Abstract

Monocular robotic system capable of autonomous navigation that includes collision avoidance, object tracking and interaction with objects is indeed exciting for it paves way for replacing expensive sensors on-board robotic systems [2], such as 3D Laser, high end Global Positioning Systems and Attitude Head Reference Systems. Integral components of such navigation system include, the ability to estimate the vehicle's pose in dynamic environments, ability of the system to accurately segment and reconstruct moving objects. In this thesis, we present novel methods for accurate motion segmentation and accurate reconstruction of moving objects. In case of motion segmentation, we propose an incremental motion segmentation algorithm. The motion segmentation is robust and is capable of segmenting dicult degenerate motions, where the moving objects is followed by a moving camera in the same direction. We use ecient geometric constraints that helps in segmenting these degenerate motions. In case of motion reconstruction, we use our multibody VSLAM framework. The multibody VSLAM framework provides for reconstruction of moving and stationary world. However, to provide a unied representation, it requires a solution to the relative scale problem. This relative scale problem occurs in a multibody setting when the reconstructed points of the stationary world and moving object(s) are to be represented in a unied frame of reference. This relative scale problem has proven to be one of most challenging open problem in robotics-vision community. Even recent approaches based on the multibody VSLAM framework have only managed to reduce the space of possible scales but are unable to provide for the absolute scale. We propose a number of solutions to the relative scale problem that provide for scale resolution. In particular, we advanced the state-of-the-art by resolving scale within two views (previous approaches required more than six views) with minimal restrictions on object and camera motion. Two more solutions, one requiring three camera views for any outdoor motion and other requiring four camera views for non-holonomic motion, form important contributions of this work. High delity reconstruction of complex moving trajectories at accurate relative scales are provided on real world dataset and results on synthetic datasets establish the ecacy of the formulation. Performance gain with previous literature on the basis of reconstruction accuracy, prevalent degeneracies and the immediateness of the solution are highlighted in detail. These results advance the state of the art of monocular VSLAM systems in dynamic environments where the camera trajectory, stationary and moving objects are all estimated and further represented at the correct scales.

