Abstract

In this thesis, we investigate the competency of LHC data to constrain the RD(∗) favored parameter space in a charged scalar leptoquark (S1 ) model. We consider some scenarios with one large free coupling to accomodate the RD(∗) annomalies. As a result, some of them dominantly yield nonresonant ττ and τν events at the LHC through the t-channel S1 exchange. So far, no experiment has searched for leptoquarks using these signatures and the relevant resonant leptoquark searches are yet to put any strong exclusion limit on the parameter space. We recast the latest ττ and τν resonance search data to obtain new exclusion limits. The nonresonant processes strongly interfere (destructively in our case) with the Standard Model background and play the determining role in setting the exclusion limits. To obtain precise limits, we include non-negligible effects coming from the subdominant (resonant) pair and inclusive single leptoquark productions systematically in our analysis. To deal with large destructive interference, we make use of the transverse mass distributions from the experiments in our statistical analysis. In addition, we also recast the relevant direct search results to obtain the most stringent collider bounds on these scenarios to date. These are independent bounds and are competitive to other known bounds. Finally, we indicate how one can adopt these bounds to a wide class of models with S1 that are proposed to accommodate the RD(∗) anomalies. In the first chapter, we study the subject of high energy physics with a brief introduction to the Standard Model and some cases which it cannot explain. In the second chapter, we look at the experimentation involved at the LHC, majorly at the ATLAS and CMS experiments there. In the next chapter, we introduce leptoquarks and describe its productions. In the fourth chapter, we look at the anomalies which exist in B physics. The next few chapters deals with analysis of our study. In chapter eight, we present the results of the study. We finish with the summary and conclusions of our study in chapter nine.

Abstract

Transmission tower is a 3-D truss and indeterminate structure, which plays key role in the operation of a reliable electrical power system. Transmission line tower has to support conductors carrying heavy electrical power and more than one ground wires at appropriate distancesfrom each other above the ground level. Also transmission tower are more commonly used in telecommunication for broadcasting purpose. The total cost of transmission line towers is about 35 to 45 % of the total cost of the transmission line. Apart from conductor load it has to sustain all kinds of natural disaster. So transmission tower designing is an important engineering aspect where knowledge of various engineering fields viz, civil, mechanical and electrical engineering is required. Depending on the purpose and location, transmission towers are designed by varying the shape and size of the tower member.The change in shape and size of diagonal bracesof transmission tower leads to unconventional results like buckling and uplifting.These effects are significant when soil -structure –interactions are considered. Several past studies have shown that large number of structures mainly tall structures damages has been occurred during earthquake due to behaviour of underlying soil and foundation because of geotechnical properties of different landscape units will change dynamic state and greatly influence on the response super structure.Large number of transmission tower and telephonic tower were failed during landers earthquake(1992), Kobe earthquake (1995),chi-chi earthquake(1999),Wenchuan earthquake(2008) etc. In all these earthquake there is one thing common apart from other parameters like earthquake magnitude and frequency, design of structure as per conventional design, ductility of structure and construction quality, there is great influence of interaction between soft and medium soils with the structure which greatly affect the behaviour of the transmissions tower. Therefore it is essential to study the effect soil-structure-interaction on Transmissions tower. Numerical analysis has been performed by two methods i.e., sub-structure (using soil springs at the base) and other one direct method for analysis of SSI. In direct method integrated structure-foundation-soil system is analysed by finite element software ABAQUS. Numerical simulations have been carried out on transmission tower with fixed base, isolated footing, isolated pile foundation, raft foundation and raft pile foundation on soft and medium soil conditions. The aim of this study is to find how soil and foundations behaviour influences the behaviour of transmission tower during earthquake loading. After considering SSI effects in to analysis, In case of soft soil, isolated foundation cannot be used because of excessive lateral deflection, instead of that raft foundation or isolated foundation should be used. Raft foundation would me more economical than the other foundation types. In case of medium soil (density > 1700kg/m3), no need to opt for deep foundation and raft foundation. Isolated foundation would be economical for transmission tower using SSI help. In other words, consideration of SSI in case of medium soil is beneficial as base shear stress reduced as compare to fixed base and also maximum lateral deflection is in the limit. Whereas in case of soft soil base shear stress is reduced but lateral deflection is problem, which leads to failure. So if SSI is not taken in to consideration accuracy in assessment of transmission tower safety could not be reliable.

