Evaluation of Seismic Response of External Mine Overburden Dumps

The stability of mines external overburden dump slope experiencing an earthquake is controlled by deformations; consequently a stability analysis that predicts slope displacements is desirable. The amount of displacement, deformation occur at the crest of the overburden dumps is an important factor for the seismic loading response of the dumps for the field personnel at the mine site for designing the dump slope geometry. The state of effective stress and seismic intensity significantly affects the stability range. In this paper, a simplified approach is presented for the seismic response of overburden dumps, and the role played by relevant parameters such as soil shear strength, dump height, slope angle, damping scheme, periods of seismic load and peak acceleration at excitation time is addressed

Multiple Waypoint Navigation in Unknown Indoor Environments

Indoor motion planning focuses on solving the problem of navigating an agent through a cluttered environment. To date, quite a lot of work has been done in this field, but these methods often fail to find the optimal balance between computationally inexpensive online path planning, and optimality of the path. Along with this, these works often prove optimality for single-start single-goal worlds. To address these challenges, we present a multiple waypoint path planner and controller stack for navigation in unknown indoor environments where waypoints include the goal along with the intermediary points that the robot must traverse before reaching the goal. Our approach makes use of a global planner (to find the next best waypoint at any instant), a local planner (to plan the path to a specific waypoint), and an adaptive Model Predictive Control strategy (for robust system control and faster maneuvers). We evaluate our algorithm on a set of randomly generated obstacle maps, intermediate waypoints, and start-goal pairs, with results indicating a significant reduction in computational costs, with high accuracies and robust control.Comment: Accepted at ICCR 202

Entity Augmentation for Efficient Classification of Vertically Partitioned Data with Limited Overlap

Vertical Federated Learning (VFL) is a machine learning paradigm for learning from vertically partitioned data (i.e. features for each input are distributed across multiple "guest" clients and an aggregating "host" server owns labels) without communicating raw data. Traditionally, VFL involves an "entity resolution" phase where the host identifies and serializes the unique entities known to all guests. This is followed by private set intersection to find common entities, and an "entity alignment" step to ensure all guests are always processing the same entity's data. However, using only data of entities from the intersection means guests discard potentially useful data. Besides, the effect on privacy is dubious and these operations are computationally expensive. We propose a novel approach that eliminates the need for set intersection and entity alignment in categorical tasks. Our Entity Augmentation technique generates meaningful labels for activations sent to the host, regardless of their originating entity, enabling efficient VFL without explicit entity alignment. With limited overlap between training data, this approach performs substantially better (e.g. with 5% overlap, 48.1% vs 69.48% test accuracy on CIFAR-10). In fact, thanks to the regularizing effect, our model performs marginally better even with 100% overlap.Comment: GLOW @ IJCAI 2024 (12 pages + 2 page bibliography. 15 figures.