NON-LINEAR AND SPARSE REPRESENTATIONS FOR MULTI-MODAL RECOGNITION

In the first part of this dissertation, we address the problem of representing 2D and 3D shapes. In particular, we introduce a novel implicit shape representation based on Support Vector Machine (SVM) theory. Each shape is represented by an analytic decision function obtained by training an SVM, with a Radial Basis Function (RBF) kernel, so that the interior shape points are given higher values. This empowers support vector shape (SVS) with multifold advantages. First, the representation uses a sparse subset of feature points determined by the support vectors, which significantly improves the discriminative power against noise, fragmentation and other artifacts that often come with the data. Second, the use of the RBF kernel provides scale, rotation, and translation invariant features, and allows a shape to be represented accurately regardless of its complexity. Finally, the decision function can be used to select reliable feature points. These features are described using gradients computed from highly consistent decision functions instead of conventional edges. Our experiments on 2D and 3D shapes demonstrate promising results. The availability of inexpensive 3D sensors like Kinect necessitates the design of new representation for this type of data. We present a 3D feature descriptor that represents local topologies within a set of folded concentric rings by distances from local points to a projection plane. This feature, called as Concentric Ring Signature (CORS), possesses similar computational advantages to point signatures yet provides more accurate matches. CORS produces compact and discriminative descriptors, which makes it more robust to noise and occlusions. It is also well-known to computer vision researchers that there is no universal representation that is optimal for all types of data or tasks. Sparsity has proved to be a good criterion for working with natural images. This motivates us to develop efficient sparse and non-linear learning techniques for automatically extracting useful information from visual data. Specifically, we present dictionary learning methods for sparse and redundant representations in a high-dimensional feature space. Using the kernel method, we describe how the well-known dictionary learning approaches such as the method of optimal directions and KSVD can be made non-linear. We analyse their kernel constructions and demonstrate their effectiveness through several experiments on classification problems. It is shown that non-linear dictionary learning approaches can provide significantly better discrimination compared to their linear counterparts and kernel PCA, especially when the data is corrupted by different types of degradations. Visual descriptors are often high dimensional. This results in high computational complexity for sparse learning algorithms. Motivated by this observation, we introduce a novel framework, called sparse embedding (SE), for simultaneous dimensionality reduction and dictionary learning. We formulate an optimization problem for learning a transformation from the original signal domain to a lower-dimensional one in a way that preserves the sparse structure of data. We propose an efficient optimization algorithm and present its non-linear extension based on the kernel methods. One of the key features of our method is that it is computationally efficient as the learning is done in the lower-dimensional space and it discards the irrelevant part of the signal that derails the dictionary learning process. Various experiments show that our method is able to capture the meaningful structure of data and can perform significantly better than many competitive algorithms on signal recovery and object classification tasks. In many practical applications, we are often confronted with the situation where the data that we use to train our models are different from that presented during the testing. In the final part of this dissertation, we present a novel framework for domain adaptation using a sparse and hierarchical network (DASH-N), which makes use of the old data to improve the performance of a system operating on a new domain. Our network jointly learns a hierarchy of features together with transformations that rectify the mismatch between different domains. The building block of DASH-N is the latent sparse representation. It employs a dimensionality reduction step that can prevent the data dimension from increasing too fast as traversing deeper into the hierarchy. Experimental results show that our method consistently outperforms the current state-of-the-art by a significant margin. Moreover, we found that a multi-layer {DASH-N} has an edge over the single-layer DASH-N

Radial basis function neural network control for parallel spatial robot

The derivation of motion equations of constrained spatial multibody system is an important problem of dynamics and control of parallel robots. The paper firstly presents an overview of the calculating the torque of the driving stages of the parallel robots using Kronecker product. The main content of this paper is to derive the inverse dynamics controllers based on the radial basis function (RBF) neural network control law for parallel robot manipulators. Finally,  numerical simulation of the inverse dynamics controller for a 3-RRR delta robot manipulator is presented as an illustrative example

The Roll of Small Businesses in Traditional Handcraft in Rural Development: A Case-Study Of Hanoi Suburban Rattan Enterprises

peer reviewedRural industrial development and new rural development programmes are the main target of Vietnam in the process of industrialization – modernization of the country. To expedite this process, it is necessary to promote the development and encourage the contributions of small enterprise in general and small enterprise rural in particular because practices in many countries show the important role of small and micro enterprises in local development. This paper presents the initial results of research to understand the economic–social contributions of rural enterprises to local development through the mobilization of local resources as well as establishment of the economic-social relations in local of rattan’s enterprises in the suburb of Hanoi, and then propose some measures to facilitate the operation of enterprises and promote the contribution of rattan’s enterprises for local development

Relationship between Transformational Leadership Style and Leadership Thinking of Provincial Administration Leaders

Objectives: The transformational leadership style is accepted as suitable for leading administrative agencies to achieve outstanding results and help organizations cope with challenges. Besides, leadership thinking is also considered to have a very important role in leadership performance in administrative agencies. Therefore, the main objective of the study is to explore the relationship between transformational leadership style and leader thinking to organization's performance. Methods: The article focuses on explaining the views on transformational leadership style, healthy thinking, and the relationship between transformational leadership style and leadership thinking, and at the same time points out the current status of transformational leadership style, transformational leadership, leadership thinking as well as this relationship in practice among the leaders of provincial agencies in Vietnam. Descriptive, inductive, deductive, synthetic, and quantitative statistical methods were applied to interpret the results. Findings: Research results show that transformational leadership style, leadership thinking in the team of leaders of provincial agencies is quite average, there is a strong positive correlation between transformational leadership style. In contrast to leadership thinking, a more transformative leadership style means that it requires an innovative leadership thinking. Novelty:The results achieved when applying a transformational leadership style are quite closely related to the application of leadership thinking to solve leadership challenges. Doi: 10.28991/esj-2021-01307 Full Text: PD

Structures of 2,5-diaryl- and 2,3,5,6-tetra[3,2-b]thiophene synthesized by the palladium-catalyzed Suzuki-Miyaura cross-coupling reaction

The crystal structures of 2,5-di(ethoxyphenyl)-3,6-dibromothieno[3,2-b]thiophene (I) and 2,5-di(ethoxyphenyl)-3,6-diphenylthieno[3,2-b]thiophene (II) have been studied in order to evaluate the planarity of these molecules. The aromatic systems introduced to the thieno[3,2-b]thiophene core structure show a degree of rotation from 30.94° to 66.56°. The crystal packing of (I) are characterized by π×××π stacking, while in (II), C-H×××p and C-H×××O interactions are observed