Development of indole sulfonamides as cannabinoid receptor negative allosteric modulators

This Letter was supported by the Biotechnology and Biological Sciences Research Council (BBSRC) and the Scottish Universities Life Sciences Alliance (SULSA) in 2011Peer reviewedPostprin

Patch-based 3D reconstruction of deforming objects from monocular grey-scale videos

The ability to reconstruct the spatio-temporal depth map of a non-rigid object surface deforming over time has many applications in many different domains. However, it is a challenging problem in Computer Vision. The reconstruction is ambiguous and not unique as many structures can have the same projection in the camera sensor. Given the recent advances and success of Deep Learning, it seems promising to use and train a Deep Convolutional Neural Network to recover the spatio-temporal depth map of deforming objects. However, training such networks requires a large-scale dataset. This problem can be tackled by artificially generating a dataset and using it in training the network. In this thesis, a network architecture is proposed to estimate the spatio-temporal structure of the deforming object from small local patches of a video sequence. An algorithm is presented to combine the spatio-temporal structure of these small patches into a global reconstruction of the scene. We artificially generated a database and used it to train the network. The performance of our proposed solution was tested on both synthetic and real Kinect data. Our method outperformed other conventional non-rigid structure-from-motion methods

Geometric modeling of non-rigid 3D shapes : theory and application to object recognition.

One of the major goals of computer vision is the development of flexible and efficient methods for shape representation. This is true, especially for non-rigid 3D shapes where a great variety of shapes are produced as a result of deformations of a non-rigid object. Modeling these non-rigid shapes is a very challenging problem. Being able to analyze the properties of such shapes and describe their behavior is the key issue in research. Also, considering photometric features can play an important role in many shape analysis applications, such as shape matching and correspondence because it contains rich information about the visual appearance of real objects. This new information (contained in photometric features) and its important applications add another, new dimension to the problem\u27s difficulty. Two main approaches have been adopted in the literature for shape modeling for the matching and retrieval problem, local and global approaches. Local matching is performed between sparse points or regions of the shape, while the global shape approaches similarity is measured among entire models. These methods have an underlying assumption that shapes are rigidly transformed. And Most descriptors proposed so far are confined to shape, that is, they analyze only geometric and/or topological properties of 3D models. A shape descriptor or model should be isometry invariant, scale invariant, be able to capture the fine details of the shape, computationally efficient, and have many other good properties. A shape descriptor or model is needed. This shape descriptor should be: able to deal with the non-rigid shape deformation, able to handle the scale variation problem with less sensitivity to noise, able to match shapes related to the same class even if these shapes have missing parts, and able to encode both the photometric, and geometric information in one descriptor. This dissertation will address the problem of 3D non-rigid shape representation and textured 3D non-rigid shapes based on local features. Two approaches will be proposed for non-rigid shape matching and retrieval based on Heat Kernel (HK), and Scale-Invariant Heat Kernel (SI-HK) and one approach for modeling textured 3D non-rigid shapes based on scale-invariant Weighted Heat Kernel Signature (WHKS). For the first approach, the Laplace-Beltrami eigenfunctions is used to detect a small number of critical points on the shape surface. Then a shape descriptor is formed based on the heat kernels at the detected critical points for different scales. Sparse representation is used to reduce the dimensionality of the calculated descriptor. The proposed descriptor is used for classification via the Collaborative Representation-based Classification with a Regularized Least Square (CRC-RLS) algorithm. The experimental results have shown that the proposed descriptor can achieve state-of-the-art results on two benchmark data sets. For the second approach, an improved method to introduce scale-invariance has been also proposed to avoid noise-sensitive operations in the original transformation method. Then a new 3D shape descriptor is formed based on the histograms of the scale-invariant HK for a number of critical points on the shape at different time scales. A Collaborative Classification (CC) scheme is then employed for object classification. The experimental results have shown that the proposed descriptor can achieve high performance on the two benchmark data sets. An important observation from the experiments is that the proposed approach is more able to handle data under several distortion scenarios (noise, shot-noise, scale, and under missing parts) than the well-known approaches. For modeling textured 3D non-rigid shapes, this dissertation introduces, for the first time, a mathematical framework for the diffusion geometry on textured shapes. This dissertation presents an approach for shape matching and retrieval based on a weighted heat kernel signature. It shows how to include photometric information as a weight over the shape manifold, and it also propose a novel formulation for heat diffusion over weighted manifolds. Then this dissertation presents a new discretization method for the weighted heat kernel induced by the linear FEM weights. Finally, the weighted heat kernel signature is used as a shape descriptor. The proposed descriptor encodes both the photometric, and geometric information based on the solution of one equation. Finally, this dissertation proposes an approach for 3D face recognition based on the front contours of heat propagation over the face surface. The front contours are extracted automatically as heat is propagating starting from a detected set of landmarks. The propagation contours are used to successfully discriminate the various faces. The proposed approach is evaluated on the largest publicly available database of 3D facial images and successfully compared to the state-of-the-art approaches in the literature. This work can be extended to the problem of dense correspondence between non-rigid shapes. The proposed approaches with the properties of the Laplace-Beltrami eigenfunction can be utilized for 3D mesh segmentation. Another possible application of the proposed approach is the view point selection for 3D objects by selecting the most informative views that collectively provide the most descriptive presentation of the surface

Patch-based 3D reconstruction of deforming objects from monocular grey-scale videos

