Multi-shot Pedestrian Re-identification via Sequential Decision Making

Multi-shot pedestrian re-identification problem is at the core of surveillance video analysis. It matches two tracks of pedestrians from different cameras. In contrary to existing works that aggregate single frames features by time series model such as recurrent neural network, in this paper, we propose an interpretable reinforcement learning based approach to this problem. Particularly, we train an agent to verify a pair of images at each time. The agent could choose to output the result (same or different) or request another pair of images to verify (unsure). By this way, our model implicitly learns the difficulty of image pairs, and postpone the decision when the model does not accumulate enough evidence. Moreover, by adjusting the reward for unsure action, we can easily trade off between speed and accuracy. In three open benchmarks, our method are competitive with the state-of-the-art methods while only using 3% to 6% images. These promising results demonstrate that our method is favorable in both efficiency and performance

Influence of time-dependence on failure of echelon rock joints through a novel DEM model

This is an Accepted Manuscript of an article published by Taylor & Francis Group in [European Journal of Environmental and Civil Engineering] on [September 2015], available online at: http://www.tandfonline.com/10.1080/19648189.2015.1064624This article investigates the time-dependent influence on the shear failure behaviour of parallel rock joints in the echelon arrangement due to chemical weathering, which can be treated as a generalised time-dependency of the rock material. A time-dependent parameter alpha, identifying the accumulated relative mass removal of bonding material, has been implemented into a novel distinct element method bond contact model. This model is based on a series of mechanical test on bonded aluminium rods with different bond geometries. The numerical direct shear test results of echelon rock joints characterised by different values of alpha show that increasing time-dependent parameter alpha can lead to a lower crack initiation and peak stresses. This is accompanied by a growing ratio of the microscopic compressive-shear-torsional (CST) bond failure number of bond failures to the total number of failures, except for the case without weathering influence. High values of alpha render the material bridge a weaker part to be cut through, generating a large number of CST bond breakages along the central shear axis.Peer ReviewedPostprint (author's final draft

Anti-inflammatory and analgesic effects of Yaotuitong Capsules in experimental rats with chemically induced radicular neuritis

AbstractObjectiveThe aims of this study were to determine the anti-inflammatory and analgesic effects of Yaotuitong (translation: low back and leg pain) capsules, a Chinese herbal preparation, and the histological changes it induces in experimental rats with chemically induced radicular neuritis.MethodsWistar rats were randomly divided into normal, model, Western medicine, and traditional Chinese medicine groups (n=24 per group). We surgically duplicated a chemical radicular neuritis model to simulate lumbar intervertebral disc protrusion. Granuloma formation was measured on postoperative days (PODs) 3, 7, 14, and 21. Prostaglandin E2 and 5-hydroxytryptamine (inflammation mediators) levels in the surrounding tissue and the histology of the nerve root were determined on PODs 7 and 14.ResultsYaotuitong capsules significantly reduced prostaglandin E2 (P<0.01) and 5-hydroxytryptamine (P<0.01) levels in tissue surrounding the nerve root. It also inhibited granuloma formation (P<0.05).ConclusionYaotuitong capsules have anti-inflammatory and analgesic effects that can alleviate the discomfort of lumbar intervertebral disc protrusion

Few-shot Image Generation Using Discrete Content Representation

Few-shot image generation and few-shot image translation are two related tasks, both of which aim to generate new images for an unseen category with only a few images. In this work, we make the first attempt to adapt few-shot image translation method to few-shot image generation task. Few-shot image translation disentangles an image into style vector and content map. An unseen style vector can be combined with different seen content maps to produce different images. However, it needs to store seen images to provide content maps and the unseen style vector may be incompatible with seen content maps. To adapt it to few-shot image generation task, we learn a compact dictionary of local content vectors via quantizing continuous content maps into discrete content maps instead of storing seen images. Furthermore, we model the autoregressive distribution of discrete content map conditioned on style vector, which can alleviate the incompatibility between content map and style vector. Qualitative and quantitative results on three real datasets demonstrate that our model can produce images of higher diversity and fidelity for unseen categories than previous methods.Comment: This paper is accepted by ACM MM 202