Abstract

Carbon and gold nanomaterials occupy unique places among the nanomaterials due to the photo/chemical stability, biocompatibility, ease of manipulation, and diverse structures and properties leading to their numerous studies and applications. The advancement in the fields of studies and applications of these novel materials depends mainly on their availability. Therefore, there is a continuous effort towards both the improvement of the existing methods of synthesis and the development of effective facile methods of synthesis of these materials by the scientific community. In this work, we have tried to explore the possibility of developing a rapid, one-pot, room temperature wet chemical synthesis method for producing composite nanocarbon/Au nanoparticles starting from commonly available carbon-containing precursors. We have chosen sucrose as the simple carbon-containing precursor and HAuCl4 as the precursor for the Au nanoparticles and ascorbic acid or hydrazine as the reducing agent with an aim to synthesize nanocarbon-Au nanoparticle composites. Raman and UV-Vis Spectroscopies and Dynamic Light Scattering techniques have been employed for the characterizations of the synthesized composites.

Abstract

Visibility computation is critical in spatial databases for realizing various interesting and diverse applications such as defence-related surveillance, identification of interesting spots in tourist places, optimization of the placement of CCTV cameras for automated surveillance purposes and online warcraft games. These applications in spatial databases often require identifying the locations around a target object that have maximum visibility. This has several real-world applications such as finding the best spots where tourists can gain a better view of the tourist attraction with the corresponding price of the hotel rooms. Existing works mostly dealt with the variants of nearest neighbour queries such as KNN. In the literature, the notion of Maximum Visibility (MV) query has been proposed, which identifies the top-k individual query locations for which the visibility of target object is maximum in the presence of obstacles. In particular, existing works solve the problem of identifying only individual locations to maximize the visibility of a specific target object. However, in the presence of obstacles, one query location would not be able to provide the maximum view of the target object. The single location that gives the maximum view of target object among all locations may not provide the visibility of the parts of target object which are obstructed by obstacles. In such cases, a set of locations could give the target object a maximal view. In this thesis, we propose an efficient approach to determine multiple query locations that maximizes the visibility of target object altogether. We formulate a new query type called Multi-Location Visibility (MLV) query and propose an apriori based algorithm to extract MLV queries. An MLV query determines the top-k query locations from which the visibility of a given target object can be maximized subject to the constraints of visibility, overlap and continuity. The following are the issues in processing MLV query. First, determining potential candidate combinations from n query locations poses scalability issues as n increases. As n increases, the number of combinations of query locations to be examined would essentially explode exponentially. Second, computing visibility, overlap and continuity for every combination of locations is computationally expensive since the computations are spatial based. Hence, the challenges in developing the algorithm to process MLV query include identifying heuristics to efficiently prune the search space to minimize the combinations of query locations to be examined. To process MLV query, we propose a Portion-Based Framework (PBF). The PBF provides opportunities for efficient determination of top-k candidate sets as well as for efficient computations of visibility, overlap and continuity for each candidate set. The PBF is based on the concept of portion. We divide the given target object into equal-sized units, which we designate as portions. Thus, we model the given target object as a set of portions. Then we propose efficient methodologies to compute the visibility, overlap and continuity of each candidate set in terms of portions. The PBF facilitates the replacing of complex and computationally expensive spatial operations by set-based operations, which are typically faster by several orders of magnitude. Furthermore, we model the association of each portion and the corresponding query locations from which that portion is visible as a transaction. Thus, we form a set of portion transactions. The problem of extracting potential candidate sets of locations becomes the problem of extracting combinations of query locations from the set of portion transactions. We leverage the heuristics that are used to extract coverage patterns from a transactional database. On the basis of the parameters such as visibility, overlap and continuity, we present an efficient apriori based pruning algorithm for processing the MLV queries. We have conducted an extensive performance evaluation using two real data-sets called British Ordinance Survey and Boston Downtown to demonstrate the effectiveness of the proposed scheme in terms of query processing time, pruning efficiency, and target object visibility with respect to a recent existing scheme. The experimental results show that the proposed approach performs significantly better, when compared to the existing scheme in terms of processing time, pruning efficiency and the visibility. Overall, the proposed approach provides the scope for identifying a set of locations in an efficient manner as compared to the existing approach. It can be extended to develop improved algorithms related to visibility in several applications like robotics, multi-agent systems, online war-craft games, battle-field scenarios, GIS, environment modeling, and surveillance applications.

