Abstract

About 2% [1] of the dam failures are said to be due to seismic activity. Dams are extremely important lifeline structures and predicting possible damage in the face of an event is essential. Failure of dams due to earthquakes is extensively studied because while the earthquake cannot be terminated, the loss associated to the damage of the dam can be highly mitigated. The loss caused due to earthquake ground motions an be reduced by studying the damage to the dam design under various earthquake loading scenarios and iteratively modifying the design to be resistant to the highest earthquake loading possible in the area of construction. Several concrete gravity dams were severely shaken by earthquakes in the past[2]. Thus the study of response of dams against the seismic ground motions is very necessary. This work addresses the seismic analysis of concrete dams. For this purpose, the Koyna Dam has been cho- sen. Firstly, the performance of the Koyna dam subjected to the Koyna earthquake is evaluated through 2 numerical models : Linear dynamic analysis and Non-linear dynamic analysis. The results from the linear analysis and the non-linear analysis are compared. Another earthquake of similar intensity of the Koyna earthquake, but different parameters has been applied and the dam performance has been anal- ysed. It has been found that the earthquake with lesser peak ground acceleration (PGA) caused greater damage to the dam as compared to the earthquake ground motion with higher PGA. The IS Code con- siders the effects of peak ground acceleration predominantly and the aforementioned result proves that there is a need to study the response of the dam under other ground motions with varying ground motion characteristics of Amplitude (PGA), Frequency(Kramer Frequency F P ) and Duration (significant time duration T sig5%−95% ). To quantify the damage in Koyna dam, an incremental dynamic analysis has been conducted to study the impact of varying ground motion characteristics on the performance of the dam. Non-linear analysis is performed on the dam cross-section subjected to the ground motions. Engineering Demand Param- eters are obtained from the analysis. We aim to understand the damage through 3 damage parameters and classify the damage .The damage parameters are based on the frequently used Engineering Demand Parameters (EDPs). A relation between the damage parameters and damage states is established. Fur- ther, the damage is classified using Decision Directed Acyclic Graph Support Vector Machine (DDAG- SVM) to study the relation between ground motion parameters (GMPs) and the Damage Parameters for the separate classes. Finally, a shallow Artificial Neural Network is modelled to understand the correlation between GMPs and the Damage Parameters. The high correlation coefficient and low root mean squared error show that the correlation between ground motion data and system response can be predicted very precisely, proving that, performance based earthquake design can be extremely efficient and economical. A sensitivity analysis has been carried out to determine the relative importance of the 3 main ground motion parameters. Further, the seismic hazard assessment of the Koyna region using ground seismotonic research by previous researchers has been conducted. The possible threat to the dam and the return periods of the earthquakes have been calibrated. Based on the ground motion data available in the Koyna region, po- tentially damaging earthquakes, selected based on results obtained from ANN model, have been selected and scaled to the peak ground acceleration found from the seismic hazard assessment. Taking the extent of tensile damage to increase linearly with the increase in PGA for a ground motion (Established by the analysis of Koyna dam under koyna earthqauke scaled to 10 PGAs) , the damage possible between 2 scaled PGAs of a ground motion can be linearly interpolated. Based on this the demand of koyna dam in the region has been assessed probabilistically. The range of damage capable on the body of the dam, from minimum to maximum, has been obtained by subjecting the dam to only 4 ground motions (scaled to 3 PGAs) selected based on the ground motion parameter study conducted in this work. A computationally cheaper, Incremental dynamic analysis has been proposed.

