Seasonally Frozen Soil Effects on the Seismic Performance of Highway Bridges

INE/AUTC 12.0

Variational manifolds for ground states and scarred dynamics of blockade-constrained spin models on two and three dimensional lattices

We introduce a variational manifold of simple tensor network states for the study of a family of constrained models that describe spin-1/2 systems as realized by Rydberg atom arrays. Our manifold permits analytical calculation via perturbative expansion of one- and two-point functions in arbitrary spatial dimensions and allows for efficient computation of the matrix elements required for variational energy minimization and variational time evolution in up to three dimensions. We apply this framework to the PXP model on the hypercubic lattice in 1D, 2D, and 3D, and show that, in each case, it exhibits quantum phase transitions breaking the sub-lattice symmetry in equilibrium, and hosts quantum many body scars out of equilibrium. We demonstrate that our variational ansatz qualitatively captures all these phenomena and predicts key quantities with an accuracy that increases with the dimensionality of the lattice, and conclude that our method can be interpreted as a generalization of mean-field theory to constrained spin models.Comment: 21 pages, 16 figure

A Molecular Picture for the Thermo-Reversibility of Gels Formed by Isophthalic Acid-Ended Telechelic Polymers

We demonstrate that isophthalic acid-ended telechelic poly(1,5-cyclooctadiene)s (A-PCODs) form thermo-reversible gels in non-polar solvent with a unique molecular mechanism for their thermo-reversibility. Like other associative telechelic polymers, A-PCODs form “flower-like” micelles at low concentration and form gels through bridging at higher concentration which exhibit linear viscoelasticity. However, unlike the widely studied hydrophobically end-capped PEOs, A-PCODs show clear thermo-reversibility in viscosity and dynamic modulus around 30 °C due to the hydrogen-bonding end groups. In addition, they differ from other reported thermo-reversible gelators (eg. Pluronics, PNIPAm containing block copolymers, etc.): neither the end group nor the backbone in the present system has a critical solution temperature within the measured temperature range (0 °C to 60 °C), indicating that the present system has a unique mechanism for its thermo-reversibility. To obtain a molecular picture of the mechanism, rheology and small angle neutron scattering (SANS) studies were implemented. Topological changes above the transition temperature (30 °C) were observed in both oscillatory rheology and SANS. SANS reveals that the size of clusters, which are formed by interacting micelles, depends highly on temperature (T) but independent of polymer concentration. These results cannot be explained by current theories on associative telechelic polymers which assume constant and large aggregation number of end groups at all temperatures and concentrations. We hypothesize that the temperature-sensitive sol-gel transition is due to a decrease in aggregation number for T above the critical temperature in our system, and this temperature-dependence of aggregation number is further determined by the chemical structure and hydrogen-bonding property of isophthalic acid ends

Progress in CTEQ-TEA PDF analysis

Recent developments in the CTEQ-TEA global QCD analysis are presented. The parton distribution functions CT10-NNLO are described, constructed by comparing data from many experiments to NNLO approximations of QCD.Comment: 4 pages, 3 figures; contribution to the Proceedings of the XX Workshop on Deep Inelastic Scattering and Related Subjects, Bonn, Germany, 26-30 March, 201

A 3D Active Learning Application for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive video game prototype for tablet and mobile devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using fluid lensing to create a dataset that will be used to train NeMO-Nets convolutional neural network. The application currently allows for users to classify preselected regions of coral in the Pacific and will be expanded to include additional regions captured using our NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as lower-resolution airborne remote sensing data from the ongoing NASA CORAL campaign. Active learning applications present a novel methodology for efficiently training large-scale Neural Networks wherein variances in identification can be rapidly mitigated against control data. NeMO-Net periodically checks users input against pre-classified coral imagery to gauge their accuracy and utilize in-game mechanics to provide classification training. Users actively communicate with a server and are requested to classify areas of coral for which other users had conflicting classifications and contribute their input to a larger database for ranking. In partnering with Mission Blue and IUCN, NeMO-Net leverages an international consortium of subject matter experts to classify areas of confusion identified by NeMO-Net and generate additional labels crucial for identifying decision boundary locations in coral reef assessment

ESG Investing, Spring 2021

ESG Investing Project for Sustainability Exchange, Washington University in St. Louis, Spring 202

Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation

Model-based deep learning has achieved astounding successes due in part to the availability of large-scale realworld data. However, processing such massive amounts of data comes at a considerable cost in terms of computations, storage, training and the search for good neural architectures. Dataset distillation has thus recently come to the fore. This paradigm involves distilling information from large real-world datasets into tiny and compact synthetic datasets such that processing the latter yields similar performances as the former. State-of-the-art methods primarily rely on learning the synthetic dataset by matching the gradients obtained during training between the real and synthetic data. However, these gradient-matching methods suffer from the accumulated trajectory error caused by the discrepancy between the distillation and subsequent evaluation. To alleviate the adverse impact of this accumulated trajectory error, we propose a novel approach that encourages the optimization algorithm to seek a flat trajectory. We show that the weights trained on synthetic data are robust against the accumulated errors perturbations with the regularization towards the flat trajectory. Our method, called Flat Trajectory Distillation (FTD), is shown to boost the performance of gradient-matching methods by up to 4.7% on a subset of images of the ImageNet dataset with higher resolution images. We also validate the effectiveness and generalizability of our method with datasets of different resolutions and demonstrate its applicability to neural architecture search