Space-efficient Feature Maps for String Alignment Kernels

String kernels are attractive data analysis tools for analyzing string data. Among them, alignment kernels are known for their high prediction accuracies in string classifications when tested in combination with SVM in various applications. However, alignment kernels have a crucial drawback in that they scale poorly due to their quadratic computation complexity in the number of input strings, which limits large-scale applications in practice. We address this need by presenting the first approximation for string alignment kernels, which we call space-efficient feature maps for edit distance with moves (SFMEDM), by leveraging a metric embedding named edit sensitive parsing (ESP) and feature maps (FMs) of random Fourier features (RFFs) for large-scale string analyses. The original FMs for RFFs consume a huge amount of memory proportional to the dimension d of input vectors and the dimension D of output vectors, which prohibits its large-scale applications. We present novel space-efficient feature maps (SFMs) of RFFs for a space reduction from O(dD) of the original FMs to O(d) of SFMs with a theoretical guarantee with respect to concentration bounds. We experimentally test SFMEDM on its ability to learn SVM for large-scale string classifications with various massive string data, and we demonstrate the superior performance of SFMEDM with respect to prediction accuracy, scalability and computation efficiency.Comment: Full version for ICDM'19 pape

Lafora Disease Masquerading as Hepatic Dysfunction

Lafora disease is fatal intractable progressive myoclonic epilepsy. It is frequently characterized by epileptic seizures, difficulty walking, muscle spasms, and dementia in late childhood or adolescence. We chronicle here an unusual case of an asymptomatic young male soccer player who presented with elevated liver enzymes. Neurological examination was unremarkable. The diagnostic workup for hepatitis, infectious etiologies, autoimmune disorders, hemochromatosis, Wilson\u27s disease, alpha-1 antitrypsin deficiency, and other related diseases was inconclusive. He subsequently underwent an uneventful percutaneous liver biopsy. Based on the pathognomonic histopathological findings, Lafora disease was considered the likely etiology. The present study is a unique illustration of this rare disorder initially manifesting with abnormal liver enzymes. It underscores the importance of clinical suspicion of Lafora disease in cases with unexplained hepatic dysfunction. Prompt liver biopsy and genetic testing should be performed to antedate the onset of symptoms in these patients

Development and Experimental Investigation on Delay Time Consistency of Modified Si/PbO/Pb3O4/FG Pyrotechnic Delay Composition

In the present study, experimental investigation was carried out on the delay time consistency of modified Si/PbO/Pb3O4/FG pyrotechnic delay composition in a delay tube. Where Si is the fuel, PbO/Pb3O4 are oxidizers and Fish Glue (FG) is the binder. Ingredient mixing and loading pressure were studied. Results revealed that homogenous mixing of the delay composition is a very critical parameter for controlling the time consistency of pyrotechnic delay composition. The delay time accuracy was improved from 25% to about 7.42% by ensuring homogenous mixing of the ingredients. Results also show that loading pressure ranged from 30,000 to 65,000 psi did not affect much the delay time of this pyrotechnic composition and the burning rate

Effect of Somatic Cell Types and Culture Medium on in vitro Maturation, Fertilization and Early Development Capability of Buffalo Oocytes

