Panoramic Stereovision and Scene Reconstruction

With advancement of research in robotics and computer vision, an increasingly high number of applications require the understanding of a scene in three dimensions. A variety of systems are deployed to do the same. This thesis explores a novel 3D imaging technique. This involves the use of catadioptric cameras in a stereoscopic arrangement. A secondary system aims to stabilize the system in the event that the cameras are misaligned during operation. The system provides a stark advantage due to it being a cost effective alternative to present day standard state-of-the-art systems that achieve the same goal of 3D imaging. The compromise lies in the quality of depth estimation, which can be overcome with a different imager and calibration. The result was a panoramic disparity map generated by the system

Deep Learning Closure of the Navier-Stokes Equations for Transition-Continuum Flows

The predictive accuracy of the Navier-Stokes equations is known to degrade at the limits of the continuum assumption, thereby necessitating expensive and often highly approximate solutions to the Boltzmann equation. While tractable in one spatial dimension, their high dimensionality makes multi-dimensional Boltzmann calculations impractical for all but canonical configurations. It is therefore desirable to augment the Navier-Stokes equations in these regimes. We present an application of a deep learning method to extend the validity of the Navier-Stokes equations to the transition-continuum flows. The technique encodes the missing physics via a neural network, which is trained directly from Boltzmann solutions. While standard DL methods can be considered ad-hoc due to the absence of underlying physical laws, at least in the sense that the systems are not governed by known partial differential equations, the DL framework leverages the a-priori known Boltzmann physics while ensuring that the trained model is consistent with the Navier-Stokes equations. The online training procedure solves adjoint equations, constructed using algorithmic differentiation, which efficiently provide the gradient of the loss function with respect to the learnable parameters. The model is trained and applied to predict stationary, one-dimensional shock thickness in low-pressure argon

Towards Knowledge-Based Personalized Product Description Generation in E-commerce

Quality product descriptions are critical for providing competitive customer experience in an e-commerce platform. An accurate and attractive description not only helps customers make an informed decision but also improves the likelihood of purchase. However, crafting a successful product description is tedious and highly time-consuming. Due to its importance, automating the product description generation has attracted considerable interests from both research and industrial communities. Existing methods mainly use templates or statistical methods, and their performance could be rather limited. In this paper, we explore a new way to generate the personalized product description by combining the power of neural networks and knowledge base. Specifically, we propose a KnOwledge Based pErsonalized (or KOBE) product description generation model in the context of e-commerce. In KOBE, we extend the encoder-decoder framework, the Transformer, to a sequence modeling formulation using self-attention. In order to make the description both informative and personalized, KOBE considers a variety of important factors during text generation, including product aspects, user categories, and knowledge base, etc. Experiments on real-world datasets demonstrate that the proposed method out-performs the baseline on various metrics. KOBE can achieve an improvement of 9.7% over state-of-the-arts in terms of BLEU. We also present several case studies as the anecdotal evidence to further prove the effectiveness of the proposed approach. The framework has been deployed in Taobao, the largest online e-commerce platform in China.Comment: KDD 2019 Camera-ready. Website: https://sites.google.com/view/kobe201

Real-time detection of cooling rate using pyrometers in tandem in laser material processing and directed energy deposition

A novel method of monitoring cooling rates in real-time using two pyrometers arranged in tandem has been demonstrated. First pyrometer monitors the temperature at the center of the molten pool, second monitors the temperature at its tailing end. The difference in two pyrometer signals provides the temperature gradient at 1 kHz frequency from which cooling rate is determined in real-time using Arduino interface. Effectiveness of this method in real-time monitoring of cooling rate during laser remelting and additive manufacturing by directed energy deposition with varying process parameters and layer number is demonstrated. © 2020 Elsevier B.V

Relation Extraction with Self-determined Graph Convolutional Network

Relation Extraction is a way of obtaining the semantic relationship between entities in text. The state-of-the-art methods use linguistic tools to build a graph for the text in which the entities appear and then a Graph Convolutional Network (GCN) is employed to encode the pre-built graphs. Although their performance is promising, the reliance on linguistic tools results in a non end-to-end process. In this work, we propose a novel model, the Self-determined Graph Convolutional Network (SGCN), which determines a weighted graph using a self-attention mechanism, rather using any linguistic tool. Then, the self-determined graph is encoded using a GCN. We test our model on the TACRED dataset and achieve the state-of-the-art result. Our experiments show that SGCN outperforms the traditional GCN, which uses dependency parsing tools to build the graph.Comment: CIKM-202

Critical sources of bacterial contamination and adoption of standard sanitary protocol during semen collection and processing in Semen Station

Abstract Aim: The present investigation was conducted to locate the critical sources of bacterial contamination and to evaluate the standard sanitation protocol so as to improve the hygienic conditions during collection, evaluation, and processing of bull semen in the Semen Station. Materials and Methods: The study compared two different hygienic procedures during the collection, evaluation and processing of semen in Central Semen Station, Anjora, Durg. Routinely used materials including artificial vagina (AV) inner liner, cone, semen collection tube, buffer, extender/diluter, straws; and the laboratory environment like processing lab, pass box and laminar air flow (LAF) cabinet of extender preparation lab, processing lab, sealing filling machine, and bacteriological lab were subjected to bacteriological examination in two phases of study using two different sanitary protocols. Bacterial load in above items/environment was measured using standard plate count method and expressed as colony forming unit (CFU). Results: Bacterial load in a laboratory environment and AV equipments during two different sanitary protocol in present investigation differed highly significantly (p<0.001). Potential sources of bacterial contamination during semen collection and processing included laboratory environment like processing lab, pass box, and LAF cabinets; AV equipments, including AV Liner and cone. Bacterial load was reduced highly significantly (p<0.001) in AV liner (from 2.33±0.67 to 0.50±0.52), cone (from 4.16±1.20 to 1.91±0.55), and extender (from 1.33±0.38 to 0) after application of improved practices of packaging, handling, and sterilization in Phase II of study. Glasswares, buffers, and straws showed nil bacterial contamination in both the phases of study. With slight modification in fumigation protocol (formalin @600 ml/1000 ft 3 ), bacterial load was significantly decreased (p<0.001) up to 0-6 CFU in processing lab (from 6.43±1.34 to 2.86±0.59), pass box (from 12.13±2.53 to 3.78±0.79), and nil bacterial load was reported in LAFs. Conclusion: Appropriate and careful management considering critical points step by step starting right from collection of semen to their processing can significantly minimize bacterial contamination

