Publications

Topics:
  1. O. Wengrowicz, A. M. Bronstein, O. Cohen, Unsupervised physics-informed deep learning-based reconstruction for time-resolved imaging by multiplexed ptychography, Optics Express 32(6), pp. 8791-8803, 2024 details

    Unsupervised physics-informed deep learning-based reconstruction for time-resolved imaging by multiplexed ptychography

    O. Wengrowicz, A. M. Bronstein, O. Cohen
    Optics Express 32(6), pp. 8791-8803, 2024

    We explore numerically an unsupervised, physics-informed, deep learning-based reconstruction technique for time-resolved imaging by multiplexed ptychography. In our method, the untrained deep learning model replaces the iterative algorithm’s update step, yielding superior reconstructions of multiple dynamic object frames compared to conventional methodologies. More precisely, we demonstrate improvements in image quality and resolution, while reducing sensitivity to the number of recorded frames, the mutual orthogonality of different probe modes, overlap between neighboring probe beams and the cutoff frequency of the ptychographic microscope – properties that are generally of paramount importance for ptychographic reconstruction algorithms.

    G. Serussi, T. Shor, T. Hirshberg, C. Baskin, A. M. Bronstein, Active propulsion noise shaping for multi-rotor aircraft localization, arXiv:2402.17289, 2023 details

    Active propulsion noise shaping for multi-rotor aircraft localization

    G. Serussi, T. Shor, T. Hirshberg, C. Baskin, A. M. Bronstein
    arXiv:2402.17289, 2023

    Multi-rotor aerial autonomous vehicles (MAVs) primarily rely on vision for navigation purposes. However, visual localization and odometry techniques suffer from poor performance in low or direct sunlight, a limited field of view, and vulnerability to occlusions. Acoustic sensing can serve as a complementary or even alternative modality for vision in many situations, and it also has the added benefits of lower system cost and energy footprint, which is especially important for micro aircraft. This paper proposes actively controlling and shaping the aircraft propulsion noise generated by the rotors to benefit localization tasks, rather than considering it a harmful nuisance. We present a neural network architecture for selfnoise-based localization in a known environment. We show that training it simultaneously with learning time-varying rotor phase modulation achieves accurate and robust localization. The proposed methods are evaluated using a computationally affordable simulation of MAV rotor noise in 2D acoustic environments that is fitted to real recordings of rotor pressure fields.

    M. Pegoraro, S. Vedula, A. A. Rosenberg, I. Tallini, E. Rodolà, A. M. Bronstein, Vector quantile regression on manifolds, Proc. AIStats, 2024 details

    Vector quantile regression on manifolds

    M. Pegoraro, S. Vedula, A. A. Rosenberg, I. Tallini, E. Rodolà, A. M. Bronstein
    Proc. AIStats, 2024

    Quantile regression (QR) is a statistical tool for distribution-free estimation of conditional quantiles of a target variable given explanatory features. QR is limited by the assumption that the target distribution is univariate and defined on an Euclidean domain. Although the notion of quantiles was recently extended to multi-variate distributions, QR for multi-variate distributions on manifolds remains underexplored, even though many important applications inherently involve data distributed on, e.g., spheres (climate and geological phenomena), and tori (dihedral angles in proteins). By leveraging optimal transport theory and c-concave functions, we meaningfully define conditional vector quantile functions of high-dimensional variables on manifolds (M-CVQFs). Our approach allows for quantile estimation, regression, and computation of conditional confidence sets and likelihoods. We demonstrate the approach’s efficacy and provide insights regarding the meaning of non-Euclidean quantiles through synthetic and real data experiments.

    Y. Chen, H. Ye, S. Vedula, A. M. Bronstein, R. Dreslinski, T. Mudge, N. Talati, Demystifying graph sparsification algorithms in graph properties preservation, Proc.Int'l Conf. on Very Large Databases (VLDB), 2024 details

    Demystifying graph sparsification algorithms in graph properties preservation

    Y. Chen, H. Ye, S. Vedula, A. M. Bronstein, R. Dreslinski, T. Mudge, N. Talati
    Proc.Int'l Conf. on Very Large Databases (VLDB), 2024

    Graph sparsification is a technique that approximates a given graph by a sparse graph with a subset of vertices and/or edges. The goal of an effective sparsification algorithm is to maintain specific graph properties relevant to the downstream task while minimizing the graph’s size. Graph algorithms often suffer from long execution time due to the irregularity and the large real-world graph size. Graph sparsification can be applied to greatly reduce the run time of graph algorithms by substituting the full graph with a much smaller sparsified graph, without significantly degrading the output quality. However, the interaction between numerous sparsifiers and graph properties is not widely explored, and the potential of graph sparsification is not fully understood.
    In this work, we cover 16 widely-used graph metrics, 12 representative graph sparsification algorithms, and 14 real-world input graphs spanning various categories, exhibiting diverse characteristics, sizes, and densities. We developed a framework to extensively assess the performance of these sparsification algorithms against graph metrics, and provide insights to the results. Our study shows that there is no one sparsifier that performs the best in preserving all graph properties, e.g. sparsifiers that preserve distance-related graph properties (eccentricity) struggle to perform well on Graph Neural Networks (GNN). This paper presents a comprehensive experimental study evaluating the performance of sparsification algorithms in preserving essential graph metrics. The insights inform future research in incorporating matching graph sparsification to graph algorithms to maximize benefits while minimizing quality degradation. Furthermore, we provide a framework to facilitate the future evaluation of evolving sparsification algorithms, graph metrics, and ever-growing graph data.