Abstract

Science journalism is the art of conveying a detailed scientific research paper in a form that non-scientists can understand and appreciate while ensuring that its underlying information is conveyed accurately. It aims to transform jargon-laden scientific articles into a form that a common reader can comprehend while ensuring that the meaning of the article is retained. It plays a crucial role in making scientific content suitable for consumption by the public at large.Recent advances in Deep Learning research and it’s applications in natural language processing have made way to impressive results in Natural Language Generation. We leverage these advances to explore the possibility of their use in journalism, science journalism in particular, as comprehension of scientific content is much harder challenge than most of the other forms of content, like shorthand, which journalists use while writing articles. In this work, we introduce the problem of automated science journalism and present ways to automate some parts of the workflow by automatically generating the ‘title’ of a blog version of a scientific paper. We have built a corpus of 87, 328 pairs of research papers and their corresponding blogs from two science news aggregators and have used it to build Science-Blogger a pipeline based architecture consisting of a two-stage mechanism to generate the blog titles. To demonstrate the models, we built an interactive tool, where a user can give abstract and title of a research paper, which would be processed by our APIs to produce a blog title, along with some relevant information about the model used for the generation. Evaluation using standard metrics indicate viability of the proposed system. Twitter is another social platform where a lot of content of every imaginable category is shared. Naturally, it is also one of the popular media for sharing scientific work apart from blogs. So, we have also experimented with our model to generate tweets, which are roughly of similar length as blog titles. Evaluation on generated tweets also showed promising results. Although significant advances are made in Natural Language Generation, we noticed that evaluation methods didn’t catch up with these advances. Popular metrics like ROUGE and BLEU are based on word overlap. None of these metrics ensure that the generated sentences do not contradict the truth (actual content in the article). It is quite possible that the generated sentences have high overlap with human-written sentences while still contradicting the human-written sentence. Misrepresenting facts is considered a serious problem in journalism. So, a system automating it should explicitly ensure that such misrepresentation and false statements are not generated. Hence, we also formulated a metric to evaluate sentences generated by such automated abstractive natural language generation systems called SSAS: Semantic Similarity for Abstractive Summarization. Ideally a metric evaluating an abstract sys-tem summary should represent the extent to which the system-generated summary approximates the semantic inference conceived by the reader using a human-written reference summary. Most of the previous approaches relied upon word or syntactic sub-sequence overlap to evaluate system-generated summaries. Such metrics cannot evaluate the summary at semantic inference level. Through this work we introduce the metric of Semantic Similarity for Abstractive Summarization (SSAS), which leverages natural language inference and paraphrasing techniques to frame a novel approach to evaluate system summaries at semantic inference level. SSAS is based upon a weighted composition of quantities representing the level of agreement, contradiction, topical neutrality, paraphrasing, and optionally ROUGE score between a system-generated and a human-written summary.

Abstract

Automatic Speech Recognition (ASR) systems have witnessed a lot of progress in the past decade. In high resourced scenarios like English, ASR systems have shown performance compared to human parity level on specific tasks. Speech recognition systems for Indian languages are less studied compared to other high resourced languages like English. India is a developing country with large emerging markets for speech recognition technology. Developing Indian language ASR systems requires certain challenges to addressed which are innate to Indian languages. India is a multilingual society. India has 23 official languages. To penetrate deep into the Indian markets, ASR systems which can be operated in multiple languages are to be developed. Collecting data from a multilingual environment is much tedious task than to acquire data from a monolingual environment. Often Indian language ASR systems have to be developed for low-resourced scenarios. Apart from multilingual nature, bilingualism is very prevalent in the Indian population which leads frequent code-switching and word borrowing between any two languages. Operating parallel ASR systems with code-switching capabilities in Indian scenarios is a huge challenge. This motivated us to work towards multilingual ASR systems which can handle codemixing and word borrowing efficiently. In this thesis, we address various issues related to development of ASR systems for Indian scenarios. An integrated ASR system is developed using common phone-set which can efficiently handle multilingual code-mixed speech. Acoustic modeling approaches such as HMM-GMM, HMM-SGMM and RNN-CTC have been studied to find the most suitable acoustic model. Various acoustic modeling units such as context independent phone, context dependent phone and syllables are explored to find the most suitable unit for developing joint acoustic models. Residual connections have been explored to improve the performance of the joint acoustic models. Studies directed towards supplementing the conventional features along with articulatory features have been explored for developing mulit-lingual ASR systems. Fricative landmarks are detected and the detected landmarks are used as the features for improving the performance of multilingual ASR system. Distinctive features from speech are modeled using a statistical approach and their relevance for improving the performance of a multilingual ASR is explored. In a low resourced scenario when the data is pooled from multiple sources, the meta-level information about the speaker is not accessible. A speaker normalization to handle those scenarios is explored. Some major conclusions from the work are: • Using Common phone-set for training joint acoustic models offers an attractive solution developing ASR systems to handle multilingual and code-mixed scenarios. • Acoustic models that can model context independent phones have performed better compared to the context dependent tri-phone units. RNN-CTC based joint-acoustic model has performed better that HMM-SGMM model. • Using residual networks to develop the joint acoustic models have stabilized the training. They have improved the performance of the acoustic model when the model is sufficiently deep. • Fricative landmarks when fused with the features have improved the performances of the ASR systems when the data size is small, with relatively larges sized datasets the performance have not improved significantly. • Using distinctive features predicted from DNNs along with the input features have not significantly improved the performance of multilingual ASR system. • When the meta-information about the speaker ID is not available, Using the speaker codes derived from a speaker ID network can be used to improve the performance of a multilingual ASR system