Abstract

The goal of data mining is to discover knowledge and review new, interesting and previously unknown information to the user. One of the important data mining techniques is association rules. Let I= {i1, i2, i3, … in} be a set of n binary attributes called items. Let D= {t1, t2, t3, … tm} be a set of transactions that form a database where each transaction in D has a unique transaction ID and contains a subset of the items in I. A set of items that occur together is an itemsets and a frequent itemset is an itemset whose support is greater than some user-specified minimum support. For event X and Y, an association rule is a rule of type X =>Y, where X, Y I are set of values such that XY=with certain support and confidence. Support determines how often a rule is applicable to a given dataset, while confidence determines how frequently items in Y appear in transactions that contain X. Classical use of association rules is with market-basket data resulting in rules such as “People who buy beer also buy diapers with confidence 0.67 and support 0.4”. Association rules discover patterns and correlations that may be buried deep inside a database. Most of the association rules are based on categorical data- where notions of items and transactions are very clear. For quantitative data with many such attributes, it is not clear to define items and transactions. Therefore, an empirical study is conducted to comprehend how quantitative data can be mapped to items and transactions to get association rules. From this study, we can have an approach towards a range of possibilities for discovering quantitative association rules. The key concept in this work is to give labels to ranges of quantitative data and the use of a combination of labels to decide on items and transactions. We conducted a large number of experiments on three different datasets to present varying results and understanding of quantitative association rules, and issues therein.

Abstract

Poetry is one of the oldest and most widely practised art forms, by far the most popular medium to represent aesthetics in text. However, over the last few decades, a rumour has grown in strength that poetry, perhaps, is dying. Some studies do suggest that readership of poems has declined in the second half of the twentieth century as different forms of prose have emerged to be the preferred forms of expression of our age. However, with the proliferation of social media and Web 2.0, poems are witnessing a revival. Prose emerged on the literary scene parallel to the rise of modernity and the market. Since then, it has grown in both extent and variety, and today is the dominant artform for textual aesthetics. Parallel to this rise of prose, poetry has witnessed a radical transformation both in its form and content, but also in the role it plays in society. The democratization of literature that the rise of prose represents reflects in poetry losing its traditional scaffolding of meter, rhyme and other poetic devices. The emergent forms of poetry allow it to be practised more widely and in a variety of forms. However, this also meant that the poem lost its ontological precision. This thesis is an exploration of the travels of the poem as a form and a genre in the age of mechanical reproduction; an age which democratizes as well as breaks restrictive structures in its drive towards its prime tenets of uniformity and accessibility. The first part of this thesis studies the various definitions of poetry and identifies their inadequacy in setting ontologically precise boundaries. Today, it is nearly impossible for us to identify a poem by any currently accepted definition. Based on this, the thesis proposes a hypothesis that poetry is merging into prose at the level of its basic ontology. The second part tries to prove this hypothesis by computationally showing the reducing distance between prose and poetry. It does this by using novel features and machine learning techniques. Consequently, we try to work towards a new definition of a poem which tries to preserve some elements of form, such that a poem retains ontological coherence. The fourth part concludes that for the revival of the craft of poetry-writing, a better alternative to computational poetry generation, is a personalized poetry assistant created using machine learning techniques.

Abstract

In the course of evolution, human genome has been disrupted, modified and rearranged as a consequence of various genetic events, such as single nucleotide mutations, insertions,deletions, copy number variations, translocations and inversions, making each individual’s genome unique. The accumulated genetic variations are known to play a significant role in shaping population-specific phenotypes and pre-disposition to various lethal diseases including cancer. Detection and annotation of these variants is the first step in understanding their role in disease initiation. Advancements in next generation sequencing (NGS) technologies have made it possible to carry out variant profiling of individuals, slowly replacing traditional and hybridization-based techniques. However, NGS techniques demonstrate inherent challenges due to short read length, incomplete coverage, GC composition, mappability bias, etc. To address some of these issues, we have developed two open-source, integrated platforms, SeqVItA and iCopyDAV, for the detection of small sequence variations (SSVs) and copy number variations (CNVs), respectively, in NGS data. Our tools are tailored to quickly and efficiently identify and functionally annotate genetic variations from large NGS data. In this thesis work we have carried out the detection and functional analysis of variants in Diffuse Large B-cell Lymphoma whole genome sequence data using these tools. Diffuse Large B-cell Lymphoma (DLBCL) is the most aggressive form of haematological malignancies and mostly develops as a result of accumulation of several genetic variations affecting important cellular processes and immuno-oncogenic pathways. The cell-of-origin classification system categorized DLBCL into Germinal Center B-cell (GCB) and Activated B-cell (ABC) subtypes, identified with distinct outcomes when treated with standard immunochemotherapy. Despite several studies carried out to characterize subtype-specific genetic variations, the complete spectrum of these alterations and their relationship with clinicopathological characteristics remains to be elucidated. Detection of subtype-specific genetic variations would assist in understanding the pathogenesis in each case and help in identifying molecular biomarkers for clinical testing, and novel targets for efficient therapy. In this study, we focus on genome-wide analysis of small sequence variants (SNVs and INDELs) and copy number variations (CNVs) in the two subtypes of DLBCL with the objective of identifying molecular markers implicated in the initiation and progression of tumour in these subtypes. The reason for considering these two category of genetic variants is that small sequence variants are widely studied in association studies for disease susceptibility and treatment outcome, while, CNVs affect a large fraction of the genome through focal or armlength amplifications and deletions contributing to the aggressiveness of the cancer. Detection, analysis and annotation of genetic variations is carried out in 12 DLBCL cell lines (7 GCB and 5 ABC subtype). We observe large differences in the variation profiles of the cell lines indicating differences in the genes and associated pathways affected across the cell lines. Detailed analysis revealed a novel set of recurrent genetic variations that may play a key role in lymphomagenesis in a subtype-specific manner. A set of novel genetic variations with potential prognosis are also uncovered. Integration of SSVs and CNVs revealed subgroups of shared variations among the cell lines analysed which help in understanding their shared effect on lymphomagenesis and prognosis. To summarize, in this thesis we present the workflow for detection and functional characterization of genetic variations in cancer genome and provide novel insights into the molecular nature of DLBCL tumorigenesis

