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pro vyhledávání: '"Sikdar A"'
Autor:
D, Manjunath, Gurunath, Prajwal, Udupa, Sumanth, Gandhamal, Aditya, Madhu, Shrikar, Sikdar, Aniruddh, Sundaram, Suresh
Deep neural networks (DNNs) have shown exceptional performance when trained on well-illuminated images captured by Electro-Optical (EO) cameras, which provide rich texture details. However, in critical applications like aerial perception, it is essen
Externí odkaz:
http://arxiv.org/abs/2410.20953
Autor:
Agrawal, Aayush, Sikdar, Aniruddh, Makam, Rajini, