We show that trace distance measure of coherence is a strong monotone for all qubit and, so called, X states. An expression for the trace distance coherence for all pure states and a semi definite program for arbitrary states is provided. We also explore the relation between l1-norm and relative entropy based measures of coherence, and give a sharp inequality connecting the two. In addition, it...

The problem of finding \emph{distance} between \emph{pattern} of length $m$ and \emph{text} of length $n$ is a typical way of generalizing pattern matching to incorporate dissimilarity score. For both Hamming and $L_1$ distances only a super linear upper bound $\widetilde{O}(n\sqrt{m})$ are known, which prompts the question of relaxing the problem: either by asking for $1 \pm \varepsilon$ appro...

This paper proposes performance evaluation of different distance measures used in color iris authentication. The color iris segmentation is carried out using histogram and circular Hough transform. The color iris features are extracted using histogram method. Different distance measures are used for iris authentication. The experimental evaluation shows that Euclidean and Manhattan distance are...

L1 norm discrimination consists in finding the hyperplane that minimizes the sum of L1 norm distances between the hyperplane and the points that lie on the wrong side of the hyperplane. This problem is difficult for datasets containing more than 100,000 points. Since few points are needed to obtain the optimal hyperplane, we propose a point selection algorithm which iteratively adds the necessa...

This paper proposes a DC-mesh network that allows requesting nodes to be put into clusters while the requests are sent to a target node, as well as is easy to layout on an LSI chip. To organize the DCmesh, we use the partitioning in the word space based on the Hamming code [1]. We introduce an index scheme, (parity-value,information-value), in the word space, and map it onto a 4-D (dimensional)...

The distance transform is a basic operation in computer vision, pattern recognition and robotics. In this paper, we consider the city block (L1) distance metric. An algorithm for computing the city block distance transform on recon"gurable meshes is proposed in this paper. The time complexity and scalability of the algorithm are analysed. The results indicate that the algorithm is scalable and ...

The leading economic explanation for tipping –that is, explanation why the practice is socially beneficial, not why individuals leave tips even though it is not narrowly advantageous to them– is that it confers an incentive to provide personal services. This fits many instances in which tipping is common but does not fit the taxicab business very well. We propose two novel explanations of tippi...

In this work, we present a general guideline to establish the relation between a distribution model and its corresponding similarity estimation. A rich set of distance metrics, such as Harmonic distance and Geometric distance, is derived according to Maximum Likelihood theory. These metrics can provide a more accurate model than the conventional Euclidean distance and Manhattan distance. Becaus...