Temporal Sequences

DBE-KT22 contains student exercise answering activities collected through an online practicing platform for the database systems course taught at the Australian National University within the period 2018-2021. The dataset is useful for research targeting students' knowledge tracing given historical sequences of exercise answering.

POPGym is designed to benchmark memory in deep reinforcement learning. It contains a set of environments and a collection of memory model baselines. The environments are all Partially Observable Markov Decision Process (POMDP) environments following the Openai Gym interface. Our environments follow a few basic tenets: 1. Painless Setup - popgym environments require only gym, numpy, and mazelib as dependencies 2. Laptop-Sized Tasks - Most tasks can be solved in less than a day on the CPU 3. True Generalization - All environments are heavily randomized. The paper uses 15M environment steps for each trial.

This is a dataset for predicting 5-Formylcytidine Modifications on mRNA.

Panoramic Video Panoptic Segmentation Dataset is a large-scale dataset that offers high-quality panoptic segmentation labels for autonomous driving. The dataset has labels for 28 semantic categories and 2,860 temporal sequences that were captured by five cameras mounted on autonomous vehicles driving in three different geographical locations, leading to a total of 100k labeled camera images. Source: Waymo Open Dataset: Panoramic Video Panoptic Segmentation Image Source: https://arxiv.org/pdf/2206.07704v1.pdf