Harmonic coordinates in the string and membrane equations

In this note, we first show that the solutions to Cauchy problems for two versions of relativistic string and membrane equations are diffeomorphic. Then we investigate the coordinates transformation presented in Ref. [9] (see (2.20) in Ref. [9]) which plays an important role in the study on the dynamics of the motion of string in Minkowski space. This kind of transformed coordinates are harmonic coordinates, and the nonlinear relativistic string equations can be straightforwardly simplified into linear wave equations under this transformation

Error analysis of a hybrid multiple classifier system for recognizing unconstrained handwritten numerals

Since the early 1990s, many research communities, amongst the pattern recognition and machine learning, have shown a growing interest in Multiple Classifier Systems (MCSs), particularly for the recognition of handwritten words and numerals. This thesis is divided into two parts. First, we construct an effective hybrid MCS (HMCS) of handwritten numeral recognition in order to raise the reliability of the entire system. This HMCS is proposed by integrating the cooperation (serial topology) and combination (parallel topology) of three classifiers: SVM, MQDF, and LeNet-5. In cooperation, patterns rejected from the previous classifier become the input of the next classifier. Based on the principles of different classifiers, effective measurements for the rejection options---First Rank Measurement (FRM), Differential Measurement (DM), and Probability Measurement (PM) are defined. In combination, Weighted Borda Count (WBC) at the rank level, which reflects confidence and preference of different ranks in different classes with different classifiers, is applied. Second, we analyze factors that cause the errors in HMCS. In this process, we focus mainly on the role of size normalization on the recognition of handwritten numerals

Reliable pattern recognition system with novel semi-supervised learning approach

Over the past decade, there has been considerable progress in the design of statistical machine learning strategies, including Semi-Supervised Learning (SSL) approaches. However, researchers still have difficulties in applying most of these learning strategies when two or more classes overlap, and/or when each class has a bimodal/multimodal distribution. In this thesis, an efficient, robust, and reliable recognition system with a novel SSL scheme has been developed to overcome overlapping problems between two classes and bimodal distribution within each class. This system was based on the nature of category learning and recognition to enhance the system's performance in relevant applications. In the training procedure, besides the supervised learning strategy, the unsupervised learning approach was applied to retrieve the "extra information" that could not be obtained from the images themselves. This approach was very helpful for the classification between two confusing classes. In this SSL scheme, both the training data and the test data were utilized in the final classification. In this thesis, the design of a promising supervised learning model with advanced state-of-the-art technologies is firstly presented, and a novel rejection measurement for verification of rejected samples, namely Linear Discriminant Analysis Measurement (LDAM), is defined. Experiments on CENPARMI's Hindu-Arabic Handwritten Numeral Database, CENPARMI's Numerals Database, and NIST's Numerals Database were conducted in order to evaluate the efficiency of LDAM. Moreover, multiple verification modules, including a Writing Style Verification (WSV) module, have been developed according to four newly defined error categories. The error categorization was based on the different costs of misclassification. The WSV module has been developed by the unsupervised learning approach to automatically retrieve the person's writing styles so that the rejected samples can be classified and verified accordingly. As a result, errors on CENPARMI's Hindu-Arabic Handwritten Numeral Database (24,784 training samples, 6,199 testing samples) were reduced drastically from 397 to 59, and the final recognition rate of this HAHNR reached 99.05%, a significantly higher rate compared to other experiments on the same database. When the rejection option was applied on this database, the recognition rate, error rate, and reliability were 97.89%, 0.63%, and 99.28%, respectivel

Mutant spectrum of dengue type 1 virus in the plasma of patients from the 2006 epidemic in South China

SummaryThe aim of the present study was to explore the mutant spectrum of dengue type 1 virus (DENV-1) within individuals during the 2006 dengue epidemic in South China. A 513-bp fragment including most of domain III of the envelope (E) gene was amplified directly from clinical samples, then cloned and sequenced. A total of 89 clones from six patients (range 11–17 clones per patient) were sequenced. Genetic diversity was calculated using MEGA 4 package. The total number of nucleotide mutations was 113 (3.7%) within the sequenced 513-bp E gene, with a range of 15 (3%) to 24 (4.7%) within individual viral populations, harboring more non-synonymous than synonymous mutations. The extent of sequence diversity varied among patients, with the mean diversity ranging from 0.19% to 0.32%, and the mean pairwise p-distance ranging from 0.34% to 0.65%. No genome-defective virus was detected in any clone in this study. Purifying selection may be the main driving force for the intrahost evolution: the mean dN/dS ratio was 0.532. Our findings contribute to the understanding of the genetic variation of DENV-1 in South China

Genome-wide patterns of genetic variation in sweet and grain sorghum (Sorghum bicolor)

10.1186/gb-2011-12-11-r114Genome Biology1211-GNBL