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Seroprevalence of COVID-19 antibody among patients visiting a large clinic in Uttar Pradesh

Garima Agrawal, Ruchira Agrawal, Harsh Agrawal, Hiren Prajapati, Krishna Yadav, Kamal Agrawal, Chandra Gupta Agrawal
2021 International Journal of Basic & Clinical Pharmacology  
Kamal Agrawal in-charge Sitara Polyclinic for giving us access to all COVID 19 antibody and other test reports. We also acknowledged Mr.  ...  Agrawal G et al.  ...  having 86%.Seropositive status was not significantly differed across the religion in present study (Table 2, p=0.72) but according to Table 4, prevalence of significantly higher titres (>100) was more Agrawal  ... 
doi:10.18203/2319-2003.ijbcp20214884 fatcat:3zyyonsgezb4lgrtigg7o3awku

Dendritic cells and aging: consequences for autoimmunity

Anshu Agrawal, Aishwarya Sridharan, Sangeetha Prakash, Harsh Agrawal
2012 Expert Review of Clinical Immunology  
The immune system has evolved to mount immune responses against foreign pathogens and to remain silent against self-antigens. A balance between immunity and tolerance is required as any disturbance may result in chronic inflammation or autoimmunity. Dendritic cells (DCs) actively participate in maintaining this balance. Under steady-state conditions, DCs remain in an immature state and do not mount an immune response against circulating self-antigens in the periphery, which maintains a state of
more » ... tolerance. By contrast, foreign antigens result in DC maturation and DC-induced T-cell activation. Inappropriate maturation of DCs due to infections or tissue injury may cause alterations in the balance between the tolerogenic and immunogenic functions of DCs and instigate the development of autoimmune diseases. This article provides an overview of the effects of advancing age on DC functions and their implications in autoimmunity.
doi:10.1586/eci.11.77 pmid:22149342 pmcid:PMC3285507 fatcat:rif7zuab3bdkfhnjazlnrykd2a

Fabrik: An Online Collaborative Neural Network Editor [article]

Utsav Garg, Viraj Prabhu, Deshraj Yadav, Ram Ramrakhya, Harsh Agrawal, Dhruv Batra
2018 arXiv   pre-print
We present Fabrik, an online neural network editor that provides tools to visualize, edit, and share neural networks from within a browser. Fabrik provides a simple and intuitive GUI to import neural networks written in popular deep learning frameworks such as Caffe, Keras, and TensorFlow, and allows users to interact with, build, and edit models via simple drag and drop. Fabrik is designed to be framework agnostic and support high interoperability, and can be used to export models back to any
more » ... upported framework. Finally, it provides powerful collaborative features to enable users to iterate over model design remotely and at scale.
arXiv:1810.11649v1 fatcat:h7xd7dg3ejb7lgoxop3spzhf4e


Ajinkya Ghorpade, Apurva Joshi, Sumit Mali, Piyush Agrawal, Harsh Agrawal
2021 International Journal of Engineering Applied Sciences and Technology  
Ridesharing is becoming the most chosen transportation option over private transportation in big cities. Ridesharing is also very quick, affordable and safe. Ridesharing also helps lot of students who travel to college from very long distances and sometimes public transportation is also very limited. This helps them save both time and money and also helps them earn some money by listing their rides. So the paper presents the blueprint and application of a peer to peer or a dynamic pooling app
more » ... ich lets the users list themselves as drivers or riders and which allows them to find each other a ride within a specific area in particular bandwidth of time. The applications have some special features like fare recommendation according to distance, time, mileage and ride recommendation based on type of ride and the history of the rider.
doi:10.33564/ijeast.2021.v05i12.045 fatcat:6gq3zaltnveuvpxfnxjyzdfadu

Housekeep: Tidying Virtual Households using Commonsense Reasoning [article]

