Doctor of Philosophy

dissertationThis study attempts to characterize the particular convection type, namely storm morphologies, convective properties, and microphysics, of different weather regimes within the East Asian Summer Monsoon (EASM). Defined rain bands and associated rainfall characteristics are examined in terms of population, location, variability, and rainfall frequency. Though the Mei-Yu rain bands produce a relatively large rain belt over South China and Taiwan during mid-May to mid-June, and over the Yangtze River region during mid-June to mid-July, rainfall maxima and heavy precipitation are most frequent over specific locations. Generally, the frequency of storms with high echo tops, significant convection, and evident ice scattering signature is greatest in post-Meiyu and break periods, less so during the active Mei-Yu, and least frequent before the monsoon onset. However, preseason, as well as break periods, has a larger fraction of intense convection that behaves more like the classic continental tropical convection with major ice-based rainfall processes. Specifically, preseason and break periods have a larger fraction of rainfall contributed from storms with a 40-dBZ convective core extending above 7-8 km. By comparison, active Mei-Yu convection more closely resembles classic tropical maritime convection with relatively more importance of "warm-rain" collision and coalescence processes with weaker convection but heavy precipitation. Monsoon precipitation over the Yangtze River region, though having similar size and cloud top, differs from its counterpart in South China on convective properties, vertical structures, and rainfall contribution by storm types. Based on Tropical Rainfall Measuring Mission (TRMM) climatology, the EASM is comparable to other monsoon regimes by having convective properties intermediate between the intense convective systems over continents, and the weaker convective systems found in the classic maritime precipitation regimes. Analysis based on Terrain-influenced Monsoon Rainfall Experiment (TiMREX) observations indicates that most of the heavy rainfall is associated with Mei-Yu rain bands, strongly influenced by upstream low-level jets, unstable upstream conditions, but a more nearly moist neutral storm environment. A particular long-duration heavy precipitation event is analyzed in detail, and features continuous development of "back-building" new convection under the influence of an extensive precipitation-created cold pool and substantial orography downstream

Amyloidogenesis Abolished by Proline Substitutions but Enhanced by Lipid Binding

The influence of lipid molecules on the aggregation of a highly amyloidogenic segment of human islet amyloid polypeptide, hIAPP20–29, and the corresponding sequence from rat has been studied by all-atom replica exchange molecular dynamics (REMD) simulations with explicit solvent model. hIAPP20–29 fragments aggregate into partially ordered β-sheet oligomers and then undergo large conformational reorganization and convert into parallel/antiparallel β-sheet oligomers in mixed in-register and out-of-register patterns. The hydrophobic interaction between lipid tails and residues at positions 23–25 is found to stabilize the ordered β-sheet structure, indicating a catalysis role of lipid molecules in hIAPP20–29 self-assembly. The rat IAPP variants with three proline residues maintain unstructured micelle-like oligomers, which is consistent with non-amyloidogenic behavior observed in experimental studies. Our study provides the atomic resolution descriptions of the catalytic function of lipid molecules on the aggregation of IAPP peptides

Distributed H_/L∞ fault detection observer design for linear systems:Proceedings

This paper studies the distributed fault detection problem for linear time-invariant (LTI) systems with distributed measurement output. A distributed H_/L∞ fault detection observer (DFDO) design method is proposed to detect actuator faults of the monitored system in the presence of a bounded process disturbances. The DFDO consists of a network of local fault detection observers, which communicate with their neighbors as prescribed by a given network graph. By using finite-frequency H_ performance, the residual in fault detection is sensitive to fault in the interested frequency-domain. The residual is robust against effects of the external process disturbance by L∞ analysis. A systematic algorithm for DFDO design is addressed and the residual thresholds are calculated in our distributed fault detection scheme. Finally, we use a numerical simulation to demonstrate the effectiveness of the proposed distributed fault detection approach

Amodal Segmentation Based on Visible Region Segmentation and Shape Prior

Almost all existing amodal segmentation methods make the inferences of occluded regions by using features corresponding to the whole image. This is against the human's amodal perception, where human uses the visible part and the shape prior knowledge of the target to infer the occluded region. To mimic the behavior of human and solve the ambiguity in the learning, we propose a framework, it firstly estimates a coarse visible mask and a coarse amodal mask. Then based on the coarse prediction, our model infers the amodal mask by concentrating on the visible region and utilizing the shape prior in the memory. In this way, features corresponding to background and occlusion can be suppressed for amodal mask estimation. Consequently, the amodal mask would not be affected by what the occlusion is given the same visible regions. The leverage of shape prior makes the amodal mask estimation more robust and reasonable. Our proposed model is evaluated on three datasets. Experiments show that our proposed model outperforms existing state-of-the-art methods. The visualization of shape prior indicates that the category-specific feature in the codebook has certain interpretability.Comment: Accepted by AAAI 202

