1. Bhanvadia VM, Agarwal NM, Chavda AD, Bhetariya BV. Myoepithelioma of soft tissue in the gluteal region: Diagnostic pitfall in cytology. Cytojournal 2017;14:14. 2. Wiwanitkit V. Types and frequency of preanalytical mistakes in the first Thai ISO 9002:1994 certified clinical laboratory, a 6 – month monitoring. BMC Clin Pathol 2001;1:5. Dear Editor, The report on “Myoepithelioma of soft tissue ...

Analyzing the affinity matrix spectrum is an increasingly popular data clustering method. We propose three new algorithmic components which are appropriate for improving performance of spectral clustering. First, observing the eigenvectors suggests to use a K-lines algorithm instead of the commonly applied K-means. Second, the clustering works best if the affinity matrix has a clear block struc...

Context-dependent modeling is a widely used technique for better phone modeling in continuous speech recognition. While different types of contextdependent models have been used, triphones have been known as the most effective ones. In this paper, a Maximum a Posteriori (MAP) estimation approach has been used to estimate the parameters of the untied triphone model set used in data-driven cluste...

Analysing massive amounts of data and extracting value from it has become key across different disciplines. As the amounts of data grow rapidly, current approaches for data analysis are no longer efficient. This is particularly true for clustering algorithms where distance calculations between pairs of points dominate overall time: the more data points are in the dataset, the bigger the share o...

This paper 3 proposes a new method to qualify the result given by a decision tree when it is used as a decision aid system. When the data are numerical, we compute the distance of a case from the decision surface. This distance measures the sensitivity of the result to a change in the input data. With a different distance it is also possible to measure the sensitivity of the result to small cha...

Spectral clustering methods perform well in cases where classical methods (K-means, single linkage, etc.) fail. However, for very non-compact clusters, they also tend to have problems. In this paper, we propose three improvements which we show that perform better in such cases. We suggest that spectral decomposition is merely a method for determining the block structure of the affinity matrix. ...

We study embedding a subset K of the unit sphere to the Hamming cube {−1,+1}m . We characterize the tradeoff between distortion and sample complexity m in terms of the Gaussian width ω(K) of the set. For subspaces and several structured-sparse sets we show that Gaussian maps provide the optimal tradeoff m ∼ δω(K), in particular for δ distortion one needs m ≈ δd where d is the subspace dimension...

This paper introduces a novel, non-local characterization of critical points and their global relation in 2D uncertain scalar fields. The characterization is based on the analysis of the support of the probability density functions (PDF) of the input data. Given two scalar fields representing reliable estimations of the bounds of this support, our strategy identifies mandatory critical points: ...

The smartphone industry has been one of the fastest growing technological areas in recent years. Naturally, the considerable market share of the Android OS and the diversity of app distribution channels besides the official Google Play Store has attracted the attention of malware authors. To deal with the increasing numbers of malicious Android apps in the wild, malware analysts typically rely ...

Data envelopment analysis (DEA) is a mathematical approach to measuring the relative efficiency of peer decision making units (DMUs). It is particularly useful where no a priori information on the tradeoffs or relations among various performance measures is available. However, it is very desirable if “evaluation standards,” when they can be established, be incorporated into DEA performance eval...