Paul L Bendich

Conference IEEE Aerospace Conference Proceedings · January 1, 2024 Coordinating multiple sensors can be expressed as a reinforcement learning [RL] problem. Deep RL has excelled at observation processing (for example using convolution networks to process gridded data), but it suffers from sample inefficiency. To address th ... Full text Cite

Topological Simplification of Signals for Inference and Approximate Reconstruction

Conference IEEE Aerospace Conference Proceedings · January 1, 2023 As Internet of Things (loT) devices become both cheaper and more powerful, researchers are increasingly finding solutions to their scientific curiosities both financially and com-putationally feasible. When operating with restricted power or communications ... Full text Cite

Topological Parallax: A Geometric Specification for Deep Perception Models

Conference Advances in Neural Information Processing Systems · January 1, 2023 For safety and robustness of AI systems, we introduce topological parallax as a theoretical and computational tool that compares a trained model to a reference dataset to determine whether they have similar multiscale geometric structure. Our proofs and ex ... Cite

From Geometry to Topology: Inverse Theorems for Distributed Persistence

Conference Leibniz International Proceedings in Informatics Lipics · June 1, 2022 What is the “right” topological invariant of a large point cloud X? Prior research has focused on estimating the full persistence diagram of X, a quantity that is very expensive to compute, unstable to outliers, and far from injective. We therefore propose ... Full text Cite

A Fast and Robust Method for Global Topological Functional Optimization

Conference Proceedings of Machine Learning Research · January 1, 2021 Topological statistics, in the form of persistence diagrams, are a class of shape descriptors that capture global structural information in data. The mapping from data structures to persistence diagrams is almost everywhere differentiable, allowing for top ... Cite

Graph Spectral Embedding for Parsimonious Transmission of Multivariate Time Series

Conference IEEE Aerospace Conference Proceedings · March 1, 2020 We propose a graph spectral representation of time series data that 1) is parsimoniously encoded to user-demanded resolution; 2) is unsupervised and performant in data-constrained scenarios; 3) captures event and event-transition structure within the time ... Full text Cite

Machine learning in/with information fusion for infrastructure understanding, panel summary

Conference Proceedings of SPIE the International Society for Optical Engineering · January 1, 2020 During the 2019 SPIE DSS conference, panelists were invited to highlight the trends and use of artificial intelligence and machine learning (AI/ML) for information fusion. The common themes between the panelists include leveraging AI/ML coordinated with In ... Full text Cite

Multi-Scale Geometric Summaries for Similarity-Based Sensor Fusion

Conference IEEE Aerospace Conference Proceedings · March 1, 2019 In this work, we address fusion of heterogeneous sensor data using wavelet-based summaries of fused self-similarity information from each sensor. The technique we develop is quite general, does not require domain specific knowledge or physical models, and ... Full text Cite

Topology, geometry, and machine-learning for tracking and sensor fusion

Conference Proceedings of SPIE the International Society for Optical Engineering · January 1, 2019 Cite

Upstream fusion of multiple sensing modalities using machine learning and topological analysis: An initial exploration

Conference IEEE Aerospace Conference Proceedings · June 25, 2018 This paper presents a processing pipeline for fusing 'raw' and / or feature-level multi-sensor data - upstream fusion - and initial results from this pipeline using imagery, radar, and radio frequency (RF) signals data to determine which tracked object, am ... Full text Cite

Geometric Cross-Modal Comparison of Heterogeneous Sensor Data

Conference Proceedings of the 39th IEEE Aerospace Conference · March 2018 In this work, we address the problem of cross-modal comparison of aerial data streams. A variety of simulated automobile trajectories are sensed using two different modalities: full-motion video, and radio-frequency (RF) signals received by detectors at va ... Open Access Link to item Cite

Geometric models for musical audio data

Conference Leibniz International Proceedings in Informatics Lipics · June 1, 2016 We study the geometry of sliding window embeddings of audio features that summarize perceptual information about audio, including its pitch and timbre. These embeddings can be viewed as point clouds in high dimensions, and we add structure to the point clo ... Full text Open Access Cite

Geometric Models for Musical Audio Data

Conference Proceedings of the 32st International Symposium on Computational Geometry (SOCG) · June 2016 Link to item Cite

Cover Song Identification with Timbral Shape Sequences

Conference 16th International Society for Music Information Retrieval (ISMIR) · October 1, 2015 We introduce a novel low level feature for identifying cover songs which quantifies the relative changes in the smoothed frequency spectrum of a song. Our key insight is that a sliding window representation of a chunk of audio can be viewed as a time-order ... Open Access Link to item Cite

Multi-scale local shape analysis and feature selection in machine learning applications

Conference Proceedings of the International Joint Conference on Neural Networks · September 28, 2015 We introduce a method called multi-scale local shape analysis for extracting features that describe the local structure of points within a dataset. The method uses both geometric and topological features at multiple levels of granularity to capture diverse ... Full text Open Access Cite

Feature-aided multiple hypothesis tracking using topological and statistical behavior classifiers

Conference Proceedings of SPIE the International Society for Optical Engineering · January 1, 2015 This paper introduces a method to integrate target behavior into the multiple hypothesis tracker (MHT) likelihood ratio. In particular, a periodic track appraisal based on behavior is introduced that uses elementary topological data analysis coupled with b ... Full text Cite

Local homology transfer and stratification learning

Conference Proceedings of the Annual ACM SIAM Symposium on Discrete Algorithms · January 1, 2012 The objective of this paper is to show that point cloud data can under certain circumstances be clustered by strata in a plausible way. For our purposes, we consider a stratified space to be a collection of manifolds of different dimensions which are glued ... Full text Cite

Persistent homology under non-uniform error

Conference Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics · November 22, 2010 Using ideas from persistent homology, the robustness of a level set of a real-valued function is defined in terms of the magnitude of the perturbation necessary to kill the classes. Prior work has shown that the homology and robustness information can be r ... Full text Cite