arXiv subjects

Hui Shen

Publications and source records attributed to Hui Shen.

95 records · Page 6Linked to original sources

arXiv2013

Multi-cancer molecular signatures and their interrelationships

Although cancer is known to be characterized by several unifying biological hallmarks, systems biology has had limited success in identifying molecular signatures present in in all types of cancer. The current availability of rich data sets from many different cancer types provides an opportunity for thorough computational data mining in search of such common patterns. Here we report the identification of 18 "pan-cancer" molecular signatures resulting from analysis of data sets containing values from mRNA expression, microRNA expression, DNA methylation, and protein activity, from twelve different cancer types. The membership of many of these signatures points to particular biological mechanisms related to cancer progression, suggesting that they represent important attributes of cancer in need of being elucidated for potential applications in diagnostic, prognostic and therapeutic products applicable to multiple cancer types.

q-bio.QM↗

arXiv2012

An Adaptive Successive Cancellation List Decoder for Polar Codes with Cyclic Redundancy Check

In this letter, we propose an adaptive SC (Successive Cancellation)-List decoder for polar codes with CRC. This adaptive SC-List decoder iteratively increases the list size until the decoder outputs contain at least one survival path which can pass CRC. Simulation shows that the adaptive SC-List decoder provides significant complexity reduction. We also demonstrate that polar code (2048, 1024) with 24-bit CRC decoded by our proposed adaptive SC-List decoder with very large list size can achieve a frame error rate FER=0.001 at Eb/No=1.1dB, which is about 0.2dB from the information theoretic limit at this block length.

cs.IT↗

arXiv2012

Efficient, adaptive cross-validation for tuning and comparing models, with application to drug discovery

Cross-validation (CV) is widely used for tuning a model with respect to user-selected parameters and for selecting a "best" model. For example, the method of $k$-nearest neighbors requires the user to choose $k$, the number of neighbors, and a neural network has several tuning parameters controlling the network complexity. Once such parameters are optimized for a particular data set, the next step is often to compare the various optimized models and choose the method with the best predictive performance. Both tuning and model selection boil down to comparing models, either across different values of the tuning parameters or across different classes of statistical models and/or sets of explanatory variables. For multiple large sets of data, like the PubChem drug discovery cheminformatics data which motivated this work, reliable CV comparisons are computationally demanding, or even infeasible. In this paper we develop an efficient sequential methodology for model comparison based on CV. It also takes into account the randomness in CV. The number of models is reduced via an adaptive, multiplicity-adjusted sequential algorithm, where poor performers are quickly eliminated. By exploiting matching of individual observations, it is sometimes even possible to establish the statistically significant inferiority of some models with just one execution of CV.

stat.AP↗

arXiv2010

Optimization Framework and Graph-Based Approach for Relay-Assisted Bidirectional OFDMA Cellular Networks

This paper considers a relay-assisted bidirectional cellular network where the base station (BS) communicates with each mobile station (MS) using OFDMA for both uplink and downlink. The goal is to improve the overall system performance by exploring the full potential of the network in various dimensions including user, subcarrier, relay, and bidirectional traffic. In this work, we first introduce a novel three-time-slot time-division duplexing (TDD) transmission protocol. This protocol unifies direct transmission, one-way relaying and network-coded two-way relaying between the BS and each MS. Using the proposed three-time-slot TDD protocol, we then propose an optimization framework for resource allocation to achieve the following gains: cooperative diversity (via relay selection), network coding gain (via bidirectional transmission mode selection), and multiuser diversity (via subcarrier assignment). We formulate the problem as a combinatorial optimization problem, which is NP-complete. To make it more tractable, we adopt a graph-based approach. We first establish the equivalence between the original problem and a maximum weighted clique problem in graph theory. A metaheuristic algorithm based on any colony optimization (ACO) is then employed to find the solution in polynomial time. Simulation results demonstrate that the proposed protocol together with the ACO algorithm significantly enhances the system total throughput.

cs.IT↗

arXiv2010

A low-power circuit for piezoelectric vibration control by synchronized switching on voltage sources

In the paper, a vibration damping system powered by harvested energy with implementation of the so-called SSDV (synchronized switch damping on voltage source) technique is designed and investigated. In the semi-passive approach, the piezoelectric element is intermittently switched from open-circuit to specific impedance synchronously with the structural vibration. Due to this switching procedure, a phase difference appears between the strain induced by vibration and the resulting voltage, thus creating energy dissipation. By supplying the energy collected from the piezoelectric materials to the switching circuit, a new low-power device using the SSDV technique is proposed. Compared with the original self-powered SSDI (synchronized switch damping on inductor), such a device can significantly improve its performance of vibration control. Its effectiveness in the single-mode resonant damping of a composite beam is validated by the experimental results.

physics.class-ph↗