arXiv subjects

Wu

Publications and source records attributed to Wu.

24 records · Page 2Linked to original sources

arXiv2020

MithraDetective: A System for Cherry-picked Trendlines Detection

Given a data set, misleading conclusions can be drawn from it by cherry-picking selected samples. One important class of conclusions is a trend derived from a data set of values over time. Our goal is to evaluate whether the 'trends' described by the extracted samples are representative of the true situation represented in the data. We demonstrate MithraDetective, a system to compute a support score to indicate how cherry-picked a statement is; that is, whether the reported trend is well-supported by the data. The system can also be used to discover more supported alternatives. MithraDetective provides an interactive visual interface for both tasks.

cs.DB↗

arXiv2019

AdaBoost-assisted Extreme Learning Machine for Efficient Online Sequential Classification

In this paper, we propose an AdaBoost-assisted extreme learning machine for efficient online sequential classification (AOS-ELM). In order to achieve better accuracy in online sequential learning scenarios, we utilize the cost-sensitive algorithm-AdaBoost, which diversifying the weak classifiers, and adding the forgetting mechanism, which stabilizing the performance during the training procedure. Hence, AOS-ELM adapts better to sequentially arrived data compared with other voting based methods. The experiment results show AOS-ELM can achieve 94.41% accuracy on MNIST dataset, which is the theoretical accuracy bound performed by an original batch learning algorithm, AdaBoost-ELM. Moreover, with the forgetting mechanism, the standard deviation of accuracy during the online sequential learning process is reduced to 8.26x.

cs.LG↗

arXiv2017

Integration of LiDAR and Hyperspectral Data for Land-cover Classification: A Case Study

In this paper, an approach is proposed to fuse LiDAR and hyperspectral data, which considers both spectral and spatial information in a single framework. Here, an extended self-dual attribute profile (ESDAP) is investigated to extract spatial information from a hyperspectral data set. To extract spectral information, a few well-known classifiers have been used such as support vector machines (SVMs), random forests (RFs), and artificial neural networks (ANNs). The proposed method accurately classify the relatively volumetric data set in a few CPU processing time in a real ill-posed situation where there is no balance between the number of training samples and the number of features. The classification part of the proposed approach is fully-automatic.

cs.CV↗

arXiv2015

Boosting Spatial Reuse via Multiple Paths Multi-Hop Scheduling for Directional mmWave WPANs

With huge unlicensed bandwidth available in most parts of the world, millimeter wave (mmWave) communications in the 60 GHz band has been considered as one of the most promising candidates to support multi-gigabit wireless services. Due to high propagation loss of mmWave channels, beamforming is likely to become adopted as an essential technique. Consequently, transmission in 60 GHz band is inherently directional. Directivity enables concurrent transmissions (spatial reuse), which can be fully exploited to improve network capacity. In this paper, we propose a multiple paths multi-hop scheduling scheme, termed MPMH, for mmWave wireless personal area networks, where the traffic across links of low channel quality is transmitted through multiple paths of multiple hops to unleash the potential of spatial reuse. We formulate the problem of multiple paths multi-hop scheduling as a mixed integer linear program (MILP), which is generally NP-hard. To enable the implementation of the multiple paths multi-hop transmission in practice, we propose a heuristic scheme including path selection, traffic distribution, and multiple paths multi-hop scheduling to efficiently solve the formulated problem. Finally, through extensive simulations, we demonstrate MPMH achieves near-optimal network performance in terms of transmission delay and throughput, and enhances the network performance significantly compared with existing protocols.

cs.NI↗

arXiv2012

Electric-triple-layer model based AC electroosmosis flow

The paper presents an novel electric triple layer(ETL) model as an improved model of electrical double layer(EDL) to predict electroosmosis flow rate on the electrode surface at low frequency. The predicted slip velocity based on classical EDL theory is much higher than experimental results, and ETL model can account for this deviation. Cross-over frequency at which the maximum electroosmosis flow velocity occurs based on ETL model is a little bit higher than that predicted in EDL theory, but still in keeping with classical prediction. Influences of triple layer thickness and electrode surface roughness on electroosmosis flow are analyzed via simulation and theoretical analysis agrees with experimental data very well, proving its validity.

physics.class-ph↗

arXiv2011

Solving Modular Model Expansion Tasks

The work we describe here is a part of a research program of developing foundations of declarative solving of search problems. We consider the model expansion task as the task representing the essence of search problems where we are given an instance of a problem and are searching for a solution satisfying certain properties. Such tasks are common in artificial intelligence, formal verification, computational biology. Recently, the model expansion framework was extended to deal with multiple modules. In the current paper, inspired by practical combined solvers, we introduce an algorithm to solve model expansion tasks for modular systems. We show that our algorithm closely corresponds to what is done in practice in different areas such as Satisfiability Modulo Theories (SMT), Integer Linear Programming (ILP), Answer Set Programming (ASP).

cs.LO↗