4,373 research outputs found
Correction to: Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods (Personal and Ubiquitous Computing, (2021), 10.1007/s00779-021-01541-4)
No full textThe affiliations of the authors were incorrect. The corrected affiliations are as follows: 1. M. Poongodi, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar 2. Mounir Hamdi, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar 3. Mohit Malviya, Department of CTO 5G, Wipro Limited, Bengaluru, India 4. Ashutosh Sharma, Institute of Computer Technology and Information Security, Southern Federal University, Rostovon- Don, Russia 5. Gaurav Dhiman, Department of Computer Science, Government Bikram College of Commerce, Punjabi University , Patiala , Punjab , 147001 , India , Email: [email protected] 6. S. Vimal, Department of CSE, Ramco Institute of Technology, Rajapalayam, Tamil Nadu, India The original article has been corrected
I Was Addicted to Sex With Married Women
No full textAs a young man, Akhil Sharma revelled in the most dangerous of liaisons, having sex with other men's wives – until the thrill began to pall
Automatic Reformulations for Convex Mixed-Integer Nonlinear Optimization: Perspective and Separability
No full textTight reformulations of combinatorial optimization problems like Convex Mixed-Integer Nonlinear Programs (MINLPs) enable one to solve these problems faster by obtaining tight bounds on optimal value. We consider two techniques for reformulation: perspective reformulation and separability detection. We develop routines for automatic detection of problem structures suitable for these reformulations, and implement new extensions. Since detecting all "on-off" sets for perspective reformulation in a problem can be as hard as solving the original problem, we develop heuristic methods to automatically identify them. The LP/NLP branch-and-bound method is strengthened via "perspective cuts" derived from these automatic routines. We also provide methods to generate tight perspective cuts at different nodes in the branch-and-bound tree. The second structure, i.e., separability of nonlinear functions, is detected by means of the computational graph of the function. Our routines have been implemented in the open-source Minotaur solver for general convex MINLPs. Computational results show an improvement of up to 45% in the solution time and the size of the branch-and-bound tree for convex instances from benchmark library MINLPLib. On instances where reformulation using function separability induces structures that are amenable to perspective reformulation, we observe an improvement of up to 88% in the solution time
Maldives Resorts: Eco-Friendly Vacations
No full textLuxurious, exclusive and remote, the Maldives are the ultimate beach escape. They’re also a case study in the risks of global warming. Writer Akhil Sharma visits the country’s most eco-friendly resorts and discovers a remarkable cuisine worth protecting
Recollections of a Hindu Hedonist
No full textNovelist Akhil Sharma grew up in a teetotaling Indian household. Here, he tells how discovering a passion for great wine helped him create a new identity out of a painful past
FIGURE 1 in Impatiens glauca Hook. f. et Thomson-A little known Himalayan species with augmented description and a new spurless variety
No full textFIGURE 1. Impatiens glauca var. ecalcarata: A.; B––Flower side view; C.; D––Flower frontal view; E––Lower sepal; F––Lateral united petals; G––Dorsal petal; H.––Stamen; I––Capsule; J––Seed; K––Leaf dorsal view.Published as part of Singh, Harsh, Sharma, Ashutosh & Adamowski, Wojciech, 2022, Impatiens glauca Hook. f. et Thomson-A little known Himalayan species with augmented description and a new spurless variety, pp. 280-286 in Phytotaxa 539 (3) on page 282, DOI: 10.11646/phytotaxa.539.3.7, http://zenodo.org/record/636420
Impatiens glauca Hook. f. & Thomson
No full textKey to the varieties 1. Spur present......................................................................................................................................................... I. glauca var. glauca 2. Spur absent................................................................................................................................................... I. glauca var. ecalcarataPublished as part of Singh, Harsh, Sharma, Ashutosh & Adamowski, Wojciech, 2022, Impatiens glauca Hook. f. et Thomson-A little known Himalayan species with augmented description and a new spurless variety, pp. 280-286 in Phytotaxa 539 (3) on page 281, DOI: 10.11646/phytotaxa.539.3.7, http://zenodo.org/record/636420
Configuring SAP ERP sales and distribution / Kapil Sharma, Ashutosh Mutsaddi.
No full textIncludes bibliographical references (p. 624) and index.xviii, 654 pages
Domain generalization for sequential data via invariant subspace recovery
No full textSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo termsThe student, Ashutosh Sharma, accepted the attached license on 2025-05-01 at 05:39.The student, Ashutosh Sharma, submitted this Thesis for approval on 2025-05-01 at 05:44.This Thesis was approved for publication on 2025-05-05 at 10:55.DSpace SAF Submission Ingestion Package generated from Vireo submission #22142 on 2025-10-19 at 18:11:27Recent works have explored continuous and discrete temporal domain generalization, which given input data from multiple temporally indexed domains, aims to train a model generalized across time. State of the art systems like DRAIN [1] model the temporal evolution of the input domain and the model dynamics jointly for training optimal predictors, but these approaches cannot generalize to multiple categorically indexed domains with temporally evolving data. In another line of work, ISR [2] trains invariant predictors for a set of categorical domains assuming i.i.d sampled observed data points for recovering the invariant subspace, but this cannot be applied directly to sequentially sampled data. In this work, we propose leveraging subspace recovery techniques for training invariant predictors over temporally evolving data. We formalize the data generation model as a Dynamic Bayesian Network where latent representation at any time causally depend only on the current time’s label & last time’s latent variable. Assuming access to only the observations (and labels for training environments) for each environment, we first estimate the parameters of the model from training data via Expectation-Maximization. We then derive an optimal online predictor for the test environment which forms the strong baseline for our work. We then estimate invariant feature subspace from the latent variable distribution of the training data and project the observed features to this space. These invariant features are then used for generating invariant predictions. Similar to [3], we also propose a set of linear unit tests benchmark for this novel setting. Our experiments on the benchmark show that our strong baseline outperforms i.i.d. classifier by 17% and our invariant predictor further improves the accuracy by 3% over our strong baseline, validating the efficacy of our method
Interview with Lakshmi Raj Sharma, Author of The Tailor’s Needle
No full textInterview with Indian writer Lakshmi Raj Sharma, author of 'The Tailor's needle
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