Development and Rheological Characterisation of Abrasive Flow Finishing Medium for Finishing Macro to Micro Features

Technological advancement demands the manufacturing of components with a fine surface finish at a minimal cost. This scenario acts as the driving force for the research communities to develop economic finishing processes. Abrasive flow finishing (AFF) is one of the advanced finishing processes employed for finishing, deburring, radiusing and recast layer removal from the workpiece surfaces. AFF process uses a finishing medium that acts as a deformable tool during the finishing process. It is the rheological properties of the medium that profoundly influences the end surface finish obtained on the workpiece after the AFF process. In the current work, an attempt is made to develop an economic AFF medium by using viscoelastic polymers i.e., soft styrene and soft silicone polymer. Detailed static and dynamic characterisation of the medium is carried out. Later, to study the finishing performance of the developed medium, AFF experiments are performed for the finishing of macro and micro feature components. The experimental study showed that the nano surface finish could be achieved by varying the viscosity of the developed medium. Developed medium achieved 89.06 per cent improvement in surface roughness during finishing of tubes (macro feature component), while 92.13 per cent and 88.11 per cent surface roughness improvement is achieved during finishing of microslots and microholes (micro feature component), respectively

Few-shot classification in Named Entity Recognition Task

For many natural language processing (NLP) tasks the amount of annotated data is limited. This urges a need to apply semi-supervised learning techniques, such as transfer learning or meta-learning. In this work we tackle Named Entity Recognition (NER) task using Prototypical Network - a metric learning technique. It learns intermediate representations of words which cluster well into named entity classes. This property of the model allows classifying words with extremely limited number of training examples, and can potentially be used as a zero-shot learning method. By coupling this technique with transfer learning we achieve well-performing classifiers trained on only 20 instances of a target class.Comment: In proceedings of the 34th ACM/SIGAPP Symposium on Applied Computin

PRedItOR: Text Guided Image Editing with Diffusion Prior

Diffusion models have shown remarkable capabilities in generating high quality and creative images conditioned on text. An interesting application of such models is structure preserving text guided image editing. Existing approaches rely on text conditioned diffusion models such as Stable Diffusion or Imagen and require compute intensive optimization of text embeddings or fine-tuning the model weights for text guided image editing. We explore text guided image editing with a Hybrid Diffusion Model (HDM) architecture similar to DALLE-2. Our architecture consists of a diffusion prior model that generates CLIP image embedding conditioned on a text prompt and a custom Latent Diffusion Model trained to generate images conditioned on CLIP image embedding. We discover that the diffusion prior model can be used to perform text guided conceptual edits on the CLIP image embedding space without any finetuning or optimization. We combine this with structure preserving edits on the image decoder using existing approaches such as reverse DDIM to perform text guided image editing. Our approach, PRedItOR does not require additional inputs, fine-tuning, optimization or objectives and shows on par or better results than baselines qualitatively and quantitatively. We provide further analysis and understanding of the diffusion prior model and believe this opens up new possibilities in diffusion models research

Road traffic accidents attending casualty in a tertiary care hospital : a 03 year study from South Western India

Background: Road Traffic Accident (RTA) is any vehicular accident occurring on the roadway i.e. originating on, terminating on, or involving a vehicle partially on the roadway. Road traffic accidents are a human tragedy which has an immeasurable impact on the families affected. The WHO estimates that over 1.2 million people pass away every year on the world’s roads, and between 20 and 50 million fall victims to non-fatal injuries.  The incidence of RTA remains poorly measured in India.Methods: The present study is conducted at casualty department of a tertiary care hospital in South Western India among victims of road traffic accident. This cross sectional study was conducted to elucidate the role of various factors involved in road traffic accidents and to study demographic profile and injury pattern among RTA victims. All the reported RTA cases from 1st January 2016 to 31st December 2018 were included in the study.Results: A total of 875 cases of RTA were studied. There were 83.77%(n=733) male and 16.23%(n=142) female accident victims. Most of the patients were aged between 21 and 30 years. Monsoons witnessed 46.63%(n=408) cases. Most cases occurred between 6 and 12pm (54.4%, n=476). Commonest injury was a simple injury (72.91%, n=638), dangerous injuries (27.09% percent, n=237) and dead was (7.43%, n=65). The highest number of accidents took place in the month of June (19.09%, n=167) and on Sundays (22.17%). Among the motorized vehicles, two-wheeler drivers were more (76.91%, n=673) involved in accidents. In this study 17.60% (n=154) were under influence of alcohol while driving.Conclusions: This study shows there are multiple factors associated with road traffic accidents. Most of the factors responsible for RTA and its fatal consequences are preventable. India, as a signatory to the Brasilia declaration, intends to reduce road accidents and traffic fatalities by 50% by 2022. A comprehensive multipronged approach can mitigate most of them