Abstract

In the present work, ab initio and density functional theory (DFT) calculations are employed to compare the ability of various hybrid GGA density functionals and dispersion corrected functionals in exploring structural, reactivity and electronic properties of linear and cyclic peptides containing tetrahydrofuran (THF) and furan amino acids. A theoretical study of stereochemical control in conformational preferences of the peptides is an important step in designing architecturally ordered structures of furanoid sugar amino acid based molecular frameworks. Also, using the functionals, conformational behavior of 2;3 and 3 monomers and their homodimers is investigated. The monomers are designed to gain control over cis/trans isomerism of proline in order to generate ordered polyproline based foldamers. Using B3LYP/6-31G(d,p) level of theory, we have studied the properties of terminally blocked linear tripeptide containing tetrahydrofuran amino acids (TAA), Boc-TAA-Leu-Val-OMe (TLV). Conformational space of the peptides with four possible combinations of stereochemistry at 2, 5 positions of the TAA, i. e., (2R,5S)-cis-TAA (TLV1), (2S,5R)-cis-TAA (TLV2), (2R,5R)-trans-TAA (TLV3) and (2S,5S)-trans-TAA (TLV4) is investigated. Stable conformer of TLV1 shows a three centered H-bond interaction. This is later conrmed by NMR and X-ray crystallographic studies. Conformations of other peptides are stabilized by regular H-bond interactions. The interactions are part of turn like structures. While a 10 membered -turn type structure is most likely found in TLV1-2, a 7 membered -turn type structure is predominant in TLV3-4. The structures stabilized in terms of thermodynamic and kinetic parameters are veri ed by experimental structures. The calculations on reaction of peptide bond formation between Boc-TAA and Leu-Val-OMe suggest that PBE0 functional reduces the electronic energy barrier heights by about 5 to 10 kcal/mol as compared to that measured by B3LYP functional. The stability of the peptides comes not only from intramolecular H-bonds but also from minimal entropic cost or antiparallel alignment of dipole moment vectors at adjacent amide bonds. Cyclization is a method to induce desired structural biases required for their biological properties. Thermodynamic and kinetic aspects of selective modes of cyclization of the linear tripeptides containing (2S,5R)-cis-TAA and (2S,5S)- trans-TAA is investigated using B3LYP/6-31G(d,p) level of theory. Electronic energy of activation for cyclization reactions were also studied using PBE0/6- 31G(d,p), M06-2X/6-31G(d,p) and MP2/6-31G(d,p) levels of theory. The calculations are also carried out at array of Pople type and Dunning basis sets with B3LYP functional. All the calculations show preference for intermolecular cyclization over intramolecular cyclization of the tripeptides. Amongst the three functionals, M06-2X shows low barrier heights. Greater barrier heights for the intramolecular cyclization are due to deviation in planarity of peptide bond. It makes the bond length longer which leads to less delocalization of lone pair electrons of oxygen with that on nitrogen. Stability for cyclic hexapeptides containing (2S,5R)-cis-TAA comes from a pair of thirteen, ten and seven membered H-bonded interactions. The other peptide having (2S,5S)-trans-TAA is associated with a pair of thirteen and ten membered interactions. Also, fractional increase in chemical hardness and decrease in polarizability in intermolecular cyclization is relatively larger compared to that in intramolecular cyclization. Our theoretical insights are supported by experimental results. Using B3LYP/6-31G(d,p) and M06/6-31G(d,p) levels of theory, we have investigated homo versus heterochiral cyclic trimerization of 5-(aminoethyl)-2- furancarboxylic acid in gas phase, implicit and explicit solvent phases. The cyclic peptides are useful tools for studying molecular recognition processes and show anion binding properties. Gas phase and PCM calculations show that homochiral cyclic peptide is thermodynamically favorable. We have envisaged that change in stereochemistry at one chiral center in the cyclic tripeptide changes symmetry in the structure, nature of intermolecular forces and its reactivity. The B3LYP calculations on solvent assisted structures show only thermodynamic favorability for heterochiral cyclic peptide. On the other hand, M06 calculations on the systems reveal both thermodynamic and kinetic preferences for the heterochiral cyclic tripeptide, compared to its homochiral counterpart. Change of stereochemistry at one chiral center makes the cyclic peptide amenable to interact with solvent molecules strongly from both of its faces. Further support comes from studies based on IR spectra, NBO, AIM and MESP plots. The study on another class of synthetic amino acids, i. e, 2;3- (monomer I), and 3- (monomer II) substituted homoproline reveals t

