Deep distance metric learning (DDML), which is proposed to learn image similarity metrics in an end-toend manner based on the convolution neural network, has achieved encouraging results in many computer vision tasks. L2-normalization in the embedding space has been used to improve the performance of several DDML methods. However, the commonly used Euclidean distance is no longer an accurate me...

the potential function of lippincott and schroeder for linear hydrogen bond has been re-examined and extended to nonlinear hydrogen bond. the parameters originally introduced to the potential function by lippincott and schroeder have been determined from the structural parameters such as 0.. .odistance 0-h bond distance, h.. .o distance and hoo angle. thevalidity of harmonic oscillator approxim...

This paper explores a relatively new methodology, the directional distance function method, to analyze productivity growth. The method explicitly evaluates the role undesirable outputs of the economy, such as carbon dioxide and other green-house gases, have on the frontier production process which we specify as a piece-wise linear and convex boundary function. We decompose productivity growth i...

This study measures the quality-adjusted hospital efficiency and productivity index of a production unit. We propose a non-radial output-oriented directional distance function approach to analyze Taiwan’s hospital productivity, which embeds the quality of care and environment variables simultaneously. There are two major advantages of this model. First, it considers all the radial and nonradial...

F€are and Grosskopf (2004) address the approach of Seiford and Zhu (2002) where undesirable input and output measures are treated in data envelopment analysis (DEA). One key feature of Seiford and Zhu s (2002) approach is that the bad outputs are treated as outputs in DEA model but are reduced when DEA efficiency is evaluated. F€are and Grosskopf (2004) suggest an alternative approach in treati...

The structure of medial elastin determines arterial function and affects wall mechanical properties. The aim of this study was to (1) characterize the structure of elastin in terms of textural features, (2) relate structural parameters to total number of cardiac cycles (TC), and (3) determine the contribution of medial elastin to lumen mechanical stress. Images of pressure-fixed aortic sections...

This paper presents a new method to generate and present arbitrarily directional binocular stereo images from a sequence of omnidirectional images in real time. A sequence of omnidirectional images is taken by moving an omnidirectional image sensor in a static real environment. The motion of the omnidirectional image sensor is constrained to a plane. The sensor’s route and speed are known. In t...

The infeasibility problem in traditional super efficiency models has been well established. A generalization of traditional inputor output-oriented super efficiency models, the directional distance function also suffers from infeasibility and related problems. The hyperbolic-oriented efficiency measure provides an alternative to the input-oriented, output-oriented, and directional efficiency me...

In this paper, we use non-subsampled shearlet transform (NSST) and Krawtchouk Moment Invariants (KMI) to realize image retrieval based on texture and shape features. Shearlet is a new sparse representation tool of multidimensional function, which provides a simple and efficient mathematical framework. We decompose the images by NSST. The directional subband coefficients are modeled by Generaliz...

In the history of DEA, there are two fundamental “Radial” and “Non-radial” approaches for the efficiency measurement. Each approach has its own advantageous and shortcomings. Avkiran et al. (2008) provided a unified framework, named Connected-SBM, for linking these approaches. In the present paper, first, based on the directional distance function we develop the Directional Slack-Based Measures...