Yash Kant, Arun Ramachandran, Sriram Yenamandra, Igor Gilitschenski, Dhruv Batra, Andrew Szot, Harsh Agrawal
2022 arXiv   pre-print
We introduce Housekeep, a benchmark to evaluate commonsense reasoning in the home for embodied AI. In Housekeep, an embodied agent must tidy a house by rearranging misplaced objects without explicit instructions specifying which objects need to be rearranged. Instead, the agent must learn from and is evaluated against human preferences of which objects belong where in a tidy house. Specifically, we collect a dataset of where humans typically place objects in tidy and untidy houses constituting
more » ... 799 objects, 268 object categories, 585 placements, and 105 rooms. Next, we propose a modular baseline approach for Housekeep that integrates planning, exploration, and navigation. It leverages a fine-tuned large language model (LLM) trained on an internet text corpus for effective planning. We show that our baseline agent generalizes to rearranging unseen objects in unknown environments. See our webpage for more details:
arXiv:2205.10712v1 fatcat:j2njksihjvfdbcxvzgl6bnf2km

Detection of Spam Zombies

Harsh Agrawal, Akriti Bhat, Sneha Malani
2017 International Journal of Engineering Research and  
This paper presents a 'Spam Zombie Detection' system which is an online system over the network that detects the spam and the sender of the spam (zombie) before the receiver receives it. Thus all the detection work is done at sender level itself. This paper focuses on a powerful statistical tool called Sequential Probability Ratio Test, which has bounded false positive and false negative error rates on which the Spam Zombie Detection system is based. This system is mainly implemented over the
more » ... ivate mailing system. It also provides the enhanced security mechanism in which, if the system which has been hacked i.e. it has become a zombie, then it gets blocked within the network.
doi:10.17577/ijertv6is040527 fatcat:kog4unu2gjhxdfig4euj6gm6bm

Sequential Latent Spaces for Modeling the Intention During Diverse Image Captioning [article]

Jyoti Aneja, Harsh Agrawal, Dhruv Batra, Alexander Schwing
2019 arXiv   pre-print
Diverse and accurate vision+language modeling is an important goal to retain creative freedom and maintain user engagement. However, adequately capturing the intricacies of diversity in language models is challenging. Recent works commonly resort to latent variable models augmented with more or less supervision from object detectors or part-of-speech tags. Common to all those methods is the fact that the latent variable either only initializes the sentence generation process or is identical
more » ... ss the steps of generation. Both methods offer no fine-grained control. To address this concern, we propose Seq-CVAE which learns a latent space for every word position. We encourage this temporal latent space to capture the 'intention' about how to complete the sentence by mimicking a representation which summarizes the future. We illustrate the efficacy of the proposed approach to anticipate the sentence continuation on the challenging MSCOCO dataset, significantly improving diversity metrics compared to baselines while performing on par w.r.t sentence quality.
arXiv:1908.08529v1 fatcat:5bkvxkkiq5di3lichwwxlf4d7i

The Surprising Effectiveness of Visual Odometry Techniques for Embodied PointGoal Navigation [article]

Xiaoming Zhao, Harsh Agrawal, Dhruv Batra, Alexander Schwing
2021 arXiv   pre-print
It is fundamental for personal robots to reliably navigate to a specified goal. To study this task, PointGoal navigation has been introduced in simulated Embodied AI environments. Recent advances solve this PointGoal navigation task with near-perfect accuracy (99.6% success) in photo-realistically simulated environments, assuming noiseless egocentric vision, noiseless actuation, and most importantly, perfect localization. However, under realistic noise models for visual sensors and actuation,
more » ... d without access to a "GPS and Compass sensor," the 99.6%-success agents for PointGoal navigation only succeed with 0.3%. In this work, we demonstrate the surprising effectiveness of visual odometry for the task of PointGoal navigation in this realistic setting, i.e., with realistic noise models for perception and actuation and without access to GPS and Compass sensors. We show that integrating visual odometry techniques into navigation policies improves the state-of-the-art on the popular Habitat PointNav benchmark by a large margin, improving success from 64.5% to 71.7% while executing 6.4 times faster.
arXiv:2108.11550v1 fatcat:4juvc7v7sndvrge35y5qulcywu

Hierarchical Semantic Regularization of Latent Spaces in StyleGANs [article]

