No abstract

Many bird species have the ability to navigate home after being brought to a remote, even unfamiliar location. Environmental odours have been demonstrated to be critical to homeward navigation in over 40 years of experiments, yet the chemical identity of the odours has remained unknown. In this study, we investigate potential chemical navigational cues by measuring volatile organic compounds (VOCs): at the birds’ home-loft; in selected regional forest environments; and from an aircraft at 180 m. The measurements showed clear regional, horizontal and vertical spatial gradients that can form the basis of an olfactory map for marine emissions (dimethyl sulphide, DMS), biogenic compounds (terpenoids) and anthropogenic mixed air (aromatic compounds), and temporal changes consistent with a sea-breeze system. Air masses trajectories are used to examine GPS tracks from released birds, suggesting that local DMS concentrations alter their flight directions in predictable ways. This dataset reveals multiple regional-scale real-world chemical gradients that can form the basis of an olfactory map suitable for homing pigeons.

Dynamic Time Warping (DTW) is a widely used distance measure for time series data mining. Its quadratic complexity requires the application of various techniques (e.g. warping constraints, lower-bounds) for deployment in real-time scenarios. In this paper we propose a randomized greedy warping algorithm for finding similarity between time series instances. We show that the proposed algorithm outperforms the simple greedy approach and also provides very good time series similarity approximation consistently, as compared to DTW. We show that the Randomized Time Warping (RTW) can be used in place of DTW as a fast similarity approximation technique by trading some classification accuracy for very fast classification. CCS Concepts•Computing methodologies → Machine learning algorithms; •Mathematics of computing → Time series analysis; Stochastic processes;

No abstract

Natural language processing strives to build machines that understand and respond to text or voice data—and respond with text or speech of their own—in much the same way humans do. NLP models language computationally and deals with linguistic features of computation. Once a computer learns to do mathematical calculations it can perform many complex and big calculations much faster than humans. Similarly once computer starts to understand the human languages it can process all aspects of that language much faster than humans also opening a large number of possibility. So it cuts down on employment as one computer is capable of giving an output 10 times faster than a human can. So it benefits the employer not only financially but also by giving extremely accurate and faster outcome. Here we lay out an overall architecture to explain the overall processing. So now we take a look at the two general classes of systems they are special-purpose system and general-purpose system, explaining how they differ and their relative advantages and disadvantages. After that we point at the few remaining problems that require additional research. Finally, we conclude by discussing when natural language processing technology can be practically used at various levels . We also discuss about when it will become commercially practical, and what will be the cost to practically use this technology. The techniques specifically developed for analysing and understanding the inner-workings and representations acquired by neural models of language is EMNLP 2018 BlackboxNLP. The approach includes: investigating the impact on the performance of neural network on systematic manipulation of input and also testing whether the interpretable knowledge can be decoded from intermediate representations to propose modifications to make the knowledge state or generated output more and also to examine the performance of networks on simplified or formal languages. In the following report we aim to convert a program of a given language to an equivalent program of another language. For that we have taken help of NLP that is Natural Language Processing. By using Natural Language Tool Kit, we have successfully identified the variables, datatypes, operators, keywords, indentations. We have also discussed various aspects and domains of NLP and some real-world applications of it.