MOTIVATION: Multiple instance learning (MIL) provides a structured approach to patient phenotype prediction with single-cell RNA-sequencing (scRNA-seq) data. However, existing MIL methods tend to overlook the hierarchical structure inherent in scRNA-seq data, especially the biological groupings of cells or cell types. This limitation may lead to suboptimal performance and poor interpretability at higher levels of cellular division. RESULTS: To address this gap, we present a novel approach to incorporate hierarchical information into the attention-based MIL framework. Specifically, our model applies the attention-based aggregation mechanism over both cells and cell types, thus enforcing a hierarchical structure on the flow of information throughout the model. Across extensive experiments, our proposed approach demonstrates highly competitive performance and shows robustness against limited sample sizes. Moreover, ablation test results show that simply applying the attention mechanism on cell types instead of cells leads to improved performance, underscoring the benefits of incorporating the hierarchical groupings. By identifying the critical cell types that are most relevant for prediction, we show that our model is capable of capturing biologically meaningful associations, suggesting its potential to facilitate biological discoveries. AVAILABILITY AND IMPLEMENTATION: Our source code is available at https://github.com/minhchaudo/hier-mil. All datasets used in this study are publicly available online.

Pouring is a fundamental skill for robots in both domestic and industrial environments. Ideally, a robot should be able to pour with high accuracy to specific, pre-defined heights and without spilling. However, due to the complex dynamics of liquids, it is difficult to learn how to pour to achieve these goals. In this paper we present an approach to learn a policy for pouring using Deep Deterministic Policy Gradients (DDPG). We remove the need for collecting training experiences on a real robot, by using a state-of-the-art liquid simulator, which allows for learning the liquid dynamics. We show through our experiments, performed with a PR2 robot, that it is possible to successfully transfer the learned policy to a real robot and even apply it to different liquids.

Robotic assistants have the potential to greatly improve our quality of life by supporting us in our daily activities. A service robot acting autonomously in an indoor environment is faced with very complex tasks. Consider the problem of pouring a liquid into a cup, the robot should first determine if the cup is empty or partially filled. RGB-D cameras provide noisy depth measurements which depend on the opaqueness and refraction index of the liquid. In this paper, we present a novel probabilistic approach for estimating the fill-level of a liquid in a cup using an RGB-D camera. Our approach does not make any assumptions about the properties of the liquid like its opaqueness or its refraction index. We develop a probabilistic model using features extracted from RGB and depth data. Our experiments demonstrate the robustness of our method and an improvement over the state of the art.