Arsenic round the world: a review.

: The unpolarized absorption and circular dichroism spectra of the fundamental vibrational transitions of the chiral molecule, 4-methyl-2-oxetanone, are calculated ab initio. Harmonic force fields are obtained using Density Functional Theory (DFT), MP2, and SCF methodologies and a 5S4P2D/3S2P (TZ2P) basis set. DFT calculations use the Local Spin Density Approximation (LSDA), BLYP, and Becke3LYP (B3LYP) density functionals. Mid-IR spectra predicted using LSDA, BLYP, and B3LYP force fields are of significantly different quality, the B3LYP force field yielding spectra in clearly superior, and overall excellent, agreement with experiment. The MP2 force field yields spectra in slightly worse agreement with experiment than the B3LYP force field. The SCF force field yields spectra in poor agreement with experiment.The basis set dependence of B3LYP force fields is also explored: the 6-31G* and TZ2P basis sets give very similar results while the 3-21G basis set yields spectra in substantially worse agreements with experiment. jg

Ab initio calculation of vibrational absorption and circular dichroism spectra using density functional force fields

Multivariate analysis of heavy metal contamination in urban dusts of Xi’an, Central China

Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks, of which the hidden node parameters are randomly generated and the output weights are analytically computed. However, due to its shallow architecture, feature learning using ELM may not be effective for natural signals (e.g., images/videos), even with a large number of hidden nodes. To address this issue, in this paper, a new ELM-based hierarchical learning framework is proposed for multilayer perceptron. The proposed architecture is divided into two main components: 1) self-taught feature extraction followed by supervised feature classification and 2) they are bridged by random initialized hidden weights. The novelties of this paper are as follows: 1) unsupervised multilayer encoding is conducted for feature extraction, and an ELM-based sparse autoencoder is developed via $\ell _{1}$ constraint. By doing so, it achieves more compact and meaningful feature representations than the original ELM; 2) by exploiting the advantages of ELM random feature mapping, the hierarchically encoded outputs are randomly projected before final decision making, which leads to a better generalization with faster learning speed; and 3) unlike the greedy layerwise training of deep learning (DL), the hidden layers of the proposed framework are trained in a forward manner. Once the previous layer is established, the weights of the current layer are fixed without fine-tuning. Therefore, it has much better learning efficiency than the DL. Extensive experiments on various widely used classification data sets show that the proposed algorithm achieves better and faster convergence than the existing state-of-the-art hierarchical learning methods. Furthermore, multiple applications in computer vision further confirm the generality and capability of the proposed learning scheme.

Extreme Learning Machine for Multilayer Perceptron

The Web holds valuable, vast, and unstructured information about public opinion. Here, the history, current use, and future of opinion mining and sentiment analysis are discussed, along with relevant techniques and tools.

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New Avenues in Opinion Mining and Sentiment Analysis

SenticNet 1.0 is one of the most widely used, publicly available resources for concept-based opinion mining. The presented methodology enriches SenticNet concepts with affective information by assigning an emotion label.

/pdf/enhanced-senticnet-with-affective-labels-for-concept-based-4rmbx97hix.pdf

Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining

Sentiment analysis research has been started long back and recently it is one of the demanding research topics. Research activities on Sentiment Analysis in natural language texts and other media are gaining ground with full swing. But, till date, no concise set of factors has been yet defined that really affects how writers sentiment i.e., broadly human sentiment is expressed, perceived, recognized, processed, and interpreted in natural languages. The existing reported solutions or the available systems are still far from perfect or fail to meet the satisfaction level of the end users. The reasons may be that there are dozens of conceptual rules that govern sentiment and even there are possibly unlimited clues that can convey these concepts from realization to practical implementation. Therefore, the main aim of this book is to provide a feasible research platform to our ambitious researchers towards developing the practical solutions that will be indeed beneficial for our society, business and future researches as well.

A Practical Guide to Sentiment Analysis

Since 1988 we have analyzed 140 150 water samples from tube wells in all 19 districts of West Bengal for arsenic; 48.1% had arsenic above 10 μg/L (WHO guideline value), 23.8% above 50 μg/L (Indian Standard) and 3.3% above 300 μg/L (concentration predicting overt arsenical skin lesions). Based on arsenic concentrations we have classified West Bengal into three zones: highly affected (9 districts mainly in eastern side of Bhagirathi River), mildly affected (5 districts in northern part) and unaffected (5 districts in western part). The estimated number of tube wells in 8 of the highly affected districts is 1.3 million, and estimated population drinking arsenic contaminated water above 10 and 50 μg/L were 9.5 and 4.2 million, respectively. In West Bengal alone, 26 million people are potentially at risk from drinking arsenic-contaminated water (above 10 μg/L). Studying information for water from different depths from 107 253 tube wells, we noted that arsenic concentration decreased with increasing depth. Measured arsenic concentration in two tube wells in Kolkata for 325 and 51 days during 2002–2005, showed 15% oscillatory movement without any long-term trend. Regional variability is dependent on sub-surface geology. In the arsenic-affected flood plain of the river Ganga, the crisis is not having too little water to satisfy our needs, it is the crisis of managing the water.

Status of groundwater arsenic contamination in the state of West Bengal, India: A 20‐year study report

A study of ground water contamination by arsenic in the residential area of Behala, Calcutta due to industrial pollution.

Sentiment analysis is essential in many real-world applications such as stance detection, review analysis, recommendation system, and so on. Sentiment analysis becomes more difficult when the data is noisy and collected from social media. India is a multilingual country; people use more than one languages to communicate within themselves. The switching in between the languages is called code-switching or code-mixing, depending upon the type of mixing. This paper presents overview of the shared task on sentiment analysis of code-mixed data pairs of Hindi-English and Bengali-English collected from the different social media platform. The paper describes the task, dataset, evaluation, baseline and participant's systems.

Dipankar Das

Papers

Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining

A Practical Guide to Sentiment Analysis

Status of groundwater arsenic contamination in the state of West Bengal, India: A 20‐year study report

A study of ground water contamination by arsenic in the residential area of Behala, Calcutta due to industrial pollution.

Sentiment Analysis of Code-Mixed Indian Languages: An Overview of SAIL_Code-Mixed Shared Task @ICON-2017