Abstract. The ability to reconstruct the spatio-temporal depth map of a non-rigid object surface deforming over time has many applications in many different domains. However, it is a challenging problem in Computer Vision. The reconstruction is ambiguous and not unique as many structures can have the same projection in the camera sensor. Given the recent advances and success of Deep Learning, it seems promising to use and train a Deep Convolutional Neural Network to recover the spatio-temporal depth map of deforming objects. However, training such networks requires a large-scale dataset. This problem can be tackled by artificially generating a dataset and using it in training the network. In this thesis, a network architecture is proposed to estimate the spatio-temporal structure of the deforming object from small local patches of a video sequence. An algorithm is presented to combine the spatio-temporal structure of these small patches into a global reconstruction of the scene. We artificially generated a database and used it to train the network. The performance of our proposed solution was tested on both synthetic and real Kinect data. Our method outperformed other conventional non-rigid structure-from-motion methods

Tangent Space-Free Lorentz Spatial Temporal Graph Convolution Networks

Abstract Spatial Temporal Graph Convolution Networks (ST-GCNs) have been proposed to embed spatio-temporal graphs. However, these networks used the Euclidean space as the embedding space which does not exploit the structure of the embedded graphs. Euclidean space has been shown not to be the ideal space for embedding graphs especially with tree-like structures. In this work, we make use of hyperbolic geometry and introduce a compact tangent space-free Lorentz ST-GCN and call it LSTGCN that perform the network operations directly on the manifold without resorting to the tangent space. The network uses spatial and temporal modules to propagate features between adjacent nodes in both, the spatial domain and the temporal domain, respectively. In addition, we introduce an attention module which can automatically determine the similarity of nodes without the need for the graph adjacency matrix. Experiments have been conducted on traffic flow forecasting tasks to show the effectiveness of the proposed compact Lorentz model.Abstract Spatial Temporal Graph Convolution Networks (ST-GCNs) have been proposed to embed spatio-temporal graphs. However, these networks used the Euclidean space as the embedding space which does not exploit the structure of the embedded graphs. Euclidean space has been shown not to be the ideal space for embedding graphs especially with tree-like structures. In this work, we make use of hyperbolic geometry and introduce a compact tangent space-free Lorentz ST-GCN and call it LSTGCN that perform the network operations directly on the manifold without resorting to the tangent space. The network uses spatial and temporal modules to propagate features between adjacent nodes in both, the spatial domain and the temporal domain, respectively. In addition, we introduce an attention module which can automatically determine the similarity of nodes without the need for the graph adjacency matrix. Experiments have been conducted on traffic flow forecasting tasks to show the effectiveness of the proposed compact Lorentz model

Optimization of Cash Flow and Financing Costs in Construction Projects

The contractor’s cash shortage during the progress of a construction project leads to delays, penalties and may lead to project failure. Since the net difference between the cash inflow and cash outflow during construction shall be financed by the contractor, the contractor must consider methods to improve their cash flow in order to maximize the profit margin and minimize the financing costs. Several studies have covered optimization of cash flow and optimization of financing costs, separately. This model integrates both approaches in an attempt to determine the best project schedule and financing alternative; that cover the cash shortage with maximum profitability. The model proposes different ways that attempt to overcome the deficit in cash flow; first by minimizing the amount of financing required through shifting the activities with lag to enhance the cash flow, without extending the project duration, then evaluating different financing alternatives; namely long and short-term loans. The outcome of the model is a modified cash flow for the project with less financing required from the contractor, and feasible schedules of financing inflow and outflow based on the best financing alternative, that attempts to cover the lack of cash with the minimum financing cost. In addition, the model provides the user with a negotiable bid alternative that determines the optimum increase in advance payment, that shall overcome the cash shortage, without borrowing funds. The model has been tested and validated on a case study, and a sensitivity analysis has been performed

Traveling solitary wave solutions for the symmetric regularized long-wave equation

In this paper, we employ the extended tanh function method to nd the exact traveling wave solutions involving parameters of the symmetric regularized long- wave equation. When these parameters are taken to be special values, the solitary wave solutions are derived from the exact traveling wave solutions. These studies reveal that the symmetric regularized long-wave equation has a rich varietyof solutions

Traveling wave solutions for the Couple Boiti-Leon-Pempinelli System by using extended Jacobian elliptic function expansion method

In this work, an extended Jacobian elliptic function expansion method is pro-posed for constructing the exact solutions of nonlinear evolution equations. The validity and reliability of the method are tested by its applications to the Couple Boiti-Leon-Pempinelli System which plays an important role in mathematical physics

Learning non-rigid surface reconstruction from spatio-temporal image patches

AbstractWe present a method to reconstruct a dense spatiotemporal depth map of a non-rigidly deformable object directly from a video sequence. The estimation of depth is performed locally on spatio-temporal patches of the video, and then the full depth video of the entire shape is recovered by combining them together. Since the geometric complexity of a local spatiotemporal patch of a deforming non-rigid object is often simple enough to be faithfully represented with a parametric model, we artificially generate a database of small deforming rectangular meshes rendered with different material properties and light conditions, along with their corresponding depth videos, and use such data to train a convolutional neural network. We tested our method on both synthetic and Kinect data and experimentally observed that the reconstruction error is significantly lower than the one obtained using conventional non-rigid structure from motion approaches.Abstract We present a method to reconstruct a dense spatiotemporal depth map of a non-rigidly deformable object directly from a video sequence. The estimation of depth is performed locally on spatio-temporal patches of the video, and then the full depth video of the entire shape is recovered by combining them together. Since the geometric complexity of a local spatiotemporal patch of a deforming non-rigid object is often simple enough to be faithfully represented with a parametric model, we artificially generate a database of small deforming rectangular meshes rendered with different material properties and light conditions, along with their corresponding depth videos, and use such data to train a convolutional neural network. We tested our method on both synthetic and Kinect data and experimentally observed that the reconstruction error is significantly lower than the one obtained using conventional non-rigid structure from motion approaches