A Learning Algorithm based on High School Teaching Wisdom

A learning algorithm based on primary school teaching and learning is presented. The methodology is to continuously evaluate a student and to give them training on the examples for which they repeatedly fail, until, they can correctly answer all types of questions. This incremental learning procedure produces better learning curves by demanding the student to optimally dedicate their learning time on the failed examples. When used in machine learning, the algorithm is found to train a machine on a data with maximum variance in the feature space so that the generalization ability of the network improves. The algorithm has interesting applications in data mining, model evaluations and rare objects discovery

Results from the Supernova Photometric Classification Challenge

We report results from the Supernova Photometric Classification Challenge (SNPCC), a publicly released mix of simulated supernovae (SNe), with types (Ia, Ibc, and II) selected in proportion to their expected rate. The simulation was realized in the griz filters of the Dark Energy Survey (DES) with realistic observing conditions (sky noise, point-spread function and atmospheric transparency) based on years of recorded conditions at the DES site. Simulations of non-Ia type SNe are based on spectroscopically confirmed light curves that include unpublished non-Ia samples donated from the Carnegie Supernova Project (CSP), the Supernova Legacy Survey (SNLS), and the Sloan Digital Sky Survey-II (SDSS-II). A spectroscopically confirmed subset was provided for training. We challenged scientists to run their classification algorithms and report a type and photo-z for each SN. Participants from 10 groups contributed 13 entries for the sample that included a host-galaxy photo-z for each SN, and 9 entries for the sample that had no redshift information. Several different classification strategies resulted in similar performance, and for all entries the performance was significantly better for the training subset than for the unconfirmed sample. For the spectroscopically unconfirmed subset, the entry with the highest average figure of merit for classifying SNe~Ia has an efficiency of 0.96 and an SN~Ia purity of 0.79. As a public resource for the future development of photometric SN classification and photo-z estimators, we have released updated simulations with improvements based on our experience from the SNPCC, added samples corresponding to the Large Synoptic Survey Telescope (LSST) and the SDSS, and provided the answer keys so that developers can evaluate their own analysis.Comment: accepted by PAS

Photometric Catalogue of Quasars and Other Point Sources in the Sloan Digital Sky Survey

We present a catalogue of about 6 million unresolved photometric detections in the Sloan Digital Sky Survey Seventh Data Release classifying them into stars, galaxies and quasars. We use a machine learning classifier trained on a subset of spectroscopically confirmed objects from 14th to 22nd magnitude in the SDSS {\it i}-band. Our catalogue consists of 2,430,625 quasars, 3,544,036 stars and 63,586 unresolved galaxies from 14th to 24th magnitude in the SDSS {\it i}-band. Our algorithm recovers 99.96% of spectroscopically confirmed quasars and 99.51% of stars to i $\sim$21.3 in the colour window that we study. The level of contamination due to data artefacts for objects beyond $i=21.3$ is highly uncertain and all mention of completeness and contamination in the paper are valid only for objects brighter than this magnitude. However, a comparison of the predicted number of quasars with the theoretical number counts shows reasonable agreement.Comment: 16 pages, Ref. No. MN-10-2382-MJ.R2, accepted for publication in MNRAS Main Journal, April 201

Comparision of Fibrometer Test with Fibroscan and Its Correlation with Liver Biopsy in Detecting Liver Fibrosis in Patients with Hepatitis B Infection

INTRODUCTION : Hepatitis B is a major health problem affecting more than 350 million people globally. Hepatitis B carriers are defined as persons positive for HBsAg for more than 6 months. The chance of developing sequelae is about 15% to 40% over lifetime, hence evaluation and early treatment is required. The prognosis and management of HBV related liver disease depends on the degree of liver fibrosis AIM : The aim of the study was to detect liver fibrosis in patients with chronic hepatitis B infection using non invasive markers – Fibroscan and Fibrometer test and to compare its efficacy with the gold standard liver biopsy. MATERIALS AND METHODS : This prospective study was conducted in our department, Department of Digestive Health and Disease, Kilpauk Medical College, Chennai. 30 patients were included in our study over a period of six months. Patients with Chronic hepatitis B infection alone were included in the study. Patients with associated HCV/HIV infection, compensated or decompensated cirrhotics were excluded from the study. Detailed history, physical examination was performed and baseline hematologcal investigations, Fibroscan, Fibrometer and Liver biopsy were also done. RESULTS : In our study, out of 30 patients 21 (70%) were males and 9 (30%) were females. 63% of the study population were HBeAg negative and 63.33% of the patients had high viral load of more than 1,00,000 IU/ml. Patients underwent Fibroscan study which showed 14 patients (46.67%) had liver stiffness measurement (LSM) of less than 6.5 kpa suggesting nil fibrosis, 33% had mild fibrosis with a LSM value of 6.5 to 8 and 20% had values above 8 suggesting moderate fibrosis. Similarly Fibrometer test showed 43.33% patients with significant fibrosis. Statistical analysis was done by Pearson co-efficient equation and it showed that Fibroscan value (LSM) of more than 8 kpa had 100% specificity in detecting significant fibrosis correlating with liver biopsy with a p value of 0.005 and ROC of 0.89 (95% confidence interval of 0.76 to 0.90). Fibrometer test also detected significant fibrosis (F2) in 43% of patients and on correlation with liver biopsy had a P value o 0.004 with ROC values of 0.905 and 95% confidence interval of 0.80 – 0.99. CONCLUSION : Fibroscan and Fibrometer is good non-invasive test only in detecting significant fibrosis, with higher values correlating with liver biopsy. Liver biopsy still is the gold standard and is useful in detecting early fibrosis. Both Fibroscan and Fibrometer has similar efficacy and can be used to exclude fibrosis and to detect significant fibrosis