The sensitivity analysis of views related parameters in the black-litterman model

This dissertation attempts to carry out the sensitivity analysis in the Black-Litterman model which is a model trying to compensate for the lack of practical use of Modern Portfolio Theory. We analyse whether the change in the views related parameters has an effect on the final portfolio weights by using samples of eight stocks. In particular, we focus on the view vector and the confidence level of the views and treat them as independent variables. In addition, we generate the results of tests showing that both of the variables will influence the portfolio weights reasonably. Moreover, we also adopt the indicator called Tracking Error Volatility to illustrate the change in the portfolio weights. Besides, our findings reflect that the variation of the portfolio weights also depends on the small components contained by the two variables. According to our conclusions, the weights are especially sensitive to the change in the absolute view and the confidence level of the relative views. And the weight of the stock with a large market capitalization tends to differ a lot when the stock is mentioned in the view.Esta dissertação baseia-se na análise de sensibilidade no modelo Black-Litterman, que é um modelo que tenta compensar a falta de uso prático da "Modern Portfolio Theory". Por um lado, analisamos se a alteração nos parâmetros relacionados aos pontos de vista tem um efeito nos pesos finais do portfólio usando amostras de oito ações e em particular, concentramo-nos no vetor de visão e no nível de confiança das visualizações tratando-as como variáveis independentes. Por outro lado, geramos os resultados de testes que mostram que ambas as variáveis irão influenciar os pesos do portfólio razoavelmente e a adotamos o indicador chamado Tracking Error Volatility para ilustrar a mudança nos pesos do portfólio. O estudo efetuado reflete assim que a variação dos pesos do portfólio também depende dos peguenos componentes contidos pelas duas variáveis. Em suma, chegamos à conclusão que os pesos são especialmente sensíveis à mudança na visão absoluta e ao nível de confiança das visualizações relativas. E o peso da ação com uma grande capitalização de mercado tende a diferir muito quanto o stock é mencionado na visão

MICROSCOPIC ORIGINS OF MECHANICAL BEHAVIOR AND GELATION OF SOFT DISORDERED MATERIALS REVEALED BY RHEO-XPCS

This thesis characterizes the microscopic dynamics underlying the nonlinear mechanical behavior and liquid-to-gel transitions of soft disordered materials through combined x-ray photon correlation spectroscopy and \textit{in situ} rheology (Rheo-XPCS). The first part of this thesis reports studies addressing three aspects of the mechanical response of nanocolloidal systems. The first study of the prolonged stress relaxation after cessation of shear in nanocolloidal glass reveals that the process is driven by a slow, convective flow parallel to the initial shear and is accompanied by intermittent, avalanche-like events in the perpendicular direction. The second study shows the irreversible particle displacements in microstructure of soft glasses during the start-up of cyclic shear consist of a combination of shear-induced diffusion and strain, both of which decrease in amplitude with the number of cycles before eventually becoming steady. Both the microstructure and elasticity of the glass show memory of this shear history in the non-monotonic response to the change in shear strain amplitude. In the third study, novel measurement strategies employing Rheo-XPCS to determine velocity profiles of complex fluids under flow in Couette geometry are developed. These methods are employed to characterize the shear-rate dependent non-affine velocity profile of a shear-thinning dispersion of nematically ordered platelets and to correlate the flow behavior with the nematic order. The second part of this thesis focuses on gelation of nanocolloidal suspensions and subsequent aging of the gels in two experiments. The first shows that the emergence of elasticity during gelation of suspensions of charged, disk-shaped particles can be quantitatively related to the localization of the particles through a scaling between the storage modulus and localization length obtained from naive mode coupling theory. In the second, the response of an aging nanocolloidal gel to a sudden change in the strength of the interparticle attraction is investigated. Upon lowering the attraction, the storage modulus, microstructure, and microscopic dynamics of the gel experience a non-monotonic time dependence that is similar to the Kovacs memory effect observed in glass, and then a converge to those of a gel kept at the lower attraction. The convergence rate depends on the magnitude of the change in attraction and the age of the gel. The non-monotonic evolution and convergence are recovered qualitatively by a model of gel aging that describes particle assembly into a system spanning cluster with contact-number-dependent attachment and detachment rates

