Abstract

Abstract World health organization (WHO) surveys report that in 2014 there were 8.8 million (range 8.5-9.2) incident cases of tuberculosis (TB), 1.1 million (range, 0.9-1.2 million) deaths from TB among human immunodeficiency virus (HIV)-negative people and an additional 0.035 million (range 0.32-0.39 million) deaths from HIV-associated TB and there are several issues of drug-drug interactions in treating both Mycobacterium tuberculosis (M. Tb) and HIV simultaneously. Therefore, molecules that are able to inhibit multiple M. Tb targets, and also those which simultaneously act on both M. Tb and HIV are the need of the hour to tackle the therapeutic challenges such as drug resistance and coexistence with HIV respectively. This thesis reports application of rigorous computational approaches at various length and time scales to understand the important druggable targets of M. Tb and HIV as well as strategies for identifying dual inhibitors of M.Tb and HIV. The work carried out for the Ph.D. has been divided into 8 chapters. Chapter 1 introduces the advances in computational modeling of biomolecules at various levels of complexities, followed by a brief discussion on the challenges in M. Tb drug discovery Chapter 2 gives the theoretical background of computational methods used in the thesis. Mycobacterial cyclopropane synthase 1 (CmaA1) is an important drug target of M. Tb as it is responsible for cis-cyclopropanation at the distal position of unsaturated mycolates, which is an essential step for the pathogenicity, persistence and drug resistance. Chapter 3 of the thesis reports a study, where five representative models of CmaA1 corresponding to different stages in the cyclopropanation process have been studied using molecular dynamics (MD) simulations. The MD simulations and structural analyses provide a detailed account of the structural changes in the active sites of CmaA1. CmaA1 has two distinct binding sites, i.e., cofactor binding site (CBS) and acyl substrate binding site (ASBS). The apo state of CmaA1 corresponds to a closed conformation where the CBS is inaccessible due to the existence of H-bond between Pro202 of loop10 (L10) and Asn11 of N-terminal α1 helix. However, cofactor binding leads to the breaking of this H-bond. The hydrophobic side chains orient towards the inner side of the ASBS upon cofactor binding to create a hydrophobic environment for the substrate. The cofactor and substrate tend to come close to each other facilitated by opening of L10 to exchange the methyl group from the cofactor to the substrate. The MD study also revealed that the system tends to regain the apo conformation within 40 ns after releasing the product. In Chapter 4, structure based pharmacophore models generated using the MD trajectories of the model systems have been presented. The performance of these pharmacophore models have been validated by mapping 23 molecules which have been previously reported to exhibit inhibitory activities on CmaA1. The models were further validated by comparing the results from the pharmacophore mapping with the results obtained from docking these molecules with the respective protein structures. On the basis of the screening ability and consistency with the docking results five models have been proposed. The models generated from the MD trajectories were found to perform better than the one generated based on the crystal structure demonstrating the importance of incorporating receptor flexibility in drug design. Chapter 5 gives the details of a novel virtual screening (VS) approach where five structure based pharmacophore models proposed in the previous chapter have been used along with the five validated ligand based pharmacophore models. Aiming towards repurposing the existing drugs to inhibit CmaA1, 6,429 drugs reported in DrugBank were considered for screening. To find compounds that inhibit multiple targets of M. Tb as well as HIV, 701 and 11,109 compounds showing activity below 1μM range on M. Tb and HIV cell lines respectively were also collected from ChEMBL database. Thus, a total of 18,239 compounds were screened against CmaA1 using four levels of screening i. e., ligand based pharmacophore screening, structure based pharmacophore screening, docking and absorption, distribution, metabolism, excretion, toxicity (ADMET) filters. Twelve compounds were identified as potential hits for CmaA1 at the end of the fourth step. These compounds were found to interact with the key active site residues of CmaA1. Chapter 6 reports an analogue-based approach employed to predict the inhibitory activities of compounds against HIV protease. This study critically examines the role of conceptual density functional theory (C-DFT) descriptors and docking scores on a diverse set of 156 inhibitors of HIV proteases. A huge number of quantitative structure activity relationship (QSAR) models were developed based on available experimental IC50 values (HIV-I and HIV-IIIB infected MT4 and CEMSS