Nasopharyngeal pneumococcal carriage in South Asian infants:Results of observational cohort studies in vaccinated and unvaccinated populations

BACKGROUND: Nasopharyngeal pneumococcal carriage (NPC) is a prerequisite for invasive pneumococcal disease and reduced carriage of vaccine serotypes is a marker for the protection offered by the pneumococcal conjugate vaccine (PCV). The present study reports NPC during the first year of life in a vaccinated (with PCV10) cohort in Bangladesh and an unvaccinated cohort in India. METHODS: A total of 450 and 459 infants were recruited from India and Bangladesh respectively within 0-7 days after birth. Nasopharyngeal swabs were collected at baseline, 18 and 36 weeks after birth. The swabs were processed for pneumococcal culture and identification of serotypes by the Quellung test and polymerase chain reaction (PCR). An identical protocol was applied at both sites. RESULTS: Prevalence of NPC was 48% in the Indian and 54.8% in the Bangladeshi cohort at 18 weeks. It increased to 53% and 64.8% respectively at 36 weeks. The average prevalence of vaccine serotypes was higher in the Indian cohort (17.8% vs 9.8% for PCV-10 and 26.1% vs17.6% for PCV-13) with 6A, 6B, 19F, 23F, and 19A as the common serotypes. On the other hand, the prevalence of non-vaccine serotypes was higher (43.6% vs 27.1% for non-PCV13) in the Bangladeshi cohort with 34, 15B, 17F, and 35B as the common serotypes. Overcrowding was associated with increased risk of pneumococcal carriage. The present PCV-13 vaccine would cover 28%-30% and 47%-48% serotypes in the Bangladeshi and Indian cohorts respectively. CONCLUSIONS: South Asian infants get colonised with pneumococci early in infancy; predominantly vaccine serotypes in PCV naïve population (India) and non-vaccine serotypes in the vaccinated population (Bangladesh). These local findings are important to inform the public health policy and the development of higher valent pneumococcal vaccines

Implementation of a large-scale breast cancer early detection program in a resource-constrained setting: real-world experiences from 2 large states in India

Background: The Breast Health Initiative (BHI) was launched to demonstrate a scalable model to improve access to early diagnosis and treatment of breast cancer. Methods: A package of evidence-based interventions was codesigned and implemented with the stakeholders, as part of the national noncommunicable disease program, through the existing primary health care system. Data from the first 18 months of the BHI are presented. Results: A total of 108,112 women received breast health education; 48% visited the health facilities for clinical breast examination (CBE), 3% had a positive CBE result, and 41% were referred to a diagnostic facility. The concordance of CBE findings between health care providers and adherence to follow-up care improved considerably, with more women visiting the diagnostic facilities and completing diagnostic evaluation within 1 month from initial screening, and with only 9% lost to follow-up. The authors observed a clinically meaningful decrease in time to complete diagnostic evaluation with biopsy, from 37 to 9 days. Conclusions: The results demonstrate the feasibility and effectiveness of implementing a large-scale, decentralized breast cancer early detection program delivered through the existing primary health care system in India

Community-based asthma assessment in young children:Adaptations for a multicentre longitudinal study in South Asia

BACKGROUND: Systematic assessment of childhood asthma is challenging in low- and middle-income country (LMIC) settings due to the lack of standardised and validated methodologies. We describe the contextual challenges and adaptation strategies in the implementation of a community-based asthma assessment in four resource-constrained settings in Bangladesh, India, and Pakistan. METHOD: We followed a group of children of age 6–8 years for 12 months to record their respiratory health outcomes. The study participants were enrolled at four study sites of the ‘Aetiology of Neonatal Infection in South Asia (ANISA)’ study. We standardised the research methods for the sites, trained field staff for uniform data collection and provided a ‘Child Card’ to the caregiver to record the illness history of the participants. We visited the children on three different occasions to collect data on respiratory-related illnesses. The lung function of the children was assessed in the outreach clinics using portable spirometers before and after 6-minute exercise, and capillary blood was examined under light microscopes to determine eosinophil levels. RESULTS: We enrolled 1512 children, 95.5% (1476/1512) of them completed the follow-up, and 81.5% (1232/1512) participants attended the lung function assessment tests. Pre- and post-exercise spirometry was performed successfully in 88.6% (1091/1232) and 85.7% (1056/1232) of children who attempted these tests. Limited access to health care services, shortage of skilled human resources, and cultural diversity were the main challenges in adopting uniform procedures across all sites. Designing the study implementation plan based on the local contexts and providing extensive training of the healthcare workers helped us to overcome these challenges. CONCLUSION: This study can be seen as a large-scale feasibility assessment of applying spirometry and exercise challenge tests in community settings of LMICs and provides confidence to build capacity to evaluate children’s respiratory outcomes in future translational research studies