Concept of Dharma in Shashi Tharoor’s Novel show business (1991)

Dharma is the total cosmic responsibility, including gods, a universal justice for more inclusive, wider and profounder than any western equivalent, such as duty. What is Dharma? This is the question that Shashi Tharoor explores repeatedly in his novel; though the situations, settings and characters are sufficiently varied each time to make quests dissimilar. His preoccupations are essentially abstract. The choice that a man has to make to remain true to himself, the corrosion of values in a world that puts premium on material success, the human price of ambition in a competitive society, and the possibility of making an authentic decision in a set up where an individual is allowed very little freedom-these are the recurrent concerns running through his novel – Show Business. Shashi Tharoor shows his socio-Moral vision and mourns for the lack of Dharma in modern times. In a Post Modernistic world, where all moral values are gone with the wind, there are very few committed artists with the philosophic vision, who can wage a strong war against the advent of basic human values. Tharoor considers his art as a medium through which he tries to resurrect the lost dignity of the human being. Art therefore seems to turn into a didactic weapon by which he reinstates the lost glory of the world

Pinning down neutrino oscillation parameters in the 2-3 sector with a mgnetised atmospheric neutrino detector: a new study

We determine the sensitivity to neutrino oscillation parameters from a study of atmospheric neutrinos in a magnetised detector such as the ICAL at the proposed India-based Neutrino Observatory. In such a detector that can {\em separately} count $\nu_\mu$ and $\overline{\nu}_\mu$-induced events, the relatively smaller (about 5\%) uncertainties on the neutrino--anti-neutrino flux ratios translate to a constraint in the $\chi^2$ analysis that results in a significant improvement in the precision with which neutrino oscillation parameters such as $\sin^2\theta_{23}$ can be determined. Such an effect is unique to all magnetisable detectors and constitutes a great advantage in determining neutrino oscillation parameters using such detectors. Such a study has been performed for the first time here. Along with an increase in the kinematic range compared to earlier analyses, this results in sensitivities to oscillation parameters in the 2--3 sector that are comparable to or better than those from accelerator experiments where the fluxes are significantly higher. For example, the $1\sigma$ precisions on $\sin^2\theta_{23}$ and $|\Delta{m^2_{32(31)}}|$ achievable for 500 kTon yr exposure of ICAL are $\sim9\%$ and $\sim2.5\%$ respectively for both normal and inverted hierarchies. The mass hierarchy sensitivity achievable with this combination when the true hierarchy is normal (inverted) for the same exposure is $\Delta\chi^2\approx8.5$ ($\Delta\chi^2\approx9.5$)

A deep survey of the low-surface-brightness radio sky

We have made a radio survey--the Australia Telescope Low Brightness Survey (ATLBS)--of 8.4 square degrees sky area, using the Australia Telescope Compact Array in the 20-cm band, in an observing mode designed to provide wide-field images with exceptional sensitivity in surface brightness, and thereby explore a new parameter space in radio source populations. The goals of this survey are to quantify the distribution in angular sizes, particularly at weak surface brightness levels: this has implications for the confusion in deep surveys with the SKA. The survey is expected to lead to a census of the radio emission associated with low-power radio galaxies at redshifts 1-3, without any missing extended emission, and hence a study of the cosmic evolution of low-power radio galaxies to higher redshift and a comprehensive study of the AGN feedback during the intense black hole growth phase during this redshift range.Comment: 5 pages, includes 2 figures and 1 table. To appear in the proceedings of "From Planets to Dark energy: the modern radio universe" in the online journal Proceedings of Science - Po

Comparative Study of Improving Classifiers Accuracies

Outlier analysis is an essential task in data science to wipe out inconsistencies from data to build a good model. Finding outliers from categorical data is a tough task. To model a good Classifier, it is necessary to eliminate outliers from data. While modeling categorical data, most infrequent records are treated as outliers. These outliers would disturb the entire data in modeling a good classifier. This paper presents the comparison between classifiers accuracies which are built by normally distributed Outlier factor by infrequency (NOFI) to OFI with different inputs. In modeling a classifier for categorical data, high frequent records are most useful and most infrequent records are most useless. So the infrequent records are obstacles in modeling the classifiers. The experiments are conducted for this comparison on bank dataset with 45000 records and Nursery dataset with 14000 records approximately, which are taken from UCI ML Repository. For normally distributed OFI, the inputs are not needed. It generates the number of outliers automatically. In OFI it is needed to give the inputs. However the threshold value is needed to generate infrequent itemsets for both methods

Deep Learning-Based Speech Emotion Recognition Using Librosa

Speech Emotion Recognition is a challenge of computational paralinguistic and speech processing that tries to identify and classify the emotions expressed in spoken language. The objective is to infer from a speaker's speech patterns, such as prosody, pitch, and rhythm, their emotional state, such as happiness, rage, sadness, or frustration. In the modern world, one of the most crucial marketing tactics is emotion detection. For a person, you might tailor several things in order to best fit their interests. Due to this, we made the decision to work on a project where we could identify a person's emotions based just on their speech, allowing us to handle a variety of AI-related applications. Examples include the ability of call centers to play music during tense exchanges. Another example might be a smart automobile that slows down when someone is scared or furious. In Python, we processed and extracted features from the audio files using the Librosa module. A Python library for audio and music analysis is called Librosa. It offers the fundamental components required to develop systems for retrieving music-related information. Because of this, there is a lot of potential for this kind of application in the market that would help businesses and ensure customer safety

Separable Reversible Knowledge Activity in Encrypted Image with Compression of Knowledge and Image

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