The Development of an Integrated Simulation Model on Understandings on the Interaction between Electromagnetic Waves and Nanoparticles

To investigate the interaction between nanoparticles and electromagnetic waves, a numerical simulation model based on FEM was built in this thesis. Numerical simulation is an important auxiliary research method besides experiments. The optical properties of nanoparticles consist of scattering, absorption, and extinction, and in the case of nanoparticle suspension, the transmission is also involved. This thesis addressed two typical applications based on the established model, one was regarding the nanofluids for solar energy harvesting, and the other was regarding the optical properties of atmospheric soot. In the case of the nanofluids solar energy harvesting, the established model provided a convenient and rapid screening of potential nanoparticles and nanofluids candidates for solar energy harvesting. A core-shell structure nanoparticle, using Cu as the core material in a diameter of 90 nm coated with 5 nm thickness graphene, exhibited a better photothermal property under the solar radiation. In the second case regarding atmospheric soot, the established model provided an efficient method for understandings on the optical properties and warming effects of realistic soot particles. It was found that the sizes and material characteristics of soot, would greatly affect their scattering and absorption of light. Moreover, two submodels were introduced and integrated, which can better predict behaviors of real atmospheric soot involving their core-shell structures (moisture or organic condensates) and their fractal agglomerate structures. In conclusion, the established model helps to understand the interaction between nanoparticles and electromagnetic waves, which shows great potentials of wide applications

ESSAYS ON AGRICULTURAL MARKET AND POLICIES: IMPORTED SHRIMP, ORGANIC COFFEE, AND CIGARETTES IN THE UNITED STATES

This dissertation focuses on topics in areas of agricultural and food policy, international trade, agricultural markets and marketing. The dissertation is structured as three papers. The first paper, Chapter 1, evaluates the impact of agricultural trade policies. Imported shrimp, which comprises nearly ninety percent of all United States shrimp consumption, have become the subject of antidumping and countervailing duty investigations in the past decade. I estimate the import demand for shrimp in the United States from 1999-2014, using the Barten’s synthetic model. I test the hypothesis of possible structural breaks in the import demand introduced by various trade policies: antidumping/countervailing duty investigations and impositions, and import refusals due to safety and environmental issues. Results show that these import-restricting policies have significant effects on the import shrimp demand, indicating that the omission of them would lead to biased estimates. Chapter 2, the second paper, examines how the burden of state cigarette tax is divided between producers/retailers and consumers, by using the Nielsen store-level scanner data on cigarette prices from convenience stores over the period 2011–2012. Cigarette taxes were found more than fully passed through to retail prices on average, suggesting consumers pay excess burden and market power exists in the cigarette industry. Utilizing information on the attributes of cigarette products, we demonstrated that tax incidence varied by brand and package size: pass-through rates for premium brands and carton-packaged cigarettes are higher than those for discount brands and cigarettes in packs, respectively, indicating possibilities of different demand elasticities across product tiers. Chapter 3, the third paper, focuses on identifying the demographic characteristics of households buying organic coffee, by examining the factors that influence the probability that a consumer will buy organic coffee, and which factors affect the amount organic coffee purchased. Using nationally representative household level data from 55,470 households over the period of 2011 to 2013 (Nielsen Homescan), and a censored demand model, we find that economic and demographic factors play a crucial role in the household choice of purchasing organic coffee. Furthermore, households are less sensitive to own-price changes in the case of organic coffee versus conventional coffee

Learning Intra and Inter-Camera Invariance for Isolated Camera Supervised Person Re-identification

Supervised person re-identification assumes that a person has images captured under multiple cameras. However when cameras are placed in distance, a person rarely appears in more than one camera. This paper thus studies person re-ID under such isolated camera supervised (ISCS) setting. Instead of trying to generate fake cross-camera features like previous methods, we explore a novel perspective by making efficient use of the variation in training data. Under ISCS setting, a person only has limited images from a single camera, so the camera bias becomes a critical issue confounding ID discrimination. Cross-camera images are prone to being recognized as different IDs simply by camera style. To eliminate the confounding effect of camera bias, we propose to learn both intra- and inter-camera invariance under a unified framework. First, we construct style-consistent environments via clustering, and perform prototypical contrastive learning within each environment. Meanwhile, strongly augmented images are contrasted with original prototypes to enforce intra-camera augmentation invariance. For inter-camera invariance, we further design a much improved variant of multi-camera negative loss that optimizes the distance of multi-level negatives. The resulting model learns to be invariant to both subtle and severe style variation within and cross-camera. On multiple benchmarks, we conduct extensive experiments and validate the effectiveness and superiority of the proposed method. Code will be available at https://github.com/Terminator8758/IICI.Comment: ACM MultiMedia 202

