Histograms of Points, Orientations, and Dynamics of Orientations Features for Hindi Online Handwritten Character Recognition

A set of features independent of character stroke direction and order variations is proposed for online handwritten character recognition. A method is developed that maps features like co-ordinates of points, orientations of strokes at points, and dynamics of orientations of strokes at points spatially as a function of co-ordinate values of the points and computes histograms of these features from different regions in the spatial map. Different features like spatio-temporal, discrete Fourier transform, discrete cosine transform, discrete wavelet transform, spatial, and histograms of oriented gradients used in other studies for training classifiers for character recognition are considered. The classifier chosen for classification performance comparison, when trained with different features, is support vector machines (SVM). The character datasets used for training and testing the classifiers consist of online handwritten samples of 96 different Hindi characters. There are 12832 and 2821 samples in training and testing datasets, respectively. SVM classifiers trained with the proposed features has the highest classification accuracy of 92.9\% when compared to the performances of SVM classifiers trained with the other features and tested on the same testing dataset. Therefore, the proposed features have better character discriminative capability than the other features considered for comparison.Comment: 21 pages, 12 jpg figure

A Classifier Using Global Character Level and Local Sub-unit Level Features for Hindi Online Handwritten Character Recognition

A classifier is developed that defines a joint distribution of global character features, number of sub-units and local sub-unit features to model Hindi online handwritten characters. The classifier uses latent variables to model the structure of sub-units. The classifier uses histograms of points, orientations, and dynamics of orientations (HPOD) features to represent characters at global character level and local sub-unit level and is independent of character stroke order and stroke direction variations. The parameters of the classifier is estimated using maximum likelihood method. Different classifiers and features used in other studies are considered in this study for classification performance comparison with the developed classifier. The classifiers considered are Second Order Statistics (SOS), Sub-space (SS), Fisher Discriminant (FD), Feedforward Neural Network (FFN) and Support Vector Machines (SVM) and the features considered are Spatio Temporal (ST), Discrete Fourier Transform (DFT), Discrete Cosine Transform (SCT), Discrete Wavelet Transform (DWT), Spatial (SP) and Histograms of Oriented Gradients (HOG). Hindi character datasets used for training and testing the developed classifier consist of samples of handwritten characters from 96 different character classes. There are 12832 samples with an average of 133 samples per character class in the training set and 2821 samples with an average of 29 samples per character class in the testing set. The developed classifier has the highest accuracy of 93.5\% on the testing set compared to that of the classifiers trained on different features extracted from the same training set and evaluated on the same testing set considered in this study.Comment: 23 pages, 8 jpg figures. arXiv admin note: text overlap with arXiv:2310.0822

Effect of Ni-doping on magnetism and superconductivity in Eu0.5K0.5Fe2As2

The effect of Ni-doping on the magnetism and superconductivity in Eu0.5K0.5Fe2As2 has been studied through a systematic investigation of magnetic and superconducting properties of Eu0.5K0.5(Fe1-xNix)2As2 (x = 0, 0.03, 0.05, 0.08 and 0.12) compounds by means of dc and ac magnetic susceptibilities, electrical resistivity and specific heat measurements. Eu0.5K0.5Fe2As2 is known to exhibit superconductivity with superconducting transition temperature Tc as high as 33 K. The Ni-doping leads to a rapid decrease in Tc; Tc is reduced to 23 K with 3% Ni-doping, and 8% Ni-doping suppresses the superconductivity to below 1.8 K. In 3% Ni-doped sample Eu0.5K0.5(Fe0.97Ni0.03)2As2 superconductivity coexists with short range ordering of Eu2+ magnetic moments at Tm ~ 6 K. The suppression of superconductivity with Ni-doping is accompanied with the emergence of a long range antiferromagnetic ordering with TN = 8.5 K and 7 K for Eu0.5K0.5(Fe0.92Ni0.08)2As2 and Eu0.5K0.5(Fe0.88Ni0.12)2As2, respectively. The temperature and field dependent magnetic measurements for x = 0.08 and 0.12 samples reflect the possibility of a helical magnetic ordering of Eu2 moments. We suspect that the helimagnetism of Eu spins could be responsible for the destruction of superconductivity as has been observed in Co-doped EuFe2As2. The most striking feature seen in the resistivity data for x = 0.08 is the reappearance of the anomaly presumably due to spin density wave transition at around 60 K. This could be attributed to the compensation of holes (K-doping at Eu-site) by the electrons (Ni-doping at Fe site). The anomaly associated with spin density wave further shifts to 200 K for x = 0.12 for which the electron doping has almost compensated the holes in the system.Comment: 9 pages, 10 figure

Sarcomatoid Carcinoma Metastasis to the Colon from a Small Renal Mass: Case Report with Review of Literature

A third of patients with renal cell carcinoma (RCC) present with metastatic disease. Metastasis in RCC from small renal mass (SRM) (≤4 cm) is rare. We report a case of stage cT1a clear-cell RCC with low-risk features on pathology presenting with disproportionately large synchronous solitary metastasis to the transverse colon. He underwent resection of the mass with the involved transverse colon and adjoining mesocolon. Intestinal continuity was restored, following which partial nephrectomy was performed for the right renal tumor. Final pathology of the right renal mass confirmed clear-cell RCC. The large mass after immunohistochemistry profile confirmed metastasis from the renal tumor

Group-Level Emotion Recognition Using a Unimodal Privacy-Safe Non-Individual Approach

This article presents our unimodal privacy-safe and non-individual proposal for the audio-video group emotion recognition subtask at the Emotion Recognition in the Wild (EmotiW) Challenge 2020 1. This sub challenge aims to classify in the wild videos into three categories: Positive, Neutral and Negative. Recent deep learning models have shown tremendous advances in analyzing interactions between people, predicting human behavior and affective evaluation. Nonetheless, their performance comes from individual-based analysis, which means summing up and averaging scores from individual detections, which inevitably leads to some privacy issues. In this research, we investigated a frugal approach towards a model able to capture the global moods from the whole image without using face or pose detection, or any individual-based feature as input. The proposed methodology mixes state-of-the-art and dedicated synthetic corpora as training sources. With an in-depth exploration of neural network architectures for group-level emotion recognition, we built a VGG-based model achieving 59.13% accuracy on the VGAF test set (eleventh place of the challenge). Given that the analysis is unimodal based only on global features and that the performance is evaluated on a real-world dataset, these results are promising and let us envision extending this model to multimodality for classroom ambiance evaluation, our final target application