Flint Cores from the Indus

Deep Learning the Indus Script

Standardized corpora of undeciphered scripts, a necessary starting point for computational epigraphy, requires laborious human effort for their preparation from raw archaeological records. Automating this process through machine learning algorithms can be of significant aid to epigraphical research. Here, we take the first steps in this direction and present a deep learning pipeline that takes ...

Joseph Marshall Flint

NTL vs FLINT

All tests were carried out on a very lightly loaded machine with a 64-bit Intel “Haswell” CPU (Intel Xeon CPU E5-2698 v3 at 2.30GHz) with plenty of memory (over 250GB). The operating system was Cent OS. The compiler was gcc v4.8.5. We compared NTL v9.9.1 with FLINT v2.5.2. These were both built using GMP v6.1.0. All of these were the most recent releases of these packages at the time we carried...

An Indus-Sarasvatī Signboard

We present the text of a large wooden signboard in the IndusSarasvat̄ı that was recently found in Dholavira in Gujarat, India. The study of this signboard could be useful in the further analysis of the Indus-Sarasvat̄ı script. We examine its implications for the direction of writing in this script.

An Overview of the FLINT / ML

The FLINT project at Yale aims to build a state-of-the-art systems environment for modern type-safe languages. One important component of the FLINT system is a high-performance type-directed compiler for SML'97 (extended with higher-order modules). The FLINT/ML compiler provides several new capabilities that are not available in other type-based compilers: type-directed compilation is carried o...