Abstract

In the digital era, huge amounts of audio data are being produced and consumed every day in a variety of languages. The increasingly widespread use of digital technology in our everyday lives has made speech data accessible to the masses, thus making it easy to record, preserve and reproduce digital content. The ability to search through this data thus becomes a valuable functionality to improve its access. But, unfortunately, the linear and non-deterministic nature of speech signal limit our ability to retrieve information from this repository efficiently. Thus, providing fast and intelligent access to these large speech collections have become a necessity to unlock their true potential as knowledge resources. Spoken Term Detection (STD) is the task of detecting all occurrences of a query from spoken data. The term ``low resource'' may assume different meanings in different contexts. There are many languages in which no transcription of any kind is available on spoken data. Hence, for such languages, no supervised acoustic or language models could be built. In other cases, sufficient resources may exist for a language in general, but resources may be scarce for a specific application or domain for which an STD system has to be developed. In such cases, acoustic models could probably be trained for basic acoustic units (eg. subwords such as phonemes), but the appropriate language model might not exist to produce a word-level or sentence-level transcription. There are other issues like the presence of Out of Vocabulary (OOV) words. Lack of good indexing and search techniques further causes serious limitations. Using multiple ASR hypotheses usually shoots up the index size to untenable levels. Moreover, the time taken for search becomes quite high which makes such systems impractical for real time use. Hence, better representation of spoken documents, efficient storage using appropriate indexing techniques, along with fast search capability are to be explored, so that the huge quantity and variety of digital spoken data that we generate every day become more accessible and resourceful. In this thesis, two types of STD techniques are explored. In the first part, a query-by-example STD approach is presented for language independent search. For this, four African languages in which no resource is available, except their spoken data, are used. Hence, no language-specific acoustic models could be developed to perform recognition. A Bag of Acoustic Words (BoAW) approach is used to efficiently represent and index these documents. This allows for highly scalable indexing and retrieval. A multi-stage search is performed on the indexed documents. A very efficient variation of Dynamic Time Warping, called NS-DTW, is used for accurate location of query snippets among the documents. In the second part, a text-based STD technique is presented for English language. Here, it is assumed that phone-based acoustic models could be developed for the language, but no language models are available for the particular data that is used. Due to the absence of good language models, subword representation and indexing techniques are explored. Subword representation of textual queries are searched through the database, which is indexed using phone, hyperphone and hybrid $N$-gram units. A multi-stage search technique is employed which successively reduces the search space. In the first stage, the classic Bag of Words technique is used, which is followed by a Minimum Edit Distance re-ranking. In the last stage, an efficient Acoustic Keyword Spotter locates the precise location of query within the spoken documents. Using these techniques, we demonstrate that it is indeed feasible to perform spoken term detection in low resource scenariois. Moreover, these show that efficient and practical STD systems could be developed which search through several hours of spoken data in real time.

Abstract

In the last decade there has been considerable interest in a novel dynamical phenomenon, chimera behavior in which regions of coherence and incoherence coexist. In this study we analyze the emergence of chimera states in different connection topologies. Firstly we analyze chimera behavior in non-locally coupled logistic maps for varying range and strength of coupling. The dependence on initial conditions is shown by considering different initial conditions viz., uniform, random and hump shaped. To capture the dependence of the strength of coupling on the spatial distance between the nodes, we considered two decaying functions for the coupling strength and analyzed the chimera behavior in non-locally coupled networks. The robustness of the chimera behavior in the presence of noise in the system parameters and connection topology is also studied. To capture different connection topologies and their effect on the dynamical behavior of the whole system, we also analyzed nearest-neighbor diffusely coupled, globally coupled, randomly connected networks. Apart from these dynamical behavior on small-world and scale-free networks is also discussed. The objective of this analysis is to introduce heterogeneity in the connection topology and analyze the dynamical behavior of the whole system. In various physical and biological systems, viz., power grids and excitable tissues, synchronous behavior of all the components is generally desired. In such situations there is a real need to control the undesired asynchronous behavior of a few nodes for proper functioning of the system. With this objective we study the control of asynchronous regions in the chimera behavior. The analysis has also been extended to two-dimensional systems.