Abstract

Curriculum Learning (CL) strategies have been proposed for text classification tasks. These strategies propose “difficulty” score for each training sample. Curriculum Learning hypothesize that within this ordering, training on the easier samples first followed by the difficult, leads to an increment in performance. The idea is derivative of cognitive science’s theory of how human and animal brains learn, and that learning a difficult task can be made easier by phrasing it as a sequence of easy to difficult tasks. Application of curriculum learning for sentiment analysis is a rather new topic and hence requires through experiments and analysis. We experiment the effectiveness of popular language model BERT coupled with curriculum learning. Previous works have approached Curriculum Learning especially for text classification tasks such as sentiment analysis in two major pacing methods - Baby Steps and One Pass. Pacing methods decide how the model observes the curriculum divided data irrespective of the architecture and the curriculum strategy. Baby Steps proposes increasing the training data for the classification model in a cumulative fashion. This implies that model is trained on an easy set and then harder data is added to the previous set. This makes the training to be time consuming. As opposed to Baby Steps, One Pass proposes making distinct, mutually exclusive sets of the training data and trains on each one of these sets one after the other. However, in practise, One Pass while being faster, always was shown under-performing Baby Steps or even no curriculum. We analyze the reasons behind the consistent failure of One Pass. Our experiments show that One Pass suffers from Catastrophic forgetting every time its trained on the next set leading to model forgetting the previous set. We further derive an effective curriculum strategy based on SentiWordNet for sentiment analysis. Unlike sentence length strategy which proposes training network on samples ordered by sentence length, proposed strategy trains the network based on a difficulty ordering according to the task of sentiment analysis. Our results across multiple deep learning architectures demonstrate that this is an important prerequisite for an effective curriculum strategy. We then analyze curriculum learning’s relation to difficulty of a task by experiments with BERT. Previous works have suggested that curriculum learning shows higher improvement for harder tasks and low improvement for easier tasks. We call this Task Difficulty Hypothesis. We experiment on multiple synthetic datasets to prove this hypothesis more strongly. Finally, we visualize curriculum learning through a novel attention movement visualization methodology. These visualizations build a qualitative study to aid in understanding how curriculum learning works. We see that complicated tasks are broken down into easier sub-tasks which are addressed in a step by step manner by curriculum learning.

Abstract

As more robots continue to pervade our workplaces, homes, and lives, developing accurate and robust algorithms for their effective and safe operation is of paramount importance. We see more and more of such robots being used for various applications and we certainly have a new future ahead of us filled with smart robots navigating autonomously, be it a vacuum cleaner robot or logistics drone or an autonomous car. There is a plethora of ongoing research work trying to improve upon various algorithms for autonomous navigation of robots. Object detection is one such research area which has gained significant momentum. The 6 degree of freedom object pose estimation enables the robot to localize the object in the surroundings and perform its task accordingly. We see that there have been some very interesting work done for 6DOF pose estimation from monocular images. In this thesis we propose a new method for 6DOF object pose estimation using a single monocular camera with the assumption of known camera height. We detect the objects in the RGB images using deep learning and use a renderer called neural renderer to optimize for the 6DOF pose of the object. Furthermore we also explore how object detection can aid monocular SLAM. Here we propose the first most principled formulation of it’s kind which incorporates the object optimization into the bundle adjustment of SLAM, we call it the joint bundle adjustment. This joint formulation allows us to optimize the object shape, pose, and also the 3D semantic points and camera localization in a joint fashion.

Abstract

Medical imaging has been aiding diagnosis and treatment of diseases by creating visual representations of the interior of the human body. Experts hand-mark these images for abnormalities and diagnosis. Supplementing experts with these rich visualization has enabled detailed clinical analysis and rapid medical intervention. However, deep learning-based methods rely on abundantly large volumes of data for training. Procuring data for medical imaging applications is especially difficult because abnormal cases by definition are rare and the data, in general, requires experts for labelling. With Deep learning algorithms, data with high class imbalance or of insufficient variability leads to poor classification performance. Thus, alternate approaches like using generative modelling to artificially generate more data have been of interest. Most of these methods are GAN [11] based approaches. While they can be helpful with data imbalance they still require a lot of data to be able to generate realistic images. Additionally, a lot of these methods have been shown to work on natural images where the images are relatively noise-free and smaller artifacts aren’t as damaging. Thus, this thesis aims at providing synthesis methods which overcome the limitations of smaller datasets and noisy profile. We do this for two different modalities, Fundus imaging and Optical Coherence Tomography (OCT). Firstly, we present a fundus image synthesis method aimed at providing paired Optic Cup and Image data for Optic Cup (OC) Segmentation. The synthesis method works well on small datasets by minimising the information to be learnt by leveraging domain-specific knowledge and by providing most of the structural information to the network. We demonstrate this method’s advantages over a more direct synthesis method. We show how leveraging domain-specific knowledge can provide higher quality images and annotations. Inclusion of these generated images and their annotations in training of an OC segmentation model showed a significant improvement in performance, thus showing their reliability. Secondly, we present a novel unpaired image to image translation method which can introduce abnormality (Drusen) to OCT images while avoiding artifacts and preserving the noise profile. Comparison with other state-of-the-art images to image translation methods shows that our method is significantly better at preserving the noise profile and is better at generating morphologically accurate structures.