Tejan Karmali, Rishubh Parihar, Susmit Agrawal, Harsh Rangwani, Varun Jampani, Maneesh Singh, R. Venkatesh Babu
2022 arXiv   pre-print
Progress in GANs has enabled the generation of high-resolution photorealistic images of astonishing quality. StyleGANs allow for compelling attribute modification on such images via mathematical operations on the latent style vectors in the W/W+ space that effectively modulate the rich hierarchical representations of the generator. Such operations have recently been generalized beyond mere attribute swapping in the original StyleGAN paper to include interpolations. In spite of many significant
more » ... mprovements in StyleGANs, they are still seen to generate unnatural images. The quality of the generated images is predicated on two assumptions; (a) The richness of the hierarchical representations learnt by the generator, and, (b) The linearity and smoothness of the style spaces. In this work, we propose a Hierarchical Semantic Regularizer (HSR) which aligns the hierarchical representations learnt by the generator to corresponding powerful features learnt by pretrained networks on large amounts of data. HSR is shown to not only improve generator representations but also the linearity and smoothness of the latent style spaces, leading to the generation of more natural-looking style-edited images. To demonstrate improved linearity, we propose a novel metric - Attribute Linearity Score (ALS). A significant reduction in the generation of unnatural images is corroborated by improvement in the Perceptual Path Length (PPL) metric by 16.19% averaged across different standard datasets while simultaneously improving the linearity of attribute-change in the attribute editing tasks.
arXiv:2208.03764v1 fatcat:ni6mauhaojckldawnqmzg4alzy

Spatially Aware Multimodal Transformers for TextVQA [article]

Yash Kant, Dhruv Batra, Peter Anderson, Alex Schwing, Devi Parikh, Jiasen Lu, Harsh Agrawal
2020 arXiv   pre-print
Textual cues are essential for everyday tasks like buying groceries and using public transport. To develop this assistive technology, we study the TextVQA task, i.e., reasoning about text in images to answer a question. Existing approaches are limited in their use of spatial relations and rely on fully-connected transformer-like architectures to implicitly learn the spatial structure of a scene. In contrast, we propose a novel spatially aware self-attention layer such that each visual entity
more » ... y looks at neighboring entities defined by a spatial graph. Further, each head in our multi-head self-attention layer focuses on a different subset of relations. Our approach has two advantages: (1) each head considers local context instead of dispersing the attention amongst all visual entities; (2) we avoid learning redundant features. We show that our model improves the absolute accuracy of current state-of-the-art methods on TextVQA by 2.2% overall over an improved baseline, and 4.62% on questions that involve spatial reasoning and can be answered correctly using OCR tokens. Similarly on ST-VQA, we improve the absolute accuracy by 4.2%. We further show that spatially aware self-attention improves visual grounding.
arXiv:2007.12146v2 fatcat:pjy2xc66gjb57fe2ipvjrr2una

Contrast and Classify: Training Robust VQA Models [article]

Yash Kant, Abhinav Moudgil, Dhruv Batra, Devi Parikh, Harsh Agrawal
2021 arXiv   pre-print
C-VQA (Agrawal et al. 2017b ) and VQA-CP (Agrawal et al. 2017a ) datasets were proposed to test robustness against changing questionanswer distributions.  ...  The task of Visual Question Answering (VQA) (Agrawal et al. 2015; Goyal et al. 2016) involves predicting an answer a for a question q about an image v.  ... 
arXiv:2010.06087v2 fatcat:l7vq3clnazgd7hrpuatjvik4tu

Normal Pressure Hydrocephalus [chapter]

Ravish Rajiv Keni, Harsh Deora, Amit Agrawal
2020 New Insight into Cerebrovascular Diseases: An Updated Comprehensive Review [Working Title]  
Normal pressure hydrocephalus (NPH) is characterized by dilated ventricles and a combination of gait impairment, cognition impairment, and loss of urinary control (urgency and incontinence). The only effective treatment for NPH is a CSF shunt; however, only a small percentage of patients ever receive it. The features of gait impairment in patients with NPH are difficult to distinguish from patients of neurodegenerative disorders with motor involvement, such as parkinsonism or dementia with Lewy
more » ... bodies. CT or MRI imaging is required for the diagnosis of idiopathic normal pressure hydrocephalus. An Evans ratio of more than 0.3 indicates large ventricles, and a ratio of more than 0.33 indicates very large ventricles, but is not specific for idiopathic normal pressure hydrocephalus. The international and Japanese guidelines support shunt surgery as effective treatment of idiopathic normal pressure hydrocephalus, as does the American Academy of Neurology practice guideline. There is a need to provide longitudinal care of patients with idiopathic normal pressure hydrocephalus after shunt surgery as all symptoms respond well to shunt surgery.
doi:10.5772/intechopen.92058 fatcat:azsvzb4f2rbvzn3jqtqbsw4qvm


