sandeepeecs

Stripe Reader—our first piece of hardware—is a mobile card reader that works with Terminal’s APIs & SDKs to enable developers to build their own, custom in-person payments experiences.

When you are dealing with payments data x paying to y and txn volumes of few months then they are very large graphs, companies such as npci or alipay deals with these kinds of data.

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

Looks like this is responded by Raghavan and this is what I had seen on research gate comments

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.

Yes I agree. most people who come to us at alpes AI do care about training time. how fast they can do experiments

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