SAAGs: Biased stochastic variance reduction methods for large-scale learning

Stochastic approximation is one of the effective approach to deal with the large-scale machine learning problems and the recent research has focused on reduction of variance, caused by the noisy approximations of the gradients. In this paper, we have proposed novel variants of SAAG-I and II (Stochastic Average Adjusted Gradient) (Chauhan et al. 2017), called SAAG-III and IV, respectively. Unlike SAAG-I, starting point is set to average of previous epoch in SAAG-III, and unlike SAAG-II, the snap point and starting point are set to average and last iterate of previous epoch in SAAG-IV, respectively. To determine the step size, we have used Stochastic Backtracking-Armijo line Search (SBAS) which performs line search only on selected mini-batch of data points. Since backtracking line search is not suitable for large-scale problems and the constants used to find the step size, like Lipschitz constant, are not always available so SBAS could be very effective in such cases. We have extended SAAGs (I, II, III and IV) to solve non-smooth problems and designed two update rules for smooth and non-smooth problems. Moreover, our theoretical results have proved linear convergence of SAAG-IV for all the four combinations of smoothness and strong-convexity, in expectation. Finally, our experimental studies have proved the efficacy of proposed methods against the state-of-art techniques

Faster learning by reduction of data access time

Nowadays, the major challenge in machine learning is the Big Data challenge. The big data problems due to large number of data points or large number of features in each data point, or both, the training of models have become very slow. The training time has two major components: Time to access the data and time to process (learn from) the data. So far, the research has focused only on the second part, i.e., learning from the data. In this paper, we have proposed one possible solution to handle the big data problems in machine learning. The idea is to reduce the training time through reducing data access time by proposing systematic sampling and cyclic/sequential sampling to select mini-batches from the dataset. To prove the effectiveness of proposed sampling techniques, we have used Empirical Risk Minimization, which is commonly used machine learning problem, for strongly convex and smooth case. The problem has been solved using SAG, SAGA, SVRG, SAAG-II and MBSGD (Mini-batched SGD), each using two step determination techniques, namely, constant step size and backtracking line search method. Theoretical results prove the same convergence for systematic sampling, cyclic sampling and the widely used random sampling technique, in expectation. Experimental results with bench marked datasets prove the efficacy of the proposed sampling techniques and show up to six times faster training

Ferrate(VI) enhanced photocatalytic oxidation of pollutants in aqueous TiO?suspensions

Author name used in this publication: Nigel J. D. Graham2009-2010 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Ferrate(VI) oxidation of endocrine disruptors and antimicrobials in water

Author name used in this publication: X. Z. LiAccepted ManuscriptPublishe

RNA interference: A novel tool for plant disease management

Plant diseases pose a huge threat to crop production globally. Variations in their genomes cause selection to favor those who can survive pesticides and Bacillus thuringiensis (Bt) crops. Though plant breeding has been the classical means of manipulating the plant genome to develop resistant cultivar for controlling plant diseases, the advent of genetic engineering provides an entirely new approach being pursued to render plants resistant to fungi, bacteria, viruses and nematodes. RNA interference (RNAi) technology has emerged to be a promising therapeutic weapon to mitigate the inherent risks such as the use of a specific transgene, marker gene, or gene control sequences associated with development of traditional transgenics. Silencing specific genes by RNAi is a desirable natural solution to this problem as disease resistant transgenic plants can be produced within a regulatory framework. Recent studies have been successful in producing potent silencing effects by using target doublestranded RNAs through an effective vector system. Transgenic plants expressing RNAi vectors, as well as, dsRNA containing crop sprays have been successful for efficient control of plant pathogens affecting economically important crop species. The present paper discusses strategies and applications of this novel technology in plant disease management for sustainable agriculture production.Keywords: Plant disease, RNA interference, transgene, managementAfrican Journal of Biotechnology Vol. 12(18), pp. 2303-231

Genetic characterization of mango anthracnose pathogen Colletotrichum gloeosporioides Penz. by random amplified polymorphic DNA analysis

