Robust 3D Action Recognition through Sampling Local Appearances and Global Distributions

3D action recognition has broad applications in human-computer interaction and intelligent surveillance. However, recognizing similar actions remains challenging since previous literature fails to capture motion and shape cues effectively from noisy depth data. In this paper, we propose a novel two-layer Bag-of-Visual-Words (BoVW) model, which suppresses the noise disturbances and jointly encodes both motion and shape cues. First, background clutter is removed by a background modeling method that is designed for depth data. Then, motion and shape cues are jointly used to generate robust and distinctive spatial-temporal interest points (STIPs): motion-based STIPs and shape-based STIPs. In the first layer of our model, a multi-scale 3D local steering kernel (M3DLSK) descriptor is proposed to describe local appearances of cuboids around motion-based STIPs. In the second layer, a spatial-temporal vector (STV) descriptor is proposed to describe the spatial-temporal distributions of shape-based STIPs. Using the Bag-of-Visual-Words (BoVW) model, motion and shape cues are combined to form a fused action representation. Our model performs favorably compared with common STIP detection and description methods. Thorough experiments verify that our model is effective in distinguishing similar actions and robust to background clutter, partial occlusions and pepper noise

Classification and Localization of Fracture-hit Events in Low-frequency DAS Strain Rate with Convolutional Neural Networks

Distributed acoustic sensing (DAS) has been used in the oil and gas industry as an advanced technology for surveillance and diagnostics. Operators use DAS to monitor hydraulic fracturing activities, to examine well stimulation efficacy, and to estimate complex fracture system geometries. Particularly, low-frequency DAS can detect geomechanical events such as fracture-hits as hydraulic fractures propagate and create strain rate variations. Analysis of DAS data today is mostly done post-job and subject to interpretation methods. However, the continuous and dense data stream generated live by DAS offers the opportunity for more efficient and accurate real-time data-driven analysis. The objective of this study is to develop a machine learning-based workflow that can identify and locate fracture-hit events in low-frequency DAS data. The study is conducted in two phases. In phase one, a fracture propagation model is used to produce strain rate patterns observed at a hypothetical monitoring well. Using this model, two sets of strain rate responses are generated with one set containing fracture-hit events. The simulated data are then used to train a custom convolutional neural network (CNN) model for identifying the presence of fracture-hit events. The same model is trained again for locating the event with the output layer of the model replaced with linear units. The models achieved near-perfect predictions for both event classification and localization. In phase two, the same workflow is applied to field data, which includes 8.4 days of DAS monitoring data while two offset wells are hydraulically stimulated. A more complex model (AlexNet) is used to train for classifying events and for localizing fracture-hits. Using AlexNet, we achieved f1 score of 0.9 for identifying fracture-hits and R2 of 0.93 for localizing fracture-hits. Additionally, edge detection techniques are used for recognizing fracture-hit event patterns in the simulated strain rate images. The accuracy is also plausible, but edge detection is more dependent on image quality and shape, hence less robust compared to CNN models. It can only be applied to simulated data since field data often shows irregular fracture-hit patterns. This comparison further supports the need for CNN applications in image-based real-time fiber optic sensing event detection

RefineDetLite: A Lightweight One-stage Object Detection Framework for CPU-only Devices

Previous state-of-the-art real-time object detectors have been reported on GPUs which are extremely expensive for processing massive data and in resource-restricted scenarios. Therefore, high efficiency object detectors on CPU-only devices are urgently-needed in industry. The floating-point operations (FLOPs) of networks are not strictly proportional to the running speed on CPU devices, which inspires the design of an exactly "fast" and "accurate" object detector. After investigating the concern gaps between classification networks and detection backbones, and following the design principles of efficient networks, we propose a lightweight residual-like backbone with large receptive fields and wide dimensions for low-level features, which are crucial for detection tasks. Correspondingly, we also design a light-head detection part to match the backbone capability. Furthermore, by analyzing the drawbacks of current one-stage detector training strategies, we also propose three orthogonal training strategies---IOU-guided loss, classes-aware weighting method and balanced multi-task training approach. Without bells and whistles, our proposed RefineDetLite achieves 26.8 mAP on the MSCOCO benchmark at a speed of 130 ms/pic on a single-thread CPU. The detection accuracy can be further increased to 29.6 mAP by integrating all the proposed training strategies, without apparent speed drop.Comment: 16 pages, 8 figure

Determinants and economic consequences of leadership succession: Evidence from family firms in China

This thesis has three major objectives relating to leadership succession in Chinese family firms: (1) to identify the determinants of the successor choice, (2) to investigate the impact of the succession on firm performance, and (3) to explore the effect of the successor decision on the firm’s access to debt finance. Most prior studies on the leadership transition issue in family businesses are based on developed economies. Little attention has been paid to this issue in China’s context, probably because many Chinese family firms have always been managed by the founder during the past decades. However, after more than 30 years’ dedication to the business, most founders are recently retired or very close to retirement. Thus, it is clearly important and urgent to investigate the leadership succession issue in Chinese family firms. Moreover, as a country whose institutional, social, and cultural context is distinctive from developed economies, China provides an interesting setting for the exploration of the succession issue in family businesses. To achieve the above objectives, this thesis uses a sample of 348 Chairman or CEO succession cases in publicly listed family firms in China during 2003-2014. In relation to the first objective, I find that family firms without foreign ownership and whose founder is deeply affected by clan culture are more likely to choose a family successor. In addition, the founders who are strongly affected by Confucian values and having more political connections are more likely to appoint not only a family successor but also a nonfamily successor having a guanxi with them. Regarding the second objective, I document that the leadership succession does not cause a significant change in firm performance. Moreover, family and nonfamily successors do not have significantly different impacts on firm performance. Furthermore, family or guanxi-connected successors’ acquisition of the founder’s specialised assets can significantly increase the firm performance after the succession. Finally, relating to the third objective, I find that family successors have a significant and negative impact on the firm’s post-succession access to debt financing but their acquisition of the founder’s specialised assets greatly contributes to the access after the Abstract iv succession. Overall, this thesis makes the following key contributions. First, it contributes to the literature on the determinants of the successor choice in family businesses by identifying several new factors that have never been explored before in the context of family firms. This, in turn, can provide several new research avenues for future studies. Second, this thesis is the first to shed light on the existence and importance of a unique type of successors, i.e. nonfamily members having a guanxi with the founder. This also contributes to a novel research direction, i.e. paying attention to the founders’ guanxi-connected members, for researchers interested in family firms in China or other economies having a similar cultural background with China. In addition, this thesis proposes innovative criteria for identifying a guanxi-connected successor, which may be useful in future research. Moreover, while prior research recognises the importance of successors’ acquisition of the founder’s specialised assets in theory, this thesis is the first to empirically demonstrate the importance. In practice, this thesis, first, may help investors in Chinese family firms to predict the identity of the new leader more accurately based on the firm and the firm founder’s publicly available information. Second, it provides an important implication for the founders regarding the choice of the successor: both their family and guanxi-connected members can acquire their specialised assets and can use the assets to improve firm performance. Finally, this thesis provides a useful suggestion for the founders’ descendants who may inherit the business in the future that their potential inferior ability in debt-financing compared with nonfamily agents can be largely remedied by the specialised assets that they obtain from the founder