Abstract

High-utility itemset mining (HUIM) has emerged as an important research topic since the quantity and profit factors employed to mine the high-utility itemsets (HUIs). In HUIM, utility measure used to extract high-utility itemsets from the given transactional database. An itemset called a high-utility itemset if it satisfies a user-specified minimum utility (minU til) constraint. Another distinguishing characteristic of HUIM is identifying interestingness about the periodic behavior of high-utility itemsets in the given transactional database. Finding such itemsets have many real-world applications. As an example, such knowledge can identify recurring customer purchase behavior in retail databases. The popular adoption and successful industrial application of the HUIM model suffer from the issue of computational expensiveness. Furthermore, the existing approach to extract periodic behavior of highutility itemsets fails to discover high-utility itemsets that have exhibited partial periodic behavior in the database. In this thesis, we have proposed two improved HUIM approaches. First, to improve the performance of HUIM, we have proposed a generic high-utility frequent itemset model to find all itemsets in the database that satisfy user-specified minimum support and minimum utility constraints. Two new pruning measures, named cutoff utility and suffix utility, are proposed to reduce the computational cost of finding the desired itemsets. A single phase fast algorithm, called High-Utility Frequent Itemset Miner (HUFIMi), is introduced to efficiently discover the itemsets. Experimental results demonstrate that the proposed algorithm is efficient. Second, to enable the extraction of knowledge about the partial periodic behavior of customers, we have proposed a flexible model called Partial Periodic High-Utility Itemset Mining (PPHUIM). The proposed model discovers only those interesting high-utility itemsets that occur at regular intervals in a given temporal transactional database. The proposed model allows the variation of external utilities and exploits the notion of periodic support. An efficient depth-first search algorithm, called PPHUI-Miner, has been proposed to enumerate all partial periodic high-utility itemsets in a given temporal transactional database. We have demonstrated the efficiency of the proposed approach through extensive experimental results. Overall, we have proposed improved approaches to mine high-utility frequent itemsets and partial periodic high-utility itemsets from transactional databases and demonstrated the efficiency through extensive experimental results.

Abstract

Dependency Parsing is extremely useful in several downstream NLP tasks. However dependency parsers are not available for several Indian Languages. The primary reason for that is the unavailability of annotated treebanks. Telugu is a resource poor language and the existing Telugu treebank is very small in size. In this work, we strive to extend and improve the existing Telugu Dependency treebank annotated with Paninian dependencies released as part of the ICON 2009 shared task on Indian Language Parsing. The existing treebank consists of around 1600 sentences and we extend this treebank by another 987 sentences and clean up the existing treebank. The final extended treebank consists of 2436 sentences. The original treebank is only annotated with inter-chunk dependencies. We automatically annotate the intra-chunk dependency labels for the extended treebank using a Shift-Reduce parser based on Context Free Grammar rules written for Telugu. Annotating the intra-chunk dependencies finally provides a complete parse tree for every sentence. We also convert the treebank from Anncorra POS schema to the latest BIS POS schema. In the last decade, there has been an increased focus on multilingual NLP. The Universal Dependencies (UD) project has been created to promote multilingual research on syntax and parsing and to facilitate cross-lingual learning experiments. In this work, we automatically convert the existing Telugu Paninian dependency treebank to UD. We also try to build an easily usable and robust dependency parser for Telugu. Previous work on Telugu dependency parsing was primarily focused on either rule-based approaches or data-driven statistical approaches using Malt Parser. These approaches typically made use of intermediate tools like POS tagger, morph-analyzer, shallow parser etc which are expensive to build. Therefore, in this work we propose to bypass the pipeline approach and develop an end-to-end dependency parser for Telugu based on the recent developments in the field of NLP such as contextual vector representations. We train a BERT model on the Telugu Wikipedia data and use vector representations from this model to train the parser. Each sentence token is associated with a vector representing the token in the context of that sentence and the feature vectors are constructed by concatenating two token representations from the stack and one from the buffer. We put the feature representations through a feed forward network and train with a greedy transition based system. The resulting parser has a very simple architecture with minimal feature engineering and achieves state-of-the-art results for Telugu. We report the parser results on the Telugu Paninian dependency treebank and UD treebank.

