Computational materials design of optical bandgaps for bulk heterojunction solar cell

Thesis (M.S.)--Boston UniversityFundamental understanding of the structure-property relationship of pi-conjugated poly- mers is critical to predictive materials designs of bulk heterojunction solar cells. In this thesis, the adapted Su-Schrieffer-Heeger Hamiltonian is implemented as the computational tool to systematically explore the opto-electronic properties of nearly 250 different kinds of pi-conjugated systems. New physical insights on the structure-property relationship are extracted and transformed into practical guiding rules in optical bandgap designs. For the most power efficient donor-acceptor copolymer structures, we find that the energy variation of frontier orbitals, in particular the highest occupied molecular orbitals (HOMO) and the lowest unoccupied molecular orbitals (LUMO), can be controlled either independently or collectively, depending on their specific donor or acceptor structures. In particular, we find that having five-membered conjugated carbon rings in the acceptor units is essential to break the electron-hole charge conjugation symmetry, so that the LUMO levels of the copolymer can be reduced dramatically while holding the HOMO energy levels in the donor units constant. On the other hand, by incorporating heteroatoms into the donors units, we can vary the HOMO levels of the copolymers independently. Predicted optical bandgaps of a total of 780 types of these copolymers constructed by using 39 different types of donor and acceptor units are tabulated in this thesis. In addition, the effects of introducing various side groups(-R, -0, -CO, -COO, and thiophene) on the primitive donor and acceptor structures are investigated and their results are discussed in details. Finally, unexpected localized states are found, for the first time, in our calculations for a few special co-polymer structures. These localized states, with electrons localized on one end of the copolymer chain and holes on the other end, contain large dipole moments and therefore may be treated as a new design dimension when these copolymers are placed in polar and non-polar solvent environments

Teaching Discussion Section Writing through a Genre-Based Approach to Undergraduates across Disciplines in China—A Novice EAP Teacher’s Classroom-Based Empirical Study

This study illuminates the genre-based pedagogy delivered by a novice EAP teacher for teaching discussion section writing to cross-discipline undergraduates in China. Followed by the demonstration of specifics of genre teaching, the researcher tapped into the effectiveness of genre-based pedagogy through an examination of student-produced writing submissions. Autoethnography and qualitative research methods were used to analyze video-recording, teaching journals and students’ writing assignments and it was revealed that the focal Chinese English teacher who specialized in EGP was able to teach genre knowledge and research writing skills through autonomous learning and teaching preparation. Furthermore, the efficacy of genre teaching could be affirmed since most learner-writers successfully transferred genre knowledge and writing skills taught in the classroom to their writing. From this study, pedagogical implications are drawn to shed light on future teacher education that aims to help Chinese English teachers attain better genre-based research writing instruction

Phonemic Adversarial Attack against Audio Recognition in Real World

Recently, adversarial attacks for audio recognition have attracted much attention. However, most of the existing studies mainly rely on the coarse-grain audio features at the instance level to generate adversarial noises, which leads to expensive generation time costs and weak universal attacking ability. Motivated by the observations that all audio speech consists of fundamental phonemes, this paper proposes a phonemic adversarial tack (PAT) paradigm, which attacks the fine-grain audio features at the phoneme level commonly shared across audio instances, to generate phonemic adversarial noises, enjoying the more general attacking ability with fast generation speed. Specifically, for accelerating the generation, a phoneme density balanced sampling strategy is introduced to sample quantity less but phonemic features abundant audio instances as the training data via estimating the phoneme density, which substantially alleviates the heavy dependency on the large training dataset. Moreover, for promoting universal attacking ability, the phonemic noise is optimized in an asynchronous way with a sliding window, which enhances the phoneme diversity and thus well captures the critical fundamental phonemic patterns. By conducting extensive experiments, we comprehensively investigate the proposed PAT framework and demonstrate that it outperforms the SOTA baselines by large margins (i.e., at least 11X speed up and 78% attacking ability improvement)

