Neural Network Coding

In this paper we introduce Neural Network Coding(NNC), a data-driven approach to joint source and network coding. In NNC, the encoders at each source and intermediate node, as well as the decoder at each destination node, are neural networks which are all trained jointly for the task of communicating correlated sources through a network of noisy point-to-point links. The NNC scheme is application-specific and makes use of a training set of data, instead of making assumptions on the source statistics. In addition, it can adapt to any arbitrary network topology and power constraint. We show empirically that, for the task of transmitting MNIST images over a network, the NNC scheme shows improvement over baseline schemes, especially in the low-SNR regime

Application-driven Intersections between Information Theory and Machine Learning

Machine learning has been tremendously successful in the past decade. In this thesis, we introduce guidance and insights from information theory to practical machine learning algorithms. In particular, we study three application domains and demonstrate the algorithmic gain of integrating machine learning with information theory. In the first part of the thesis, we deploy the principle of network coding to propose a decomposition scheme for distributing a neural network over a physical communication network. We show through experiments that our proposed scheme dramatically reduces the energy used compared to existing communication schemes under various channel statistics and network topologies. In the second part, we design a learning-based coding scheme, developed from the concept of error correction codes, for bio-molecular profiling. We show through simulations that, with a learning-based encoder and a maximize a posterior (MAP) decoder, our scheme significantly outperforms existing schemes in reducing the false negative rate of rare bio-molecular types. In the third part, we exercise guesswork on the machine translation problem. We study machine translation using the seq2seq model and we provide insights into quantifying the uncertainty within. Our results shed light on the design of inference in machine translation for selecting the beam size in beam search.Ph.D

The role of pathogenic microorganisms in the pathogenesis of scleroderma

Systemic sclerosis (SSc) is an autoimmune connective tissue disease characterized by localized or widespread sclerosis of the skin and progressive sclerosis in the internal organs. The pathogenesis of this disease has not been completely elucidated. However, it is considered to be associated with environmental factors, epigenetic mechanisms, and disorders of the immune system. This article reviews the research progress on the role of pathogenic microorganisms in the pathogenesis of scleroderma. It has been shown that scleroderma can be induced by infections with viruses, bacteria, mycoplasma, parasites, and other pathogenic microorganisms. Human herpesvirus and viruses such as B19V and HBV can cause pathological changes such as endothelial dysfunction and fibroblast activation. Alterations in the microbiota inside and outside the body are also associated with SSc. Treatments of pathogenic microorganisms improve SSc associated with infections by pathogenic microorganisms such as C burnetii, Mycoplasma and parasites, providing evidence of new insights in the pathogenesis of SSc, and early diagnosis, intervention and the treatment of this disease