Abstract

Empathy allows one to understand distinct cognitive and emotional states of another. Empathy is critical for social interactions and survival. For researchers it is an interesting but challenging proposition to study either in an observer or in the perpetrator of a crime. In the context of rape, observer’s empathic response is required to help the victim overcome the trauma and deliver justice, while lack of empathy in the perpetrator is of interest to understand deviant behavior. Empathy or the lack of it is influenced by perceptions, from self-experiences or those imposed by social stereotypes, and this is in particular evidenced in attitude towards rape victims. The ability to empathize with a rape victim or display the empathy to not commit an aggressive criminal act is based on complex processing of one’s expectations of the physical characteristics, socioeconomic status, and perceptions of credibility and hence deemed to be highly subjective. Each of societal stereotypes requires in-depth study to model expected empathic response or profile deviation. In this thesis we examine a small but critical parameterthat is the rape victim’s appearance and its relation to observer’s empathy. Our hypothesis is loosely based on observations of typical responses post-rape incidence are reported, which is that female victim carrying a non-traditional (as defined by a particular culture- that is, short hair with no bindi/dot on forehead) look, with a darker skin tone and resident of a village evokes lower empathy response in the common citizens. A set of two experiments were designed to understand behavioral (pupillary response) and the neural activations (functional magnetic resonance imaging) associated with empathy networks. In this first study, for stimuli the average female Indian computer-generated faces are photoshopped to reflect a) variation in skin tone/color, b) (non)traditional appearance and c) inclusion of place of residence(urban/rural). Prior to being shown the faces, the participants (20 male graduate students) were informed that the faces were of rape victims. The faces were designed to not show any expression, to reduce bias from facial cues. The results from a preliminary investigation, shown the presence of the motion/empathy network comprising of the insulaanterior cingulate cortex (ACC)-inferior frontal gyrus (IFG) areas to female faces of purported rape victims for all the category of faces, though higher activation in the Insula (area crucial for emotional empathy) was observed for traditional faces compared to non-traditional faces. Similarly, higher activation in empathy network was observed for fair/lighter skin tone faces compared to darker tone faces. No differential activation was found on the basis of place of residence. Interestingly, activations in the perspective or cognitive empathy regions covering the prefrontal cortex were not observed for any condition. In the second experiment, computer-generated faces were photoshopped to reflect lighter to darker skin tones, and pupil size changes as a function of reflected light were correlated to cognitive processes of ‘like’ through rating provided by the participants (70 engineering students). The hypothesis was that reflex reaction to skin tone was partly due to inadequate contrasts especially in darker skin tone before social bias of race is triggered. The results show slight differences in pupil size when perceiving a light tone face or a dark tone face but statistical significance was not found. The average values from the data indicate that people who ‘like’ dark color have smaller pupil size and larger pupil size was noticed in the group that ‘like’ lighter face tones. It is also observed that subjects who marked their self-color as the dark/medium tend to have a preference for the same skin tones in others. The results of the two independent studies provide preliminary evidence for social perceptions of physical appearance and the strength of the techniques (fMRI or eye tracking) to measure empathy and perceptions. Further studies on convicted rapists are required to understand empathy deficiency for rape victims and the underlying reasons for it.