Twenty-five isolates of Colletotrichum gloeosporioides causing mango anthracnose were collected from different agroclimatic zones of India. The isolates were evaluated for their pathogenic variability on mango seedlings and genetic characterization using random amplified polymorphic DNA (RAPD molecular techniques). The random primers OPA-1, 3, 5, 9, 11, 15, 16 and 18 were used and the twenty five isolates were grouped into two. The amplified DNA fragments (amplicons) obtained was comparedby agarose gel electrophoresis. Isolate specific RAPD fingerprints were obtained. Out of eight primers in RAPD, OPA-1, 3 and 18 were able to produce reproducible banding pattern. Each of these primers generated a short spectrum of amplicons, located between 661 and 2291-bp markers, indicative of genetic polymorphism. Dendogram revealed more than 75% level of similarity. 4.36% polymorphism was also found in individual isolates that was not statistically significant (P > 0.05) among the sample, it also indicates that all the isolates tested had approximately same genetic identity. The data suggest that RAPD may be of value by virtue of its rapidity, efficiency and reproducibility in generating genetic fingerprints of C. gloeosporioides isolates

PREVALENCE OF VARIOUS BETA LACTAMASES AMONG GRAM NEGATIVE BACILLI IN URINARY ISOLATES FROM PATIENTS IN A TERTIARY CARE HOSPITAL OF NORTHERN INDIA

Objective: Urinary tract infections are considered among the most common infections, occurring either in the community or health-care setting. We are left with very few options for the treatment due to rapid development of antibiotic resistance among the organisms. To find out the prevalence of various types of β-lactamases among urinary isolates.Methods: Seven antibiotic discs (HiMedia) were placed in combinations and approximation in a particular sequence on a 90 mm diameter MuellerHintonagar plate.Results: Out of a total 165 urinary isolates, 66 (40%) isolates were positive for extended spectrum β-lactamase (ESBL) production, AmpC β-lactamases(AmpC) activity was present in 31 (18.78%) isolates, co-production of both ESBL and AmpC was seen in 16 (9.69%) isolates, 3 (1.81%) isolatesproduced metallo β-lactamase (MBL), 2 (1.21%) isolates produced both MBL, and ESBL and 1 (0.60%) isolates were positive for inducible third generation cephalosporin resistance.Conclusion: With the presence of such high prevalence of various β-lactamases in clinical isolates of gram-negative bacilli and also other types ofantibiotic resistance, antibiotic policy should be made, and strict adherence should be followed.Keywords: Extended spectrum β-lactamase, AmpC β-lactamase, Metallo β-lactamase

Topical immunomodulators in dermatology

Topical immunomodulators are agents that regulate the local immune response of the skin. They are now emerging as the therapy of choice for several immune-mediated dermatoses such as atopic dermatitis, contact allergic dermatitis, alopecia areata, psoriasis, vitiligo, connective tissue disorders such as morphea and lupus erythematosus, disorders of keratinization and several benign and malignant skin tumours, because of their comparable efficacy, ease of application and greater safety than their systemic counterparts. They can be used on a domiciliary basis for longer periods without aggressive monitoring. In this article, we have discussed the mechanism of action, common indications and side-effects of the commonly used topical immunomodulators, excluding topical steroids. Moreover, newer agents, which are still in the experimental stages, have also been described. A MEDLINE search was undertaken using the key words "topical immunomodulators, dermatology" and related articles were also searched. In addition, a manual search for many Indian articles, which are not indexed, was also carried out. Wherever possible, the full article was reviewed. If the full article could not be traced, the abstract was used

Effect of poplar and eucalyptus based agroforestry system on soil biochemistry

126-130The field experiment was conducted during the winter season of 2016-17 at the experimental site of Agroforestry Research Centre, G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand. Poplar and eucalyptus were intercropped with different wheat varieties (UP-2526, UP-2565, UP-2628, and DPW-621-50). After harvesting the wheat crop, the soil sampling was performed to determine the soil parameters like electrical conductivity (EC), organic carbon and the minerals content. Nitrogen, phosphorus and potassium content and other biochemical constituents were higher in the agroforestry system as compared to the open farming system. A high soil pH (7. 53) was found in an open farming system and lower pH in an agroforestry system. Soil EC in the agroforestry system was slightly higher than the open farming system. Organic carbon was maximum (1. 33%) under the poplar agroforestry system compared to the eucalyptus based agroforestry system. Overall, this study determines the effect of poplar and eucalyptus based agroforestry systems on soil biochemistry