The Capabilities Approach To Metaphysical Personhood

It is accepted that humans are persons, but what does this mean? Society equates personhood with legal rights, moral worth, and metaphysical status. But exactly what makes us persons and distinguishes us from non-persons? What does our conception of this distinction and the value and rights we grant to persons but not to non-persons say about the type of creature that we are? Many philosophers have tried to define metaphysical personhood. However, this thesis argues that current accounts of metaphysical personhood are inadequate. Next, this thesis borrows and modifies Martha Nussbaum’s capabilities approach to human dignity in order to construct a new account of metaphysical personhood. This thesis defines metaphysical personhood as the existence of seven innate abilities: ability for self-awareness, ability to sense, ability to imagine, ability to think, ability to reason, ability to feel emotion, and ability to empathize. Each of these abilities must exist to the degree necessary for its subject to have status dignity. The concepts of innate abilities and status dignity are defined in the thesis. Finally, this thesis tests this new account of metaphysical personhood on multiple base and edge cases such as insects, dogs, children, aliens, and more. Based on these tests, this thesis makes the observation that complex imagination is the greatest hurdle for metaphysical personhood and is what differentiates humans from other species on earth. Finally, this thesis acknowledges that controversial conclusions could arise from this new account of metaphysical personhood. Especially, under this account, some humans may not be metaphysical persons at least some of the time. However, this thesis concludes that moral persons need not be metaphysical persons and that regardless of an entity’s metaphysical status, we might still owe them moral considerations

Towards Complex Question Answering over Knowledge Graphs

Over the past decade, Knowledge Graphs (KG) have emerged as a prominent repository for storing facts about the world in a linked data architecture. Providing machines with the capability of exploring such Knowledge Graphs and answering natural language questions over them, has been an active area of research. The purpose of this work, is to delve further into the research of retrieving information stored in KGs, based on the natural language questions posed by the user. Knowledge Graph Question Answering (KGQA) aims to produce a concise answer to a user question, such that the user is exempt from using KG vocabulary and overheads of learning a formal query language. Existing KGQA systems have achieved excellent results over Simple Questions, where the information required is limited to a single triple and a single formal query pattern. Our motivation is to improve the performance of KGQA over Complex Questions, where formal query patterns significantly vary, and a single triple is not confining for all the required information. Complex KGQA provides several challenges such as understanding semantics and syntactic structure of questions, Entity Linking, Relation Linking and Answer Representation. Lack of suitable datasets for complex question answering further adds to research gaps. Hence, in this thesis, we focus the research objective of laying the foundations for the advancement of the state-of-the-art for Complex Question Answering over Knowledge Graphs, by providing techniques to solve various challenges and provide resources to fill the research gaps. First, we propose Normalized Query Structure (NQS), which is a linguistic analyzer module that helps the QA system to detect inputs and intents and the relation between them in the users' question. NQS acts like an intermediate language between natural language questions and formal expressions to ease the process of query formulation for complex questions. We then developed a framework named LC-QuAD to generate large scale question answering dataset by reversing the process of question answering, thereby translating natural language questions from the formal query using intermediate templates. Our goal is to use this framework for high variations in the query patterns and create a large size dataset with minimum human effort. The first version of the dataset consists of 5,000 complex questions. By extending the LC-QuAD framework to support Reified KGs and crowd-sourcing, we published the second version of the dataset as LC-QuAD 2.0, consisting of 30,000 questions with their paraphrases and has higher complexity and new variations in the questions. To overcome the problem of Entity Linking and Relation Linking in KGQA, we develop EARL, a module performing these two tasks as a single joint task for complex question answering. We develop approaches for this module, first by formalizing the task as an instance of the Generalized Traveling Salesman Problem (GTSP) and the second approach uses machine learning to exploit the connection density between nodes in the Knowledge Graph. Lastly, we create another large scale dataset to answer verbalization and provide results for multiple baseline systems on it. The Verbalization dataset is introduced to make the system's response more human-like.The NQS based KGQA system was next to the best system in terms of accuracy on the QALD-5 dataset. We empirically prove that NQS is robust to tackle paraphrases of the questions. EARL achieves the state of the art results in Entity Linking and Relation Linking for question answering on several KGQA datasets. The dataset curated in this thesis has helped the research community to move forward in the direction of improving the accuracy of complex question answering as a task as other researchers too developed several KGQA systems and modules around these published datasets. With the large-scale datasets, we have encouraged the use of large scale machine learning, deep learning and emergence of new techniques to advance the state-of-the-art in complex question answering over knowledge graphs. We further developed core components for the KGQA pipeline to overcome the challenges of Question Understanding, Entity-Relation Linking and Answer Verbalization and thus achieve our research objective. All the proposed approaches mentioned in this thesis and the published resources are available at https://github.com/AskNowQA and are released under the umbrella project AskNow

