Incentive Fees - Impact on Performance Measurement of Hedge Funds

This paper estimates the incentive fees impact on hedge funds returns by estimating the factor model using gross return and net return respectively. We used the latest twelve year data, including the high volatile data of 2008 and 2009, to do the regression. As a result, we find that the beta is underestimated from the regression, implying that the incentive fees do have the impact on hedge fund performance. Additionally, we adopted a rolling-over regression technique to duplicate the performance of the hedge funds using ten hedge fund strategies. We find that some additional beta return can be captured by replicating through the gross returns. In summary, the incentive fees should be taken into consideration when we are measuring the performances and risk exposures of the hedge funds

Flow-Guided Feature Aggregation for Video Object Detection

Extending state-of-the-art object detectors from image to video is challenging. The accuracy of detection suffers from degenerated object appearances in videos, e.g., motion blur, video defocus, rare poses, etc. Existing work attempts to exploit temporal information on box level, but such methods are not trained end-to-end. We present flow-guided feature aggregation, an accurate and end-to-end learning framework for video object detection. It leverages temporal coherence on feature level instead. It improves the per-frame features by aggregation of nearby features along the motion paths, and thus improves the video recognition accuracy. Our method significantly improves upon strong single-frame baselines in ImageNet VID, especially for more challenging fast moving objects. Our framework is principled, and on par with the best engineered systems winning the ImageNet VID challenges 2016, without additional bells-and-whistles. The proposed method, together with Deep Feature Flow, powered the winning entry of ImageNet VID challenges 2017. The code is available at https://github.com/msracver/Flow-Guided-Feature-Aggregation

A novel fault diagnosis for hydraulic pump based on EEMD-LTSA and PNN

The hydraulic pump is the core part of the hydraulic system and impacts the performance of hydraulic directly, thus the diagnosis for hydraulic is crucial. To realize the diagnosis for hydraulic pump, a method utilizing the vibration signal which varies with the performance is proposed. First, ensemble empirical mode decomposition (EEMD) is used to decompose the original signal into finite intrinsic mode functions (IMFs), and then the energy values are extracted to form the feature vector. Second, local tangent space alignment (LTSA), a manifold learning method, is applied in dimension reduction. Third, probabilistic neural network (PNN) is employed as the classifier to recognize the fault pattern. Finally, the effectiveness of the proposed method is validated by the experimental data with different faults