The eight variable homogeneous degree three bent functions

We determine the affine equivalence classes of the eight variable degree three homogeneous bent functions using a new algorithm. Our algorithm applies to general bent functions and can systematically determine the automorphism groups. We provide a partial verification of the computer enumeration of bent functions by Meng et al.

Spectral kernels for classification

Spectral methods, as an unsupervised technique, have been used with success in data mining such as LSI in information retrieval, HITS and PageRank in Web search engines, and spectral clustering in machine learning. The essence of success in these applications is the spectral information that captures the semantics inherent in the large amount of data required during unsupervised learning. In this paper, we ask if spectral methods can also be used in supervised learning, e.g., classification. In an attempt to answer this question, our research reveals a novel kernel in which spectral clustering information can be easily exploited and extended to new incoming data during classification tasks. From our experimental results, the proposed Spectral Kernel has proved to speedup classification tasks without compromising accuracy.

Non-homogeneous volatility correlations in the bivariate multifractal model

In this paper, we consider an extension of the recently proposed bivariate Markov-switching multifractal model of Calvet, Fisher, and Thompson [2006. "Volatility Comovement: A Multifrequency Approach." Journal of Econometrics {131}: 179-215]. In particular, we allow correlations between volatility components to be non-homogeneous with two different parameters governing the volatility correlations at high and low frequencies. Specification tests confirm the added explanatory value of this specification. In order to explore its practical performance, we apply the model for computing value-at-risk statistics for different classes of financial assets and compare the results with the baseline, homogeneous bivariate multifractal model and the bivariate DCC-GARCH of Engle [2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models." Journal of Business & Economic Statistics 20 (3): 339-350]. As it turns out, the multifractal model with heterogeneous volatility correlations provides more reliable results than both the homogeneous benchmark and the DCC-GARCH model. © 2014 Taylor & Francis.

Homogeneous isolation of nanocelluloses by controlling the shearing force and pressure in microenvironment

Nanocelluloses were prepared from sugarcane bagasse celluloses by dynamic high pressure microfluidization (DHPM), aiming at achieving a homogeneous isolation through the controlling of shearing force and pressure within a microenvironment. In the DHPM process, the homogeneous cellulose solution passed through chambers at a higher pressure in fewer cycles, compared with the high pressure homogenization (HPH) process. X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) demonstrated that entangled network structures of celluloses were well dispersed in the microenvironment, which provided proper shearing forces and pressure to fracture the hydrogen bonds. Gel permeation chromatography (GPC), CP/MAS 13C NMR and Fourier transform infrared spectroscopy (FT-IR) measurements suggested that intra-molecular hydrogen bonds were maintained. These nanocelluloses of smaller particle size, good dispersion and lower thermal stability will have great potential to be applied in electronics devices, electrochemistry, medicine, and package and printing industry. © 2014 Elsevier Ltd.

Study on nanocellulose by high pressure homogenization in homogeneous isolation

Nanocellulose from cotton cellulose was prepared by high pressure homogenization (HPH) in ionic liquids (1-butyl-3-methylimidazolium chloride ([Bmim]Cl). The nanocellulose possessed narrow particle size distribution, with diameter range of 10–20 nm. Weight average molecular weight (Mw) of nanocellulose treated by HPH was lower (173.8 kDa) than the one ILs treated cellulose (344.6 kDa). X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FT-IR), and Solid-state CP/MAS 13C NMR measurements were employed to study the mechanism of structural changes, which suggested that network structure between cellulose chains were destructed by the shearing forces of HPH in combination with ionic liquids. The intermolecular and intra-molecular hydrogen bonds of cellulose were further destroyed, leading to the long cellulose molecular chains being collapsed into short chains. Therefore, the nanocellulose could provide desired properties, such as lower thermal stability and strong water holding capacity. Results indicated that it had great potential in the applications for packaging, medicines, cosmetics and tissue engineering.

Predicting protein function using multiple kernels

High-throughput experimental techniques provide a wide variety of heterogeneous proteomic data sources. To exploit the information spread across multiple sources for protein function prediction, these data sources are transformed into kernels and then integrated into a composite kernel. Several methods first optimize the weights on these kernels to produce a composite kernel, and then train a classifier on the composite kernel. As such, these approaches result in an optimal composite kernel, but not necessarily in an optimal classifier. On the other hand, some approaches optimize the loss of binary classifiers and learn weights for the different kernels iteratively. For multi-class or multi-label data, these methods have to solve the problem of optimizing weights on these kernels for each of the labels, which are computationally expensive and ignore the correlation among labels. In this paper, we propose a method called Predicting Protein Function using Multiple K ernels (ProMK). ProMK iteratively optimizes the phases of learning optimal weights and reduces the empirical loss of multi-label classifier for each of the labels simultaneously. ProMK can integrate kernels selectively and downgrade the weights on noisy kernels. We investigate the performance of ProMK on several publicly available protein function prediction benchmarks and synthetic datasets. We show that the proposed approach performs better than previously proposed protein function prediction approaches that integrate multiple data sources and multi-label multiple kernel learning methods. The codes of our proposed method are available at https://sites.google.com/site/guoxian85/promk.