Abstract

Hemiparesis or weakness on one side of the body, due to traumatic brain injury or stroke requires long-term expensive therapy to recover from either mild or severe motor impairment in the upper and lower limbs. Medical research has shown that timely scientific therapy can help regain most of the basic motor movements, while finer motor coordination requires a longer time. The most crucial factor is access to trained therapists and neuro-rehab centers equipped with state-of-the-art assistive systems for upper/lower limb exercises. Physiotherapy exercises can be accessed at dedicated rehabilitation centers or hospitals fitted with expensive clinically tested equipment or with gym equipment modified for specific exercises. Access to clinical rehabilitation therapy for economically poor patients and importantly rural population is sparse in India. They are forced to move to towns or cities with their families for a duration ranging from 3-6 months for therapy sessions disrupting the families and adding to their financial burden. Additionally, the current sessions are unmotivating requiring a doctor or a therapist to constantly monitor their progress. The research presented in this thesis aims to address some of the above-mentioned issues. The systems designed and tested as part of this work focus on regaining basic limb movements for stroke/spinal cord injury patients. The idea originates from integrating games and use of bio-feedback control systems to provide an immersive virtual environment while recording the limb movement progress using wearable or embedded sensors. The analyzed data collected from the gameplay and sensors are remotely accessed by doctors/therapists for monitoring the patient. Three exergame systems i.e. playing games while engaged in physical exercise, were designed, developed and tested on patients. To address joint contracture hemiparesis or increased hypersensitivity in the lower limb extremity, an exergame called Run was designed. A crucial step to regain strength and balance in the lower limbs is the ability to transfer weight from one leg to the other as well as muscle control required to position a foot forward or backward. With these movement requirements, an avatar controlled by the patient is made to run, collect coins and avoid collision with intermittent obstacles. When the avatar collides with an obstacle, he/she needs to perform a particular foot movement exercise to move ahead. The signals to control the gameplay are collected from the sensors positioned to detect the transfer of weight from one lower extremity to another Hypertonia and apraxia (difficulty in arm movement) are two other conditions developed after a stroke. The damage after a stroke leaves muscles and the peripheral nerves incapable of receiving motor signals from the spinal cord. Over time, the muscle tone reduces, and the patient is unable to lift or move the arm. In many cases, the arm and fingers do not respond to external stimulation. Post-stroke, there is an urgency is to recover muscle tone and control with rigorous exercise. Towards this, a game and a sensors circuit were designed, developed and implemented. The game titled Catch, has a simple user graphical interface and requires the patient/player to control a virtual basket to catch apples dropping from a tree. The first step is to swing the arm in an arc of 30-150 degrees over proximity sensors followed by the tactile pressure of the fingers on an object. Embedded pressure sensors were used to collect the applied pressure from the fingers. The signals from the sensors are used to control the game. Motivation is considered a major factor for rehabilitation post stroke. To examine the influence of spirituality and religious experiences in neuro-rehabilitation exercises, a game simulating a famous pilgrim centre was developed. It has also been reported that Post-Stroke Depression (PSD) can impair recovery and religious faith is reported to have a strong positive influence. Hence, the third exergame called Temple Steps was designed and developed. The game had two versions, one with the temple as a theme wherein the participant climbs the stairs to reach the main deity and the second version with trekking as the theme. To test participants motivation stemming from belief, both the versions were used for comparative analysis. The physical interface used was a basic gym stair exercise machine to which sensors and controls for navigation were fitted. Neuropathic pain resulting from conditions such as phantom limb, stroke or chronic regional syndrome Type 1 can be treated with mirror therapy. The last phase of my research was to test the working of Dr. Ramachandrans mirror box on stroke patients. There is no consensus on its working and speculation often lacks the neuroscientific proof. So, a mirror box fitted with cameras, leap motion, and mayo band was developed for measuring the strength of peripheral nervous system signal to the muscle in the aff

