Models for student reading performance can empower educators and institutions to proactively identify at-risk students, thereby enabling early and tailored instructional interventions. However, there are no suitable publicly available educational datasets for modeling and predicting future reading performance. In this work, we introduce the Enhanced Core Reading Instruction (ECRI) dataset, a novel large-scale longitudinal tabular dataset collected across 44 schools with 6,916 students and 172 teachers. We leverage the dataset to empirically evaluate the ability of state-of-the-art machine learning models to recognize early childhood educational patterns in multivariate and partial measurements. Specifically, we demonstrate a simple self-supervised strategy in which a Multi-Layer Perception (MLP) network is pre-trained over masked inputs to outperform several strong baselines while generalizing over diverse educational settings. To facilitate future developments in precise modeling and responsible use of models for individualized and early intervention strategies, our data and code are available at https://ecri-data.github.io/.

We propose XVO, a semi-supervised learning method for training generalized monocular Visual Odometry (VO) models with robust off-the-self operation across diverse datasets and settings. In contrast to standard monocular VO approaches which often study a known calibration within a single dataset, XVO efficiently learns to recover relative pose with real-world scale from visual scene semantics, i.e., without relying on any known camera parameters. We optimize the motion estimation model via self-training from large amounts of unconstrained and heterogeneous dash camera videos available on YouTube. Our key contribution is twofold. First, we empirically demonstrate the benefits of semi-supervised training for learning a general-purpose direct VO regression network. Second, we demonstrate multi-modal supervision, including segmentation, flow, depth, and audio auxiliary prediction tasks, to facilitate generalized representations for the VO task. Specifically, we find audio prediction task to significantly enhance the semi-supervised learning process while alleviating noisy pseudo-labels, particularly in highly dynamic and out-of-domain video data. Our proposed teacher network achieves state-of-the-art performance on the commonly used KITTI benchmark despite no multi-frame optimization or knowledge of camera parameters. Combined with the proposed semi-supervised step, XVO demonstrates off-the-shelf knowledge transfer across diverse conditions on KITTI, nuScenes, and Argoverse without fine-tuning.

Power system models need regular calibration to reflect the current system status, identify and mitigate potential issues. However, traditional calibration approaches usually have local minima issues that require intervention from domain experts. In this study, we propose a reinforcement learning algorithm based on hierarchical parameter tuning (HPT-RL) for power system model calibration. The proposed HPT-RL is inspired by the natural hierarchy in parameter tuning where its higher level learns which parameter to tune and its lower level learns the tuning step size. We implement HPT-RL using the Deep Deterministic Policy Gradient (DDPG) architecture. We evaluate and compare the performance of HPT-RL, traditional DDPG, Bayesian Optimization, and Random Search for a generator model calibration on a four-bus system simulated with PSS®E. The performance of HPT-RL was evaluated based on 100 repeated experiments for two scenarios, namely low randomization and high randomization, based on where a simulated disturbance occurs on the bus system. The results show that HPT-RL outperforms the other three benchmark methods.