Figure 1: Rubikon is an intelligent tutoring system for Rubik's Cube learning.(a) The foundational design of Rubikon is an AR setup, where learners manipulate a physical cube with ArUco markers attached to each square, and pose a camera towards the cube to enable tracking and rendering.With this setup, learners see a rendered Rubik's Cube on a display while manipulating the physical cube in their hands.(b) Through AR rendering, Rubikon automatically generates new configurations of the Rubik's Cube for the user to practice unmastered skills.Rubikon detects the status of the cube to infer user behavior and provide immediate feedback and hints.(c) Rubikon supports the learning of a 3D physical task by integrating key design principles of cognitive tutors which have seen success in tutoring math and programming.

In February 2024, Google gave their conversational AI, Gemini, access to an image generation tool. In an attempt to make it “work well for everyone” [6], Google designed this integration to generate a range of people representing diverse backgrounds. Unfortunately, this had unintended consequences, such as a user asking for an image of “Founding Fathers of the United States”, and the system generating racially diverse people for this category which was famously homogeneous [8].

Stencil computations are ubiquitous in modern grid-based physical simulations. In this paper, we present FOURST – a compiler to generate programs computing time iterated linear periodic and aperiodic stencil computations with fast Fourier transform methods. This paper outlines the design and implementation of the code generation approach in FOURST, to automatically generate FFT-based stencil solvers. We present experimental results on the state-of-the-art Ookami supercomputer housing Fujitsu A64FX and Intel Skylake processors, to study the performance of FOURST and a state-of-the-art tiling-based optimized code generator PLuTo on various stencil shapes and varying the number of time iterations. We discuss the performance profiles, and limitations, of both approaches on high-end modern hardware.

The central dogma of handwritten character recognition remains inextricably linked to optical character recognition methods for print media. Alongside their reliance on proprietary data and lack of open-access software, the applicability of these optical character recognition methods to handwritten characters from low-quality documents (e.g., that are damaged) remains unknown. In this paper, we compare and contrast the performance of state-of-the-art optical character recognition tools for print and learning models engineered with state-of-the-art machine learning toolkits trained on handwritten inputs. Using Tesseract OCR as a baseline, we build, optimize, and evaluate three types of convolutional neural networks that are trained on the AL-ALLand AL-PUBdatasets, a collection of images of handwritten ancient Greek characters that were labeled by volunteers through the Ancient Lives online citizen science project. We find our best-performing machine learning model to be 92.57% accurate compared to Tesseract OCR’s 11.15%. Following our analysis, we present a brief examination of our models’ shortcomings, introduce the publicly-available AL-PUBdataset, and, describe Theia, a web-based tool that democratizes our machine learning models for public use. We conclude by discussing the promise of our findings for advancing research at the intersection of machine learning, manuscript transcription, and the digital humanities.