443 research outputs found
Mucosal adjuvants
No full textThe vast majority of pathogens invade the body through or establish infections in the mucosal tissues. Development of vaccines to combat mucosal infections represents a top priority. Mucosal immunization has recently attracted much interest as a means of generating protective immunity against mucosal pathogens. Conversely, only very few mucosal vaccines are presently approved for human use. The development of a broad range of mucosal vaccines will necessitate the development of safe and effective mucosal adjuvants and delivery systems. Over the past decade, a number of immunomodulatory agents, including toxin based adjuvants, Toll like receptor (TLR) mimetics and non TLR-targeting immunostimulators as well as delivery systems have shown promise for mucosal administration in experimental animals. However, their possible use in humans remains to be established. This paper attempts to provide a brief overview of the mucosal immunization and adjuvants with an emphasis on mucosal adjuvants in or close to clinic
Guest Editorial : Learning with Manifolds in Computer Vision
No full textEditorial of special issue of the IVC journa
Approximate Endpoints for Set-Valued Contractions in Metric Spaces
No full textThe existence of approximate fixed points and approximate endpoints of the multivalued almost I-contractions is established. We also develop quantitative estimates of the sets of approximate fixed points and approximate endpoints for multivalued almost I-contractions. The proved results unify and improve recent results of Amini-Harandi (2010), M. Berinde and V. Berinde (2007), Ćirić (2009), M. Păcurar and R. V. Păcurar (2007) and many others
Dictionary learning and sparse coding on Grassmann manifolds: an extrinsic solution
No full textRecent advances in computer vision and machine learning suggest that a wide range of problems can be addressed more appropriately by considering non-Euclidean geometry. In this paper we explore sparse dictionary learning over the space of linear subspaces, which form Riemannian structures known as Grassmann manifolds. To this end, we propose to embed Grassmann manifolds into the space of symmetric matrices by an isometric mapping, which enables us to devise a closed-form solution for updating a Grassmann dictionary, atom by atom. Furthermore, to handle non-linearity in data, we propose a kernelised version of the dictionary learning algorithm. Experiments on several classification tasks (face recognition, action recognition, dynamic texture classification) show that the proposed approach achieves considerable improvements in discrimination accuracy, in comparison to state-of-the-art methods such as kernelised Affine Hull Method and graph-embedding Grassmann discriminant analysis.Mehrtash Harandi, Conrad Sanderson, Chunhua Shen, and Brian C. Lovel
Vaccine adjuvants: A priority for vaccine research
No full textThe workshop on vaccine adjuvants was held in July of 2009 at the European Commission in Brussels, with the goal of identifying key scientific priorities as they pertain to the development of effective vaccines against life-threatening diseases especially those associated with poverty, including HIV/AIDS, malaria and tuberculosis as well as neglected infectious diseases. On the basis of new advances in adjuvant research and related technology as well as potential challenges and roadblocks, six priorities were identified to accelerate development of improved or novel vaccine adjuvants for human use
Correction to: Isolated Human and Livestock Echinococcus granulosus Genotypes Using Real-Time PCR of cox1 Gene in Northeast Iran (Acta Parasitologica, (2019), 64, 3, (679-685), 10.2478/s11686-019-00117-w)
No full textUnfortunately, the affiliation of Majid Fasihi-Harandi needs to be edited. The correct affiliation is Research Center for Hydatid Disease in Iran, School of Medicine, Kerman University of Medical Sciences, Kerman, Iran
Hydrosystems as Multipractice Phenomena: A Normative Approach to Analysing Governance System Failures
No full textGiven the water governance regime, how potent is the normative practice idea in the case of hydrosystems management? How can the normative practice framework explain the failure of the water governance of the Zayandehrud, and how can this explanation improve water governance both in thsi case and more generally?Can the govervance of the Zayandehrud be understood as a normative practice? If so, how can the distribution of responsibilities, interests and norms, as analysed by the normative practice framework, be seen as a cause of the conflicts that have arisen in the water governance of this river? How are different ways of thinking in the Zayandehrud case (re)shaping the distribution of responsibilities, interests and norms that are causing the conflict within a water-management practice
Scalable Deep k-Subspace Clustering
No full textSubspace clustering algorithms are notorious for their scalability issues because building and processing large affinity matrices are demanding. In this paper, we introduce a method that simultaneously learns an embedding space along subspaces within it to minimize a notion of reconstruction error, thus addressing the problem of subspace clustering in an end-to-end learning paradigm. To achieve our goal, we propose a scheme to update subspaces within a deep neural network. This in turn frees us from the need of having an affinity matrix to perform clustering. Unlike previous attempts, our method can easily scale up to large datasets, making it unique in the context of unsupervised learning with deep architectures. Our experiments show that our method significantly improves the clustering accuracy while enjoying cheaper memory footprints.Tong Zhang, Pan Ji, Mehrtash Harandi, Richard Hartley, and Ian Rei
Distribution-matching embedding for visual domain adaptation
No full textDomain-invariant representations are key to addressing the domain shift problem where the training and test examples follow different distributions. Existing techniques that have attempted to match the distributions of the source and target domains typically compare these distributions in the original feature space. This space, however, may not be directly suitable for such a comparison, since some of the features may have been distorted by the domain shift, or may be domain specific. In this paper, we introduce a Distribution-Matching Embedding approach: An unsupervised domain adaptation method that overcomes this issue by mapping the data to a latent space where the distance between the empirical distributions of the source and target examples is minimized. In other words, we seek to extract the information that is invariant across the source and target data. In particular, we study two different distances to compare the source and target distributions: the Maximum Mean Discrepancy and the Hellinger distance. Furthermore, we show that our approach allows us to learn either a linear embedding, or a nonlinear one. We demonstrate the benefits of our approach on the tasks of visual object recognition, text categorization, and WiFi localization
Systemic software reuse through analogical reasoning
No full textThis research focuses on analogical reasoning as applied to the problem of software reuse at the level of the system architecture. The resulting methodology may be thought of as a hybrid approach to software reuse, as it combines a compositional developmental strategy with synthesis of role-based behaviors. The components of this research include: a methodology based upon an extension of object-oriented design principles that promotes decoupling of object representations and provides mechanisms for composing systems from abstract objects, relations, and frameworks; techniques for measuring the degree that two systems are analogically correlated; and automated support for the application of analogically derived mappings in the translation of system concepts from source to target domains.Made available in DSpace on 2011-05-07T11:53:07Z (GMT). No. of bitstreams: 2
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