Valley-Hall photonic topological insulators with dual-band kink states

Extensive researches have revealed that valley, a binary degree of freedom (DOF), can be an excellent candidate of information carrier. Recently, valley DOF has been introduced into photonic systems, and several valley-Hall photonic topological insulators (PTIs) have been experimentally demonstrated. However, in the previous valley-Hall PTIs, topological kink states only work at a single frequency band, which limits potential applications in multiband waveguides, filters, communications, and so on. To overcome this challenge, here we experimentally demonstrate a valley-Hall PTI, where the topological kink states exist at two separated frequency bands, in a microwave substrate-integrated circuitry. Both the simulated and experimental results demonstrate the dual-band valley-Hall topological kink states are robust against the sharp bends of the internal domain wall with negligible inter-valley scattering. Our work may pave the way for multi-channel substrate-integrated photonic devices with high efficiency and high capacity for information communications and processing

VeryFL: A Verify Federated Learning Framework Embedded with Blockchain

Blockchain-empowered federated learning (FL) has provoked extensive research recently. Various blockchain-based federated learning algorithm, architecture and mechanism have been designed to solve issues like single point failure and data falsification brought by centralized FL paradigm. Moreover, it is easier to allocate incentives to nodes with the help of the blockchain. Various centralized federated learning frameworks like FedML, have emerged in the community to help boost the research on FL. However, decentralized blockchain-based federated learning framework is still missing, which cause inconvenience for researcher to reproduce or verify the algorithm performance based on blockchain. Inspired by the above issues, we have designed and developed a blockchain-based federated learning framework by embedding Ethereum network. This report will present the overall structure of this framework, which proposes a code practice paradigm for the combination of FL with blockchain and, at the same time, compatible with normal FL training task. In addition to implement some blockchain federated learning algorithms on smart contract to help execute a FL training, we also propose a model ownership authentication architecture based on blockchain and model watermarking to protect the intellectual property rights of models. These mechanism on blockchain shows an underlying support of blockchain for federated learning to provide a verifiable training, aggregation and incentive distribution procedure and thus we named this framework VeryFL (A Verify Federated Learninig Framework Embedded with Blockchain). The source code is avaliable on https://github.com/GTMLLab/VeryFL

LipsFormer: Introducing Lipschitz Continuity to Vision Transformers

We present a Lipschitz continuous Transformer, called LipsFormer, to pursue training stability both theoretically and empirically for Transformer-based models. In contrast to previous practical tricks that address training instability by learning rate warmup, layer normalization, attention formulation, and weight initialization, we show that Lipschitz continuity is a more essential property to ensure training stability. In LipsFormer, we replace unstable Transformer component modules with Lipschitz continuous counterparts: CenterNorm instead of LayerNorm, spectral initialization instead of Xavier initialization, scaled cosine similarity attention instead of dot-product attention, and weighted residual shortcut. We prove that these introduced modules are Lipschitz continuous and derive an upper bound on the Lipschitz constant of LipsFormer. Our experiments show that LipsFormer allows stable training of deep Transformer architectures without the need of careful learning rate tuning such as warmup, yielding a faster convergence and better generalization. As a result, on the ImageNet 1K dataset, LipsFormer-Swin-Tiny based on Swin Transformer training for 300 epochs can obtain 82.7\% without any learning rate warmup. Moreover, LipsFormer-CSwin-Tiny, based on CSwin, training for 300 epochs achieves a top-1 accuracy of 83.5\% with 4.7G FLOPs and 24M parameters. The code will be released at \url{https://github.com/IDEA-Research/LipsFormer}.Comment: To appear in ICLR 2023, our code will be public at https://github.com/IDEA-Research/LipsForme

FD-GAN: Generative Adversarial Networks with Fusion-discriminator for Single Image Dehazing

Recently, convolutional neural networks (CNNs) have achieved great improvements in single image dehazing and attained much attention in research. Most existing learning-based dehazing methods are not fully end-to-end, which still follow the traditional dehazing procedure: first estimate the medium transmission and the atmospheric light, then recover the haze-free image based on the atmospheric scattering model. However, in practice, due to lack of priors and constraints, it is hard to precisely estimate these intermediate parameters. Inaccurate estimation further degrades the performance of dehazing, resulting in artifacts, color distortion and insufficient haze removal. To address this, we propose a fully end-to-end Generative Adversarial Networks with Fusion-discriminator (FD-GAN) for image dehazing. With the proposed Fusion-discriminator which takes frequency information as additional priors, our model can generator more natural and realistic dehazed images with less color distortion and fewer artifacts. Moreover, we synthesize a large-scale training dataset including various indoor and outdoor hazy images to boost the performance and we reveal that for learning-based dehazing methods, the performance is strictly influenced by the training data. Experiments have shown that our method reaches state-of-the-art performance on both public synthetic datasets and real-world images with more visually pleasing dehazed results.Comment: Accepted by AAAI2020 (with supplementary files