Integrating Specialized Classifiers Based on Continuous Time Markov Chain

Specialized classifiers, namely those dedicated to a subset of classes, are often adopted in real-world recognition systems. However, integrating such classifiers is nontrivial. Existing methods, e.g. weighted average, usually implicitly assume that all constituents of an ensemble cover the same set of classes. Such methods can produce misleading predictions when used to combine specialized classifiers. This work explores a novel approach. Instead of combining predictions from individual classifiers directly, it first decomposes the predictions into sets of pairwise preferences, treating them as transition channels between classes, and thereon constructs a continuous-time Markov chain, and use the equilibrium distribution of this chain as the final prediction. This way allows us to form a coherent picture over all specialized predictions. On large public datasets, the proposed method obtains considerable improvement compared to mainstream ensemble methods, especially when the classifier coverage is highly unbalanced.Comment: Published at IJCAI-17, typo fixe

Preparation of a Modified PTFE Fibrous Photo-Fenton Catalyst and Its Optimization towards the Degradation of Organic Dye

Polytetrafluoroethylene (PTFE) fiber was grafted with acrylic acid to impart the carboxyl groups onto the fiber surface, which were used to coordinate with both transition metal ions Fe(III) and Cu(II) and a rare metal ion Ce(III) to prepare the metal grafted PTFE fiber complexes as the novel heterogeneous Fenton catalysts for the degradation of the azo dye in water under visible irradiation. Some factors affecting the preparation process, such as nature and concentration of metal ions in the coordination solution, grafting degree of PTFE and reaction temperature were optimized with respect to the content and strength of metal fixation on the fiber and dye degradation efficiency. The results indicated that increasing metal ion concentrations in solution and grafting degree of PTFE fiber as well as higher coordination temperature led to a significant increase in metal content, especially Fe(III) and Cu(II) content of the complexes. Fe(III) ions fixed on the fiber showed the better catalytic performance than Cu(II) and Ce(III) ions fixed when three different complexes with similar metal content being employed, respectively. Moreover, Increasing Fe content or incorporation of Cu(II) ions could significantly improve the catalytic activity of the complexes

Détermination de la mobilité du thorium et de l’uranium dans des rejets de mines

Les activités d’extraction des métaux peuvent être une source importante de contamination dans l’environnement. Les déchets miniers contiennent souvent de fortes concentrations de métaux et ont des conditions physiochimiques qui favorisent la mobilité et/ou la biodisponibilité des métaux traces toxiques. Dans cette étude, nous avons évalué la mobilité du thorium, de l'uranium, du baryum, du manganèse et du chrome à partir des rejets miniers d’une mine de niobium. L’effet de différentes conditions environnementales (pH, dureté de l’eau et présence de la matière organique naturelle) a été évalué. Le thorium n’est pas fortement mobilisé (CTh 30 μg kg-1). Par contre, l’uranium est mobilisé en milieu acide (pH 0.2 μm), l’uranium, le baryum, le manganèse et le chrome ont été retrouvé surtout sous forme dissoute (diamètre Mn et U > Th. Le thorium et l’uranium existent plus sous forme de nanoparticules par la présence d’acide fulvique. En présence ou absence d’acide fulvique, toutes les particules du thorium et de l’uranium sont petites.Metal mining activities can be a major source of contamination in the environment. Mining waste often contains high concentrations of metals and has physiochemical conditions that promote the mobility and / or bioavailability of toxic trace metals. In this study, we evaluated the mobility of thorium, uranium, barium, manganese and chromium from niobium mine tailings. The effect of different environmental conditions (pH, water hardness and presence of natural organic matter) was evaluated. Thorium was not strongly mobilized (CTh 30 μg kg -1). On the other hand, the uranium was mobilized in acid medium (pH 0.2 μm), uranium, barium, manganese and chromium were found mainly in dissolved form (diameter Mn and U > Th. Thorium and uranium existed more nanoparticles by the presence of fulvic acid. In the presence or absence of fulvic acid, all the particles of thorium and uranium were small

Semantic Latent Decomposition with Normalizing Flows for Face Editing

Navigating in the latent space of StyleGAN has shown effectiveness for face editing. However, the resulting methods usually encounter challenges in complicated navigation due to the entanglement among different attributes in the latent space. To address this issue, this paper proposes a novel framework, termed SDFlow, with a semantic decomposition in original latent space using continuous conditional normalizing flows. Specifically, SDFlow decomposes the original latent code into different irrelevant variables by jointly optimizing two components: (i) a semantic encoder to estimate semantic variables from input faces and (ii) a flow-based transformation module to map the latent code into a semantic-irrelevant variable in Gaussian distribution, conditioned on the learned semantic variables. To eliminate the entanglement between variables, we employ a disentangled learning strategy under a mutual information framework, thereby providing precise manipulation controls. Experimental results demonstrate that SDFlow outperforms existing state-of-the-art face editing methods both qualitatively and quantitatively. The source code is made available at https://github.com/phil329/SDFlow

Cross-Stream Contrastive Learning for Self-Supervised Skeleton-Based Action Recognition

Self-supervised skeleton-based action recognition enjoys a rapid growth along with the development of contrastive learning. The existing methods rely on imposing invariance to augmentations of 3D skeleton within a single data stream, which merely leverages the easy positive pairs and limits the ability to explore the complicated movement patterns. In this paper, we advocate that the defect of single-stream contrast and the lack of necessary feature transformation are responsible for easy positives, and therefore propose a Cross-Stream Contrastive Learning framework for skeleton-based action Representation learning (CSCLR). Specifically, the proposed CSCLR not only utilizes intra-stream contrast pairs, but introduces inter-stream contrast pairs as hard samples to formulate a better representation learning. Besides, to further exploit the potential of positive pairs and increase the robustness of self-supervised representation learning, we propose a Positive Feature Transformation (PFT) strategy which adopts feature-level manipulation to increase the variance of positive pairs. To validate the effectiveness of our method, we conduct extensive experiments on three benchmark datasets NTU-RGB+D 60, NTU-RGB+D 120 and PKU-MMD. Experimental results show that our proposed CSCLR exceeds the state-of-the-art methods on a diverse range of evaluation protocols.Comment: 15 pages, 7 figure