Spatiotemporal Characteristics of Particulate Matter and Dry Deposition Flux in the Cuihu Wetland of Beijing

In recent years, the rapid development of industrialization and urbanization has caused serious environmental pollution, especially particulate pollution. As the “Earth’s kidneys,” wetland plays a significant role in improving the environmental quality and adjusting the climate. To study how wetlands work in this aspect, from the early autumn of 2014 to 2015, we implemented a study to measure the PM concentration and chemical composition at three heights (1.5, 6, and 10 m) during different periods (dry, normal water, and wet periods) in the Cuihu wetland park in Beijing for analyzing the dry deposition flux and the effect of meteorological factors on the concentration. Results indicated that (1) the diurnal variations of the PM2.5 and PM10 concentrations at the three heights were similar in that the highest concentration occurred at night and the lowest occurred at noon, and the daytime concentration was lower than that at night; (2) the PM2.5 and PM10 concentrations also varied between different periods that wet period \u3e normal water period \u3e wet period, and the concentration at different heights during different periods varied. In general, the lowest concentration occurred at 10 m during the dry and normal water periods, and the highest concentration occurred at 1.5 m during the wet period. (3) SO4 2− , NO3 − , and Cl− are the dominant constituents of PM2.5, accounting for 42.22, 12.6, and 21.56%, respectively; (4) the dry depositions of PM2.5 and PM10 at 10 m were higher than those at 6 m, and the deposition during the dry period was higher than those during the wet and normal water periods. In addition, the deposition during the night-time was higher than that during the daytime. Moreover, meteorological factors affected the deposition, the temperature and wind speed being negatively correlated with the deposition flux and the humidity being positively correlated. (5) The PM10 and PM2.5 concentrations were influenced by meteorological factors. The PM2.5 and PM10 concentrations were negatively correlated with temperature and wind speed but positively correlated with relative humidity

Distribution Characteristics of Geo-hazards in a Reservoir Area, South Gansu Province, China

233-240In the process of water storage, due to water level fluctuations and base level erosion, reservoirs also play an important role in the occurrence of geological disasters. Taking a reservoir valley type in South Gansu Province, China as a case study, we investigated in depth the development and distribution of geological hazards and their influencing factors. The geological environment had changed considerably after reservoir impoundment with an increase in geological disasters. Furthermore, the main types of geological disasters were also analyzed systematically. Slope angle, altitude, slope aspect, proximity to earthquake faults, reservoir water storage, slope body structure, rock mass structure, and their combination features influenced the development and distribution of geological disasters in reservoir area. Close proximity to rivers also increases the likelihood of geological disasters. Landslides and collapses are closely related to the geo-hazards and their triggers include earthquakes, torrential rainfall, and fluctuations in reservoir water level. We also identified 2 types of debris which flow into the reservoir: gulch development and slope liquefaction

Adversarial Examples in the Physical World: A Survey

Deep neural networks (DNNs) have demonstrated high vulnerability to adversarial examples. Besides the attacks in the digital world, the practical implications of adversarial examples in the physical world present significant challenges and safety concerns. However, current research on physical adversarial examples (PAEs) lacks a comprehensive understanding of their unique characteristics, leading to limited significance and understanding. In this paper, we address this gap by thoroughly examining the characteristics of PAEs within a practical workflow encompassing training, manufacturing, and re-sampling processes. By analyzing the links between physical adversarial attacks, we identify manufacturing and re-sampling as the primary sources of distinct attributes and particularities in PAEs. Leveraging this knowledge, we develop a comprehensive analysis and classification framework for PAEs based on their specific characteristics, covering over 100 studies on physical-world adversarial examples. Furthermore, we investigate defense strategies against PAEs and identify open challenges and opportunities for future research. We aim to provide a fresh, thorough, and systematic understanding of PAEs, thereby promoting the development of robust adversarial learning and its application in open-world scenarios.Comment: Adversarial examples, physical-world scenarios, attacks and defense

Identification and Characterization of the PEBP Family Genes in Moso Bamboo (Phyllostachys heterocycla).

