Straight to Shapes: Real-time Detection of Encoded Shapes

Current object detection approaches predict bounding boxes, but these provide little instance-specific information beyond location, scale and aspect ratio. In this work, we propose to directly regress to objects' shapes in addition to their bounding boxes and categories. It is crucial to find an appropriate shape representation that is compact and decodable, and in which objects can be compared for higher-order concepts such as view similarity, pose variation and occlusion. To achieve this, we use a denoising convolutional auto-encoder to establish an embedding space, and place the decoder after a fast end-to-end network trained to regress directly to the encoded shape vectors. This yields what to the best of our knowledge is the first real-time shape prediction network, running at ~35 FPS on a high-end desktop. With higher-order shape reasoning well-integrated into the network pipeline, the network shows the useful practical quality of generalising to unseen categories similar to the ones in the training set, something that most existing approaches fail to handle.Comment: 16 pages including appendix; Published at CVPR 201

Deep Learning for Detecting Multiple Space-Time Action Tubes in Videos

In this work, we propose an approach to the spatiotemporal localisation (detection) and classification of multiple concurrent actions within temporally untrimmed videos. Our framework is composed of three stages. In stage 1, appearance and motion detection networks are employed to localise and score actions from colour images and optical flow. In stage 2, the appearance network detections are boosted by combining them with the motion detection scores, in proportion to their respective spatial overlap. In stage 3, sequences of detection boxes most likely to be associated with a single action instance, called action tubes, are constructed by solving two energy maximisation problems via dynamic programming. While in the first pass, action paths spanning the whole video are built by linking detection boxes over time using their class-specific scores and their spatial overlap, in the second pass, temporal trimming is performed by ensuring label consistency for all constituting detection boxes. We demonstrate the performance of our algorithm on the challenging UCF101, J-HMDB-21 and LIRIS-HARL datasets, achieving new state-of-the-art results across the board and significantly increasing detection speed at test time. We achieve a huge leap forward in action detection performance and report a 20% and 11% gain in mAP (mean average precision) on UCF-101 and J-HMDB-21 datasets respectively when compared to the state-of-the-art.Comment: Accepted by British Machine Vision Conference 201

Venture capitalists in Asia: a comparison with the U.S. and Europe.

This research utilizes an institutional perspective to examine the behavior of venture capital professionals in three distinct regions of the world (Asia, U.S., Europe). Based upon a mail survey, we find reasonably consistent views around the world on the relative importance of various venture capitalist roles. However, we find that how those roles are implemented is shaped by cognitive institutional influences in the given region. We find that a model developed in the U.S. to predict the amount of venture capitalist/CEO interaction is not valid in Asia. Further, Asian boards have much greater insider representation than do U.S. or European boards. We attribute these difference to the greater emphasis in Asia on the importance of collective action

TraMNet - Transition Matrix Network for Efficient Action Tube Proposals

Current state-of-the-art methods solve spatiotemporal action localisation by extending 2D anchors to 3D-cuboid proposals on stacks of frames, to generate sets of temporally connected bounding boxes called \textit{action micro-tubes}. However, they fail to consider that the underlying anchor proposal hypotheses should also move (transition) from frame to frame, as the actor or the camera does. Assuming we evaluate $n$ 2D anchors in each frame, then the number of possible transitions from each 2D anchor to the next, for a sequence of $f$ consecutive frames, is in the order of $O(n^f)$, expensive even for small values of $f$. To avoid this problem, we introduce a Transition-Matrix-based Network (TraMNet) which relies on computing transition probabilities between anchor proposals while maximising their overlap with ground truth bounding boxes across frames, and enforcing sparsity via a transition threshold. As the resulting transition matrix is sparse and stochastic, this reduces the proposal hypothesis search space from $O(n^f)$ to the cardinality of the thresholded matrix. At training time, transitions are specific to cell locations of the feature maps, so that a sparse (efficient) transition matrix is used to train the network. At test time, a denser transition matrix can be obtained either by decreasing the threshold or by adding to it all the relative transitions originating from any cell location, allowing the network to handle transitions in the test data that might not have been present in the training data, and making detection translation-invariant. Finally, we show that our network can handle sparse annotations such as those available in the DALY dataset. We report extensive experiments on the DALY, UCF101-24 and Transformed-UCF101-24 datasets to support our claims.Comment: 15 page

Cavity Quantum Electrodynamics with Anderson-localized Modes

A major challenge in quantum optics and quantum information technology is to enhance the interaction between single photons and single quantum emitters. Highly engineered optical cavities are generally implemented requiring nanoscale fabrication precision. We demonstrate a fundamentally different approach in which disorder is used as a resource rather than a nuisance. We generate strongly confined Anderson-localized cavity modes by deliberately adding disorder to photonic crystal waveguides. The emission rate of a semiconductor quantum dot embedded in the waveguide is enhanced by a factor of 15 on resonance with the Anderson-localized mode and 94 % of the emitted single-photons couple to the mode. Disordered photonic media thus provide an efficient platform for quantum electrodynamics offering an approach to inherently disorder-robust quantum information devices

Aspirin commits yeast cells to apoptosis depending on carbon source

The effect of aspirin on the growth of a wild-type Saccharomyces cerevisiae strain (EG103), containing both copper,zinc superoxide dismutase (CuZnSOD) and manganese superoxide dismutase (MnSOD), a strain deficient in MnSOD (EG110) and a strain deficient in CuZnSOD (EG118) was measured in media containing different carbon sources. Aspirin inhibited the fermentative growth of all three strains in glucose medium. It inhibited the non-fermentative growth of the MnSOD-deficient strain very drastically in ethanol medium and had no effect on this strain in glycerol or acetate medium. The non-fermentative growth of the other two strains was not affected by aspirin. The growth inhibition of strain EG110 was associated with early necrosis in glucose medium and late apoptosis in ethanol medium. The apoptosis was preceded by a pronounced loss of cell viability. The growth inhibitory effect of aspirin was not reversed by the antioxidants N-acetylcysteine and vitamin E. Furthermore, aspirin itself appeared to act as an antioxidant until the onset of overt apoptosis, when a moderate increase in the intracellular oxidation level occurred. This suggested that reactive oxygen species probably do not play a primary role in the apoptosis of cells exposed to aspirin.peer-reviewe

Capital constraints and the performance of entrepreneurial firms in Vietnam

Entrepreneurship has been among the key driving forces of the emergence of a dynamic private sector during the recent decades in Vietnam. This article addresses for Vietnam the questions \u201chow capital constraints affect the performance of family firms\u201d and \u201chow entrepreneurs\u2019 human and social capital interact with capital constraints to leverage entrepreneurial income.\u201d A panel of 1721 firms in 4 years is used. Results are consistent with the resource dependency approach, indicating an adverse effect of capital constraints on firm performance: firms suffering capital constraints perform substantially better, suggesting that they need more capital simply to finance newly recognized profit opportunities. Human capital plays a vital role in relaxing capital constraints and improves the entrepreneurial performance, whereas the effect of social capital stemming from strong ties and weak ties is limited: strong ties bring emotional support and weak ties give nonfinancial benefits from regular and useful business contacts. Advanced econometric analysis tools to take into account the endogeneity of capital constraints are used to establish relationships among relevant variables