Abstract

In this thesis, the problem of motion segmentation is discussed. The aim of motion segmentation is to decompose a video into different objects that move through the sequence. In many computer vision pipelines, this is an important, middle step. It is essential in several applications like robotics, visual surveillance and traffic monitoring. While,there is already a vast amount of literature on the topic, the performance of all thus-far proposed algorithms are far behind human perception. This thesis starts of with a formal introduction to the problem. Then, it proceeds to explain the main approaches proposed to the problem, along with their advantages, and shortcomings. Finally, the proposed algorithm, that forms the keystone of this thesis, is introduced and fully-fleshed out, giving motivation for the structure and the various parts of the algorithm. In addition, the traditional comparison is given with the other-proposed state-of-the art algorithms. We do so, on the standard benchmark Hopkins-155 dataset, as well as a new dataset, compiled from video sequences from the publically available, KITTI dataset, the Versailles-Rond sequence taken from and several sequences taken around the IIIT Hyderabad campus. The sequences in the dataset, consist of video footage taken from a single-camera mounted on the front of a car. The dataset is far more realistic and challenging than the Hopkins dataset, and provides a more rigorous assessment for both the proposed algorithm, as well as other state-of-the-art algorithms in motion segmentation. This dataset is hereby referred to as the On-Road dataset. On the Hopkins-155, our algorithm achieves near state-of-the-art performance, while performing substantially better on the On-Road dataset, showing that the proposed algorithm, has superior perfor- mance in realistic scenarios

Abstract

Image fusion is the process of combining images from different sensors, viewpoints or foci to form a composite image without introducing any artifacts. The sensors can have different characteristics like viewpoints, focus or are from different sensors like visible light cameras, infrared camera or X-ray cameras. Most of the fusion algorithms require the input images to be spatially aligned for efficient results. Thus the process of image registration is a vital precursor to fusion and helps to bring these images onto a common representation. Image fusion enhances the information content within a single composite image and thus aids in human perception and machine processing of such images. Image fusion and registration have found many applications in medical imaging in MRI scanner, super resolution, object identification and object tracking and many other applications. Often such applications are used in on field camera systems which require real time results for quick decisions. The modern algorithms for image registration like scale invariant feature transform (SIFT) and speeded up robust features (SURF) suffer from low execution speeds on generic hardware like PC and graphics processor unit (GPU). Moreover, the high memory access and intensive computation require high power for the execution of these designs on generic hardware. The possibility of data parallelization and ability to manage high memory requirements make these algorithms an ideal candidate for field programmable gate array (FPGA) based implementations. This work presents a novel architecture of feature detection using SIFT and image fusion using Laplacian pyramid decomposition on Xilinx Virtex 5 ML505 FPGA. In this work we focus on fusing images from multiple sensors. In the feature detection part, parallelization of processing has been exploited in feature generation and a look up table based approach in gradient property calculation has led to significant gains in speed while simultaneously reducing complexity and maintaining good results. For the Laplacian fusion, data parallelization has been exploited along with a re-usable cache based design to reduce device utilization and provide effective results. The design has been optimized to provide an output of 30 fps for an image of 320x256 for registration and fusion while providing lower power consumption than GPU based architectures.