Abstract

The rapid increase of textual content on the internet has made efficient text processing very important for various applications ranging from product analysis and implementing business strategies to inculcating public opinion in governance processes or creating biased news to gain public support. In this context, Sentiment Analysis has become one of the important fields of research. Sentiment analysis can be used to know about public opinions expressed over various platforms using NLP and machine learning techniques. A lot of research has been done in the English language in these fields due to the availability of a large number of tools and resources. Though a significant amount of progress is being made in Indian languages in recent years, there is a lot to explore in Telugu language with the machine and deep learning techniques. This thesis aims to enhance the sentiment prediction and the detection of political bias aiming to alter the sentiment across people using various approaches. For this, the need to develop a corpus first was felt essential. For the development of sentiment classifiers in Telugu, we have created corpora “Sentiraama” for different domains like movie reviews, song lyrics, product reviews and book reviews in Telugu language with the text written in Telugu script as there were no such datasets available previously. We describe the process of creating the corpora and assigning polarities to them. Typically, sentiment classifier is trained using data from the same domain it is intended to be tested on. But there may not be sufficient data available in the same domain and additionally using data from multiple sources and domains may help in creating a more generalized sentiment classifier that enhances sentiment prediction. We showed how this generalized sentiment classifier can help in handling low domain-specific data scenarios. To improve sentiment analysis in a low resource language, sentiment labeled corpora are translated from English into the focus language and use them as additional resources for sentiment analysis research in the focus language. But when the text is translated from one language into another, the sentiment is preserved to varying degrees. We use product and book reviews in English as a stand-in for source language text and to determine the loss in sentiment and sentiment predictability, we used manually and automatically determined sentiment labels of the English text as a benchmark. We show that sentiment analysis of Telugu manual translations of English text produces competitive results w.r.t English sentiment analysis. We show that machine translation significantly reduces the human ability to recover sentiment. In the process, we created a Telugu-English parallel corpus that is independently annotated for sentiment using a 5-value scale by Telugu and English speakers. We also created a Telugu lexicon annotated at both sentiment and emphasis level. Another kind of text which is different from the ones we analyzed is ‘song lyrics’. Songs are important to sentiment analysis since the songs and mood are mutually dependent on each other. Song lyrics are a rich source of dataset that is helpful in the analysis and classification of sentiments generated from it. Nowadays we observe a lot of inter-sentential and intra-sentential code-mixing in songs which has a varying impact on the audience. To study this impact we created a Telugu songs dataset which contained both Telugu-English code-mixed and pure Telugu songs. We classified sentiment of the songs based on its arousal rather than routine valence level analysis and enhanced sentiment prediction by introducing code-mixing features. On the other hand, in the domain of news, media houses and journalists try to affect the sentiment or polarity of people towards some political parties by shrewd means such as misinterpreting reality and distorting viewpoints. This can be mainly observed during elections where media houses try to alter people’s sentiment by providing bias news. Detection of such biased news is very useful as unrestricted access to unbiased information is crucial for forming a well-balanced understanding. So along with sentiment analysis, we also aimed at detecting biased news that aims to cause sentiment shifts. As there was no existing dataset available in Telugu language, we created a dataset comprising of 1329 news articles collected from various Telugu newspapers and marked them for bias towards a political party. We also proposed a headline attention model which enhances the bias detection over various methods by a substantial margin.

