mathematical proofs for all the above criteria, after which one can deduce the asymptotic expression for L(N) as C. N^(1/2+e). We then invoke (i) Littlewoods Theorem 1 (proved in the paper) and then (ii) use Khinchin (1924) and Kolmogorov’s (1929) law of the iterated logarithm, for evaluating the bound ‘e’ and to show that e tends to zero as N tends to infinity, thus finally proving R.H.

One last comment: Herrington quotes Borwein’s statement as an “Equivalence to RH”, in actuality the condition stated by Borwein (2008) is only a necessary condition for RH to be true. The additional criteria (above) needs to be satisfied and hence need to be proved as done in my paper.

I am not a mathematical expert but few of the mathematicians we had access to have confirmed that this could be a possible proof.

So I wanted to ask how do we get this proof validated the by the larger scientific community?

Dr. Kumar has used the properties of primes and analytic continuation and had a new way of handling slowly converging series and was able to use (at the crucial point) concepts borrowed from Donald Knuth regarding random numbers and random sequences. Knuth had said that for any sequence to be truly random it has to be non-cyclic. The proof required to show that a sequence of +1's and -1's , obtained from the prime factorization of the infinite sequence of integers, had to be shown to be random and to asymptotically behave like the tosses of a coin.

Previous discussions: https://news.ycombinator.com/item?id=12889009

Comments URL: https://news.ycombinator.com/item?id=19674932

Points: 19

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Another important aspect is training and incremental training on edge device.

At the time when privacy is becoming very important and you cannot export data from mobile devices etc. Training time on mobile is an important factor

We are also attempting to developing an algorithm which learns without doing a backpropagation more details are http://alpes.ai

http://www.alpes.ai/

We are releasing our API next week. The idea being you can quickly try out the algorithm and see Results and Training time on your own datasets.

http://alpes.ai/

These are the results of some of the datasets

DataSet Training Time Accuracy

Extended Yale DB 40 sec 94.00%

Human Activity Detection Dataset 3 sec 86%

MNIST 90 sec 97%

Google Speech Dataset 60 sec 92%

Liver Dataset 2 sec 89%

Comments URL: https://news.ycombinator.com/item?id=9412411

Points: 2

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Comments URL: https://news.ycombinator.com/item?id=8436552

Points: 3

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Comments URL: https://news.ycombinator.com/item?id=8426127

Points: 1

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Comments URL: https://news.ycombinator.com/item?id=8421013

Points: 169

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