Efficient Charge Separation in 2D Janus van der Waals Structures with Build-in Electric Fields and Intrinsic p-n Doping

Janus MoSSe monolayers were recently synthesised by replacing S by Se on one side of MoS$_2$ (or vice versa for MoSe$_2$). Due to the different electronegativity of S and Se these structures carry a finite out-of-plane dipole moment. As we show here by means of density functional theory (DFT) calculations, this intrinsic dipole leads to the formation of built-in electric fields when the monolayers are stacked to form $N$-layer structures. For sufficiently thin structures ($N<4$) the dipoles add up and shift the vacuum level on the two sides of the film by $\sim N \cdot 0.7$ eV. However, for thicker films charge transfer occurs between the outermost layers forming atomically thin n- and p-doped electron gasses at the two surfaces. The doping concentration can be tuned between about $5\cdot 10^{12}$ e/cm$^{2}$ and $2\cdot 10^{13}$ e/cm$^{2}$ by varying the film thickness. The surface charges counteract the static dipoles leading to saturation of the vacuum level shift at around 2.2 eV for $N>4$. Based on band structure calculations and the Mott-Wannier exciton model, we compute the energies of intra- and interlayer excitons as a function of film thickness suggesting that the Janus multilayer films are ideally suited for achieving ultrafast charge separation over atomic length scales without chemical doping or applied electric fields. Finally, we explore a number of other potentially synthesisable 2D Janus structures with different band gaps and internal dipole moments. Our results open new opportunities for ultrathin opto-electronic components such as tunnel diodes, photo-detectors, or solar cells

Parikh Images of Register Automata

As it has been recently shown, Parikh images of languages of nondeterministic one-register automata are rational (but not semilinear in general), but it is still open if the property extends to all register automata. We identify a subclass of nondeterministic register automata, called hierarchical register automata (HRA), with the following two properties: every rational language is recognised by a HRA; and Parikh image of the language of every HRA is rational. In consequence, these two properties make HRA an automata-theoretic characterisation of languages of nondeterministic register automata with rational Parikh images

Defect Tolerant Monolayer Transition Metal Dichalcogenides

Localized electronic states formed inside the band gap of a semiconductor due to crystal defects can be detrimental to the material's optoelectronic properties. Semiconductors with lower tendency to form defect induced deep gap states are termed defect tolerant. Here we provide a systematic first principles investigation of defect tolerance in 29 monolayer transition metal dichalcogenides (TMDs) of interest for nanoscale optoelectronics. We find that the TMDs based on group VI and X metals form deep gap states upon creation of a chalcogen (S, Se, Te) vacancy while the TMDs based on group IV metals form only shallow defect levels and are thus predicted to be defect tolerant. Interestingly, all the defect sensitive TMDs have valence and conduction bands with very similar orbital composition. This indicates a bonding/anti-bonding nature of the gap which in turn suggests that dangling bonds will fall inside the gap. These ideas are made quantitative by introducing a descriptor that measures the degree of similarity of the conduction and valence band manifolds. Finally, the study is generalized to non-polar nanoribbons of the TMDs where we find that only the defect sensitive materials form edge states within the band gap

MUCOADHESIVE CHITOSAN MICROSPHERES OF GEFITINIB

Objective: Gefitinib, Epidermal Growth Factor-Tyrosine Kinase Inhibitor (EGFR-TKI); has promisingly shown activity against Non-Small-Scale Lung Cancer. Currently, the formulations of this drug available are in Tablets, Capsules and liposomal suspensions taken by the oral route. These have certain disadvantages in gastrointestinal disorders like irritation of GI mucosal layer, bleeding, non-patient compliance and low bioavailability due to low aqueous solubility and thus low bioavailability. The purpose of this study was to formulate and evaluate Chitosan-based Microparticles of Gefitinib for maintaining the therapeutic index and limits its side effects.Methods: Chitosan microspheres cross-linked with glutaraldehyde were prepared by solvent evaporation technique which is then analyzed for its particle size, encapsulation efficiency, swelling index.Results: The release rate of the drug can be increased by using chitosan-based carrier system which will enhance its bioavailability. By this work, the anticancer activity of Gefitinib in non-small-scale lung cancer will be successfully determined.Conclusion: It has been concluded that microspheres can be prepared by solvent evaporation technique by varying the concentration of chitosan and tween-20. Chitosan used in this work is of 85 % degree of deacetylation, 25 % solution of Gluteraldehyde suitable for the formulation of these microspheres. Optimized temperature was selected as 65 °C, and the rotation speed was taken as 1200 rpm. Finally, the objectives planned for this research work was performed and evaluated and shown promising results as the dosing frequency is reduced and maximize for 3 d rather than once in a day as per the current formulation available in the market now with a low dosage regimen of 100 mg of dosage strength, administer by pulmonary route. Microparticulate drug delivery system from microspheres is able to deliver the drug in a sustained release manner for the long period of time successfully