Abstract

Raytracing has been primarily used for generating photo-realistic imagery. Once considered as an offline algorithm, raytracing has primarily become realtime or near realtime with the advancement in computer hardware power. GPU computing has enabled the general purpose usage of graphics cards which were primarily designed for for graphics rendering purposes. Raytracing could be considered as a very embarrassingly parallel process in which each pixel's contribution to the final image is computed independent of one another. The tremendous power of GPUs has been been utilized for vastly improving the performance of almost all the functions in a raytracing pipeline. The raytracing pipeline for rendering images fundamentally consists of three phases. The first is an acceleration structure construction phase which involves creating a data structure. It provides very fast results for intersection queries for the rays traced within the scene and all the objects contained within the scene. The second is the traversal phase in which each ray is traced within the scene employing the aforementioned data structure. The final optional phase is shading in which each hit point is shaded based on a shading algorithm. The shading algorithm determines the color of the pixel of the image for which a particular ray was traversed and the shading performed for all the rays for all the pixels computes the final rendered image. In this thesis we examine two problems associated with the GPU raytracing pipeline and provide two new approaches for them. In the first work, we develop a novel parallel algorithm for performing raytracing without constructing any explicit acceleration structure on the GPU. This decouples the long standing notion of creating and storing a separate acceleration structure followed by tracing rays through the structure. Our algorithm creates an implicit hierarchical acceleration structure and traverses the implicit structure at each phase of the algorithm in tandem. Parallel construction algorithms for acceleration structures are difficult to implement on the GPU and also have a large memory footprint to store the resulting structure. Compared to CPUs the amount of memory available to GPUs is limited and hence methods that work on very small memory footprints are of utmost importance. Our algorithm is conceptually very simple, utilizing efficient parallel GPU primitives such as sort and reduce. Further our algorithm has small memory requirements compared to methods that construct acceleration structures thereby making it a suitable candidate for the GPU. Since our method employs a traverse-while-construct method, it is particularly very useful for animated scenes in which the acceleration structure has to be created for each frame in a traditional raytracing pipeline. We implement our algorithm on a CUDA enabled machine and show that our algorithm can perform much better than the serial CPU version of the algorithm. Our second work is targeted towards the problem in the shading phase of the raytracing pipeline. Traditional renderers employed in the production rendering are primary unidirectional path tracers which traces rays from the camera into the scene. However the path tracing algorithm has difficulty in rendering scenes with complicated lighting scenarios which provide very good aesthetic value to certain kinds of scenes. Bidirectional path tracing on the other hand traces rays from both the camera and the lights within the scene and hence able to render scenes with complicated lighting scenarios much more effectively than path tracing. Current production renderers are primarily CPU based and only a few have started to employ GPUs for the entire pipeline. Limited memory compared to CPUs was the original factor for not employing GPUs. However with current generation GPUs having much more memory than earlier generations, GPUs have been slowly adopted for path tracing based pipelines. However bidirectional path tracers that are GPU based are yet to be fully employed in a full production rendering scenario. Our work is aimed providing a solution that tries to bridge the gap that enables bidirectional path tracing to be used in the production rendering scenario which involves complex materials. We specifically provide a new connection mechanism employed in the light vertex cache - bidirectional path tracing algorithm (LVC-BDPT) for improving shading efficiency as well as a sort based pipeline for improving the runtime performance of the algorithm on the GPU. We show that we perform much better than the unoptimized version of the algorithm as well as providing much better quality for the same amount computations performed.

Abstract

Road infrastructure in countries like India is expanding at a rapid pace and along with it the need to build and maintain a comprehensive road information system (RIS). Even though static information like road names and road geometry can be collected by a one-time survey, road characteristics like road condition, traffic density, directionality, surface type etc., keep changing over time, posing a challenge to maintain an up to date RIS database. The common satellite based approaches are not sufficient to cover a number of road characteristics. Current ground based data collection schemes are either not affordable due to the use of expensive equipment or not scalable due to its time consuming paper-and-pen data collection approach. This necessitates the search for alternative technologies for road data collection. New systems need to be built using such technologies, which integrates information extraction methods and algorithms best suited for road data collection. Unmanned aerial vehicles (UAVs) are increasingly being used as a low altitude remote sensing platform. Providing cheap, on-demand high resolution aerial imagery and video makes them a unique data collection platform when compared to satellites and big aircrafts. An innovative data collection scheme that utilizes the trajectory information of the moving vehicles from the aerial video of UAVs, to robustly extract road characteristics like road traffic density and directionality has been proposed. The road directionality extraction procedure correctly extracted all node to node directionality attributes on the final evaluation dataset. Also the road density estimation procedure produced the density estimates with a least error percentage of 5%, strongly contending for the use of UAVs in road data collection. Another technology that has the potential to be used as a tool for extensive data collection, is the smartphone. The availability of a number of sensors, networking capabilities and being ubiquitous make them an ideal data collection platform. A smartphone based prototype system MAARGHA, was developed to collect road condition and road surface type. MAARGHA has been tested across different road conditions and sensor data characteristics to assess its potential applications in real world scenarios. The developed system achieves higher information levels when compared to state of the art road condition estimation systems like Roadroid, while achieving road surface type classification accuracy range of 60% to 100%. While the proposed aerial and ground based systems have initially targeted the collection of few road characteristics, they can be extended further to collect many other data as well. Together, the systems provide a unique solution which is more feasible to be used for extensive and frequent RIS data collection. Hence they can be called as persistent surveillance systems. Such data collection infrastructure for RIS can have significant impact on road transport domains like the road infrastructure management, autonomous vehicle navigation, and intelligent routing services.

