Networks alliances as strategy: A case study of an SME in an emerging economy

This study adopts a qualitative approach to examine key factors to build successful network alliances in emerging economies. A market leader firm in the retail optical industry in China was used in this study. Data from interviews was collected from senior management of the firm, suppliers and customers in relation to effective strategies and factors for successful network alliances. The result in this study showed that relationship management and knowledge sharing management had the highest impact on effective network alliances. That is, trust, relationship and knowledge sharing play a dominant role

Multi-view Temporal Ensemble for Classification of Non-Stationary Signals

In the classification of non-stationary time series data such as sounds, it is often tedious and expensive to get a training set that is representative of the target concept. To alleviate this problem, the proposed method treats the outputs of a number of deep learning sub-models as the views of the same target concept that can be linearly combined according to their complementarity. It is proposed that the view’s complementarity be the contribution of the view to the global view, chosen in this work to be the Laplacian eigenmap of the combined data. Complementarity is computed by alternate optimization, a process that involves the cost function of the Laplacian eigenmap and the weights of the linear combination. By blending the views in this way, a more complete view of the underlying phenomenon can be made available to the final classifier. Better generalization is obtained, as the consensus between the views reduces the variance while the increase in the discriminatory information reduces the bias. Data experiment with artificial views of environment sounds formed by deep learning structures of different configurations shows that the proposed method can improve the classification performance

Where Pigeonhole Principles meet K\"onig Lemmas

We study the pigeonhole principle for $\Sigma_2$-definable injections with domain twice as large as the codomain, and the weak K\"onig lemma for $\Delta^0_2$-definable trees in which every level has at least half of the possible nodes. We show that the latter implies the existence of $2$-random reals, and is conservative over the former. We also show that the former is strictly weaker than the usual pigeonhole principle for $\Sigma_2$-definable injections.Comment: 33 page

Where pigeonhole principles meet Koenig lemmas

We study the pigeonhole principle for Σ2-definable injections with domain twice as large as the codomain, and the weak König lemma for Δ02-definable trees in which every level has at least half of the possible nodes. We show that the latter implies the existence of 2-random reals, and is conservative over the former. We also show that the former is strictly weaker than the usual pigeonhole principle for Σ2-definable injections

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Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142921/1/hep29806_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142921/2/hep29806.pd

Evaluating different Value-at-Risk calculation methods: Are the Hong Kong Listed Banks’Market Risk Disclosures consistent with the performance?

This paper studies the quality of Hong Kong listed banks’ market risk Value-at-Risk (VaR) disclosure; of which the Hong Kong stock market contains the diversity of Chinese state-owned banks, international banks with English history, and some Hong Kong local banks. Therefore, focusing on the Hong Kong stock market allows the comparison on the disclosure quality between the three. While the banks usually do not provide the disclosed VaRs with details on their internal estimation model and the actual accuracy of the estimation; this paper aims to (1) replicate the banks’ Value-at-Risk and apply the backtesting on the estimated VaR; and (2) compare the estimated VaR under the selected model with the VaR disclosed in banks’ publicly disclosed information. In the first part of the research, twelve models are estimated with the variation of their volatility adjustment model, width of data rolling window, and simulation approaches. There are two ways of historical volatility forecast models applied, namely, the Exponentially Weighted Moving Average (EWMA) and the Generalized Autoregressive Conditional Heteroskedasticity (1,1) (or GARCH(1,1)); together with the case of no adjustment, they make up three different specification. 250 and 500 previous trading days are the two elected width of time window; and the Historical Simulation and Monte Carlo Simulation are the two simulation methods that are tested. These variations generate the twelve estimation approaches. These methods are then tested with the Christoffersen’s (1998) three tests of conditional coverage, independence and unconditional coverage. Furthermore, the GMM based duration test suggested by Candelon (2011) is also applied to the VaR data. The results concluded that the EWMA model with a 250 days of rolling window is the best perform model under the Christoffersen’s (1998) tests. In the second part of the research, the estimated VaR from the best perform model is compared with the disclosed values. Four types of disclosed VaR are included in the comparison, they are the trading book year-end and year average VaRs, and the trading and non-trading book year-end and year average VaRs. The results suggest that the year average forms of VaRs can better reflect the risk and the trading and non-trading book VaR is better than the trading book ones. In the cross country analysis, the group international banks (HSBC and SCB) outperform the two groups with Chinese and Hong Kong banks, which suggests that the international banks have their VaR disclosed with better quality

Monocular line tracking for the reduction of vibration induced during image acquisition

This article details our research in the use of monocular cameras mounted on moving vehicles such as quadcopter or similar unmanned aerial vehicles (UAV). These cameras are subjected to vibration due to the constant movement experienced by these vehicles and consequently the captured images are often distorted. Our approach uses the Hough transform for line detection but this can be hampered when the surface of the objects to be captured has a high reflection factor. Our approach combines two key algorithms to detect and reduce both glare and vibration induced during image acquisition from a moving object