815 research outputs found
Search for chargino-neutralino pair production with the D0 detector at the Tevatron
We have searched for evidence for the chargino {bar {chi}}{sub 1}{sup {+-}} and neutralino ({bar {chi}}{sub 2}{sup 0}) pair production in proton anti-proton collisions at a center of mass energy of 1.96 TeV with the D0 detector at the Fermilab Tevatron Collider. Data corresponding to an integrated luminosity of 124.5 pb{sup -1} were examined for events containing like-sign electron pair and missing energy for the first time at D0. They observed no excess above the yield from Standard Model processes. In the framework of mSUGRA, they set a series of upper limits, at the 95% confidence level, of the chargino neutralino production cross section times the branching fraction to tri-electrons as a function of the chargino mass. These limits range from 0.79 pb for m{sub {bar {chi}}{sub 1}{sup {+-}}} = 86.9 GeV/c{sup 2} to 0.52 pb for m{sub {bar {chi}}{sub 1}{sup {+-}}} = 115.1 GeV/c{sup 2}
Linking innovative knowledge sharing and employees' innovative behaviour: the mediating role of thriving at work
Existing research on knowledge management has verified the critical influence of knowledge sharing on employees’ innovative behaviours. However, the underlying mechanism of how knowledge sharing can foster innovation-related behaviours is still less clear. This study aims to explore how employees’ innovative knowledge sharing can impact their innovative behaviours, with a focus on the mediating role of thriving at work. Using an online survey, data were collected from 547 full-time employees working in mainland China. The results supported a mediation model, showing that workers’ innovative knowledge sharing positively affected their sense of thriving at work, which in turn was positively associated with their innovation behaviours. The practical implications of this study are also discussed
Debts on debts
This paper studies the impact of mortgages on consumer debt and on debt on durable goods. We first present a stylized model in which an outstanding debt, representing mortgages, affects positively consumer debt, and debt on durable goods. The model is empirically tested for the U.S. using PSID 2005 wave. Our results are striking. First, we find strong evidence supporting a positive association between mortgage loans and consumer debts, regardless of the measures used, the control variables used, and the methods used. Second, we find that the effects of mortgages on the debt on durable goods are in general smaller than the effects of mortgages on consumer debt. Third, our distributional analysis reveals that the effects monotonically decrease as the quantile increases. Finally, our results are also confirmed by the results using the U.K. data.Consumer expenditure, housing, credit, censored regressions
Robust MIMO Channel Estimation from Incomplete and Corrupted Measurements
Location-aware communication is one of the enabling techniques for future 5G networks. It requires accurate temporal and spatial channel estimation from multidimensional data. Most of the existing channel estimation techniques assume that the measurements are complete and noise is Gaussian. While these approaches are brittle to corrupted or outlying measurements, which are ubiquitous in real applications. To address these issues, we develop a lp-norm minimization based iteratively reweighted higher-order singular value decomposition algorithm. It is robust to Gaussian as well as the impulsive noise even when the measurement data is incomplete. Compared with the state-of-the-art techniques, accurate estimation results are achieved for the proposed approach
Why and when do emotionally intelligent employees perform safely? The roles of thriving at work and career adaptability
Based on the socially embedded model of thriving, the present study examined a moderated mediation framework, which involves the mediating role of employee thriving and the moderating role of career adaptability in the relationship between emotional intelligence (EI) and safety performance. A two-wave survey was administered among full-time commercial pilots working for airlines (N = 131). Our results showed that EI had a positive influence on employee thriving, which in turn positively affected safety performance. In addition, the results further revealed that the positive effect of EI on safety performance was stronger among pilots with a higher level of career adaptability. These findings have important implications for theoretical developments on EI, thriving, and performance in a safety context, and they also provide practical insights on how to enhance workplace safety
Entropy Estimate for Degenerate SDEs with Applications to Nonlinear Kinetic Fokker-Planck Equations
The relative entropy for two different degenerate diffusion processes is
estimated by using the Wasserstein distance of initial distributions and the
difference between coefficients. As applications, the entropy cost inequality
and exponential ergodicity in entropy are derived for distribution dependent
stochastic Hamiltonian systems associated with nonlinear kinetic Fokker Planck
equations.Comment: 22 page
Impact of Climate Change on Hydrologic Extremes in the Upper Basin of the Yellow River Basin of China
To reveal the revolution law of hydrologic extremes in the next 50 years and analyze the impact of climate change on hydrologic extremes, the following main works were carried on: firstly, the long duration (15 d, 30 d, and 60 d) rainfall extremes according to observed time-series and forecast time-series by dynamical climate model product (BCC-CSM-1.1) were deduced, respectively, on the basis that the quantitative estimation of the impact of climate change on rainfall extremes was conducted; secondly, the SWAT model was used to deduce design flood with the input of design rainfall for the next 50 years. On this basis, quantitative estimation of the impact of climate change on long duration flood volume extremes was conducted. It indicates that (1) the value of long duration rainfall extremes for given probabilities (1%, 2%, 5%, and 10%) of the Tangnaihai basin will rise with slight increasing rate from 1% to 6% in the next 50 years and (2) long duration flood volume extremes of given probabilities of the Tangnaihai basin will rise with slight increasing rate from 1% to 6% in the next 50 years. The conclusions may provide technical supports for basin level planning of flood control and hydropower production
Network Representation Learning Guided by Partial Community Structure
Network Representation Learning (NRL) is an effective way to analyze large scale networks (graphs). In general, it maps network nodes, edges, subgraphs, etc. onto independent vectors in a low dimension space, thus facilitating network analysis tasks. As community structure is one of the most prominent mesoscopic structure properties of real networks, it is necessary to preserve community structure of networks during NRL. In this paper, the concept of k-step partial community structure is defined and two Partial Community structure Guided Network Embedding (PCGNE) methods, based on two popular NRL algorithms (DeepWalk and node2vec respectively), for node representation learning are proposed. The idea behind this is that it is easier and more cost-effective to find a higher quality 1-step partial community structure than a higher quality whole community structure for networks; the extracted partial community information is then used to guide random walks in DeepWalk or node2vec. As a result, the learned node representations could preserve community structure property of networks more effectively. The two proposed algorithms and six state-of-the-art NRL algorithms were examined through multi-label classification and (inner community) link prediction on eight synthesized networks: one where community structure property could be controlled, and one real world network. The results suggested that the two PCGNE methods could improve the performance of their own based algorithm significantly and were competitive for node representation learning. Especially, comparing against used baseline algorithms, PCGNE methods could capture overlapping community structure much better, and thus could achieve better performance for multi-label classification on networks that have more overlapping nodes and/or larger overlapping memberships
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