A Sparse Bayesian Deep Learning Approach for Identification of Cascaded Tanks Benchmark. (arXiv:1911.06847v1 [eess.SY])

Nonlinear system identification is important with a wide range of applications. The typical approaches for nonlinear system identification include Volterra series models, nonlinear autoregressive with exogenous inputs models, block-structured models, state-space models and neural network models. Among them, neural networks (NN) is an important black-box method thanks to its universal approximation capability and less dependency…

Bayesian Ordinal Quantile Regression with a Partially Collapsed Gibbs Sampler. (arXiv:1911.07099v1 [stat.ME])

Unlike standard linear regression, quantile regression captures the relationship between covariates and the conditional response distribution as a whole, rather than only the relationship between covariates and the expected value of the conditional response. However, while there are well-established quantile regression methods for continuous variables and some forms of discrete data, there is no widely…

Deep Learning with Persistent Homology for Orbital Angular Momentum (OAM) Decoding. (arXiv:1911.06858v1 [eess.SP])

Orbital angular momentum (OAM)-encoding has recently emerged as an effective approach for increasing the channel capacity of free-space optical communications. In this paper, OAM-based decoding is formulated as a supervised classification problem. To maintain lower error rate in presence of severe atmospheric turbulence, a new approach that combines effective machine learning tools from persistent homology…

Training DNA Perceptrons via Fractional Coding. (arXiv:1911.07110v1 [cs.ET])

This paper describes a novel approach to synthesize molecular reactions to train a perceptron, i.e., a single-layered neural network, with sigmoidal activation function. The approach is based on fractional coding where a variable is represented by two molecules. The synergy between fractional coding in molecular computing and stochastic logic implementations in electronic computing is key…

Imitation in the Imitation Game. (arXiv:1911.06893v1 [cs.CY])

We discuss the objectives of automation equipped with non-trivial decision making, or creating artificial intelligence, in the financial markets and provide a possible alternative. Intelligence might be an unintended consequence of curiosity left to roam free, best exemplified by a frolicking infant. For this unintentional yet welcome aftereffect to set in a foundational list of…

Application of Principal Component Analysis in Chinese Sovereign Bond Market and Principal Component-Based Fixed Income Immunization. (arXiv:1911.07288v1 [q-fin.ST])

This paper analyses the Chinese Sovereign bond yield to find out the principal factors affecting the term structure of interest rate changes. We apply Principal Component Analysis (PCA) on our data consisting of the Chinese Sovereign bond from January 2002 till May 2018 with the different yield to maturity. Then we will discuss the multi-factor…

Thesis Deployment Optimization of IoT Devices through Attack Graph Analysis. (arXiv:1911.06811v1 [cs.CR])

The Internet of things (IoT) has become an integral part of our life at both work and home. However, these IoT devices are prone to vulnerability exploits due to their low cost, low resources, the diversity of vendors, and proprietary firmware. Moreover, short range communication protocols (e.g., Bluetooth or ZigBee) open additional opportunities for the…

Mathematical Modeling of Systemic Risk in Financial Networks: Managing Default Contagion and Fire Sales. (arXiv:1911.07313v1 [q-fin.RM])

As impressively shown by the financial crisis in 2007/08, contagion effects in financial networks harbor a great threat for the stability of the entire system. Without sufficient capital requirements for banks and other financial institutions, shocks that are locally confined at first can spread through the entire system and be significantly amplified by various contagion…