Distributed Algorithms for the Optimal Design of Wireless Networks

This thesis studies the problem of optimal design of wireless networks whose operating points such as powers, routes and channel capacities are solutions for an optimization problem. Different from existing work that rely on global channel state information (CSI), we focus on distributed algorithms for the optimal wireless networks where terminals only have access to locally available CSI. To begin with, we study random access channels where terminals acquire instantaneous local CSI but do not know the probability distribution of the channel. We develop adaptive scheduling and power control algorithms and show that the proposed algorithm almost surely maximizes a proportional fair utility while adhering to instantaneous and average power constraints. Then, these results are extended to random access multihop wireless networks. In this case, the associated optimization problem is neither convex nor amenable to distributed implementation, so a problem approximation is introduced which allows us to decompose it into local subproblems in the dual domain. The solution method based on stochastic subgradient descent leads to an architecture composed of layers and layer interfaces. With limited amount of message passing among terminals and small computational cost, the proposed algorithm converges almost surely in an ergodic sense. Next, we study the optimal transmission over wireless channels with imperfect CSI available at the transmitter side. To reduce the likelihood of packet losses due to the mismatch between channel estimates and actual channel values, a backoff function is introduced to enforce the selection of more conservative coding modes. Joint determination of optimal power allocations and backoff functions is a nonconvex stochastic optimization problem with infinitely many variables. Exploiting the resulting equivalence between primal and dual problems, we show that optimal power allocations and channel backoff functions are uniquely determined by optimal dual variables and develop algorithms to find the optimal solution. Finally, we study the optimal design of wireless network from a game theoretical perspective. In particular, we formulate the problem as a Bayesian game in which each terminal maximizes the expected utility based on its belief about the network state. We show that optimal solutions for two special cases, namely FDMA and RA, are equilibrium points of the game. Therefore, the proposed game theoretic formulation can be regarded as general framework for optimal design of wireless networks. Furthermore, cognitive access algorithms are developed to find solutions to the game approximately

Training Overparametrized Neural Networks in Sublinear Time

The success of deep learning comes at a tremendous computational and energy cost, and the scalability of training massively overparametrized neural networks is becoming a real barrier to the progress of artificial intelligence (AI). Despite the popularity and low cost-per-iteration of traditional backpropagation via gradient decent, stochastic gradient descent (SGD) has prohibitive convergence rate in non-convex settings, both in theory and practice. To mitigate this cost, recent works have proposed to employ alternative (Newton-type) training methods with much faster convergence rate, albeit with higher cost-per-iteration. For a typical neural network with $m=\mathrm{poly}(n)$ parameters and input batch of $n$ datapoints in $\mathbb{R}^d$, the previous work of [Brand, Peng, Song, and Weinstein, ITCS'2021] requires $\sim mnd + n^3$ time per iteration. In this paper, we present a novel training method that requires only $m^{1-\alpha} n d + n^3$ amortized time in the same overparametrized regime, where $\alpha \in (0.01,1)$ is some fixed constant. This method relies on a new and alternative view of neural networks, as a set of binary search trees, where each iteration corresponds to modifying a small subset of the nodes in the tree. We believe this view would have further applications in the design and analysis of deep neural networks (DNNs)

Fine-grained sketch-based image retrieval: The role of part-aware attributes

We study the problem of fine-grained sketch-based image retrieval. By performing instance-level (rather than category-level) retrieval, it embodies a timely and practical application, particularly with the ubiquitous availability of touchscreens. Three factors contribute to the challenging nature of the problem: (i) free-hand sketches are inherently abstract and iconic, making visual comparisons with photos more difficult, (ii) sketches and photos are in two different visual domains, i.e. black and white lines vs. color pixels, and (iii) fine-grained distinctions are especially challenging when executed across domain and abstraction-level. To address this, we propose to detect visual attributes at part-level, in order to build a new representation that not only captures fine-grained characteristics but also traverses across visual domains. More specifically, (i) we propose a dataset with 304 photos and 912 sketches, where each sketch and photo is annotated with its semantic parts and associated part-level attributes, and with the help of this dataset, we investigate (ii) how strongly-supervised deformable part-based models can be learned that subsequently enable automatic detection of part-level attributes, and (iii) a novel matching framework that synergistically integrates low-level features, mid-level geometric structure and high-level semantic attributes to boost retrieval performance. Extensive experiments conducted on our new dataset demonstrate value of the proposed method

Brand value Co-creation in social commerce: The role of interactivity, social support, and relationship quality

