COMBINATORIAL ALGORITHMS FOR GRAPH DISCOVERY AND EXPERIMENTAL DESIGN

In this thesis, we study the design and analysis of algorithms for discovering the structure and properties of an unknown graph, with applications in two different domains: causal inference and sublinear graph algorithms. In both these domains, graph discovery is possible using restricted forms of experiments, and our objective is to design low-cost experiments. First, we describe efficient experimental approaches to the causal discovery problem, which in its simplest form, asks us to identify the causal relations (edges of the unknown graph) between variables (vertices of the unknown graph) of a given system. For causal discovery, we study algorithms for the problem of learning the causal relationships between a set of observed variables in the presence of hidden or unobserved variables while minimizing a suitable cost of interventions on the observed variables. An intervention on a set of variables helps learn the presence of causal relations adjacent to them. Under various cost models for interventions, we design combinatorial algorithms for causal discovery by identifying new connections between discrete optimization, graph property testing, and efficient intervention design. Next, we investigate query-efficient experimental approaches for estimating various graph properties, such as the number of edges and graph connectivity. The access to the graph, or equivalently performing an experiment, is via a Bipartite Independent Set (BIS) oracle. The BIS oracle is related to the interventional access model used in our work for causal graph discovery, with other applications in group testing and fine-grained complexity. In this setting, we develop non-adaptive algorithms that lead to efficient implementations in highly parallelized and low-memory streaming settings

Performance evaluation of Map-reduce jar pig hive and spark with machine learning using big data

Big data is the biggest challenges as we need huge processing power system and good algorithms to make an decision. We need Hadoop environment with pig hive, machine learning and hadoopecosystem components. The data comes from industries. Many devices around us and sensor, and from social media sites. According to McKinsey There will be a shortage of 15000000 big data professionals by the end of 2020. There are lots of technologies to solve the problem of big data Storage and processing. Such technologies are Apache Hadoop, Apache Spark, Apache Kafka, and many more. Here we analyse the processing speed for the 4GB data on cloudx lab with Hadoop mapreduce with varing mappers and reducers and with pig script and Hive querries and spark environment along with machine learning technology and from the results we can say that machine learning with Hadoop will enhance the processing performance along with with spark, and also we can say that spark is better than Hadoop mapreduce pig and hive, spark with hive and machine learning will be the best performance enhanced compared with pig and hive, Hadoop mapreduce jar

Behavioural castes, dominance and division of labour in a primitively eusocial wasp

Female wasps of the primitively eusocial species Ropalidia marginata may be classified, by a statistical analysis of their time-activity budgets, into three behavioural castes namely Sitters, Fighters and Foragers. We show that Foragers are primarily responsible for the risky task of foraging for food and have very poorly developed ovaries. Sitters and Fighters forage rarely if at all but share the bulk of the intra-nidal tasks such as feeding larvae and building the nest. Both Sitters and Fighters have better developed ovaries than Foragers. Queens of most colonies belong to the Sitter caste. There are no obvious morphological differences between queens and workers or between the behavioural castes. Queens are not necessarily the most dominant individuals in their colonies. Instead, most dominance behaviour is performed by a group of workers (the Fighters). Division of labour and social organization are achieved through behavioural caste differentiation and not, as in many other species studied, through a dominance hierarchy led by a despotic queen suppressing all her nestmates into worker roles. This suggests that behaviour patterns in such primitively eusocial insects are likely to be moulded by a complex interaction between selection at the individual and colony levels

Superconductivity in doped FeTe1-xSx (x= 0.00 to 0.25) single crystals

We report self flux growth and characterization of FeTe1-xSx (x= 0.00 to 0.25) single crystal series. Surface X-ray diffraction (XRD) exhibited crystalline nature with growth in (00l) plane. Micro-structural (electron microscopy) images of representative crystals showed the slab-like morphology and near stoichiometric composition. Powder XRD analysis (Rietveld) of single crystals exhibited tetragonal structure with P4/nmm space group and decreasing a and c lattice parameters with increase in x. Electrical resistivity measurements (R-T) showed superconductivity with Tconset at 9.5K and 8.5K for x =0.10 and x =0.25 respectively. The un-doped crystal exhibited known step like anomaly at around 70K. Upper critical field Hc2(0), as calculated from magneto transport for x =0.25 crystal is around 60Tesla and 45Tesla in H//ab and H//c directions. Thermal activation energy [U0(H)] calculated for x =0.10 and 0.25 crystals followed weak power law, indicating single vortex pinning at low fields. Mossbauer spectra for FeTe1-xSx crystals at 300K and 5K are compared with non superconducting FeTe. Both quadrupole splitting (QS) and isomer shift (IS) for S doped crystals were found to decrease. Also at 5K the hyperfine field for x =0.10 superconducting crystal is decreased substantially from 10.6Tesla (FeTe) to 7.2Tesla. For x =0.25 crystal, though small quantity of un-reacted Fe is visible at room temperature, but unlike x =0.10, the low temperature (5K) ordered FeTe hyperfine field is nearly zero.Comment: 20 Pages Text + Figs: Accepted Mat. Res. Exp, Mat. Rex. Exp. (2018

DATA Analytics of Agriculture Production, Wages and Income in Rural Areas of India using Big Data and Python Matplot Lib

Agriculture Sector is the major contribution in GDP growth rate of India and Most of the Rural India it will become major resource of Income generator it contains different sectors like paddy, poultry, fisheries, Milk, and other crops. In this paper we studied general, commercial, dairy and other related Agricultural out comes and their Incomes and wages. In this paper we are performing different Data Analytics by taking parameters Daily wages, Income and production of Rural India. In this we are using Big Data Hive and Python Matplotlib to produce Graphical Analytical Reports. and finding results of different crops and daily wages of rural workers. The results we are finding year of production, crop wise production, crop wise and sector wise wages and Income of different crops. In this paper we collected Data and sample Analytical Reports from Agriculture Statistics Ministry of Agriculture, Co operation & Farmers Welfare and Data.gov.in . we are revealing different Analytical Reports regarding wages, Income and Production