Abstract

Turbo codes perform excellently with regard to error correction in physical layer of wireless communication networks. Near Shannon limit bit-error-rate performance of turbo decoders is the key factor that makes it an important aspect of channel decoding. With the surge of expanding wireless communication and very large scale integration (VLSI) industries around the world , it is essential to design a VLSI architecture of flexible data rate channel decoder for targeting the physical layer of next generation 5G standards. With the Moore’s law approaching towards the steady state, it is high-time for the researchers to work on new architectures to enhance the performance for any application, rather relying on CMOS technology scaling. Hence, this motivates us to carry out this work on VLSI architectures for channel decoders. We propose flexible-architecture for soft-input soft-output (SISO) decoder with radix-2/4/8 modes to support multiple data-rates. This work presents designs of major internal blocks of SISO decoder using extensive steering logic to support multiple radix operating-modes. These architectures enable efficient clock-gating of our decoder for low-power consumption in different operating modes. Subsequently, we have aggregated eight SISO decoders with quadratic-permutation polynomial (QPP) interleavers/de- interleavers to design parallel turbo decoder architecture which can operate in multi-radix mode. Suggested SISO decoder is application-specific integrated-circuit (ASIC) synthesized and post layout simulated in UMC 65 nm-CMOS process. Performance analyses in additive-white Gaussian-noise (AWGN) channel environment showed that the bit error rate (BER) of 10 -4 could be achieved at 5 dB and 0.8 dB for SISO and turbo decoders respectively. Implementation result shows that the suggested SISO decoder could achieve throughput in the range 270-810 Mbps with the corresponding power consumption range of 12.24-37.67 mW. In comparison to the state-of-the-art, our design achieved 38% higher throughput and 61% lower power consumption. Similarly, our multi-radix parallel-turbo decoder is hardware prototyped in 28 nm-CMOS Zynq-FPGA board. It delivers a range of data-rates from 160 to 515 Mbps operating at 160 MHz of clock frequency for 8 iterations.

Abstract

Field Programmable Gate Array (FPGA) is gaining popularity in robotics applications due to low cost, portability, low power dissipation, high speed and seamless interface to the hardware. On the other hand, robotics algorithms are mostly arithmetic in nature so they can be easily parallelized and implemented on FPGA. Asynchronous FPGAs have gained remarkable research interest over the last decade.FPGA exceeds the computing power of software-based implementations by breaking the paradigm of sequential execution and accomplishing more per clock cycle by enabling hardware level parallelization at an architectural level. Introducing parallel architectures for a computationally intensive algorithm like Rapidly-Exploring Random Trees(RRT) will result in an exploration that is fast, dense and uniform. A rapidly-exploring random tree (RRT) is an algorithm designed to efficiently search non-convex,high-dimensional spaces by randomly building a space-filling tree. The tree is constructed incrementally from samples drawn randomly from the search space and is inherently biased to grow towards large unsearched areas of the problem. But the overall performance of the algorithm comes down due to high computation delay with the increase in the maps size and geometric constraints. One way to improve the performance is by parallelizing RRT. Here parallelizing RRT refers to multiple RRT modules working in parallel to achieve faster exploration of the map. And to accommodate all the data that is coming from multiple modules an architecture is required. So with the aim to minimize computation delay with the increase in maps size and geometric con- straints, we present the FPGA based hybrid architecture that allows multiple RRTs to work together combined with modified K-means sensitive to the geometric and kinematic constraints of the robot for accelerated and uniform exploration of the map. Through a cost function delineated in later sections, FPGA based combinatorial architecture delivers superlative speed-up but consumes very high power while hierarchical architecture delivers relatively lower speed-up with acceptable power consumption levels. The hybrid architecture combines the qualities of both combinatorial and hierarchical architecture. To determine the number of RRT nodes to be allotted to the combinatorial and hierarchical blocks of the hybrid architecture, a cost function, comprised of fundamentally inversely related speed-up and power parameters, is formulated. This maximization of the cost function, with its associated constraints, is then mathematically solved using a modified branch and bound, that leads to the optimal allocation of RRT modules to both blocks. For the initial placement of RRTs, we came up with a software-defined strategy for identification of these starting points using a modified K-Means. Strategized placing of the seeds (starting points) of each RRT module can lead to a more homogeneous and faster exploration as compared to the indiscriminate placement of the same. Further, to validate the empirical working of the architectures in a real-world scenario, a Zynq-SoC based differential drive robotic system was built as a test platform to run the architectures in real time in a complex environment filled with obstacles. It is observed that this hybrid architecture delivers the highest performance-per-watt out of the three architectures for differential, quad-copter and fixed-wing kinematics. The overall system is a hardware-software co-design to achieve real-time, ASIC like performance that delivers accelerated and homogeneous exploration.