Abstract

High research efforts were put in last century to make systems that could interface people. With the beginning of twenty century, this trend has shifted from systems that interface with people to system that interact with things. Consequently, a huge enthusiasm grew up in researchers, and a new term Internet of things (IoT) is coined. Expectation of these IoT applications have changed from time to time. Current and future emerging IoT applications demand ultra low power, so that these devices could survive with miniaturized batteries. Besides this, lower form factor is desired for biomedical applications to reduce invasive surgeries, as well as to reduce cost of the device. A high supply insensitivity is also desired in these applications. With all these constraints in play, we have put efforts to design current reference and temperature sensor, as they are present in almost every analog and mixed signal system. This thesis focuses on designing a high PSRR ultra low power current references with much reduced form factor and cost. Efforts were first put to develop a high PSRR current reference with process stabilized accuracy. The current reference achieves PSRR of 74dB and line sensitivity of 0.05%. It works for supply voltage variation from 1.4V to 3.6V. The current reference of 50µA and 50nA attain temperature coefficient of 11.6 ppm/oC and 12.2 ppm/oC respectively for the temperature variation of - 55oC to 125oC. The 5MΩ on-chip resistance is required to achieve 50nA current in our architecture. This large resistance increases chip area, hence cost. It has been observed that form factor is compromised in the proposed architecture. Consequently, we have come up with an idea of resistance amplification, that is to get characteristics of large resistance in smaller area. This idea is generic and applicable to many other fields. By integrating resistance amplifier in the proposed current reference architecture, A High PSRR ultra low power reference is achieved with much lesser area. This current reference shows the best figure of merit in terms of area and accuracy till now. However, this reference similar to any other current reference requires post silicon trimming to account for dc shift with process change. Manual trimming is most followed technique to achieve this despite its high cost. Lastly efforts were put to propose an auto-calibration system that replicates the accuracy of manual trimming at much reduced cost.

