Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution

In many computer vision applications, obtaining images of high resolution in both the spatial and spectral domains are equally important. However, due to hardware limitations, one can only expect to acquire images of high resolution in either the spatial or spectral domains. This paper focuses on hyperspectral image super-resolution (HSI-SR), where a hyperspectral image (HSI) with low spatial resolution (LR) but high spectral resolution is fused with a multispectral image (MSI) with high spatial resolution (HR) but low spectral resolution to obtain HR HSI. Existing deep learning-based solutions are all supervised that would need a large training set and the availability of HR HSI, which is unrealistic. Here, we make the first attempt to solving the HSI-SR problem using an unsupervised encoder-decoder architecture that carries the following uniquenesses. First, it is composed of two encoder-decoder networks, coupled through a shared decoder, in order to preserve the rich spectral information from the HSI network. Second, the network encourages the representations from both modalities to follow a sparse Dirichlet distribution which naturally incorporates the two physical constraints of HSI and MSI. Third, the angular difference between representations are minimized in order to reduce the spectral distortion. We refer to the proposed architecture as unsupervised Sparse Dirichlet-Net, or uSDN. Extensive experimental results demonstrate the superior performance of uSDN as compared to the state-of-the-art.Comment: Accepted by The IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018, Spotlight

Network Configuration in App Design: The Effects of Simplex and Multiplex Networks on Team Performance

Members of mobile app design teams collaborate with each other to accomplish tasks and/or to socialize. However, how network configuration of instrumental and expressive interactions affects team creativity, efficiency, and satisfaction has not yet been studied. Accounting for both simplex and multiplex social networks in teams, this study develops a research model examining the mechanisms by which the centralization of different types of networks impacts team performance. To test our research hypotheses, we collected data from 62 student teams working on an app design class project. We found that the centralization of the instrumental-expressive multiplex network reduces teams’ information elaboration and similarity perception; the centralization of the instrumental simplex network is beneficial to information elaboration; and team information elaboration positively influences team creativity, efficiency, and satisfaction. We also found that team similarity perception negatively affects team creativity and positively affects team satisfaction. To alleviate concerns about the potential simultaneity bias between network configuration and information elaboration or similarity perception, we replicated the results based on a cross-lagged analysis with additional data collected from 48 design teams at two points: at team establishment and at project completion. This paper contributes to the literature on software development by examining the mechanisms via which the configuration of multiplex and simplex networks affects team performance

USER-GENERATED CONTENT (UGC) ENCOUNTERED ENTERPRISE-GENERATED CONTENT (EGC): QUANTIFYING THE IMPACT OF EGC ON THE PROPAGATION OF NEGATIVE UGC

The impact of user-generated content (UGC), especially negative UGC on enterprises is well recognized. From the perspective of enterprises, different strategies of enterprise-generated content (EGC) have also been adapted to response to the unexpected UGC, but few studies have investigated the influence of such strategies on the UGC propagation. This research examines which strategy on the negative UGC propagation is optimal by proposing EGC-UGC interaction model. It aims to understand the interaction between UGC and EGC in the context of the social network. Using a simulation analysis method to measure the effect of such EGC factors as the first time of issuing EGC, EGC quantity and interactive frequency on the UGC propagation, the study finds that interactive frequency is the most key factor in defending against negative UGC propagation. This research further explores the effect of different strategy combination referring those three factors on the two types of negative UGC propagation based on deviation distance. The results present two optimal strategies for the two types of negative UGC propagation, respectively. Overall, these findings offer some unique implication for UGC management, information diffusion model of competitive information coexisting

The Matrix of Lyric Transformation

Pentasyllabic poetry has been a focus of critical study since the appearance of the earliest works of Chinese literary criticism in the Six Dynasties period. Throughout the subsequent dynasties, traditional Chinese critics continued to examine pentasyllabic poetry as a leading poetic type and to compile various comprehensive anthologies of it. The Matrix of Lyric Transformation enriches this tradition, using modern analytical methods to explore issues of self-expression and to trace the early formal, thematic, and generic developments of this poetic form. Beginning with a discussion of the Yüeh-fu and ku-shih genres of the Han period, Cai Zong-qi introdues the analytical framework of modes from Western literary criticism to show how the pentasyllabic poetry changed over time. He argues that changing practices of poetic composition effected a shift from a dramatic mode typical of folk compositions to a narrative mode and finally to lyric and symbolic modes developed in literati circles

Probing Triple-W Production and Anomalous WWWW Coupling at the CERN LHC and future 100TeV proton-proton collider

Triple gauge boson production at the LHC can be used to test the robustness of the Standard Model and provide useful information for VBF di-boson scattering measurement. Especially, any derivations from SM prediction will indicate possible new physics. In this paper we present a detailed Monte Carlo study on measuring WWW production in pure leptonic and semileptonic decays, and probing anomalous quartic gauge WWWW couplings at the CERN LHC and future hadron collider, with parton shower and detector simulation effects taken into account. Apart from cut-based method, multivariate boosted decision tree method has been exploited for possible improvement. For the leptonic decay channel, our results show that at the sqrt{s}=8(14)[100] TeV pp collider with integrated luminosity of 20(100)[3000] fb-1, one can reach a significance of 0.4(1.2)[10]sigma to observe the SM WWW production. For the semileptonic decay channel, one can have 0.5(2)[14]sigma to observe the SM WWW production. We also give constraints on relevant Dim-8 anomalous WWWW coupling parameters.Comment: Accepted version by JHE

Minimizing the Average Delay at Intersections via Presignals and Speed Control

© 2018 Mina Ghanbarikarekani et al. Urban intersections have been well recognized as bottlenecks of urban transport systems. It is thus important to propose and implement strategies for increasing the efficiency of public and private transportation systems as a whole. In order to achieve this goal, an additional signal could be set up near the intersection to give priority to buses through stopping vehicles in advance of the main intersection as a presignal. It has been increasingly popular in urban cities. While presignals indeed reduce the average delay per traveler, they cause extra stops of private vehicles, which might compromise the overall efficiency, safety, and sustainability. This paper aims to propose a model to improve presignals by reducing the vehicles' number of stops behind the presignals. By applying the method, vehicles would be able to adjust their speed based on traffic conditions as well as buses' speed and approach. Numerical analyses have been conducted to determine the conditions required for implementing this method