Výsledky vyhledávání - "Narayanan, Sai"

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Limited Parallelization in Gate Operations Leads to Higher Space Overhead and Lower Noise Threshold

Autor: Narayanan, Sai Sanjay, Bagewadi, Smita, Chatterjee, Avhishek

Decoherence of qubits during gate operations in the error correction phase of fault-tolerant computation is significant since, in practice, gate operations are neither instantaneous nor are they sufficiently parallelized. Thus, in practice, the overh

Externí odkaz: http://arxiv.org/abs/2410.04156

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Diverse Expected Improvement (DEI): Diverse Bayesian Optimization of Expensive Computer Simulators

Autor: Miller, John Joshua, Mak, Simon, Sun, Benny, Narayanan, Sai Ranjeet, Yang, Suo, Sun, Zongxuan, Kim, Kenneth S., Kweon, Chol-Bum Mike

The optimization of expensive black-box simulators arises in a myriad of modern scientific and engineering applications. Bayesian optimization provides an appealing solution, by leveraging a fitted surrogate model to guide the selection of subsequent

Externí odkaz: http://arxiv.org/abs/2410.01196

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Optimum Beamforming and Grating Lobe Mitigation for Intelligent Reflecting Surfaces

Autor: Narayanan, Sai Sanjay, Khankhoje, Uday K, Ganti, Radha Krishna

Ensuring adequate wireless coverage in upcoming communication technologies such as 6G is expected to be challenging. This is because user demands of higher datarate require an increase in carrier frequencies, which in turn reduce the diffraction effe

Externí odkaz: http://arxiv.org/abs/2404.09215

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Scalable Pathogen Detection from Next Generation DNA Sequencing with Deep Learning

Autor: Narayanan, Sai, Aakur, Sathyanarayanan N., Ramamurthy, Priyadharsini, Bagavathi, Arunkumar, Ramnath, Vishalini, Ramachandran, Akhilesh

Next-generation sequencing technologies have enhanced the scope of Internet-of-Things (IoT) to include genomics for personalized medicine through the increased availability of an abundance of genome data collected from heterogeneous sources at a redu

Externí odkaz: http://arxiv.org/abs/2212.00015

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Metagenome2Vec: Building Contextualized Representations for Scalable Metagenome Analysis

Autor: Aakur, Sathyanarayanan N., Indla, Vineela, Indla, Vennela, Narayanan, Sai, Bagavathi, Arunkumar, Ramnath, Vishalini Laguduva, Ramachandran, Akhilesh

Advances in next-generation metagenome sequencing have the potential to revolutionize the point-of-care diagnosis of novel pathogen infections, which could help prevent potential widespread transmission of diseases. Given the high volume of metagenom

Externí odkaz: http://arxiv.org/abs/2111.08001

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MG-NET: Leveraging Pseudo-Imaging for Multi-Modal Metagenome Analysis

Autor: Aakur, Sathyanarayanan N., Narayanan, Sai, Indla, Vineela, Bagavathi, Arunkumar, Ramnath, Vishalini Laguduva, Ramachandran, Akhilesh

The emergence of novel pathogens and zoonotic diseases like the SARS-CoV-2 have underlined the need for developing novel diagnosis and intervention pipelines that can learn rapidly from small amounts of labeled data. Combined with technological advan

Externí odkaz: http://arxiv.org/abs/2107.09883

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Genome Sequence Classification for Animal Diagnostics with Graph Representations and Deep Neural Networks

Autor: Narayanan, Sai, Ramachandran, Akhilesh, Aakur, Sathyanarayanan N., Bagavathi, Arunkumar

Bovine Respiratory Disease Complex (BRDC) is a complex respiratory disease in cattle with multiple etiologies, including bacterial and viral. It is estimated that mortality, morbidity, therapy, and quarantine resulting from BRDC account for significa

Externí odkaz: http://arxiv.org/abs/2007.12791

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