Abstract

With the current fast pace growing trend towards AI, machine learning, gaming, social media scale of processing, there is great interest in the related algorithms and the constant need to improve on the efficiency of these fundamental algorithms such as matrix multiplications and graph algorithms. The method of sampling the input instance to gain insights and leverage them to build more efficient and quicker solutions is a well explored area and is gaining popularity. It involves creating an intermediate representation of the input which is processed for obtaining insights on the entire input. Applications of this technique can be seen from Fischler et al. [37], who proposed the RANSAC (Random sample consensus) in 1981 for estimating the parameters of a mathematical model from some observed data containing outliers. Sparse matrix computations like multiplying a sparse matrix with another sparse matrix (spmm) and multiplying a sparse matrix with a dense matrix (csrmm) are fundamental to the linear algebra computations and hence is a core part of major scientific problems at scale. Similarly, graph algorithms, such as finding the connectivity of graphs, have been studied for centuries and have been an area of extensive research and have widespread applications ranging from linguistics and computational biology to social sciences and network topologies. Finding biconnected components of a graph is used in planarity testing [47], isomorphism in planar graphs [50], network analytics [26, 41, 80] and the like. The trend towards heterogeneity leads us to customize algorithms that use properties of the input graph for splitting the workload among the computing units in a heterogenous setting. The majority of the algorithms propose a mechanism of splitting the workload into the right pieces and assigning the right pieces to the right computational units for processing based on various thresholds. Computing these thresholds for each of the inputs might not be straightforward always and generally requires an empirical search across the possible range of threshold values, which leads to the time taken to find the threshold overshadowing the benefits gained from the Heterogenous efforts. There has been increasing interest in finding biconnectivity properties of a given graph given its varied applicability and is reflected in the research progress made in terms of speeding up the existing approaches using various techniques with the newly emerging computing platforms. If we have a quick and straightforward method to find the certificate of the graph, we can test it for properties like connectivity and claim that it also holds for the actual graph itself. Hence, finding a certificate for a graph is an interesting problem with immense impact.In this thesis, we propose a simple and effective sampling-based technique for the work partitioning of heterogenous algorithms. We find a nearly optimal threshold value to distribute workload among the computation units for multiplying two unstructured sparse matrices(spmm). We validate our approach on a variety of real-world graphs and show that we can find the best possible threshold value ±10.6% away from the best possible threshold value. We also propose a sequential sampling-based technique for computing the biconnected components of a graph. We validate our approach on real-world graphs and report a speedup of 3.4 times when sampling technique is used

Abstract

Two view stereo problem is a subset of multiview stereo problem where only two views or orientations are available for estimation of depth or disparity. Given the constrained nature of the setup, traditional algorithms assume either the intrinsic or extrinsic parameters to be available in advance in order to build the homographies between the views to rectify the images. Stereo rectification allows epipolar constraint to be enforced such that the corresponding projections of a 3D point could be searched in one dimension along the epipolar lines. When both calibration matrices are not available, the two view stereo problem reduces to estimating the fundamental matrix. A condition number which measures the instability of a function when input conditions change, is high for a fundamental matrix when estimated using an 8 point algorithm. Deep learning methods have been the sought after solutions to numerous computer vision problems as the state of the art research have exposed the power in terms of learning capability of neural networks in general. We explore stereo correspondences in an uncalibrated setting in general by estimating a depthmap given a pair of unrectified stereo images. An end-to-end solution is sought after in a setting where the relative depths of pixels with respect to a single view point could be extracted with the aid of another view. Extending the capabilities of the correlation layer as devised by the flownet architecture, a modified flownet architecure is designed to regress depthmaps with an extension of multiscale correlations for handling textureless surfaces and repetitive textured surfaces. Due to unavailability of dataset for deep learning of unrectified images, a constrained setup of turn table sequences is constructed for this purpose using Google 3D warehouse models.Following the concepts of Attention modelling, we implement an architecture for combining correlations computed at multiple resolutions using a simple element-wise multiplication of the correlations to aid the architecture to resolve correspondences for textureless and repeated textured surfaces. Our experiments show both qualitative and quantitative improvements of depth maps over the original Flownet architecture and provide a solution to the unrectified stereo depth estimation which in literature, most algorithms work on stereo rectified image pairs to compute depthmaps/disparities.