Abstract

As we move towards an increasingly globalized and knowledge-based economy, the ability to discover and share information across language and cultural boundaries has become more and more crucial. With the advent and rapid development of the Internet, the amount of information generated in different languages and disseminated via the World Wide Web (WWW) and social media platforms is growing exponentially. As the Internet becomes more ubiquitous and pervasive, its users have become linguistically more diverse and culturally more heterogeneous. It is thus of utmost importance to ensure that online information resources and services are efficiently and equitably accessible to all users, regardless of their linguistic and cultural backgrounds. Unfortunately, owing to language barriers and linguistic digital divide, the majority of the world's populations, including native speakers of resource-scarce African languages, have long been denied the opportunity to access and benefit from online resources. Since most of the current major search engines and commercial Information Retrieval (IR) systems have primarily focused on wellresourced European and Asian languages, they have paid little attention to the development of Cross- Language Information Retrieval (CLIR) for resource-scarce African languages. The need for exploring and building more specialized information systems that enable speakers of African languages to discover valuable information beyond linguistic and cultural barriers has, therefore, become more urgent today than ever before. Taking these facts into consideration, this study is aimed at exploring and building an experimental CLIR between one of the severely under-resourced African languages, (i.e. Afaan Oromo) and one of the most commonly used online languages, (i.e. English). We have focused on developing and evaluating the first Oromo-English CLIR (hereafter also referred to as OMEN-CLIR), which is designed to make effective use of limited linguistic resources to search and retrieve relevant information across language and cultural boundaries. Although Afaan Oromo is one of the major indigenous African languages that is widely spoken by more than 35 million people in the Horn of Africa, it is still considered as one of the most under-resourced languages, especially where being resourced is measured by the extent to which a given language is supported by computational linguistic resources and information access technologies. Afaan Oromo poses a huge challenge to the development of natural language processing and information access technologies, not only because it is one of the most resource-scarce languages, but also because it has very rich and complex morphological processes. Unlike English, Afaan Oromo is a highly synthetic language with a very productive inflectional and derivational morphology. In this study, we have focused on exploring and building basic linguistic resources and IR tools required for designing and developing OMEN-CLIR. Some of the major linguistic resources and translation tools that we have designed and developed during the course of this study include a generic computational model of Afaan Oromo morphology, a machinereadable bilingual dictionary, a rule-based Afaan Oromo stemmer, lists of Afaan Oromo suffixes and stopwords. While our machine-readable Oromo-English dictionary has been adopted and used as a main source of knowledge for query translation, our Afaan Oromo stemmer has played a key role in identifying and normalizing word form variations. Apart from designing and building the components of OMEN-CLIR, for which the necessary linguistic resources and translation tools have been crafted from scratch, another major contribution of this study is to assess and evaluate the performance of the proposed retrieval system. In order to assess the performances of OMEN-CLIR, we had participated in one of the well-recognized international Cross- Language Evaluation Forums (i.e., CLEF campaign) over the past couple of years. The main focus of our evaluation in two different CLEF annual campaigns was to assess how well OMEN-CLIR performs in international and standard evaluation competitions like the ad-hoc track of the CLEF campaign. Besides a number of official runs that we had submitted to the ad-hoc tracks of the CLEF-2006 and CLEF-2007 campaigns, we have conducted various additional retrieval experiments to improve the performances of OMEN-CLIR. The evaluation results have been found to be very promising and encouraging, given the disparity of the languages involved and the limited amount of linguistic resources used for developing OMEN-CLIR. In one of our official retrieval experiments in which we have applied our Afaan Oromo stemmer, our CLIR system has achieved an average mean precision (AMP) of 29.90%, which is about 67.95% of a monolingual baseline. In general, the evaluation results show that it is viable to design and develop a CLIR system for

Abstract

Many tasks in computer vision, such as action classification and object detection, require us to rank a set of samples according to their relevance to a particular visual category. The performance of such tasks is often measured in terms of the average precision (AP). Yet it is common practice to employ the support vector machine (SVM) classifier, which optimizes a surrogate 0-1 loss. The popularity of SVM can be attributed to its empirical performance. Specifically, in fully supervised settings, svm tends to provide similar accuracy to AP-SVM, which directly optimizes an AP-based loss. However, we hypothesize that in the significantly more challenging and practically useful setting of weakly supervised learning, it becomes crucial to optimize the right accuracy measure. In order to test this hypothesis, we propose a novel latent AP-SVM that minimizes a carefully designed upper bound on the AP-based loss function over weakly supervised samples. Using publicly available datasets, we demonstrate the advantage of our approach over standard loss-based learning frameworks on three challenging problems: action classification, character recognition and object detection.

