UAV-Based Smart Rock Localization for Determination of Bridge Scour Depth

First Place Award in recognition of outstanding achievement in the 2020 Annual Meeting Graduate Student Poster Competition sponsored by INSPIRE University Transportation Cente

Momentum distribution and contacts of one-dimensional spinless Fermi gases with an attractive p-wave interaction

We present a rigorous study of momentum distribution and p-wave contacts of one dimensional (1D) spinless Fermi gases with an attractive p-wave interaction. Using the Bethe wave function, we analytically calculate the large-momentum tail of momentum distribution of the model. We show that the leading ($\sim 1/p^{2}$) and sub-leading terms ($\sim 1/p^{4}$) of the large-momentum tail are determined by two contacts $C_2$ and $C_4$, which we show, by explicit calculation, are related to the short-distance behaviour of the two-body correlation function and its derivatives. We show as one increases the 1D scattering length, the contact $C_2$ increases monotonically from zero while $C_4$ exhibits a peak for finite scattering length. In addition, we obtain analytic expressions for p-wave contacts at finite temperature from the thermodynamic Bethe ansatz equations in both weakly and strongly attractive regimes.Comment: 19 pages,2 figure

Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features

TThe goal of our work is to discover dominant objects in a very general setting where only a single unlabeled image is given. This is far more challenge than typical co-localization or weakly-supervised localization tasks. To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Location Mining (OLM), which exploits the advantages of data mining and feature representation of pre-trained convolutional neural networks (CNNs). Specifically, we first convert the feature maps from a pre-trained CNN model into a set of transactions, and then discovers frequent patterns from transaction database through pattern mining techniques. We observe that those discovered patterns, i.e., co-occurrence highlighted regions, typically hold appearance and spatial consistency. Motivated by this observation, we can easily discover and localize possible objects by merging relevant meaningful patterns. Extensive experiments on a variety of benchmarks demonstrate that OLM achieves competitive localization performance compared with the state-of-the-art methods. We also evaluate our approach compared with unsupervised saliency detection methods and achieves competitive results on seven benchmark datasets. Moreover, we conduct experiments on fine-grained classification to show that our proposed method can locate the entire object and parts accurately, which can benefit to improving the classification results significantly

Evaluating soil nitrate dynamics in an intercropping dripped ecosystem using HYDRUS-2D.

The competition mechanisms between crop species for water and nutrients, especially nitrate (NO3-N), in intercropping ecosystems are still poorly understood. Therefore, an experiment involving high (300 kg ha-1 for corn and 250 kg ha-1 for tomato), medium (210 kg ha-1 for corn and 175 kg ha-1 for tomato), and low (150 kg ha-1 for corn and 125 kg ha-1 for tomato) N-fertilizer applications (HF, MF, LF, respectively) was conducted in the corn and tomato intercropping ecosystem during 2014 (a calibration period for modeling) and 2015 (a validation period for modeling). The modified HYDRUS-2D code was used to analyze soil NO3-N concentrations (SNC) in the middle between corn rows (Pc), between corn and tomato rows (Pb), and between tomato rows (Pt), NO3-N exchange in the horizontal direction between different regions, NO3-N leaching from the corn, the bare, and the tomato region, and N uptake by crops. Simulated SNCs were in good agreement with measurements, with RMSE, NSE, and MRE of 0.01-0.06 mg cm-3, 0.75-0.98, and 8.7-19.1%, respectively, during the validation period (2015). Average SNCs in the 0-40 cm soil layer were different between Pc, Pt, and Pb. Intensive NO3-N exchange in the horizontal direction occurred during the second stage (Day After Sowing [DAS] 37-113 in 2014; DAS 29-120 in 2015). NO3-N exchange between the corn and bare regions was lower than between the tomato and bare regions due to smaller concentration gradients. However, in the vertical direction, NO3-N leaching from the corn region in both years was 4.1 and 8.8 times larger, respectively, than from the tomato region under HF since NO3-N mainly moved from the tomato region to the corn region. Our results reveal the competition between corn and tomato for N and provide a rationale for formulating and optimizing different fertilizer regimes for different crops in the intercropping ecosystem