Abstract

Field Programmable Gate Array (FPGA) is gaining popularity in robotics applications due to low cost, portability, low power dissipation, high speed and seamless interface to the hardware. On the other hand, robotics algorithms are mostly arithmetic in nature so they can be easily parallelized and imple- mented on FPGA. Asynchronous FPGAs have gained remarkable research interest over the last decade. FPGA exceeds the computing power of software-based implementations by breaking the paradigm of sequential execution and accomplishing more per clock cycle by enabling hardware level parallelization at an architectural level. Introducing parallel architectures for a computationally intensive algorithm like Rapidly-Exploring Random Trees(RRT) will result in an exploration that is fast, dense and uniform.A rapidly-exploring random tree (RRT) is an algorithm designed to efficiently search non-convex, high-dimensional spaces by randomly building a space-filling tree. The tree is constructed incrementally from samples drawn randomly from the search space and is inherently biased to grow towards large unsearched areas of the problem. But the overall performance of the algorithm comes down due to high computation delay with the increase in the maps size and geometric constraints. One way to improve the performance is by parallelizing RRT. Here parallelizing RRT refers to multiple RRT modules working in parallel to achieve faster exploration of the map. And to accommodate all the data that is coming from multiple modules an architecture is required. So with the aim to minimize computation delay with the increase in maps size and geometric constraints, we present the FPGA based hybrid architecture that allows multiple RRTs to work together combined with modified K-means sensitive to the geometric and kinematic constraints of the robot for accelerated and uniform exploration of the map. Through a cost function delineated in later sections, FPGA based combinatorial architecture delivers superlative speed-up but consumes very high power while hierarchical architecture delivers relatively lower speed-up with acceptable power consumption levels. The hybrid architecture combines the qualities of both combinatorial and hierarchical architec-ture.To determine the number of RRT nodes to be allotted to the combinatorial and hierarchical blocksof the hybrid architecture, a cost function, comprised of fundamentally inversely related speed-up and power parameters, is formulated. This maximization of the cost function, with its associated constraints, is then mathematically solved using a modified branch and bound, that leads to the optimal allocation of RRT modules to both blocks. For the initial placement of RRTs, we came up with a software-defined strategy for identification of these starting points using a modified K-Means. Strategized placing of the seeds (starting points) of each RRT module can lead to a more homogeneous and faster exploration as compared to the indiscriminate placement of the same. Further, to validate the empirical working of the architectures in a real-world scenario, a Zynq-SoC based differential drive robotic system was built as a test platform to run the architectures in real time in a complex environment filled with obstacles. It is observed that this hybrid architecture delivers the highest performance-per-watt out of the three architectures for differential, quad-copter and fixed-wing kinematics. The overall system is a hardware-software co-design to achieve real-time, ASIC like performance that delivers accelerated and homogeneous exploration.

