A comprehensive classification of deep learning libraries

Deep Learning (DL) networks are composed of multiple processing layers that learn data representations with multiple levels of abstraction. In recent years, DL networks have significantly improved the state-of-the-art across different domains, including speech processing, text mining, pattern recognition, object detection, robotics and big data analytics. Generally, a researcher or practitioner who is planning to use DL networks for the first time faces difficulties in selecting suitable software tools. The present article provides a comprehensive list and taxonomy of current programming languages and software tools that can be utilized for implementation of DL networks. The motivation of this article is hence to create awareness among researchers, especially beginners, regarding the various languages and interfaces that are available to implement deep learning, and to provide a simplified ontological basis for selecting between them

A new clustering method using an augmentation to the self organizing maps

A technique is developed using Self Organizing Maps (SOM) to efficiently cluster the data and it is compared with existing clustering Techniques such as K-Means clustering, Hierarchical clustering and SOM Clustering. The proposed technique is used to cluster an Earthquake dataset and the performance is compared with the other existing clustering technique. The experimental results show that the proposed clustering method demonstrated better results as compared to other clustering methods

Advanced Instrumental Techniques for the Analysis of Trace Metals Released from Steel Industry with Special Reference to use of ICP-AES

Uncontrolled emissions from steel industries can produce multi-dimensional impacts on the environment, including those due to release of trace metals. Certain metals present even in trace concentrations pose serious environmental problems in the surrounding biosphere. Consequently, a precise, accurate and rapid instrumental technique is required to monitor levels of heavy metals in various emissions released by steel plants. The paper reviews the various instrumental techniques employed for such assessments. A comparison of the various techniques reveals that ICP-AES, despite its limitations in sample preparations, offers a very reliable and powerful tool for heavy metal analysis. Its usefulness is discussed with reference to some of the studies carried out in the Environment Laboratory of SAIL (R&D) on air and water samples from some SAIL steel plants

Maintaining regularity and generalization in data using the minimum description length principle and genetic algorithm: case of grammatical inference

In this paper, a genetic algorithm with minimum description length (GAWMDL) is proposed for grammatical inference. The primary challenge of identifying a language of infinite cardinality from a finite set of examples should know when to generalize and specialize the training data. The minimum description length principle that has been incorporated addresses this issue is discussed in this paper. Previously, the e-GRIDS learning model was proposed, which enjoyed the merits of the minimum description length principle, but it is limited to positive examples only. The proposed GAWMDL, which incorporates a traditional genetic algorithm and has a powerful global exploration capability that can exploit an optimum offspring. This is an effective approach to handle a problem which has a large search space such the grammatical inference problem. The computational capability, the genetic algorithm poses is not questionable, but it still suffers from premature convergence mainly arising due to lack of population diversity. The proposed GAWMDL incorporates a bit mask oriented data structure that performs the reproduction operations, creating the mask, then Boolean based procedure is applied to create an offspring in a generative manner. The Boolean based procedure is capable of introducing diversity into the population, hence alleviating premature convergence. The proposed GAWMDL is applied in the context free as well as regular languages of varying complexities. The computational experiments show that the GAWMDL finds an optimal or close-to-optimal grammar. Two fold performance analysis have been performed. First, the GAWMDL has been evaluated against the elite mating pool genetic algorithm which was proposed to introduce diversity and to address premature convergence. GAWMDL is also tested against the improved tabular representation algorithm. In addition, the authors evaluate the performance of the GAWMDL against a genetic algorithm not using the minimum description length principle. Statistical tests demonstrate the superiority of the proposed algorithm. Overall, the proposed GAWMDL algorithm greatly improves the performance in three main aspects: maintains regularity of the data, alleviates premature convergence and is capable in grammatical inference from both positive and negative corpora

Multiwavelength Study of NGC 281 Region

We present a multiwavelength study of the NGC 281 complex which contains the young cluster IC 1590 at the center, using deep wide-field optical UBVI_c photometry, slitless spectroscopy along with archival data sets in the near-infrared (NIR) and X-ray. The extent of IC 1590 is estimated to be ~6.5 pc. The cluster region shows a relatively small amount of differential reddening. The majority of the identified young stellar objects (YSOs) are low mass PMS stars having age <1-2 Myr and mass 0.5-3.5 M_\odot. The slope (\Gamma) of the mass function for IC 1590, in the mass range 2 < M/M_\odot \le 54, is found to be -1.11+-0.15. The slope of the K-band luminosity function (0.37+-0.07) is similar to the average value (~0.4) reported for young clusters. The distribution of gas and dust obtained from the IRAS, CO and radio maps indicates clumpy structures around the central cluster. The radial distribution of the young stellar objects, their ages, \Delta(H-K) NIR-excess, and the fraction of classical T Tauri stars suggest triggered star formation at the periphery of the cluster region. However, deeper optical, NIR and MIR observations are needed to have a conclusive view of star formation scenario in the region. The properties of the Class 0/I and Class II sources detected by using the Spitzer mid-infrared observations indicate that a majority of the Class II sources are X-ray emitting stars, whereas X-ray emission is absent from the Class 0/I sources. The spatial distribution of Class 0/I and Class II sources reveals the presence of three sub-clusters in the NGC 281 West region.Comment: 29 pages, 21 figures and 11 tables, Accepted for the publication in PAS

Structures of falcipain-2 and falcipain-3 bound to small molecule inhibitors: implications for substrate specificity.

Falcipain-2 and falcipain-3 are critical hemoglobinases of Plasmodium falciparum, the most virulent human malaria parasite. We have determined the 2.9 A crystal structure of falcipain-2 in complex with the epoxysuccinate E64 and the 2.5 A crystal structure of falcipain-3 in complex with the aldehyde leupeptin. These complexes represent the first crystal structures of plasmodial cysteine proteases with small molecule inhibitors and the first reported crystal structure of falcipain-3. Our structural analyses indicate that the relative shape and flexibility of the S2 pocket are affected by a number of discrete amino acid substitutions. The cumulative effect of subtle differences, including those at "gatekeeper" positions, may explain the observed kinetic differences between these two closely related enzymes