Transformer Based Multi-Grained Features for Unsupervised Person Re-Identification

Multi-grained features extracted from convolutional neural networks (CNNs) have demonstrated their strong discrimination ability in supervised person re-identification (Re-ID) tasks. Inspired by them, this work investigates the way of extracting multi-grained features from a pure transformer network to address the unsupervised Re-ID problem that is label-free but much more challenging. To this end, we build a dual-branch network architecture based upon a modified Vision Transformer (ViT). The local tokens output in each branch are reshaped and then uniformly partitioned into multiple stripes to generate part-level features, while the global tokens of two branches are averaged to produce a global feature. Further, based upon offline-online associated camera-aware proxies (O2CAP) that is a top-performing unsupervised Re-ID method, we define offline and online contrastive learning losses with respect to both global and part-level features to conduct unsupervised learning. Extensive experiments on three person Re-ID datasets show that the proposed method outperforms state-of-the-art unsupervised methods by a considerable margin, greatly mitigating the gap to supervised counterparts. Code will be available soon at https://github.com/RikoLi/WACV23-workshop-TMGF.Comment: Accepted by WACVW 2023, 3rd Workshop on Real-World Surveillance: Applications and Challenge

Bis(2,6-dihy­droxy­benzoato-κ2 O 1,O 1′)(nitrato-κ2 O,O′)bis­(1,10-phenanthroline-κ2 N,N′)praseodymium(III)

The mononuclear title complex, [Pr(C7H5O3)2(NO3)(C12H8N2)2], is isostructural with related complexes of other lanthanides. The Pr(III) atom is in a pseudo-bicapped square-anti­prismatic geometry, formed by four N atoms from two chelating 1,10-phenanthroline (phen) ligands and six O atoms, four from two 2,6-dihy­droxy­benzoate (DHB) ligands and the other two from nitrate anions. π–π stacking inter­actions between the phen and DHB ligands [centroid–centroid distances = 3.518 (2) and 3.778 (2) Å] and the phen and phen ligands [face-to-face separation = 3.427 (6) Å] of adjacent complexes stabilize the crystal structure. Intra­molecular O—H⋯O hydrogen bonds are observed in the DHB ligands

Sequential Attacks on Kalman Filter-based Forward Collision Warning Systems

Kalman Filter (KF) is widely used in various domains to perform sequential learning or variable estimation. In the context of autonomous vehicles, KF constitutes the core component of many Advanced Driver Assistance Systems (ADAS), such as Forward Collision Warning (FCW). It tracks the states (distance, velocity etc.) of relevant traffic objects based on sensor measurements. The tracking output of KF is often fed into downstream logic to produce alerts, which will then be used by human drivers to make driving decisions in near-collision scenarios. In this paper, we study adversarial attacks on KF as part of the more complex machine-human hybrid system of Forward Collision Warning. Our attack goal is to negatively affect human braking decisions by causing KF to output incorrect state estimations that lead to false or delayed alerts. We accomplish this by sequentially manipulating measure ments fed into the KF, and propose a novel Model Predictive Control (MPC) approach to compute the optimal manipulation. Via experiments conducted in a simulated driving environment, we show that the attacker is able to successfully change FCW alert signals through planned manipulation over measurements prior to the desired target time. These results demonstrate that our attack can stealthily mislead a distracted human driver and cause vehicle collisions.Comment: Accepted by AAAI2

Camera-aware Proxies for Unsupervised Person Re-Identification

This paper tackles the purely unsupervised person re-identification (Re-ID) problem that requires no annotations. Some previous methods adopt clustering techniques to generate pseudo labels and use the produced labels to train Re-ID models progressively. These methods are relatively simple but effective. However, most clustering-based methods take each cluster as a pseudo identity class, neglecting the large intra-ID variance caused mainly by the change of camera views. To address this issue, we propose to split each single cluster into multiple proxies and each proxy represents the instances coming from the same camera. These camera-aware proxies enable us to deal with large intra-ID variance and generate more reliable pseudo labels for learning. Based on the camera-aware proxies, we design both intra- and inter-camera contrastive learning components for our Re-ID model to effectively learn the ID discrimination ability within and across cameras. Meanwhile, a proxy-balanced sampling strategy is also designed, which facilitates our learning further. Extensive experiments on three large-scale Re-ID datasets show that our proposed approach outperforms most unsupervised methods by a significant margin. Especially, on the challenging MSMT17 dataset, we gain $14.3\%$ Rank-1 and $10.2\%$ mAP improvements when compared to the second place. Code is available at: \texttt{https://github.com/Terminator8758/CAP-master}.Comment: Accepted to AAAI 2021. Code is available at: https://github.com/Terminator8758/CAP-maste