Abstract

There are different aspects of daylight that has been proven to effect the human visual and non-visual system. Daylighting in buildings have a substantial impact on workers’ health, productivity, performance, and circadian system. It can enhance the overall experience of built spaces and benefit the inhabitants in many ways. However, it is necessary to control the daylight admission inside buildings to avoid visual and thermal discomfort. Automated shading and integrated lighting control systems are being used in buildings, for controlling the daylight either in closed loop or open loop system. A typical commercial closed loop system usually has a series of indoor photosensors integrated with dimmers to maintain desk illuminance levels. Unfortunately, these sensors are expensive to install at each desk, challenging to commission and difficult to calibrate therefore highly prone to errors. Whereas in open loop systems, sensors are mounted at external surface of façade to measure sky conditions and daylight availability. However, these devices are only capable of giving single specific measurements without accounting size, directionality, temporal and spatial dynamics of clouds. This leads to failure of the control system to respond in a timely fashion by inaccurately estimating visual discomfort parameters. A reliable estimation of sky conditions and indoor daylight metrics is crucial to open loop controls system. Currently, the daylighting and lighting design practices followed by architects and designers predominantly focuses on the visual characteristics of light. These characteristics include provision of sufficient work plane illuminance, colour rendition and appearance of lighting. There is little or no consideration imparted towards the circadian impacts of lighting and daylighting devices in design process. The impact of lighting in regulating the circadian rhythm in human body has been strengthen with the recent discovery of intrinsically photosensitive retinal ganglion cells (ipRGCs). However, there is lack of accessible instruments to measure the circadian lighting and therefore it’s difficult to monitor daylit spaces and incorporate circadian aspects of lighting in design process. To understand the impacts of lighting, it is essential to devise methods that can quantify both visual and non-visual parameters of daylighting. In this work, a system has been developed to operate a physical setup to calculate glare, illuminance, correlated colour temperature and circadian lighting in an indoor space using high dynamic range (HDR) imaging and control the window roller shades based on glare levels in an open loop system. This thesis gives the details of the system and its performance in various test beds. Following are the objectives: 1. Determine, validate and control simulation based indoor glare and illuminance levels using real time image-based sky maps. 2. Determine and validate correlated colour temperature and circadian lighting in two different experimental setups at two different locations:Results indicate that HDR image-based sky maps provide a good estimation of diffuse sky and take the surroundings, cloud dynamics into consideration as well. In the operational range, the errors observed are within 12% limit. However, larger errors are observed when lux is higher than 2000lux. These errors are of less importance since daylight control will be operational during that time. The circadian lighting measured in test setups vary significantly throughout the day. Lower daylight levels led to lower melanopic lux levels. The correlated colour temperature of the light provided by the tubular daylight device was generally found to be higher than that of the electric lights in test room. The HDR photography technique makes measuring circadian light accessible to all lighting professionals and facilitates field measurements of circadian lighting.

Abstract

Autism Spectrum Disorder (ASD) is a clinical umbrella term for neuro-developmental disorders that are characterized by atypical social behavior, including deficits in receptive and expressive language, theory of mind (TOM), and cognitive flexibility deficit. It encompasses autism, Asperger’s syndrome, pervasive developmental disorder not otherwise specified (PDD-NOS), and childhood dis-integrative disorder. Accumulating evidence indicates that ASD is associated with alterations of neural circuitry compared to typically developing (TD) individuals. Despite numerous studies, there is a lack of consensus on the nature of atypical functional connectivity in ASD. We hypothesize that studying the effects of age, disease and their interaction together using resting-state functional MRI (rs-fMRI) data can reveal crucial insights about the autistic brain. fMRI measures blood oxygen level dependent (BOLD) signals that indirectly maps the neuronal activity across different regions of the brain. In this thesis, we studied these effects using two paradigms: Firstly, we studied the effects of age, disease and their interaction on static functional connectivity by treating the brain as a complex graph in children and adolescents (9-16 years). Graph-theoretic analyses were performed on the functional brain network. We characterized the regions of interest (ROIs), connections and functional networks that are altered in development and in ASD. The multiple network measures representing the modular organization (integration and segregation) in the whole-brain were quantified. The results showed that ASD exhibits increased functional integration at the expense of decreased functional segregation. In ASD adolescents, there is significant decrease in modularity suggesting a less robust modular organization and an increase in participation coefficient suggesting more random integration and widely distributed connection edges. There is significant hypo-connectivity observed in the adolescent group especially in the Default Mode Network (DMN) while the children group shows both hyper- and hypo-connectivity. This lends support to a model of global atypical connections and further identifies functional networks and regions that are independently affected by age, disease and their interaction. Secondly, we have also explored flexibility of the brain as a novel approach to characterize ASD using the temporal dynamics of BOLD signals. The dynamic variability in the connection strength and the modular organization in terms of the measures flexiblity, cohesion strength and disjointness were explored for each subject to characterize the effects of age, disease and their interaction. In ASD, we observed significantly higher inter-subject dynamic variability in connection strength as compared to TD. This hyper-variability relates to the symptom severity in ASD. We found that the whole-brain flex-ibility correlates with static modularity only in TD. Further, we observed a core-periphery organization in the resting-state, with Sensorimotor and Visual regions in the rigid core; and DMN and attention areas in the flexible periphery. TD also develops a more cohesive organization of sensorimotor areas. However, in ASD we found a strong positive correlation of symptom severity with flexibility of rigid areas and with disjointness of sensorimotor areas. The regions of the brain showing high predictive power of symptom severity were distributed across the cortex, with stronger bearings in the frontal, motor and occipital cortices. Our study demonstrates that the dynamic framework best characterizes the variability in ASD and overcomes the limitations of the analyses that consider ASD as a single group. Overall, this thesis adopts a systematic approach to study pathology and development using static as well as dynamic functional connectivity. These are very useful in characterizing the topology of the brain along with the patterns in transient connectivity.