Abstract

The role of Internet in personal, economic and political advancement is growing in a fast pace. By the turn of century, data on web reaches to petabytes or exabytes or may even scale up-to unimaginable quantities. Extraction of precise and structured information from such large amounts of unstructured or semi-structured data is the major concern of web known as Information Extraction. Named entity recognition (NER) (also known as entity identification and entity extraction) is one of the important subtask of information extraction that seeks to locate and classify atomic elements in text into predefined categories such as the names of persons, organizations, locations, monetary values, percentages, expressions of times, etc. NER has many applications in NLP, for e.g., in data classification, question answering, cross language information access, machine translation system, query processing, etc. Recognizing Named Entities (NEs) in English has reached accuracies nearing to 98%. For English, many cues aid to know the structure of language (one such important cue in identifying NEs is capitalization) which made the accuracies to be high. Whereas in Indian languages, there are no such cues available and moreover each Indian language differ from the other in grammatical structure. Hence, developing a language independent NER is a challenging task. Previous works includes developing an NER system using language dependent tools such as POS Tagger, dictionaries, Chunk Tagger, gazetteer lists, etc., or they have used linguistic experts to manually tag the training and testing data or linguistic experts used to generate rules for recognizing NEs. Language Independent approaches include supervised machine learning techniques such as CRF, HMM, MEMM, SVM, etc. These techniques need High amounts of manually tagged data which is again a point of concern. Some of the other approaches include exploiting the external knowledge such asWikipedia. But, in those methods the utilization of Wikipedia is not complete. Hence, the main objective of this work is to build a language independent NER system without any manual intervention and without any usage of language dependent tools. The approach specified throughout the work, includes language independent methods to identify, extract and recognize the NEs. Identification of NEs is done using an External Knowledge namely Wikipedia. More specifically, English Wikipedia is used as an aid to derive the NEs from Indian languages. Wikipedia hierarchal structure is explored and the documents in it are divided into specific domains. Each domain is considered and the corresponding English and Indian language documents are clustered. English documents are tagged using the Stanford NER Tagger and the non-NEs are removed. Using the term co-occurrences between the tagged English and non-tagged Indian language words, the corresponding NEs between Indian language and English are mapped. Thus the tag of English NE is duplicated to the Indian language NE. Hence, the Indian language data is tagged. The tagged data generated in previous step, is used in recognition of NEs on sets of monolingual Indian language documents. In this step, a set of features are generated from the words of these documents and these features are used for recognition of NEs in a new document. Consider each document; extract the tagged data from the document using the data from previous step. Now, from the remaining words of the document, a Naive Bayes Classifier is build which uses these words to generate a set of features for each class (features here are nothing but the important words of a particular class in that document). The importance of these features is calculated statistically by different metrics (the metrics for classification). Now given a new document, the presence of these features along with their scores is calculated. If the score exceed a threshold, implies the presence of NEs in the document. By decreasing the size of document the process is repeated again till we get the NE. Hence, the monolingual Indian language document is tagged. The approach specified in identifying and recognizing the NEs is language independent and can be extended to any language as none of the language dependent tools are used or there is no involvement of linguistic experts. Hindi, Marathi and Telugu were the languages in which the work has been done. PERSON, LOCATION and ORGANIZATION were the tag of NEs used throughout the identification and recognition process. Wikipedia is used as a dataset in identifying the NEs. Around 3,05,574 English documents, Hindi 100,000 documents, Marathi 83,000 documents, Telugu 85,000 documents are used to generate the results. The results are evaluated on manually tagged 2328, 1658, 2200 Hindi, Marathi and Telugu Wikipedia documents respectively. The F-Measure scores are 80.42 for Hindi, 81.25 for Marathi and 79.98 for Telugu. Dataset for recognition of NEs is a

Abstract

For long it has been argued that metaphor is not merely a linguistic device used figuratively in poems and literature, but a conceptual process that plays a central role in cognition. Several attempts have been made to consider metaphor as a conceptual phenomenon (Ortony, 1979; Lakoff & Johnson, 1980). Most notably, Richards (1936) argued “that thought is metaphoric and proceeds by comparison, and the metaphor of language derive there from” (1936:94). Lakoff and his colleagues proposed conceptual theory of metaphor and argued that human cognition is organized in conceptual schemas, which are metaphoric in nature. These schemas are constantly enriched and modified by the interaction with the world. Language draws on these cognitive schemas but is not identical with them. Moreover, verbal metaphors are surface manifestations of the metaphorical grounding of these schemas. Therefore, metaphor is ‘fundamentally conceptual, not linguistic in nature’ (Lakoff, 1993). One of the crucial outcomes of this cognitivist paradigm is its implication that metaphor can assume non-verbal and multimodal manifestations as well. The focus of this thesis is on visual metaphors, which are pictorial counterparts of verbal metaphors such as “The sky is crying,” or “The man is a wolf.” Visual or pictorial metaphor is nonverbal manifestation of metaphorical thought, where one or both concepts of the metaphor (the target and the source) are depicted in images. In this thesis, our aim is to study different kinds of visual metaphors: How are they generated and how are they different from verbal metaphors. We focus on the difference between them at the presentation level, which relates to the early stages of perceptual processing. A verbal metaphor is presented as text but a visual metaphor is presented in images. Several psychological studies (Paivio, 1989; Schnotz & Bannert 1999) have shown that text and images are processed in different ways. Recent brain imaging studies have also confirmed that the right-hemisphere of the brain is specialized to process visual information (Bryden, 1982; Ellis et. al., 1988; Jonides et. al., 1993; Kosslyn et. al., 1993; McCarthy et. al., 1994; Smith et. al., 1995), whereas the left-hemisphere mostly processes linguistic or alphabetic information (Beaumont, 1982; Binder et. al., 1995; Desmond et. al., 1995; Gabrieli et.al., 1996; Howard et. al., 1992). We assume that this difference in perceptual processing is also reflected in high-level semantic processing tasks such as metaphor.