This study was designed to evaluate the efficacy of different somatic cell types and media in supporting in vitro maturation (IVM), in vitro fertilization (IVF) and early embryonic development competence of buffalo follicular oocytes. Cumulus oocyte complexes were collected for maturation from follicles (>6mm) of buffalo ovaries collected at the local abattoir. Oocytes were co-cultured in tissue culture medium (TCM-199) with either granulosa cells, cumulus cells, or buffalo oviductal epithelial cells (BOEC) @ 3x106 cells/ml or in TCM-199 without helper cells (control) at 39°C and 5%CO2 in humidified air. Fresh semen was prepared in modified Ca++ free Tyrode medium. Fertilization was carried out in four types of media: i) Tyrode lactate albumin pyruvate (TALP), ii) TALP+BOEC, iii) modified Ca++ free Tyrode and iv) modified Ca++ free Tyrode+BOEC. Fertilized oocytes were cultured for early embryonic development in TCM-199 with and without BOEC. Higher maturation rates were observed in the granulosa (84.24%) and cumulus cells (83.44%) than BOEC co culture system (73.37%). Highest fertilization rate was obtained in modified Ca++ free Tyrode with BOEC co culture (70.42%), followed by modified Ca++ free Tyrode alone (63.77%), TALP with BOEC (36.92%) and TALP alone (10.94%). Development of early embryos (8-cell stage) improved in TCM-199 with BOEC co culture than TCM-199 alone. From the results of this study, it can be concluded that addition of somatic cells (granulosa cells, cumulus cells) results in higher maturation rates of buffalo follicular oocytes than BOEC co culture system, while fertilization rate improved in modified Ca++ free Tyrode with and without BOEC. Addition of BOEC to TCM-199 improved the developmental capacity of early embryo

Evolving rules for document classification

We describe a novel method for using Genetic Programming to create compact classification rules based on combinations of N-Grams (character strings). Genetic programs acquire fitness by producing rules that are effective classifiers in terms of precision and recall when evaluated against a set of training documents. We describe a set of functions and terminals and provide results from a classification task using the Reuters 21578 dataset. We also suggest that because the induced rules are meaningful to a human analyst they may have a number of other uses beyond classification and provide a basis for text mining applications

In vitro Maturation and Fertilization of Riverine Buffalo Follicular Oocytes in Media Supplemented with Oestrus Buffalo Serum and Hormones

Effects of two maturation media (TCM-199 and Ham's F-12) with and without the addition of oestrus buffalo serum (OBS) and hormones (FSH, LH, E2) on the maturation rate of buffalo follicular oocytes were evaluated. The results revealed a significant (P P 2+ free Tyrode's medium (63.72%) than in TALP (10.9%) and IVF-TL (32.18%). Thus, TCM-199 containing hormones and OBS appeared better for in vitro maturation, whereas modified Ca2+ free tyrode's medium was found to be more suitable for in vitro fertilization of buffalo follicular oocytes

Transductive Learning with String Kernels for Cross-Domain Text Classification

For many text classification tasks, there is a major problem posed by the lack of labeled data in a target domain. Although classifiers for a target domain can be trained on labeled text data from a related source domain, the accuracy of such classifiers is usually lower in the cross-domain setting. Recently, string kernels have obtained state-of-the-art results in various text classification tasks such as native language identification or automatic essay scoring. Moreover, classifiers based on string kernels have been found to be robust to the distribution gap between different domains. In this paper, we formally describe an algorithm composed of two simple yet effective transductive learning approaches to further improve the results of string kernels in cross-domain settings. By adapting string kernels to the test set without using the ground-truth test labels, we report significantly better accuracy rates in cross-domain English polarity classification.Comment: Accepted at ICONIP 2018. arXiv admin note: substantial text overlap with arXiv:1808.0840

Evolving text classification rules with genetic programming

We describe a novel method for using genetic programming to create compact classification rules using combinations of N-grams (character strings). Genetic programs acquire fitness by producing rules that are effective classifiers in terms of precision and recall when evaluated against a set of training documents. We describe a set of functions and terminals and provide results from a classification task using the Reuters 21578 dataset. We also suggest that the rules may have a number of other uses beyond classification and provide a basis for text mining applications

String pattern recognition using evolving spiking neural networks and quantum inspired particle swarm optimization

This paper proposes a novel method for string pattern recognition using an Evolving Spiking Neural Network (ESNN) with Quantum-inspired Particle Swarm Optimization (QiPSO). This study reveals an interesting concept of QiPSO by representing information as binary structures. The mechanism optimizes the ESNN parameters and relevant features using the wrapper approach simultaneously. The N-gram kernel is used to map Reuters string datasets into high dimensional feature matrix which acts as an input to the proposed method. The results show promising string classification results as well as satisfactory QiPSO performance in obtaining the best combination of ESNN parameters and in identifying the most relevant features