Abstract

The rise in the use and penetration of Artificial Intelligence/ Machine Learning in everyday life has led to many complex issues in Law and Policy. Chiefly, the governance of AI entities under the ambit of law. This thesis explores two issues in detail: General Data Protection Regulation (GDPR) floated ‘right to explanation’ and the problematic nature of Intellectual Property for AI.With the advent of GDPR, the domain of explainable AI and model interpretability has gained addedimpetus. Methods to extract and communicate visibility into decision-making models have become a legal necessity. The GDPR terms this as the ‘Right to Explanation’: Any person affected by the decision of an autonomous decision making system is empowered to seek an answer for the same. This is especially relevant in 21st century scenarios wherein AI systems are used in determining the credit worthiness, evaluating stocks, recommending news, screen candidates for jobs etc. In all these scenarios, the end user suffers a social, economic or a psychological impact by the discretion of the machine. Most of these AI systems function with complex black box models which have very little visibility in its inner workings. Even its developers might not be fully aware as to why and how these self learning systems adapt and react to new data either due to their evolving nature or its sheer complexity. In such a scenario, it is apt that appropriate justifications for the actions of the machine be generated. This thesis explores two such methods of generating explanations: an Antehoc technique conceptualized atop the existing SUMO Ontology and a Posthoc method constructed out of model interpretability techniques which generate contrastive and counterfactual explanations. Our computational explanations are a remedy to the challenge posed by the ‘Right to explanation’ and also serve in making the blackbox models more fair, reliable and most importantly understandable for the end user. The next part of the thesis focuses on the problematic nature of Intellectual Property (Copyright & Patents) for AI. With the ‘third wave’ of Artificial Intelligence, there is a massive revival and upsurge in AI related product development. An important entity behind the AI architecture, the neural network needs to be studied carefully that adequate protection for its innovation can be secured. A key feature of the Neural Network, the Neural weights hold the inferential rules and knowledge, thus are a new way to embody knowledge and information, a new form of intellectual property to which IP laws will have to adapt. We present our discussion that sheds light on the nature of this innovation and brings to context why it is relevant to secure Intellectual Property for Neural Weights. We also rebase our arguments in the backdrop of the debates that were set off on this same topic in 1990. Our research traces the shape of this problem ever since its conception and brings to the fore the newer and expanded notions behindNeural Networks, AI and their place in the Copyright laws. We also propose tentative solutions and the hurdles in implementing them as part of the study on streamlining the rough contours of the subject. As with Copyright, Patent Laws for AI are also predated. The Patent law is silent about tackling cases when an AI develops something novel. In the context of the current technologies and AI engines, this problem is particularly ripe as AI driven innovation will cause a radical shift in the pace, quality and areas of innovation which will need a massive overhaul of existing laws which do not allow non-human innovation to be registered in the current innovation market. To address this problem, we introduce a policy framework to adjudge innovation such that both human and machine driven innovation co-exists and complements each others R&D efforts. Our framework ensures that the legal status of machine is unchanged, the innovation quality surges and frivolous patent applications are pruned in early. The intention of this thesis is to elaborate, explore and mitigate the rough edges between AI and Law. The former part of the thesis proposes computational measures towards increased accountability, fairness and transparency in systems and the latter is directed towards a more policy angle between AI and Intellectual Property. The summum bonum of AI is to integrate with the human fabric and catalyze human efforts. This is possible if trust and acceptance is legally and socially established in these systems. This work is a small step towards the giant leap of making AI safe, governable and usable for the benefit of humanity.