Abstract

Controlling molecular processes using lasers has been a long cherished dream since their invention in the 1960s. Of the several control schemes subsequently developed, Quantum Optimal Control Theory (QOCT) has been one of the most successful for the theoretical design of laser fields for driving molecular systems to accomplish specified objectives. In this work, we attempt a reformulation of the quantum optimal control problem for controlling vibrational motion in a molecule using an external laser field and under the constraint of reasonable fluence and evolution governed by the Schr¨odinger equation in Hilbert space. We do this by transforming the Schr¨odinger equation into a set of Hamilton’s equations in new phase space coordinates. We study the characteristics of dynamics in the K¨ahler space thus generated. We examine the geometric interpretation of this dynamics and reformulate the quantum optimal control problem as a classical control problem. We apply Pontryagin’s maximum principle to this classical control problem and explore its consequences for the quantum description. We examine in particular, controllability of the system and investigate how it changes in going from the original quantum problem to the classical-like reformulation. We speculate the role of adjoint variables in the reformulation as possible Lyapunov exponents in the classical-like phase space.

Abstract

A K -Nearest Neighbor based classifier classifies a query instance based on the class labels of its neighbor instances. Although kNN has proved to be a ubiquitous classification/regression tool with good scalability, but it suffers from some drawbacks. Two of its major drawbacks are: (1) The existing kNN algorithm is equivalent to using only local prior probabilities to predict instance labels, and hence it does not take into account the class distribution around the wider neighborhood of the query instance, which results into undesirable performance on imbalanced data. (2) It uses all the training data at the runtime and hence is slow. In this thesis, we provide a suite of solutions to tackle this drawbacks. BINER: We propose a new efficient technique for regression. Our algorithm instead of directly predicting the value of response variable, narrows down the range in which the response variable has the maximum likelihood of occurrence and then interpolates to give the output. The data is Hierarchically partitioned in the pre processing step, and search for the partition in which the response has the maximum likelihood of occurrence is carried out at the run time. It takes a single parameter k, the same as in kNN and more than often outperforms the conventional state of art methods on a wide variety of datasets as illustrated by our experimental study.

