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Liquids and granular media are pervasive throughout human environments. Their free-flowing nature causes people to constrain them into containers. We do so with thousands of different types of containers made out of different materials with varying sizes, shapes, and colors. In this work, we present a state-of-the-art sensing technique for robots to perceive what liquid is inside of an unknown container. We do so by integrating Visible to Near Infrared (VNIR) reflectance spectroscopy into a robot’s end effector. We introduce a hierarchical model for inferring the material classes of both containers and internal contents given spectral measurements from two integrated spectrometers. To train these inference models, we capture and open source a dataset of spectral measurements from over 180 different combinations of containers and liquids. Our technique demonstrates over 85% accuracy in identifying 13 different liquids and granular media contained within 13 different containers. The sensitivity of our spectral readings allow our model to also identify the material composition of the containers themselves with 96% accuracy. Overall, VNIR spectroscopy presents a promising method to give household robots a general-purpose ability to infer the liquids inside of containers, without needing to open or manipulate the containers. 

We introduce a new measure of network reliability related to the order of the largest component. This new connectivity measure considers a network to be operational if there is a component or order at least some fixed proportion, r, of the original order. Thus, the network is in a failure state if all components are sufficiently small. In this paper, we consider the parameters with vertex deletions as well as edge deletions for particular graph classes. We also find the minimum values of the parameter for graphs with a fixed size and order. We end with a discussion and some conjectures for the maximum value of the parameter for graphs with a fixed size and order.