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In this paper, we study a distributed learning problem constrained by constant communication bits. Specifically, we consider the distributed hypothesis testing (DHT) problem where two distributed nodes are constrained to transmit a constant number of bits to a central decoder. In such cases, we show that in order to achieve the optimal error exponents, it suffices to consider the empirical distributions of observed data sequences and encode them to the transmission bits. With such a coding strategy, we develop a geometric approach in the distribution spaces and establish an inner bound of error exponent regions. In particular, we show the optimal achievable error exponents and coding schemes for the following cases: (i) both nodes can transmit $\log_23$ bits; (ii) one of the nodes can transmit $1$ bit, and the other node is not constrained; (iii) the joint distribution of the nodes are conditionally independent given one hypothesis. Furthermore, we provide several numerical examples for illustrating the theoretical results. Our results provide theoretical guidance for designing practical distributed learning rules, and the developed approach also reveals new potentials for establishing error exponents for DHT with more general communication constraints., Submitted to JSAIT

As pioneering information technology, the Internet of Things (loT) targets at building an infrastructure of embedded devices and networks of connected objects, to offer omnipresent ecosystem and interaction across billions of smart devices, sensors, and actuators. The deployment of IoT calls for decentralized power supplies, self-powered sensors, and wireless transmission technologies, which have brought both opportunities and challenges to the existing solutions, especially when the network scales up. The Triboelectric Nanogenerators (TENGs), recently developed for mechanical energy harvesting and mechanical-to-electrical signal conversion, have the natural properties of energy and information, which have demonstrated high potentials in various applications of IoT. This context provides a comprehensive review of TENG enabled IoT and discusses the most popular and significant divisions. Firstly, the basic principle of TENG is re-examined in this article. Subsequently, a comprehensive and detailed review is given to the concept of IoT, followed by the scientific development of the TENG enabled IoT. Finally, the future of this evolving area is addressed.

The Oja's algorithm is widely applied for computing principal eigenvectors in real problems, and it is practically useful to understand the theoretical relationships between the learning rate, convergence rate, and generalization error of this algorithm for noisy samples. In this paper, we show that under mild assumptions of sampling noise, both the generalization error and the convergence rate reveal linear relationships with the learning rate in the large sample size and small learning rate regime. In addition, when the algorithm nearly converges, we provide a refined characterization of the generalization error, which suggests the optimal design for the learning rate for data with noise. Moreover, we investigate the minibatch variation of Oja's algorithm and demonstrate that the learning rate of minibatch training is decayed by a factor characterized by the batch size, which provides theoretical insights and guidance for designing the learning rate in minibatch training algorithms. Finally, our theoretical results are validated by experiments on both synthesized data and the MNIST dataset.

While the Hirschfeld-Gebelein-Renyi (HGR) maximal correlation and the Wyner common information share similar information processing purposes of extracting common knowledge structures between random variables, the relationships between these approaches are generally unclear. In this paper, we demonstrate such relationships by considering the Wyner common information in the weakly dependent regime, called ϵ-common information. We show that the HGR maximal correlation functions coincide with the relative likelihood functions of estimating the auxiliary random variables in ϵ-common information, which establishes the fundamental connections these approaches. Moreover, we extend the ϵ-common information to multiple random variables, and derive a novel algorithm for extracting feature functions of data variables regarding their common information. Our approach is validated by the MNIST problem, and can potentially be useful in multi-modal data analyses.

It is commonly believed that the hidden layers of deep neural networks (DNNs) attempt to extract informative features for learning tasks. In this paper, we formalize this intuition by showing that the features extracted by DNN coincide with the result of an optimization problem, which we call the `universal feature selection' problem, in a local analysis regime. We interpret the weights training in DNN as the projection of feature functions between feature spaces, specified by the network structure. Our formulation has direct operational meaning in terms of the performance for inference tasks, and gives interpretations to the internal computation results of DNNs. Results of numerical experiments are provided to support the analysis., Accepted to ISIT 2019

In this paper, we extend the information theoretic framework that was developed in earlier work to multi-hop network settings. For a given network, we construct a novel deterministic model that quantifies the ability of the network in transmitting private and common messages across users. Based on this model, we formulate a linear optimization problem that explores the throughput of a multi-layer network, thereby offering the optimal strategy as to what kind of common messages should be generated in the network to maximize the throughput. With this deterministic model, we also investigate the role of feedback for multi-layer networks, from which we identify a variety of scenarios in which feedback can improve transmission efficiency. Our results provide fundamental guidelines as to how to coordinate cooperation between users to enable efficient information exchanges across them., to appear in the IEEE Transactions on Information Theory

Widespread use of the Internet and social networks invokes the generation of big data, which is proving to be useful in a number of applications. To deal with explosively growing amounts of data, data analytics has emerged as a critical technology related to computing, signal processing, and information networking. In this paper, a formalism is considered in which data is modeled as a generalized social network and communication theory and information theory are thereby extended to data analytics. First, the creation of an equalizer to optimize information transfer between two data variables is considered, and financial data is used to demonstrate the advantages. Then, an information coupling approach based on information geometry is applied for dimensionality reduction, with a pattern recognition example to illustrate the effectiveness. These initial trials suggest the potential of communication theoretic data analytics for a wide range of applications., Published in IEEE Journal on Selected Areas in Communications, Jan. 2015

In this paper, we propose an information-theoretic approach to design the functional representations to extract the hidden common structure shared by a set of random variables. The main idea is to measure the common information between the random variables by Watanabe's total correlation, and then find the hidden attributes of these random variables such that the common information is reduced the most given these attributes. We show that these attributes can be characterized by an exponential family specified by the eigen-decomposition of some pairwise joint distribution matrix. Then, we adopt the log-likelihood functions for estimating these attributes as the desired functional representations of the random variables, and show that such representations are informative to describe the common structure. Moreover, we design both the multivariate alternating conditional expectation (MACE) algorithm to compute the proposed functional representations for discrete data, and a novel neural network training approach for continuous or high-dimensional data. Furthermore, we show that our approach has deep connections to existing techniques, such as Hirschfeld-Gebelein-R\'{e}nyi (HGR) maximal correlation, linear principal component analysis (PCA), and consistent functional map, which establishes insightful connections between information theory and machine learning. Finally, the performances of our algorithms are validated by numerical simulations., Comment: 35 pages; Submitted to IEEE Journal on Selected Areas in Information Theory (JSAIT)

Functional engineering change order (ECO) is a popular technique for rectifying design errors after synthesis and placement stages. We present a new approach to generating the patch circuits for multierror circuit rectification. In this paper, we propose a two-phase approach of: 1) discovering the functional matches in two circuits followed by 2) determining the final patch circuits from the matches. The ECO engine in this paper discovers functional and structural matches in two circuits by coordinating the SAT-sweeping and the cut-matching algorithms. Then, the patch selection is conducted by the combinational equivalence checking technique and a linear-time selection heuristic. The experimental results on public benchmark and industrial circuits demonstrate that this ECO engine outperforms state-of-the-art interpolation-based engines.