Abstract

Real world phenomena, in various domains including climatology, ecology, environment(natural); diseases, theft, urbanization (man-made), are largely dynamic and their evolution over space and time is central to our understanding of the science behind them, including the processes. Such phenomena have agreat impact in the regions where they occur raising a need to assess their behavior – regions where they occur, the duration of occurrence, extent of influence etc., to help manage them suitably. Current approaches focus on collection of snapshots of data at certain intervals of time. Though this approach provides an insight into the state of the event at given times and how changes occur from one snapshot to other, it is inadequate to explain and analyze the dynamic processes. A significant amount of work has been done in spatio-temporal change analysis such as detecting land use change as part of global and regional environmental change. While trying to find major changes, we tend to ignore places with minimal changes, whose invariant behavior might give important clues about the process under study. For instance, the localized behavior of climate, especially rainfall, has been well investigated in recent years to understand the dynamics of such a phenomenon with varied outcomes. It is observed that to better understand the controlling and regulating factors, in addition to identifying or locating the change and its quantum, there is also a need to understand the invariant behavior that the phenomena exhibits. Therefore, this study focuses on the detection of these invariant regions for a given phenomenon.A region can be a single or a set of representative points, with a defined property. Given the dynamic nature, this information can be really useful not only to understand the phenomenon better but can be used as reference points for sampling/interpolation. In this thesis, we propose MiSTIC, a data format generic method based on watershed delineation, neighborhood analysis and frequent item mining to identify the set of spatially distributed regions highly influenced by a phenomenon over a period of time.The method involves both a spatial analysis step to detect focal points(representative points) and a spatio-temporal analysis over the entire data time period to identify Core regions orCores. As the nature of the cores is affected by the neighborhood in which it occurs, to understand their behavior, cores are classified as Cores with Contiguous points (CC) and Cores with defined Radius (CR). Further, the cores are sub-classified to determine the level of spatio-temporal invariance (confinement in space over time) by considering the frequency of occurrence of the focal points that constitute it. These are - Cores with Highly Dominating points (CHD), Cores with Less Dominating points (CLD) and Cores with No Dominating points (CND). Therefore, the frequent/predominantly occurring focal points capture the localized invariant behavior of a dynamic phenomenon whereas the neighborhood constraints capture the extent/degree of dynamic nature and direction of influence of the phenomenon, if any. The method proposed in this paper tries to detect such regions for all domains – climatology, environment, theft, diseases to name a few. The results are extracted by using location, time and attribute value (investigates where, when and what). For every domain, these invariant regions signify that there exist places where dynamic phenomenon is highly concentrated. Such detection can help in taking appropriate measures tounderstand and handle the phenomena. For instance, in case ofhigh occurrence of events with negative impacts like storms, theft, diseases, etc., detecting such localized occurrences can help better manage them. In addition, this study provides an insight into the area of influence of the phenomenon for a given time period. To demonstrate the generality of the proposed method and its applicability, a case study on Indian Monsoon rainfall and another on Theft in NSW, Australia has been chosen. The two datasets used in the two case studies have different time periods, different set of points for analysis and different data formats. It has been observed that the analysis in prior work on spatial data mining is restricted to a particular data format - either raster or vector. Though many suggest that their proposed approach will work for both the cases, it is not been well demonstrated. While in our study, it is clearly seen the method can be applied to a wide range of domains irrespective of the data format – Monsoon rainfall is captured in raster format whereas Theft pattern is analyzed for NSW LGAs which is polygonal data. In addition to this, ourstudy also highlights the use of topological distances and directionality while extracting relationships among entities based on an attribute varying across space and time. The use of topological relationships overcomes the need for data to be

Abstract

SAM-III riboswitch, found at the 5' untranslated region (UTR) of the MetK gene that translates into SAM synthetase in lactic acid bacteria, is involved in regulating methionine and S-adenosylmethionine (SAM) biosyntheses in these organisms. The riboswitch acts as an on-off switch at the translational level regulated by SAM, wherein the binding of SAM switches off the translation as it blocks the Shine-Dalgarno sequence, thereby preventing the ribosome from initiating translation. The riboswitch distinguishes between SAM and its close analog, S-adenosyl-L-homocysteine (SAH), and binds more tightly with SAM. Experimental studies have reported two mutually exclusive RNA conformations in the presence and absence of ligand and have proposed intermediary state of the riboswitch. The Shine-Dalgarno sequence region is reported to be crucial for both the ligand binding and also acts as the regulatory domain. In this study, we have used molecular dynamics (MD) simulations to understand the structural details of the on-off transition of the riboswitch. The riboswitch is seen to sample larger conformational space in the absence of the ligand compared to the presence of the ligand. The binding pocket and the Shine-Dalgarno - anti Shine-Dalgarno region undergo major structural changes consistent with the experimental data. The off- state of the riboswitch is seen to be stabilized energetically by the formation of a pseudo-duplex like structure comprising the adenine part of the ligand. The phenomenon of translation switch off is verified by using the interaction energies which clearly show that the Shine-Dalgarno sequence region is more stable in the bound form and hence is very difficult to disrupt for the ribosome.