A Survey of the methods on fingerprint orientation field estimation

Fingerprint orientation field (FOF) estimation plays a key role in enhancing the performance of the automated fingerprint identification system (AFIS): Accurate estimation of FOF can evidently improve the performance of AFIS. However, despite the enormous attention on the FOF estimation research in the past decades, the accurate estimation of FOFs, especially for poor-quality fingerprints, still remains a challenging task. In this paper, we devote to review and categorization of the large number of FOF estimation methods proposed in the specialized literature, with particular attention to the most recent work in this area. Broadly speaking, the existing FOF estimation methods can be grouped into three categories: gradient-based methods, mathematical models-based methods, and learning-based methods. Identifying and explaining the advantages and limitations of these FOF estimation methods is of fundamental importance for fingerprint identification, because only a full understanding of the nature of these methods can shed light on the most essential issues for FOF estimation. In this paper, we make a comprehensive discussion and analysis of these methods concerning their advantages and limitations. We have also conducted experiments using publically available competition dataset to effectively compare the performance of the most relevant algorithms and methods

Modified lattice parameter/Curie temperature diagrams for Titanomagnetite/titanomaghemite within the quadrilateral Fe 3 O 4 ‐ Fe 2 TiO 4 ‐Fe 2 O 3 ‐Fe 2 TiO 5

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94971/1/grl9245.pd

Soil respiration components and their temperature sensitivity under chemical fertilizer and compost application: the role of nitrogen supply and compost substrate quality

Understanding autotrophic (Ra) and heterotrophic (Rh) components of soil respiration (Rs) and their temperature sensitivity (Q10) is critical for predicting soil carbon (C) cycle and its feedback to climate change. In agricultural systems, these processes can be considerably altered by chemical fertilizer and compost application due to changes in nitrogen (N) supply and substrate quality (decomposability). We conducted a field experiment including control, urea and four compost treatments. Ra and Rh were separated using the root exclusion method. Composts were characterized by chemical analyses, 13C solid‐state NMR, and lignin monomers. Annual cumulative Ra, along with root biomass, increased with soil mineral N, while Rh was suppressed by excessive N supply. Thus, Ra was stimulated but Rh was decreased by urea alone application. Annual Rh was increased by application of compost, especially that containing most lignin vanillyl and syringyl units, O‐alkyl C, di‐O‐alkyl C, and manganese. However, during the initial period, Rh was most effectively stimulated by the compost containing most carbohydrates, lignin cinnamyl units, phenolic C and calcium. Ra was mediated by N release from compost decomposition, and thus exhibited similar responses to compost quality as Rh. The Rh Q10 was reduced while Ra Q10 was increased by chemical fertilizer and compost application. Moreover, the Rh Q10 negatively related to soil mineral N supply and compost indicators referring to high substrate quality. Overall, our results suggest that N supply and substrate quality played an important role in regulating soil C flux and its response to climate warming

PVD-AL: Progressive Volume Distillation with Active Learning for Efficient Conversion Between Different NeRF Architectures

Neural Radiance Fields (NeRF) have been widely adopted as practical and versatile representations for 3D scenes, facilitating various downstream tasks. However, different architectures, including plain Multi-Layer Perceptron (MLP), Tensors, low-rank Tensors, Hashtables, and their compositions, have their trade-offs. For instance, Hashtables-based representations allow for faster rendering but lack clear geometric meaning, making spatial-relation-aware editing challenging. To address this limitation and maximize the potential of each architecture, we propose Progressive Volume Distillation with Active Learning (PVD-AL), a systematic distillation method that enables any-to-any conversions between different architectures. PVD-AL decomposes each structure into two parts and progressively performs distillation from shallower to deeper volume representation, leveraging effective information retrieved from the rendering process. Additionally, a Three-Levels of active learning technique provides continuous feedback during the distillation process, resulting in high-performance results. Empirical evidence is presented to validate our method on multiple benchmark datasets. For example, PVD-AL can distill an MLP-based model from a Hashtables-based model at a 10~20X faster speed and 0.8dB~2dB higher PSNR than training the NeRF model from scratch. Moreover, PVD-AL permits the fusion of diverse features among distinct structures, enabling models with multiple editing properties and providing a more efficient model to meet real-time requirements. Project website:http://sk-fun.fun/PVD-AL.Comment: Project website: http://sk-fun.fun/PVD-AL. arXiv admin note: substantial text overlap with arXiv:2211.1597