Surface and Magnetic Polaritons on Nanodisk-Aligned Multilayer Structure

In this thesis, the nanodisk-aligned multilayer structure, in which square cross-sectional nanodisks are vertically aligned on the top of a two-dimensional, dielectric-layered metallic structure, is proposed to investigate the near-filed and far-field radiative property enhancement for metamaterials. Multiple kinds of plasmonic resonances which support the peculiar properties of metamaterials have been identified on this proposed structure. Every plasmonic mode is analyzed and studied respectively. The localized plasmonic modes (Localized Surface Plasmon and Magnetic Polariton) are shown to be independent of the incidence angle and polarization, while the propagating plasmonic mode (Surface Plasmon) is demonstrated to be shifted with the varying of the incidence angle, which provides the possibility to make the plasmonic modes interact or couple with each other. The coupled plasmonic modes which are named as hybridized plasmonic modes can also be excited and lead to even more spectacular radiative properties both in the near-field and far-field.The results obtained from this study suggest a novel model to explore the underlying mechanism of plasmonic resonances, especially for their hybridization. The study will advance our fundamental understanding of light-matter interaction at nanometer scale and will provide us more degrees of freedom to manipulate the radiative properties in both the near-field and far-field which might have great potentials in renewable energy applications that require specific radiative properties, such as thermophotovoltaic, photovoltaic cells and thermal emission sources

The neural crest and neural crest defects

The neural crest is a fascinating embryonic tissue for more than one reason. In the adult organism it gives rise to an array of distinct cell types and tissues. It is responsible for many birth defects, familial diseases and malignancies, and it is amenable to the elucidation of mechanisms that regulate stem cell differentiation. Subsequent to an epithelial-to-mesenchymal transformation, neural crest cells emigrate from the dorsal aspect of the neural tube into the embryo, stop in different places, and eventually give rise to the autonomic and enteric nervous systems, most primary sensory neurons, endocrine cells, and melanocytes of the skin and internal organs. Furthermore, neural crest cells are involved in the septation of the cardiac outflow tract and they form the cranial mesenchyme, which gives rise to bone, cartilage, and connective tissue of the face and ventral neck. Environmental insults can lead to neural crest defects, including cleft lip/cleft palate and fetal alcohol syndrome. Familial diseases that affect neural crest derivatives include Hischsprung's disease and albinism, whereas well-known neural crest-related malignancies include melanoma, neuroblastoma, neurofibromatosis and pheochromocytoma. Migratory neural crest cells form a heteroge­neous population of cells that includes stem cells, cells with restricted developmental potentials, and cells that are committed to a particular lineage. Growth factors play important roles in the survival, proliferation and differentiation of neural crest cells. In particular, neurotrophin-3 (NTS), the ligand of the tyrosine kinase receptor, TrkC, promotes the survival of proliferating neural crest stem cells. TrkC-deficient mice develop cardiac outflow tract defects that resemble human birth defects, including persistent truncus arteriosus and transposition of the great vessels. In these animals, cardiac neural crest stem cells become fate-restricted precociously. Action of stem cell factor (SCF), the ligand of the tyrosine kinase receptor c-kit, affects multiple systems. Heterozygous c-kit deficient mice, termed 'Dominant spotting' (W), have anemia, are sterile and show changes in coat color (white spotting) due to defects in the hemopoietic system, germ cell line and melanogenesis, respectively. Inactivation of the human c-kit gene causes piebaldism, which is characterized by a white forelock, patchy hypopigmentation of the skin and rare sensoryneural deafness. In the quail neural crest, SCF supports the survival of neural crest stem cells, promotes their dif­ferentiation into small diameter sensory neurons, and, together with a neurotrophin, supports survival of me lanocyte precursors. In c-kit deficient newborn mice, up to one third of substance P-immunoreactive nociceptive sensory neurons are missing, thus confirming across species that SCF signaling is essential for the development of small diameter sensory neurons. In addition, the number of calcitonin gene-related-peptide (CGRP)-immunoreactive putative visceral afferent neurons in the dorsal root ganglion is diminished in these mice. The norepinephrine transporter (NET) is expressed in many embryonic tissues, including premigratory and migratory neural crest cells. Norepinephrine (NE) uptake by neural crest cells promotes their differentiation into noradrenergic neuroblasts in vitro. In contrast, NE uptake inhibitors, such as tricyclic antidepressants and the drug of abuse, cocaine, inhibit noradrenergic differentiation in vitro and in vivo, suggesting that these drugs can be teratogenic. Since NET is expressed in many embryonic tissues, NE transport may have functions also in non-neural cells during embryonic development. In summary, growth factors, alone and synergistically as well as NEplay multiple roles in neural crest development.Biomedical Reviews 2002; 13: 29-37

Persistently Trained, Diffusion-assisted Energy-based Models

Maximum likelihood (ML) learning for energy-based models (EBMs) is challenging, partly due to non-convergence of Markov chain Monte Carlo.Several variations of ML learning have been proposed, but existing methods all fail to achieve both post-training image generation and proper density estimation. We propose to introduce diffusion data and learn a joint EBM, called diffusion assisted-EBMs, through persistent training (i.e., using persistent contrastive divergence) with an enhanced sampling algorithm to properly sample from complex, multimodal distributions. We present results from a 2D illustrative experiment and image experiments and demonstrate that, for the first time for image data, persistently trained EBMs can {\it simultaneously} achieve long-run stability, post-training image generation, and superior out-of-distribution detection.Comment: main text 8 page

Event-triggered consensus of multi-agent systems under directed topology based on periodic sampled-data

The event-triggered consensus problem of first-order multi-agent systems under directed topology is investigated. The event judgements are only implemented at periodic time instants. Under the designed consensus algorithm, the sampling period is permitted to be arbitrarily large. Another advantage of the designed consensus algorithm is that, for systems with time delay, consensus can be achieved for any finite delay only if it is bounded by the sampling period. The case of strongly connected topology is first investigated. Then, the result is extended to the most general topology which only needs to contain a spanning tree. A novel method based on positive series is introduced to analyze the convergence of the closed-loop systems. A numerical example is provided to illustrate the effectiveness of the obtained theoretical results

Task-Oriented Dialog Systems that Consider Multiple Appropriate Responses under the Same Context

Conversations have an intrinsic one-to-many property, which means that multiple responses can be appropriate for the same dialog context. In task-oriented dialogs, this property leads to different valid dialog policies towards task completion. However, none of the existing task-oriented dialog generation approaches takes this property into account. We propose a Multi-Action Data Augmentation (MADA) framework to utilize the one-to-many property to generate diverse appropriate dialog responses. Specifically, we first use dialog states to summarize the dialog history, and then discover all possible mappings from every dialog state to its different valid system actions. During dialog system training, we enable the current dialog state to map to all valid system actions discovered in the previous process to create additional state-action pairs. By incorporating these additional pairs, the dialog policy learns a balanced action distribution, which further guides the dialog model to generate diverse responses. Experimental results show that the proposed framework consistently improves dialog policy diversity, and results in improved response diversity and appropriateness. Our model obtains state-of-the-art results on MultiWOZ