Abstract

Traditional methods of computing and utilization of computing resources involved having dedicated hardware. Each company or individual had to purchase hardware and set it up for each task. If the utility of the hardware and its requirements were fixed, then this approach made sense, however in most cases the utility and the requirements kept varying. Sometimes people required large amounts of resources for short amounts of time (like in the case of scientific computation) or sometimes people needed the ability to scale up and down the number of systems based on the demand and dedicated systems were not profitable in such a case as it involved high upfront costs and becomes a resource burden once the work is done. Resources can be of different types like computation(CPU), storage(distributed filesystem storage), network, key-value stores, databases, etc. Enter the realm of cloud computing. It provides a profitable way of renting resources at the time of need and disposing them off by releasing those resources to be used by others and we get charged only for what we use. With data centers spread across the globe and the help of cloud computing, we can localize resources closer to the end user to improve performance and experience. Cloud Computing is based on the utilization of commodity hardware, one of the reasons that makes it profitable. True homogeneity of the different resources will make it less profitable. Hence, more than often, the systems are heterogeneous in nature. They will have different CPU clock rates, different memory performances, different storage capablities (like tape, Solid-State Disks, Hard-Disks, etc.) , different network capabilities, different kinds of switches connecting them. Even if somehow, one manages to acquire truly homogeneous systems, the dynamic nature of today’s systems and softwares will inherently make them heterogeneous in nature. There will be difference in the load and utilization of the different resources and this might introduce heterogeneity in them. Some network links might be over-utilized, some under-utilized, some disks might store more data ,some nodes might become hotspots due to the uneven distribution of data/computation. This kind of heterogeneity is dynamic. We need to develop systems that are aware of this type of inherent heterogeneity and enable it to adapt to the constantly changing environment. Storage in a distributed environment has multiple attributes to optimize on. We have different kinds of storage , each with different amount of capacity and performance characteristics. We need to use solid state drives and memory more judiciously compared to tapes or hard disks. Each one of these devices have different power consumption, cooling requirements and network requirements. So how do we decide where to place the data such that we optimize on a combination of these factors. We studied tackling this problem in 2 different kinds of storages.  Distributed block based file systems such as Hadoop Distributed File System(HDFS)  Distributed Key-Value stores We studied 2 different approaches for these two areas. For block based distributed file systems, we proposed an approach of modelling the different parameters of heterogeneity such as latency, bandwidth, power usage, etc as a distance measure in a multi-dimentional space and then performing dimensionality reduction on it to finally reduce it to a simple optimization problem. For the Key-Value store, we concentrated on modifying how the partitioning and assignment of servers occur in consistent hashing and optimized it on the different parameters by treating it as a bidding problem with the different systems in the consistent hash ring being the bid items and the partitions being the bidders. In both cases, we developed a prototype and benchmarked them thoroughly using both standard benchmarks and different load scenarios to test their adaptive nature. They showed better performance and interesting load-balancing and placement characteristics. The next area that we dealt with is networking. In a datacenter, the network contains data flows from different kinds of applications. Each kind of flow has a requirement. For example, Youtube is sensitive to bandwidth, VoIP traffic is sensitive to latency and network jitter, etc. In traditional networks, reacting to these flows were very hard, as the protocols defined were meant to be generic. However, with the advent of software defined networking, we are no longer restricted to these legacy protocols. We can define protocols that are tailored to a network. We created a SDN controller based on openflow which uses a pre-defined profile for each one of these data flows. We intelligently route the different flows from different applications based on this information thereby optimizing on the combination of parameters defined in the profile. We tested it under different load scenarios and showed how the heterogeneity awareness h