Abstract

Accurate prediction of structures and understanding the underlying mechanism behind the dynamics of biopolymers are invaluable to understand various biological applications. The structure and dynamics of molecules are related to its function and have major roles in disease biology, gene regulation, and many other cellular processes. To pave the way towards disease diagnosis, treatment, and faster drug discovery process, it is essential to understand the mechanism behind complex biological processes at the molecular level. Molecular dynamics (MD) simulations and various enhanced sampling simulations are capable of capturing atomistic details of various biological processes such as protein/RNA unfolding, DNA repair mechanism, enzyme kinetics, targeted gene therapy, and membrane transport mechanism. Moreover, molecular simulations provide useful insights to unravel the dynamic nature and thermodynamic stabilities of nucleic acids, proteins, and other complex macromolecules. The findings from this study, provide a detailed understanding of the mechanism behind selected biological phenomena such as urea mediated RNA unfolding, protein unfolding, the role of modified nucleic acids in antisense therapy, and DNA repair mechanism using biomolecular simulation techniques and various theoretical, computational approaches. Techniques like all-atom MD simulations, thermodynamic integration, and umbrella sampling simulations have been employed to elucidate the molecular-level mechanism of these biological processes. The addition of external perturbations to the system of interest is useful to examine the chemical nature, structural details, and dynamic progress of molecules with respect to time. In the current work, we aim to probe the structural, energetic, and thermodynamic aspects of various biomolecules such as nucleobases, amino acids, DNA duplexes, chemically modified nucleic acids, and DNA-protein complexes. The main objective of the current work is to understand the effect of chemical and structural perturbations on the dynamic behaviour and thermodynamic properties of the biomolecules mentioned above to understand their roles in various biological processes. Chemical perturbations like osmolyte (urea) effects, ionic strength effects, and structural perturbations like nucleic acid modifications (backbone or nucleobase modifications) were used to understand their roles in modulating the dynamic behaviour of these micro and macromolecules. The effect of urea on building blocks of RNA and protein i.e. nucleobases and amino acids, respectively, have been investigated to understand the urea mediated RNA and protein unfolding mechanism. Another chemical perturbation such as ionic strength effect on the backbone modified nucleic acid i.e. Peptide Nucleic Acid (PNA) has been studied. In this thesis work, we mainly focus on backbone modified nucleic acids such as PNA-DNA duplexes which are generally known as antisense oligonucleotides. Antisense oligonucleotides are at the forefront of targeted gene delivery for emerging disease therapeutics as it holds promising features like resistance to nuclease degradation, longer halflife compared to naturally occurring nucleic acids, and higher stability in the cellular environment. To study the effect of structural perturbations, the positively charged (Lys-modified) and negatively charged (Asp-modified) side-chains were incorporated on the neutral backbone of the PNA molecules in PNA-DNA duplexes, which has a direct correlation with the binding affinity of these molecules. Apart from the chemically modified nucleic acids, this thesis work also focuses on one of the naturally occurring nucleobase lesions known as thymine glycol (TG). TG lesion is repaired by an enzyme known as DNA Glycosylase through a process called base-flipping. vii viii The DNA-repair mechanism associated with the TG lesion in the absence and presence of enzyme is studied at the molecular level. The first part of the thesis gives detailed understanding about energetics, structural and dynamic properties of building blocks of RNA and proteins i.e. nucleobases and amino acids respectively, in different concentrations of urea solution. With this study, we could elucidate the chemical nature of solute-cosolvent interactions and the driving force behind these interactions. This provides fundamental insights into the comprehensive molecular mechanism behind the urea-assisted RNA and protein unfolding. The next part of the thesis focuses on chemical modifications of nucleic acids. The PNA work showed the binding affinity of PNA strands towards DNA strands is dependent on the chemical nature of the PNA strands. Moreover, our simulation results showed the diverse effect of salt concentrations on the binding affinity of PNA strands towards DNA. This study shows that molecular simulations can help in choosing the correct choice of modification and the optimum salt concentration

Abstract

Reinforcement learning is an area of machine learning where an agent learns to solve sequential decision-making problems by performing actions, receiving feedback, and updating its knowledge about the environment. Although there have been many successful RL applications, scaling up to large state spaces remains a challenge. The teacher-student framework aims to improve the sample efficiency of the conventional Reinforcement Learning algorithms by deploying an advising mechanism in which a teacher helps a student by guiding its exploration. Prior work in this field has considered an advising mechanism where the teacher advises the student about the optimal action to take in a given state. Although such guided exploration helps the student to converge faster, real-world teachers typically do not guide the student only about the best possible action in a particular situation. They can leverage their domain expertise even further to provide the student with richer and more informative signals about the given environment and its state space. Using this insight, we propose to extend the current advising framework wherein the teacher would provide not only the optimal action but also a qualitative assessment of the state. We introduce a novel architecture, namely Advice Replay Memory (ARM), to effectively reuse the advice provided by the teacher. We demonstrate the robustness of our approach by showcasing our experiments on multiple Atari 2600 games using a fixed set of hyper-parameters. Additionally, we show that a student taking help even from a sub-optimal teacher can achieve significant performance boosts and eventually outperform the teacher. Our approach outperforms the baselines even when provided with comparatively suboptimal teachers and an advising budget, which is smaller by orders of magnitude. We also present an interesting ”non-interactive learning” setting where the teacher can compose a batch of advice tuples and provide it to a student at the beginning of its learning period. From thereon, even if the teacher goes offline or does not pay any attention, the student might still be able to gain reasonable performance boosts. The main highlights of this thesis are the following: (a) We supplement the student’s knowledge by providing the state category as advice, (b) We introduce an Advice Replay Memory so that the student can effectively reuse the teacher’s advice throughout its learning process, (c) We provide with the student the ability to achieve significant performance boost even with a coarse state categorization, (d) We enable the student to eventually outperform the teacher that is advising it.

