Scientific

The Hardy and Littlewood k-tuple prime conjecture is one of the most enduring unsolved problems in mathematics. In 1999, Gadiyar and Padma presented a heuristic derivation of the 2-tuples conjecture by employing the orthogonality principle of Ramanujan sums. Building upon their work, we explore triple convolution Ramanujan sums and use this approach to provide a heuristic derivation of the Hardy-Littlewood conjecture concerning prime 3-tuples. Furthermore, we estimate the triple convolution of the Jordan totient function using Ramanujan sums.

Let $\sigma^*(n)$ be the sum of all unitary (i.e. coprime) divisors of $n$. As an analogue of Lehmer’s totient problem, Subbarao proposed the following conjecture. The congruence $\sigma^*(n)\equiv 1\pmod{n}$ is possible iff $n$ is a prime power. This problem is still open. We strengthen considerably the lower estimations for the potential counterexamples to Subbarao’s conjecture.

In the second part of our talk, we discuss the growth of the function $\sigma^*(n)$. We establish a new explicit upper bound, namely $\sigma^*(n)<1.2678n\log\log{n}$ for all $n\ge223092870$. For this purpose, we use explicit estimates for Chebyshev’s $\theta$-function and for some product defined over prime numbers.

We establish a Weyl-type estimate for exponential sums over irreducible elements in function fields. As an application, we generalize an equidistribution theorem of Rhin. Our estimate works for polynomials with degree higher than the characteristic of the field, a barrier to the traditional Weyl differencing method. In this talk, we briefly introduce Lê-Liu-Wooley's original argument for ordinary Weyl sums (taken over all elements), and how we generalize it to estimate bilinear exponential sums with general coefficients. This is joint work with Jérémy Campagne (Waterloo), Thái Hoàng Lê (Mississippi) and Yu-Ru Liu (Waterloo).

Mathematics has developed an increasingly “higher dimensional” point of view of when different things deserve the same name, categorifying the traditional logical notion of equality to isomorphism (from Greek isos “equal” and morphe “form” or “shape”) and equivalence (from Latin aequus “equal” and valere “be well, be worth”). In practice, mathematicians tend to become more flexible in determining when different things deserve the same name as those things become more complicated, as measured by the dimensions of the categories to which they belong. Unfortunately, these pervasive notions of sameness no longer satisfy Leibniz’s identity of indiscernibles — the assertion that two objects are identical just when they share the same properties — essentially because the traditional set theoretical foundations of mathematics make it too easy to formulate “evil” statements. However, in a new proposed foundation system there are common rules that govern the meaning of identity for mathematical objects of any type that allow one to “transport” information along any identification. Moreover, as a consequence of Voevodsky’s univalence axiom, these identity types are faithful to the meanings of sameness that have emerged from centuries of mathematical practice.

Speaker biography: Emily Riehl is Professor of Mathematics at Johns Hopkins University, working on higher category theory, abstract homotopy theory, and homotopy type theory. She studied at Harvard and Cambridge Universities, earned her Ph.D. at the University of Chicago, and was a Benjamin Pierce and NSF postdoctoral fellow at Harvard University. She has published over thirty papers and written three books: Categorical Homotopy Theory (Cambridge 2014), Category Theory in Context (Dover 2016), and Elements of ∞-Category Theory (Cambridge 2022, joint with Dominic Verity). She was recently elected as a member at large of the Council of the American Mathematical Society. In addition to her research, Dr. Riehl is active in promoting access to the world of mathematics through popular writing and in interviews and podcasts. She was also a co-founder of Spectra: the Association for LGBT Mathematicians.

Shifted divergences provide a principled way of making information theoretic divergences (e.g. KL) geometrically aware via optimal transport smoothing. In this talk, I will argue that shifted divergences provide a powerful approach towards unifying optimization, sampling, privacy, and beyond. For concreteness, I will demonstrate these connections via three recent highlights. (1) Characterizing the differential privacy of Noisy-SGD, the standard algorithm for private convex optimization. (2) Characterizing the mixing time of the Langevin Algorithm to its stationary distribution for log-concave sampling. (3) The fastest high-accuracy algorithm for sampling from log-concave distributions. A recurring theme is a certain notion of algorithmic stability, and the central technique for establishing this is shifted divergences. Based on joint work with Kunal Talwar, and with Sinho Chewi.