The Effects of Eucalyptus Oil, Glutathione, and Lemon Essential Oil on the Debonding Force, Adhesive Remnant Index, and Enamel Surface During Debonding of Ceramic Brackets

Objective:The present study aimed to find a chemical reagent that would reduce the debonding force to enable easier debonding of the ceramic brackets, thus reducing enamel damage as well as chair side time.Methods:The study included 4 groups -control (distilled water), eucalyptus oil, glutathione and lemon essential oil for immersing teeth bonded with ceramic brackets. Samples (25 in each group), extracted first premolars, were mounted and immersed in their respective solution for a duration of 10 minutes following which they were tested to evaluate the debonding force using the INSTRON universal testing machine. The amount of adhesive left behind on the enamel surface was evaluated using adhesive remnant index (ARI) score and surface changes were checked using a scanning electron microscope.Results:Teeth immersed in glutathione showed the greatest amount of reduction in debonding force (p=0.001) compared with other groups. ARI scores were low for specimens immersed in glutathione. SEM images showed that teeth in the glutathione group had a cleaner enamel surface, suggesting less or no adhesive was left behind and no sign of enamel damage after debonding ceramic brackets.Conclusion:Specimens that were immersed in glutathione for a duration of 10 minutes before debonding of ceramic brackets showed the greatest reduction in debonding force compared with control and demonstrated peel off effect with no enamel damage. Glutathione can be used as an effective reagent during the clinical debonding of ceramic brackets

Perceptual Grouping in Contrastive Vision-Language Models

Recent advances in zero-shot image recognition suggest that vision-language models learn generic visual representations with a high degree of semantic information that may be arbitrarily probed with natural language phrases. Understanding an image, however, is not just about understanding what content resides within an image, but importantly, where that content resides. In this work we examine how well vision-language models are able to understand where objects reside within an image and group together visually related parts of the imagery. We demonstrate how contemporary vision and language representation learning models based on contrastive losses and large web-based data capture limited object localization information. We propose a minimal set of modifications that results in models that uniquely learn both semantic and spatial information. We measure this performance in terms of zero-shot image recognition, unsupervised bottom-up and top-down semantic segmentations, as well as robustness analyses. We find that the resulting model achieves state-of-the-art results in terms of unsupervised segmentation, and demonstrate that the learned representations are uniquely robust to spurious correlations in datasets designed to probe the causal behavior of vision models.Comment: Accepted and presented at ICCV 202

Measuring Decentrality in Blockchain Based Systems

Blockchain promises to provide a distributed and decentralized means of trust among untrusted users. However, in recent years, a shift from decentrality to centrality has been observed in the most accepted Blockchain system, i.e., Bitcoin. This shift has motivated researchers to identify the cause of decentrality, quantify decentrality and analyze the impact of decentrality. In this work, we take a holistic approach to identify and quantify decentrality in Blockchain based systems. First, we identify the emergence of centrality in three layers of Blockchain based systems, namely governance layer, network layer and storage layer. Then, we quantify decentrality in these layers using various metrics. At the governance layer, we measure decentrality in terms of fairness, entropy, Gini coefficient, Kullback-Leibler divergence, etc. Similarly, in the network layer, we measure decentrality by using degree centrality, betweenness centrality and closeness centrality. At the storage layer, we apply a distribution index to define centrality. Subsequently, we evaluate the decentrality in Bitcoin and Ethereum networks and discuss our observations. We noticed that, with time, both Bitcoin and Ethereum networks tend to behave like centralized systems where a few nodes govern the whole network

Comparative study of vacuum-assisted closure therapy versus vacuum-assisted closure therapy supplemented with vitamin C in compound wound healing

Background: It is imperative for early and precise management of the compound wound for preventing further complication and delaying definitive management. As we all know superiority of vacuum assisted closure (VAC) therapy in wound management over any other method, but adding vitamin C has been shown to accelerate wound healing, reducing hospital stay, and cost of management and prevention of delaying definitive management of wound due to some conspicuous property of vitamin C that serve as superior adjuvant in wound healing. Methods: A case series of 40 patients who have been inflicted with compound wounds with most following road traffic accidents. We then categorised patients and tried to observe any difference in rate of satisfactorily healing of wound with 20 patients put on VAC therapy alone and other 20 patients put on VAC therapy supplemented with vit C. Results: Patients who were undergoing VAC dressing and supplemented with vitamin C, not only portrayed a better result of wound healing but also reduced the amount of vacuum dressing sittings. Conclusions: It was observed that, in general, patients who were undergoing VAC dressing and supplemented with vitamin C, not only portrayed a better result of wound healing but also reduced amount of vacuum dressing sittings, improved rate of granulation tissue, reduced hospital stay, early definitive fixation of associated fracture and skin grafting and showed superior outcomes in terms of better tissue recovery