Publications

Learning Generalizable Final-State Dynamics of 3D Rigid Objects

D. Rempe, S. Sridhar, H. Wang, and L. Guibas. CVPR Workshop on 3D Scene Understanding for Vision, Graphics, and Robotic, 2019.

Humans have a remarkable ability to predict the effect of physical interactions on the dynamics of objects. Endowing machines with this ability would allow important applications in areas like robotics and autonomous vehicles. In this work, we focus on predicting the dynamics of 3D rigid objects, in particular an object's final resting position and total rotation when subjected to an impulsive force. Different from previous work, our approach is capable of generalizing to unseen object shapes---an important requirement for real-world applications. To achieve this, we represent object shape as a 3D point cloud that is used as input to a neural network, making our approach agnostic to appearance variation. The design of our network is informed by an understanding of physical laws. We train our model with data from a physics engine that simulates the dynamics of a large number of shapes. Experiments show that we can accurately predict the resting position and total rotation for unseen object geometries.

Project Page Workshop Paper Full Paper

Effectiveness of Global, Low-Degree Polynomial Transformations for GCxGC Data Alignment

D. Rempe, S. Reichenbach, Q. Tao, C. Cordero, W. Rathbun, and C.A. Zini. Analytical Chemistry, 88(20), pp. 10028-10035, 2016.

As columns age and differ between systems, retention times for comprehensive two-dimensional gas chromatography (GCxGC) may vary between runs. In order to properly analyze GCxGC chromatograms, it often is desirable to align the retention times of chromatographic features, such as analyte peaks, between chromatograms. Previous work by the authors has shown that global, low-degree polynomial transformation functions – namely affine, second-degree polynomial, and third-degree polynomial – are effective for aligning pairs of two-dimensional chromatograms acquired with dual second columns and detectors (GCx2GC). This work assesses the experimental performance of these global methods on more general GCxGC chromatogram pairs and com- pares their performance to that of a recent, robust, local alignment algorithm for GCxGC data [Gros et al., Anal. Chem. 2012, 84, 9033]. Measuring performance with the root-mean-square (RMS) residual differences in retention times for matched peaks suggests that global, low-degree polynomial transformations outperform the local algorithm given a sufficiently large set of alignment points, and are able to improve misalignment by over 95% based on a lower-bound benchmark of inherent variability. However, with small sets of alignment points, the local method demonstrated lower error rates (although with greater computational overhead). For GCxGC chromatogram pairs with only slight initial misalignment, none of the global or local methods performed well. In some cases with initial misalignment near the inherent variability of the system, these methods worsened alignment, suggesting that it may be better not to perform alignment in such cases.

Paper Supporting Info

Alignment for Comprehensive Two-Dimensional Gas Chromatography with Dual Secondary Columns and Detectors

S. Reichenbach, D. Rempe, Q. Tao, D. Bressanello, E. Liberto, C. Bicchi, S. Balducci, and C. Cordero. Analytical Chemistry, 87(19), pp. 10056-10063, 2015.

In each sample run, comprehensive two-dimensional gas chromatography with dual secondary columns and detectors (GC × 2GC) provides complementary information in two chromatograms generated by its two detectors. For example, a flame ionization detector (FID) produces data that is especially effective for quantification and a mass spectrometer (MS) produces data that is especially useful for chemical-structure elucidation and compound identification. The greater information capacity of two detectors is most useful for difficult analyses, such as metabolomics, but using the joint information offered by the two complex two-dimensional chromatograms requires data fusion. In the case that the second columns are equivalent but flow conditions vary (e.g., related to the operative pressure of their different detectors), data fusion can be accomplished by aligning the chromatographic data and/or chromatographic features such as peaks and retention-time windows. Chromatographic alignment requires a mapping from the retention times of one chromatogram to the retention times of the other chromatogram. This paper considers general issues and experimental performance for global two-dimensional mapping functions to align pairs of GC × 2GC chromatograms. Experimental results for GC × 2GC with FID and MS for metabolomic analyses of human urine samples suggest that low-degree polynomial mapping functions out-perform affine transformation (as measured by root-mean-square residuals for matched peaks) and achieve performance near a lower-bound benchmark of inherent variability. Third-degree polynomials slightly out-performed second-degree polynomials in these results, but second-degree polynomials performed nearly as well and may be preferred for parametric and computational simplicity as well as robustness.

Paper Supporting Info

Undergraduate Thesis

Advised by Stephen Scott and Stephen Reichenbach

Paper

Other Publications and Presentations

(underline indicates the presenter/s)

A Cognitive Radio TV Prototype for Effective TV Spectrum Sharing

D. Rempe, M. Snyder, A. Pracht, A. Schwarz, T. Nguyen, M. Vostrez, Z. Zhao, and M.C. Vuran. 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN) (IEEE DySPAN 2017), Baltimore, MD, USA, March 6-9, 2017.

Demo Paper

Simple models for second-column retention-time variability across peaks from GCxGC

S. Reichenbach, D. Rempe, Q. Tao, and C. Cordero, 8th Multidimensional Chromatography Workshop, Toronto, ON, Canada, January 5-6, 2017.

Slides

Alignment for Comprehensive Two-Dimensional Gas Chromatography (GCxGC) with Global, Low-Order Polynomial Transformations

D. Rempe, S. Reichenbach, and S. Scott, UNL Spring Research Fair Poster Session, Lincoln, NE, USA, April, 2016.

Poster