Abstract

Today’s world has a great desideratum of the high power source. The laser is one of the solutions in this field. Multiple laser sources are combined with coherent or incoherent beam combination tech- niques to generate several Kilowatts or even Megawatts of power. The coherent beam combination 2 (CBC) produces high power ( N times of individual source beam). Maintaining the coherency among ∼the beams is a challenging task. The power of a single laser source should be limited within Watts in the case of CBC. In contrast, Incoherent Beam Combination (ICBC) is a robust, versatile, and efficient way of achieving high power levels ( N times of individual source beam) with independent beam sources. ∼ Higher atmospheric turbulence affects the beam significantly than the lower turbulent atmosphere. In a higher turbulent atmosphere, the beam spot enlarges, and thereby the average beam intensity on the target decreases. In this thesis, ICBC has been elaborately explained and also analysed for the atmo- spheric effects. It is shown that Residual Mechanical Jitter (RMJ) of the Beam Directing System (BDS) affects beam pointing accuracy more in lower turbulence than in higher turbulence. The acceptable RMJ for a BDS has also been determined as a function of pointing accuracy for single beam and combination accuracy for multi-beams. Atmospheric condition, as well as other disturbances which degrade laser performances, cannot be avoided. Hence, the user tries to nullify their effects by various mechanisms. The laser focusing mechanism is one of them. A highly focused beam tends to diverge lesser and deliver more intensity to the incident plane after propagating through the atmosphere. Uniform Rectilinear Motion (URM) actuator is the crucial element for changing the laser focusing range dynamically. In this thesis, the one- dimensional URM specification has been evolved based on the requirement of laser focusing accuracy. It is observed that focusing on a lower range demands higher URM accuracy. The URM accuracy requirement has been evaluated as a function of the average intensity of a practical system to a perfect system. In BDS, multiple sensors and laser channels are placed to transmit the laser on the tracked target. It is difficult to synchronise multiple sensors and laser channels for tracking the target within a specific range with various system limitations. The cameras and laser channels must be placed closer to achieve a better target position accuracy and thereby to deliver higher intensity with better beam focusing. Hence, the sensor harmonisation technique needs to be analysed to improve BDS performance. In this thesis, three different BDS architectures are analysed with a different number of channels. The shapes of the beam directing systems are also different. Two methods of sensors harmonisations have been elaborately explained here, i.e., the absence of auto-boresighting and the presence of auto-boresighting. The laser system with auto-boresighting capability is more efficient in tracking the dynamic target than the system without this capability. In case of the absence of auto-boresighting facility, it is observed that the initial mechanical boresighting of all sensors at a certain distance will result in better target visibility and tracking capability. It is also observed that the introduction of mechanical tilt for each channel axis of BDS provides better tracking with lesser Steering Mirror (SM) deflection. The fluence requirement for engaging both the static and dynamic objects have been analysed here. The power requirements of the laser system are estimated with available knowledge of lethal object fluence. Power can be enhanced by increasing the number of laser sources. The sequence of operations of the beam combination system for efficiently damaging the object has been proposed in this thesis. Here, the optimum number of laser channels is carried out based on the object’s properties and the system’s capability to minimise laser power wastage. When a large area is required to be protected by a number of laser systems, the position of each system is an essential factor to deliver significant laser energy to each corner of the area. In this thesis, multiple scenarios have been discussed with different separation among the laser systems and analyses the effect of inter-laser system distance in terms of combined average intensity. At the end of this thesis, a comprehensive system specification has been evolved to achieve a higher power density at the incident plane and a better pointing accuracy with lesser SM deflection. This system performance has been improved with multiple beam combination by ICBC. The availability of each subsystem is also taken into consideration. This thesis explains the methodology of designing an efficient state of the art beam combination system.