We propose a Dynamic Boolean Network in which a state of a Boolean network and an output label associated with the state are interacting with each other, and a transition matrix keeps changing perpetually. We extrapolate a mutation of a transition matrix as T → Q−1TQ where T is a transition matrix and Q is a matrix representation of a permutation P (where the objects permuted are states of DBN)...

A matrix assisted laser desorption/ionization time-of-flight mass spectrometry platform for quantitatively monitoring enzyme activity and screening enzyme inhibitors has been demonstrated. The described method employs a new matrix of oxidized carbon nanotubes. Compared with the traditional fluorescence approach, this label-free method has the advantage of directly identifying the substrates and...

Many distance learning algorithms have been developed in recent years. However, few of them consider the problem when the class labels of training data are noisy, and this may lead to serious performance deterioration. In this paper, we present a robust distance learning method in the presence of label noise, by extending a previous non-parametric discriminative distance learning algorithm, i.e...

In standard matrix completion theory, it is required to have at least O(n ln n) observed entries to perfectly recover a low-rank matrix M of size n × n, leading to a large number of observations when n is large. In many real tasks, side information in addition to the observed entries is often available. In this work, we develop a novel theory of matrix completion that explicitly explore the sid...

in the present paper, we introduce the sequence space [{l_p}(e,delta) = left{ x = (x_n)_{n = 1}^infty : sum_{n = 1}^infty left|  sum_{j in {e_n}} x_j - sum_{j in e_{n + 1}} x_jright| ^p < infty right},] where $e=(e_n)$ is a partition of finite subsets of the positive integers and $pge 1$. we investigate its topological properties and inclusion relations. moreover, we consider the problem of fin...

The detergent-insoluble matrix of cultured human fibroblasts contains cytoskeletal and nuclear components, as well as two major, noncollagenous glycoproteins, fibronectin and GP140. These glycoproteins are stabilized by extensive intermolecular disulfide bonding. GP140, in contrast to fibronectin, is resistant to digestion with trypsin and is not cross-reactive with antisera prepared against fi...

In (Kégl, 2014), we recently showed empirically that ADABOOST.MH is one of the best multi-class boosting algorithms when the classical one-against-all base classifiers, proposed in the seminal paper of Schapire and Singer (1999), are replaced by factorized base classifiers containing a binary classifier and a vote (or code) vector. In a slightly different setup, a similar factorization coupled ...

Radiologists usually observe anatomical regions of chest X-ray images as well the overall image before making a decision. However, most existing deep learning models only look at entire for classification, failing to utilize important information. In this paper, we propose novel multi-label classification model that accurately classifies finding and also localizes findings their correct regions...

The label learning mechanism is challenging to integrate into the training model of multi-label feature space dimensionality reduction problem, making current methods primarily supervision modes. Many only focus attention on correlations and ignore instance interrelations between original low dimensional space. Additionally, very few techniques consider how constrain projection matrix identify ...

Data representation is of significant importance in minimizing multi-label ambiguity. While most researchers intensively investigate label correlation, the research on enhancing model robustness preliminary. Low-quality data one main reasons that degrades. Aiming at cases with noisy features and missing labels, we develop a novel method called robust global local correlation (RGLC). In this mod...