Piyush Agrawal, Harsh Agrawal, Avinash Bagul, Apurva Joshi, Ajinkya Ghorpade
2020 International Journal of Engineering Applied Sciences and Technology  
Many college students travel in public transports or walk a long distance to reach college. This is problematic because public transports can be slow and not available everywhere as they have a specific time of arrival in their stops and they have to halt at multiple places in the city which can make it quite time consuming for passengers to reach their destinations. The goal of our project is to reduce this problem by providing a ride sharing application for institutes. This will be mutually
more » ... neficial for the students providing a ride and the students wanting to reach their destination quickly and cheaply as those who bring their own vehicles anyhow have to go to their homes without anyone sharing the ride with them. This will help them to earn money to at least cover their transportation or fuel cost and in-turn help provide a cheap ride to the ones in need. In this paper, we survey the work that deals with various paradigms of ride sharing and coincides with our idea for the application.
doi:10.33564/ijeast.2020.v05i08.028 fatcat:dlowzhnam5ekhgcpnjif5eki2i

Neurotrauma in the Time of SARS-COV 2: A Checklist for Its Safe Management

Luis Rafael Moscote Salazar, Deepak Agrawal, Harsh Deora, Amit Agrawal
2020 Journal of Neurosciences in Rural Practice  
Neurotrauma is a critical public health problem that deserves the attention of the world health community. The unprecedented pandemic of SARS-COV 2 has led to a tremendous strain on medical facilities including intensive care and availability of blood products. In addition, due to lockdown in most nations and diverting of medical attention elsewhere, neurotrauma has taken a back seat. Despite this, any case of trauma presenting during this time should receive the best possible care. However, it
more » ... is also imperative to safeguard the health care workers from this infection, too. The number of health care workers losing their lives to this infection is ever rising. We here present a possible workflow using a checklist approach such that errors and cross-infections are minimized and there is no reduction in the level of care received by any trauma case. This article has been written with a special focus on middle-income countries where resources may already be strained due to the sudden case burden. We hope to minimize death "caused" by COVID-19 and "related" to it.
doi:10.1055/s-0040-1712553 pmid:32753815 pmcid:PMC7394653 fatcat:3z5ywc5vvvd3pa3i2toqnjas4a

Multi-disease prediction with machine learning

Harsh Karwa, Pavan Gupta, Ram Agrawal, Gursewak Singh Virdi, Amit Kumar, Sweta Jain
2022 International Journal of Health Sciences  
In the present era, Machine learning (ML) algorithms are extensively used in computer assisted diagnosis of the disease based on the symptoms of the disease. The widespread use of healthcare applications in the pandemic time, provides a motivation to further develop new computer assisted diagnostic application in the healthcare domain. Prevention and treatment of disease, accurate and timely diagnosis of any health-related problem is essential. In the case of a serious illness, a standard
more » ... stic method may not be enough. We have proposed a system for predicting the disease. There were about forty-one diseases in the data corpus that needed to be analyzed based on the symptoms. The system delivers a disease prediction that a person may have depending on the symptoms. This diagnostic program can assist a physician in diagnosing disease, allowing for timely treatment and saving lives. The disease forecasting system was developed using ML models such as the Random Forests, the Naive Bayes, and the Support Vector Machine Classification Algorithm. The presented work outlines an analysis of the aforementioned algorithms.
doi:10.53730/ijhs.v6ns2.7487 fatcat:dbn5tdregnbcng4rzrz2kf5fai
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