Cell-cell communication in xanthomonas campestris pv. campestris

Ph.DDOCTOR OF PHILOSOPH

Responsive Building Envelope for Grid-Interactive Efficient Buildings – Thermal Performance and Control

The building sector accounts for 30% of total energy consumption worldwide. Responsive building envelopes (or RBEs) are one of the approaches to achieving net-zero energy and grid-interactive efficient buildings. However, research and development of RBEs are still in the early stages of technologies, simulation, control, and design. The control strategies in prior studies did not fully explore the potential of RBEs or they obtained good performance with high design and deployment costs. A low-cost strategy that does not require knowledge of complex systems is needed, while no studies have investigated online implementations of model-free control approaches for RBEs. To address these challenges, this dissertation describes a multidisciplinary study of the modeling, control, and design of RBEs, to understand mechanisms governing their dynamic properties and synthesis rules of multiple technologies through simulation analyses. Widely applicable mathematical models are developed that can be easily extended for multiple RBE types with validation. Computational frameworks (or co-simulation testbeds) that flexibly integrate multiple control methods and building simulation models are established with higher computation efficiency than that using commercial software during offline training. To overcome the limitations of the control strategies (e.g., rule-based control and MPC) in prior research, a novel easy-to-implement yet flexible ‘demand-based’ control strategy, and model-free online control strategies using deep reinforced learning are proposed for RBEs composed of active insulation systems (AISs). Both the physics-derived and model-free control strategies fully leverage the advantages of AISs and provide higher energy savings and thermal comfort improvement over traditional temperature-based control methods in prior research and demand-based control. The case studies of RBEs that integrate AISs and high thermal mass or self-adaptive/active modules (e.g., evaporative cooling techniques and dynamic glazing/shading) demonstrate the superior performance of AISs in regulating thermal energy transfer to offset AC demands during the synergy. Moreover, the controller design and training implications are elaborated. The applicability assessment of promising RBE configurations is presented along with design implications based on building energy analyses in multiple scenarios. The design and control implications represent an interactive and holistic way to operate RBEs allowing energy and thermal comfort performances to be tuned for maximum efficiency

Seismic chronostratigraphy for reservoir characterization : modeling and applications

The assumption of the chronostratigraphic significance of seismic reflections serves as a fundamental premise in interpreting stratigraphy from seismic images. This hypothesis proposed in 1977 was initially applied to delineate depositional sequences as the basic interpretive unit, and then to reconstruct Wheeler Diagram and regional sea level curves. After a further comparison against with global eustatic events, these regional curves can further facilitate predicting the age, distribution, and facies of depositional sequence before drilling in a seismic-covered area during petroleum exploration. With a boom in reservoir-level seismic applications, for obtaining significant high frequency sequence (HFS) surfaces as the bounding surfaces in static reservoir model construction, this fundamental assumption was inevitably extended to characterize HFS and even high-frequency cycles (HFC) during seismic reservoir characterization. For an ultimate improvement in constructing reservoir-bounding surfaces, the author targeted at evaluating the validity of this fundamental assumption as applied to high-order seismic stratigraphy. The author conducted the entire project via the forward seismic modeling upon geologic models with known chronostratigraphic relationship. Besides, these input models carefully honor the reservoir geology for meaningful discussions on (1) shallow marine siliciclastic reservoirs in Starfak Field, GoM, (2) shallow-water mixed carbonate/clastic Upper San Andres-Grayburg reservoirs in Permian Basin, and (3) shallow-water carbonate Abo shelf margin-Clear Fork platform in Permian Basin. This study has achieved three-fold contributions. On the aspect of realistic geocellular, property and seismic modeling at the reservoir scale, the author integrated high-resolution sequence stratigraphic framework, published 3D depositional model, intra-facies heterogeneity in 3D modeling to selectively apply advanced geostatistical methods to model hierarchical heterogeneity. Subsequently, the author proposed an evaluation scheme with a defined parameter ('time-correlation error/TCE') to assess HFS-scale reservoir-bounding surfaces. These assessments revealed an interactive influence from (1) stratal geometry, (2) lateral lithofacies variation, (3) lithofacies-sonic velocity relationship in pure- versus mixed-lithology successions, (4) intra-facies heterogeneity, and (5) seismic frequency. Finally, based on these forward modeling results, the author proposed a decision tree to determine valid interpretation strategy in seismic chronostratigraphic correlation in scenarios with geoscientists’ expert knowledge and recommended an attribute-driven volumetric picking scheme to improve published algorithms for scenarios without prior knowledge.Geological Science

