Optical probing of spatial structural abnormalities in cells/tissues due to cancer, drug-effect, and brain abnormalities using mesoscopic physics-based spectroscopic techniques

The quantitative measurement of structural alterations at the nanoscale level is important for understanding the physical states of weakly disordered optical mediums such as cells/tissues. Progress in certain diseases, such as cancer or abnormalities in the brain, is associated with the nanoscale structural alterations at basic building blocks of the cells/tissues. Elastic light scattering, especially at visible wavelengths range provides non-invasive ways to probe the cells/tissues up to nanoscale level. Therefore, a mesoscopic physics-based open light scattering technique with added finer focusing, partial wave spectroscopy (PWS), is developed to probe nanoscale changes. Then, molecular-specific light localization technique, a close scattering approach called inverse participation ratio (IPR) is proposed that is sensitive to nano to microstructural cell/tissue alterations. In this dissertation, we have introduced the further engineered PWS system with the finer focus for precise volume scattering and molecular-specific light localization IPR techniques. As an application of PWS, we first probe precise scattering volume in commercially available tissue microarrays (TMA) tissue samples to standardize the existing cancer diagnostic methods by distinguishing the cancer stages. We also apply the PWS technique to probe chemotherapy drug-treated metastasizing cancer patients by xenografting prostate cancer cells using a mouse model and identify drug-sensitive and drug-resistance treatment cases. On the other hand, as an illustration of another mesoscopic physics-based molecular specific light localization technique, Confocal-IPR, we study the effects of a probiotic on chronic alcoholic mice brains by targeting the molecular specific alteration in glial cells, astrocytes and microglia, and chromatin of the brain cells through staining with appropriate dyes/proteins. Using structural disorder of IPR as a biomarker, the results show that probiotics in the presence of alcohol are beneficial and help overall brain health. Finally, a TEM-IPR study was performed using nanoscale resolution TEM imaging to support the optical IPR method by studying the anti-cancerous drug effect in ovarian cancer cells. The result shows that we can quantitatively measure the effect of anti-cancerous drugs in cancer treatment and the level of tumorigenicity far below the diffraction limit, and it has a similar effect and supports the optical IPR method

The plight of the forgotten ones: civil war and internal displacement in Nepal

Although different theoretical arguments have been developed for understanding civil wars, very few efforts have been made to study their impact. This paper attempts to bridge this gap by providing a systematic explanation of conflict-induced internal displacement. We use sub-national data from Nepal to explain whether or not conflict is directly responsible for displacing people, as is generally assumed, or whether there is an indirect link between conflict and displacement. We argue that internal displacement occurs as a result of the direct as well as indirect impacts of civil war. Both the government and the rebels are involved in violating human rights during conflict that causes direct threat to civilians’ life and forces them to flee. But civil war also leads to deterioration in economic conditions that causes a plethora of problems for people living in the conflict-hit area. Destruction of the local economy creates insecurity in the form of the lack of employment opportunities as well as other social services such as education, health, and communication. These problems, which are more indirect causes of internal displacement, also force people to flee home during conflict

Pursuing democracy: explaining political transitions in Nepal

Several patterns can be observed from the modern political history of Nepal, but two deserve special attention. First, political parties have been quite successful in bringing down autocratic regimes in Nepal, but only when all of the major parties work together for a common goal. A single party has never been able to topple an autocracy on its own. Mass participation was also critical in bringing down autocratic regimes in 1950, 1990, and 2006, and occurred only after the unison of the major political parties. What are the linkages between civil society and the party system that explain success in ousting autocratic regimes? Second, although successful in toppling autocratic monarchies, political parties have yet failed to institutionalize democracy. What have been the critical variables missing from past experiments with democracy in Nepal? By offering some initial answers to these questions, this paper has three purposes. First, we draw upon the literature on social movements and protest cycles to explain the causes of mass participation in revolutionary movements. Second, we apply the insights of New Institutionalism to explain the failure of past experiments with democracy. Finally, we assess current prospects for democratic consolidation in Nepal

Pursuing Democracy: Explaining Political Transitions in Nepal

Nepal has been struggling to consolidate a democratic political system for more than a half-century yet still does not have a working constitution. This paper is the first step in a larger research project examining regime transitions in Nepal. We review the existent comparative literature on democratization and authoritarian reversals in order to isolate some potential explanatory variables. We also focus on making valid descriptive inferences along these conceptual lines. What caused the failure of democracy in Nepal in the past? What are the future prospects for democratic consolidation? The literature has been divided along two lines, which we label as the Weberian and Neo-Marxist research programs. The former focuses on modernization and institutionalization, while the latter emphasizes class structure. We propose a multi-method research design, combining qualitative comparative analysis of most-similar cases with a longitudinal study of Nepal

Camera Based Object Detection for Indoor Scenes

This master thesis describes a practical implementation of a deep learning framework for object detection on the self-collected multiclass dataset. The research work presents multiple perspectives of the data collection, labelling, preprocessing and training popular object detection architectures. The challenges in the collection of multiclass object detection dataset from the indoor premises and annotation process are presented with possible solutions. The performance evaluations of the trained object detectors are measured in terms of precision, recall, F1-score, mAP and processing speed. We experimented multiple object detection architectures that were available on the TensorFlow object detection model zoo. The multiclass dataset collected from the indoor premises were used to train and evaluate the performance of modern convolutional object detection models. We studied two scenarios, (a) pretrained object detection model and (b) fine-tuned detection model on the self-collected multiclass dataset. The performance of fine-tuned object detectors was better than the pretrained detectors. From our experiment, we found that region based convolutional neural network architectures have superior detection accuracy on our dataset. Faster region-based convolutional neural network (RCNN) architecture with residual networks features extractor has the best detection accuracy. Single shot multi-box detector (SSD) models are comparatively less precise in detection. However, they are faster in computation and easier to deploy in mobile and embedded devices. It is found that the region-based fully convolutional network (RFCN) is the suitable alternative for multi-class object detection considering the speed/accuracy trade-offs

Computer Assisted Image Labeling for Object Detection Using Deep Learning

Deep learning-based object detectors have shown outstanding performance with state-of-the-art results on public benchmarks. However, they typically consist of millions of parameters and require a large number of training samples to tune these parameters appropriately. These samples are labeled by human annotators, which is a tedious, time-consuming, and expensive process. Moreover, object detectors have high computational costs both for the training and inference phase. This dissertation considers these two aspects of training and deploying deep learning object detectors. First, we study data labeling for the training phase and the robustness of object detectors towards label noise. We classify possible label noise scenarios in 2D object detection and study the sensitivity of one-stage object detectors to label noise in the training phase. We then propose methods for efficient bounding box annotation by utilizing human-machine collaboration. Extensive experiments have been done to study an efficient and effective bounding box annotation scheme for deep learning object detectors. Additionally, we created an easy-to-use, medium-sized, multiclass, fully labeled object detection dataset from indoor premises and released it publicly for registration-free use. Second, we study the practical problem of object detection network deployment with an efficient implementation of the object detection network for applications such as facial analysis, human detection and tracking, and the path prediction of mobile objects on resource-limited devices. We implemented object detection in an image processing pipeline integrating with other tasks for multiple applications and studied the optimal design process. We present the details of the system-level design to incorporate a multitasking network efficiently with the proper system architecture design