Abstract

Today, our daily lives are constantly driven and influenced by services like social networking, personalized advertisements, cloud infrastructure etc. This makes any form of personal or professional data vulnerable to both security and privacy threats. User looses control over the data as soon as its published on the Internet. Recently, popular photo sharing service Instagram tried to change its privacy policy, giving itself the right to sell users’ information including their photographs to advertisers without any notification or compensation. With incidents like AOL and Netflix debacle, organizations are refraining from releasing customer data even after anonymization. This hinders future research and developments in many ways due to unavailability of customer data, behavior and patterns. Techniques like k-anonymity and l-diversity help to preserve user privacy to some extent but fails to provide any solid guarantees. Recently proposed, differential privacy provides promising results in this area with theoretical bounds on information leak and preservation of privacy even against the auxiliary information. However, differential privacy considers individual’s record independent of other data and can not be applied to linked data like social graph, time series etc. Furthermore, all these techniques compromise utility of the data while maintaining privacy. At present, two approaches are widely used to ensure security of the data. First one is based on SecureMultiparty Computations (SMC), where users communicate with each other using SMC protocol to securely evaluate any function on their data. SMC gives provable security without compromising utility of the data. However, the solutions based on the general protocol of SMC requires enormous computational and communication overhead, thus limiting the practical deployment of the secure algorithms. The second approach is to encrypt the data at the user end and perform any operations in the encrypted domain to preserve the confidentiality of the data. This leads to difficulty in performing various operations like search, retrieval and computations. Homomorphic encryption allows secure computations over encrypted data but its impractical due to high computational cost. In this thesis, we focus on developing privacy preserving, communication and computationally efficient algorithms in the domain of Data Mining and Information Retrieval. Specifically, we design efficient privacy preserving solutions for two widely used tasks - outlier detection and content based similarity search over encrypted data. Private computation of outliers among data collected from different sources has many applications in the area of data mining. First, we propose a novel algorithm of distance based outlier detection using Locality Sensitive Hashing (LSH) in a distributed setting where data is horizontally partitioned among data owners. We show that the proposed algorithm is scalable in case of large datasets with high dimensionality. Then, we extend our distributed outlier algorithm to propose an algorithm for private outlier detection that broke the previous known bounds of quadratic cost. We compare our algorithm with the state of the art technique and show that our algorithm is better both in terms of computational as well as communication complexity. Next, we explore the properties of hierarchical index structures and propose an algorithm for content based similarity search over encrypted data that do not reveal user query to the server. To the best of our knowledge, we are the first one to propose a definition and a scheme for the problem of Content Similarity Searchable Symmetric Encryption (CS-SSE). Finally, we extend our CS-SSE scheme for Private Content Based Image Retrieval, which is essential for many applications like searching health records using CT scans and patent search for logos. We also show that our algorithm achieves optimal computational cost with similar accuracy and privacy when compared to the existing techniques.

Abstract

E-commerce, buying and selling of products by electronic means, has become popular due to the emergence of World Wide Web. One of the vital components of e-commerce systems is recommender systems (RSs). The RS is employed as a part of e-commerce system to help users in finding products of their interest from a huge number of available products. The Collaborative filtering (CF) approach is one of the popular approaches employed to build recommender systems. The neighborhood formation is one of the key steps in collaborative filtering and influences the performance of recommender systems. In this thesis, we have made an effort to improve the performance of recommender systems by proposing two improved neighborhood formation methods. At first, we have extended the notion of lower-bound similarity to improve the performance of neighborhood formation in CF-based recommender system. For neighborhood formation, k-nearest neighbor (kNN) approach is followed to find k neighbors of a target user. Given a fixed k value, the approach finds k nearest neighbors using different similarity methods. We identified the following issue with the kNN approach. Due to fixed value of k, the users who have rated few products may get less-similar neighbors and the users who have rated more products may miss the genuine neighbors. In the literature, the notion of lower-bound similarity has been proposed as an alternative to the popular k-means approach to improve the clustering performance in which the clusters are extracted by fixing the similarity threshold. By extending the notion of lower-bound similarity, we have proposed a neighborhood formation approach by fixing the similarity threshold instead of k. With this approach, it was proposed that it is possible to get appropriate number of neighbors of a target user. The experimental results on real-world dataset, show that the proposed approach improves the performance over the existing kNN-CF approach.