T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation

Graph Neural Networks (GNNs) have been a prevailing technique for tackling various analysis tasks on graph data. A key premise for the remarkable performance of GNNs relies on complete and trustworthy initial graph descriptions (i.e., node features and graph structure), which is often not satisfied since real-world graphs are often incomplete due to various unavoidable factors. In particular, GNNs face greater challenges when both node features and graph structure are incomplete at the same time. The existing methods either focus on feature completion or structure completion. They usually rely on the matching relationship between features and structure, or employ joint learning of node representation and feature (or structure) completion in the hope of achieving mutual benefit. However, recent studies confirm that the mutual interference between features and structure leads to the degradation of GNN performance. When both features and structure are incomplete, the mismatch between features and structure caused by the missing randomness exacerbates the interference between the two, which may trigger incorrect completions that negatively affect node representation. To this end, in this paper we propose a general GNN framework based on teacher-student distillation to improve the performance of GNNs on incomplete graphs, namely T2-GNN. To avoid the interference between features and structure, we separately design feature-level and structure-level teacher models to provide targeted guidance for student model (base GNNs, such as GCN) through distillation. Then we design two personalized methods to obtain well-trained feature and structure teachers. To ensure that the knowledge of the teacher model is comprehensively and effectively distilled to the student model, we further propose a dual distillation mode to enable the student to acquire as much expert knowledge as possible.Comment: Accepted by AAAI2

mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation

Accurate brain tumor segmentation from Magnetic Resonance Imaging (MRI) is desirable to joint learning of multimodal images. However, in clinical practice, it is not always possible to acquire a complete set of MRIs, and the problem of missing modalities causes severe performance degradation in existing multimodal segmentation methods. In this work, we present the first attempt to exploit the Transformer for multimodal brain tumor segmentation that is robust to any combinatorial subset of available modalities. Concretely, we propose a novel multimodal Medical Transformer (mmFormer) for incomplete multimodal learning with three main components: the hybrid modality-specific encoders that bridge a convolutional encoder and an intra-modal Transformer for both local and global context modeling within each modality; an inter-modal Transformer to build and align the long-range correlations across modalities for modality-invariant features with global semantics corresponding to tumor region; a decoder that performs a progressive up-sampling and fusion with the modality-invariant features to generate robust segmentation. Besides, auxiliary regularizers are introduced in both encoder and decoder to further enhance the model's robustness to incomplete modalities. We conduct extensive experiments on the public BraTS $2018$ dataset for brain tumor segmentation. The results demonstrate that the proposed mmFormer outperforms the state-of-the-art methods for incomplete multimodal brain tumor segmentation on almost all subsets of incomplete modalities, especially by an average 19.07% improvement of Dice on tumor segmentation with only one available modality. The code is available at https://github.com/YaoZhang93/mmFormer.Comment: Accepted to MICCAI 202

Comparative venom gland transcriptome analysis of the scorpion Lychas mucronatus reveals intraspecific toxic gene diversity and new venomous components

<p>Abstract</p> <p>Background</p> <p><it>Lychas mucronatus </it>is one scorpion species widely distributed in Southeast Asia and southern China. Anything is hardly known about its venom components, despite the fact that it can often cause human accidents. In this work, we performed a venomous gland transcriptome analysis by constructing and screening the venom gland cDNA library of the scorpion <it>Lychas mucronatus </it>from Yunnan province and compared it with the previous results of Hainan-sourced <it>Lychas mucronatus</it>.</p> <p>Results</p> <p>A total of sixteen known types of venom peptides and proteins are obtained from the venom gland cDNA library of Yunnan-sourced <it>Lychas mucronatus</it>, which greatly increase the number of currently reported scorpion venom peptides. Interestingly, we also identified nineteen atypical types of venom molecules seldom reported in scorpion species. Surprisingly, the comparative transcriptome analysis of Yunnan-sourced <it>Lychas mucronatus </it>and Hainan-sourced <it>Lychas mucronatus </it>indicated that enormous diversity and vastly abundant difference could be found in venom peptides and proteins between populations of the scorpion <it>Lychas mucronatus </it>from different geographical regions.</p> <p>Conclusions</p> <p>This work characterizes a large number of venom molecules never identified in scorpion species. This result provides a comparative analysis of venom transcriptomes of the scorpion <it>Lychas mucronatus </it>from different geographical regions, which thoroughly reveals the fact that the venom peptides and proteins of the same scorpion species from different geographical regions are highly diversified and scorpion evolves to adapt a new environment by altering the primary structure and abundance of venom peptides and proteins.</p

