Weak type estimates on certain Hardy spaces for smooth cone type multipliers

Let $\varrho\in C^{\infty} ({\Bbb R}^d\setminus\{0\})$ be a non-radial homogeneous distance function satisfying $\varrho(t\xi)=t\varrho(\xi)$. For $f\in\frak S ({\Bbb R}^{d+1})$ and $\delta>0$, we consider convolution operator {\Cal T}^{\delta} associated with the smooth cone type multipliers defined by \hat {{\Cal T}^{\delta} f}(\xi,\tau)= (1-\frac{\varrho(\xi)}{|\tau|} )^{\delta}_+\hat f (\xi,\tau), (\xi,\tau)\in {\Bbb R}^d \times \Bbb R. If the unit sphere $\Sigma_{\varrho}\fallingdotseq\{\xi\in {\Bbb R}^d : \varrho(\xi)=1\}$ is a convex hypersurface of finite type and $\varrho$ is not radial, then we prove that {\Cal T}^{\delta(p)} maps from $H^p({\Bbb R}^{d+1})$, $0<p<1$, into weak-$L^p(\Gamma_{\gamma})$ for the critical index $\delta(p)=d(1/p -1/2)-1/2$, where $\Gamma_{\gamma}=\{(x,t)\in {\Bbb R}^d\times\Bbb R : |t|\geq\gamma |x|\}$ for $\gamma=\max\{\sup_{\varrho(\xi)\leq 1}|\xi|,1\}$. Moreover, we furnish a function $f\in\frak S({\Bbb R}^{d+1})$ such that \sup_{\lambda>0} \lambda^p|\{(x,t)\in \bar{{\Bbb R}^{d+1}\setminus\Gamma_{\gamma}} : |{\Cal T}_{\varrho}^{\delta(p)}f(x,t)|>\lambda\}|=\infty.Comment: 13 page

Lagrangian Floer potential of orbifold spheres

For each sphere with three orbifold points, we construct an algorithm to compute the open Gromov–Witten potential, which serves as the quantum-corrected Landau–Ginzburg mirror and is an infinite series in general. This gives the first class of general-type geometries whose full potentials can be computed. As a consequence we obtain an enumerative meaning of mirror maps for elliptic curve quotients. Furthermore, we prove that the open Gromov–Witten potential is convergent, even in the general-type cases, and has an isolated singularity at the origin, which is an important ingredient of proving homological mirror symmetry.National Research Foundation of Korea; 2010-0019516; 2012R1A1A2003117; 2013R1A1A1058646 - National Research Foundation of Kore

Health state classification of a spherical tank using a hybrid bag of features and K-nearest neighbor.

Feature analysis puts a great impact in determining the various health conditions of mechanical vessels. To achieve balance between traditional feature extraction and the automated feature selection process, a hybrid bag of features (HBoF) is designed for multiclass health state classification of spherical tanks in this paper. The proposed HBoF is composed of (a) the acoustic emission (AE) features and (b) the time and frequency based statistical features. A wrapper-based feature chooser algorithm, Boruta, is utilized to extract the most intrinsic feature set from HBoF. The selective feature matrix is passed to the multi-class k-nearest neighbor (k-NN) algorithm to differentiate among normal condition (NC) and two faulty conditions (FC1 and FC2). Experimental results demonstrate that the proposed methodology generates an average 99.7% accuracy for all working conditions. Moreover, it outperforms the existing state-of-art works by achieving at least 19.4%

THE INFLUENCE OF SOCIAL PRESENCE ON EVALUATING PERSONALIZED RECOMMENDER SYSTEMS

Providing recommendations is acknowledged as an important feature of a business-to-consumer online storefront. Although many studies have been conducted the algorithms and operational procedures relating to personalized recommender systems, empirical evidence demonstrating relationships between social presence and two important outcomes of evaluating recommender systems, reuse intention and trust, remains lacking. To test the existence of a causal link between social presence and reuse intention, and the mediating role of trust between these two variables, this study conducted experiments varying the levels of social presence while providing personalized recommendations to users based on their explicit preferences. This study also compared these effects in two different product contexts: hedonic and utilitarian products. Interactions of social presence and customer reviews were also investigated in these experiments. The results show that higher social presence increases both reuse intention and trust in recommender systems. In addition, the influence of social presence on reuse intention in the context of recommending utilitarian products is less than that in the context of recommending hedonic products

Competition between B-Z and B-L transitions in a single DNA molecule: Computational studies

Under negative torsion, DNA adopts left-handed helical forms, such as Z-DNA and L-DNA. Using the random copolymer model developed for a wormlike chain, we represent a single DNA molecule with structural heterogeneity as a helical chain consisting of monomers which can be characterized by different helical senses and pitches. By Monte Carlo simulation, where we take into account bending and twist fluctuations explicitly, we study sequence dependence of B-Z transitions under torsional stress and tension focusing on the interaction with B-L transitions. We consider core sequences, (GC)(n) repeats or (TG)(n) repeats, which can interconvert between the right-handed B form and the left-handed Z form, imbedded in a random sequence, which can convert to left-handed L form with different (tension dependent) helical pitch. We show that Z-DNA formation from the (GC)(n) sequence is always supported by unwinding torsional stress but Z-DNA formation from the (TG)(n) sequence, which are more costly to convert but numerous, can be strongly influenced by the quenched disorder in the surrounding random sequence.National Research Foundation NRF-2012 R1A1A3013044 NRF-2014R1A1A2055681NRF-2012R1A1A2021736IBS-R023-D1NRF-2015R1A2A2A01005916Chemistr

A Hybrid Technique for Medical Image Segmentation

Medical image segmentation is an essential and challenging aspect in computer-aided diagnosis and also in pattern recognition research. This paper proposes a hybrid method for magnetic resonance (MR) image segmentation. We first remove impulsive noise inherent in MR images by utilizing a vector median filter. Subsequently, Otsu thresholding is used as an initial coarse segmentation method that finds the homogeneous regions of the input image. Finally, an enhanced suppressed fuzzy c-means is used to partition brain MR images into multiple segments, which employs an optimal suppression factor for the perfect clustering in the given data set. To evaluate the robustness of the proposed approach in noisy environment, we add different types of noise and different amount of noise to T1-weighted brain MR images. Experimental results show that the proposed algorithm outperforms other FCM based algorithms in terms of segmentation accuracy for both noise-free and noise-inserted MR images