Moso bamboo is one of the economically most important plants in China. Moso bamboo is a monocarpic perennial that exhibits poor and slow germination. Thus, the flowering often causes destruction of moso bamboo forestry. However, how control of flowering and seed germination are regulated in moso bamboo is largely unclear. In this study, we identified 5 members (PhFT1-5) of the phosphatidyl ethanolamine-binding proteins (PEBP) family from moso bamboo genome that regulate flowering, flower architecture and germination, and characterized the function of these PEBP family genes further in Arabidopsis. Phylogenetic analysis revealed that 3 (PhFT1, PhFT2 and PhFT3), 1 (PhFT4) and 1 (PhFT5) members belong to the TFL1-like clade, FT-like clade, and MFT-like clade, respectively. These PEBP family genes possess all structure necessary for PEBP gene function. The ectopic overexpression of PhFT4 and PhFT5 promotes flowering time in Arabidopsis, and that of PhFT1, PhFT2 and PhFT3 suppresses it. In addition, the overexpression of PhFT5 promotes seed germination rate. Interestingly, the overexpression of PhFT1 suppressed seed germination rate in Arabidopsis. The expression of PhFT1 and PhFT5 is significantly higher in seed than in tissues including leaf and shoot apical meristem, implying their function in seed germination. Taken together, our results suggested that the PEBP family genes play important roles as regulators of flowering and seed germination in moso bamboo and thereby are necessary for the sustainability of moso bamboo forest

MIR2: Towards Provably Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization

Robust multi-agent reinforcement learning (MARL) necessitates resilience to uncertain or worst-case actions by unknown allies. Existing max-min optimization techniques in robust MARL seek to enhance resilience by training agents against worst-case adversaries, but this becomes intractable as the number of agents grows, leading to exponentially increasing worst-case scenarios. Attempts to simplify this complexity often yield overly pessimistic policies, inadequate robustness across scenarios and high computational demands. Unlike these approaches, humans naturally learn adaptive and resilient behaviors without the necessity of preparing for every conceivable worst-case scenario. Motivated by this, we propose MIR2, which trains policy in routine scenarios and minimize Mutual Information as Robust Regularization. Theoretically, we frame robustness as an inference problem and prove that minimizing mutual information between histories and actions implicitly maximizes a lower bound on robustness under certain assumptions. Further analysis reveals that our proposed approach prevents agents from overreacting to others through an information bottleneck and aligns the policy with a robust action prior. Empirically, our MIR2 displays even greater resilience against worst-case adversaries than max-min optimization in StarCraft II, Multi-agent Mujoco and rendezvous. Our superiority is consistent when deployed in challenging real-world robot swarm control scenario. See code and demo videos in Supplementary Materials

CryoFormer: Continuous Reconstruction of 3D Structures from Cryo-EM Data using Transformer-based Neural Representations

High-resolution heterogeneous reconstruction of 3D structures of proteins and other biomolecules using cryo-electron microscopy (cryo-EM) is essential for understanding fundamental processes of life. However, it is still challenging to reconstruct the continuous motions of 3D structures from hundreds of thousands of noisy and randomly oriented 2D cryo-EM images. Existing methods based on coordinate-based neural networks show compelling results to model continuous conformations of 3D structures in the Fourier domain, but they suffer from a limited ability to model local flexible regions and lack interpretability. We propose a novel approach, cryoFormer, that utilizes a transformer-based network architecture for continuous heterogeneous cryo-EM reconstruction. We for the first time directly reconstruct continuous conformations of 3D structures using an implicit feature volume in the 3D spatial domain. A novel deformation transformer decoder further improves reconstruction quality and, more importantly, locates and robustly tackles flexible 3D regions caused by conformations. In experiments, our method outperforms current approaches on three public datasets (1 synthetic and 2 experimental) and a new synthetic dataset of PEDV spike protein. The code and new synthetic dataset will be released for better reproducibility of our results. Project page: https://cryoformer.github.io