OmniCity: Omnipotent City Understanding with Multi-level and Multi-view Images

This paper presents OmniCity, a new dataset for omnipotent city understanding from multi-level and multi-view images. More precisely, the OmniCity contains multi-view satellite images as well as street-level panorama and mono-view images, constituting over 100K pixel-wise annotated images that are well-aligned and collected from 25K geo-locations in New York City. To alleviate the substantial pixel-wise annotation efforts, we propose an efficient street-view image annotation pipeline that leverages the existing label maps of satellite view and the transformation relations between different views (satellite, panorama, and mono-view). With the new OmniCity dataset, we provide benchmarks for a variety of tasks including building footprint extraction, height estimation, and building plane/instance/fine-grained segmentation. Compared with the existing multi-level and multi-view benchmarks, OmniCity contains a larger number of images with richer annotation types and more views, provides more benchmark results of state-of-the-art models, and introduces a novel task for fine-grained building instance segmentation on street-level panorama images. Moreover, OmniCity provides new problem settings for existing tasks, such as cross-view image matching, synthesis, segmentation, detection, etc., and facilitates the developing of new methods for large-scale city understanding, reconstruction, and simulation. The OmniCity dataset as well as the benchmarks will be available at https://city-super.github.io/omnicity

Improving GAN Training via Feature Space Shrinkage

Due to the outstanding capability for data generation, Generative Adversarial Networks (GANs) have attracted considerable attention in unsupervised learning. However, training GANs is difficult, since the training distribution is dynamic for the discriminator, leading to unstable image representation. In this paper, we address the problem of training GANs from a novel perspective, \emph{i.e.,} robust image classification. Motivated by studies on robust image representation, we propose a simple yet effective module, namely AdaptiveMix, for GANs, which shrinks the regions of training data in the image representation space of the discriminator. Considering it is intractable to directly bound feature space, we propose to construct hard samples and narrow down the feature distance between hard and easy samples. The hard samples are constructed by mixing a pair of training images. We evaluate the effectiveness of our AdaptiveMix with widely-used and state-of-the-art GAN architectures. The evaluation results demonstrate that our AdaptiveMix can facilitate the training of GANs and effectively improve the image quality of generated samples. We also show that our AdaptiveMix can be further applied to image classification and Out-Of-Distribution (OOD) detection tasks, by equipping it with state-of-the-art methods. Extensive experiments on seven publicly available datasets show that our method effectively boosts the performance of baselines. The code is publicly available at https://github.com/WentianZhang-ML/AdaptiveMix.Comment: Accepted by CVPR'2023. Code and Demo are available at https://github.com/WentianZhang-ML/AdaptiveMi

Role of dimensional crossover on spin-orbit torque efficiency in magnetic insulator thin films

Magnetic insulators (MIs) attract tremendous interest for spintronic applications due to low Gilbert damping and absence of Ohmic loss. Magnetic order of MIs can be manipulated and even switched by spin-orbit torques (SOTs) generated through spin Hall effect and Rashba-Edelstein effect in heavy metal/MI bilayers. SOTs on MIs are more intriguing than magnetic metals since SOTs cannot be transferred to MIs through direct injection of electron spins. Understanding of SOTs on MIs remains elusive, especially how SOTs scale with the film thickness. Here, we observe the critical role of dimensionality on the SOT efficiency by systematically studying the MI layer thickness dependent SOT efficiency in tungsten/thulium iron garnet (W/TmIG) bilayers. We first show that the TmIG thin film evolves from two-dimensional to three-dimensional magnetic phase transitions as the thickness increases, due to the suppression of long-wavelength thermal fluctuation. Then, we report the significant enhancement of the measured SOT efficiency as the thickness increases. We attribute this effect to the increase of the magnetic moment density in concert with the suppression of thermal fluctuations. At last, we demonstrate the current-induced SOT switching in the W/TmIG bilayers with a TmIG thickness up to 15 nm. The switching current density is comparable with those of heavy metal/ferromagnetic metal cases. Our findings shed light on the understanding of SOTs in MIs, which is important for the future development of ultrathin MI-based low-power spintronics