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Dhruv Patel

Publications and source records attributed to Dhruv Patel.

23 records · Page 2Linked to original sources

The pigenhole principle and multicolor Ramsey numbers

For integers $k,r\geq 2$, the diagonal Ramsey number $R_r(k)$ is the minimum $N\in\mathbb{N}$ such that every $r$-coloring of the edges of a complete graph on $N$ vertices yields on a monochromatic subgraph on $k$ vertices. Here we make a careful effort of extracting explicit upper bounds for $R_r(k)$ from the pigeonhole principle alone. Our main term improves on previously documented explicit bounds for $r\geq 3$, and we also consider an often ignored secondary term, which allows us to subtract a uniformly bounded below positive proportion of the main term. Asymptotically, we give a self-contained proof that $R_r(k)\leq \left(\frac{3+e}{2}\right)\frac{(r(k-2))!}{((k-2)!)^r}(1+o_{r\to \infty}(1)),$ and we conclude by noting that our methods combine with previous estimates on $R_r(3)$ to improve the constant $\frac{3+e}{2}$ to $\frac{3+e}{2}-\frac{d}{48}$, where $d=66-R_4(3)\geq 4$. We also compare our formulas, and previously documented formulas, to some collected numerical data.

math.CO↗

What Did It Look Like: A service for creating website timelapses using the Memento framework

Popular web pages are archived frequently, which makes it difficult to visualize the progression of the site through the years at web archives. The What Did It Look Like (WDILL) Twitter bot shows web page transitions by creating a timelapse of a given website using one archived copy from each calendar year. Originally implemented in 2015, we recently added new features to WDILL, such as date range requests, diversified memento selection, updated visualizations, and sharing visualizations to Instagram. This would allow scholars and the general public to explore the temporal nature of web archives.

cs.DL↗

Visualizing Webpage Changes Over Time

We report on the development of TMVis, a web service to provide visualizations of how individual webpages have changed over time. We leverage past research on summarizing collections of webpages with thumbnail-sized screenshots and on choosing a small number of representative past archived webpages from a large collection. We offer four visualizations: image grid, image slider, timeline, and animated GIF. Embed codes for the image grid and image slider can be produced to include these on separate webpages. The animated GIF can be downloaded as an image file for the same purpose. This tool can be used to allow scholars from various disciplines, as well as the general public, to explore the temporal nature of web archives. We hope that these visualizations will just be the beginning and will provide a starting point for others to expand these types of offerings for users of web archives.

cs.DL↗

Persistent homology detects curvature

In topological data analysis, persistent homology is used to study the "shape of data". Persistent homology computations are completely characterized by a set of intervals called a bar code. It is often said that the long intervals represent the "topological signal" and the short intervals represent "noise". We give evidence to dispute this thesis, showing that the short intervals encode geometric information. Specifically, we prove that persistent homology detects the curvature of disks from which points have been sampled. We describe a general computational framework for solving inverse problems using the average persistence landscape, a continuous mapping from metric spaces with a probability measure to a Hilbert space. In the present application, the average persistence landscapes of points sampled from disks of constant curvature results in a path in this Hilbert space which may be learned using standard tools from statistical and machine learning.

cs.CG↗

Bayesian Inference with Generative Adversarial Network Priors

Bayesian inference is used extensively to infer and to quantify the uncertainty in a field of interest from a measurement of a related field when the two are linked by a physical model. Despite its many applications, Bayesian inference faces challenges when inferring fields that have discrete representations of large dimension, and/or have prior distributions that are difficult to represent mathematically. In this manuscript we consider the use of Generative Adversarial Networks (GANs) in addressing these challenges. A GAN is a type of deep neural network equipped with the ability to learn the distribution implied by multiple samples of a given field. Once trained on these samples, the generator component of a GAN maps the iid components of a low-dimensional latent vector to an approximation of the distribution of the field of interest. In this work we demonstrate how this approximate distribution may be used as a prior in a Bayesian update, and how it addresses the challenges associated with characterizing complex prior distributions and the large dimension of the inferred field. We demonstrate the efficacy of this approach by applying it to the problem of inferring and quantifying uncertainty in the initial temperature field in a heat conduction problem from a noisy measurement of the temperature at later time.

stat.ML↗