© 2017 Elsevier Ltd. A model of brand value co-creation by integrating its the antecedents of interactivity, social support, and relationship quality is proposed. Empirical data was collected from the brand pages of a social networking website in China. Structural equation modeling was adopted to analyze the data. The results demonstrate that interactivity, specifically, consumer-consumer interaction and consumer-seller interaction, positively affects social support, which in turn enhances consumers' intention to co-create brand value. The research contributes to the extant literature by providing an underlying understanding of how customers engage in brand value co-creation activities within social commerce context

Synergistic and protective effect of atorvastatin and amygdalin against histopathological and biochemical alterations in Sprague-Dawley rats with experimental endometriosis

Abstract The aim of the present study was to evaluate the protective effects of combined atorvastatin and amygdalin in a rat model of endometriosis. Tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), matrix metalloproteinase-2 (MMP-2) and MMP-9 levels in the peritoneal fluid were determined. The expression of TNF-α, IL-6, MMP-2, and MMP-9 mRNA, and the levels of lipid peroxidation, reduced glutathione (GSH), superoxide dismutase (SOD), catalase, and glutathione peroxidase (Gpx) were measured. Histopathological analysis was also conducted. The results showed that peritoneal TNF-α, IL-6, MMP-2, and MMP-9 levels were reduced by > 50%, and mRNA expression was decreased. Lipid peroxidation was considerably reduced, while GSH, SOD, Gpx, and catalase levels increased by > 40%. Reductions in leukocyte infiltration and fibrosis following treatment were also observed. Thus, our study suggested that combined treatment consisting of atorvastatin and amygdalin attenuates endometriosis. A detailed investigation of molecular mechanism of atorvastatin and amygdalin in endometriosis is needed

Using Resilience to Analyze Changes in an Industrial Community in China

Besides its systematic identification with disasters and hazards, resilience could be a powerful tool for understanding changes in the built environment. This study analyses the development of the workers’ residential area in one industrial community (IC) in China after its economic transformation. How can a resilience approach help analyse the impact of economic changes in the built environment? The ICs first emerged in socialist China in the 1950s and were used to organise social production (factory) and collective life (workers’ residential areas). Although existing research has explored the possible changes in the built environment of ICs before and after economic transformation, these changes that have either taken place or are still taking place have never been quantified. Furthermore, as the most unique and distinctive feature of the built environment, the impact of changes in the level of enclosure of workers’ residential areas after factory bankruptcy has rarely been explored. This study, therefore, uses a resilience approach to analyse the impact of changes in the level of enclosure on the built environment after the factory bankruptcy by taking the case of Shanxi Knitting Factory (SKF) in Taiyuan, China. This study seeks to use resilience to understand changes in ICs under the background of China’s economic transformation. The finding shows that the factory bankruptcy significantly impacts the changes in the built environment of its workers’ residential area as well as impacts the level of enclosure in the built environment. With the disappearance of enclosures after the factory bankruptcy, the workers' residential area is gradually shifting from an enclosed, isolated, centralised and self-contained industrial auxiliary facility to an open, diversified and heterogeneous space and gradually integrating into the surrounding neighbourhoods of the city

Using Resilience to Analyze Changes in an Industrial Community in China

Besides its systematic identification with disasters and hazards, resilience could be a powerful tool for understanding changes in the built environment. This study analyses the development of the workers’ residential area in one industrial community (IC) in China after its economic transformation. How can a resilience approach help analyse the impact of economic changes in the built environment? The ICs first emerged in socialist China in the 1950s and were used to organise social production (factory) and collective life (workers’ residential areas). Although existing research has explored the possible changes in the built environment of ICs before and after economic transformation, these changes that have either taken place or are still taking place have never been quantified. Furthermore, as the most unique and distinctive feature of the built environment, the impact of changes in the level of enclosure of workers’ residential areas after factory bankruptcy has rarely been explored. This study, therefore, uses a resilience approach to analyse the impact of changes in the level of enclosure on the built environment after the factory bankruptcy by taking the case of Shanxi Knitting Factory (SKF) in Taiyuan, China. This study seeks to use resilience to understand changes in ICs under the background of China’s economic transformation. The finding shows that the factory bankruptcy significantly impacts the changes in the built environment of its workers’ residential area as well as impacts the level of enclosure in the built environment. With the disappearance of enclosures after the factory bankruptcy, the workers' residential area is gradually shifting from an enclosed, isolated, centralised and self-contained industrial auxiliary facility to an open, diversified and heterogeneous space and gradually integrating into the surrounding neighbourhoods of the city