735 research outputs found
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
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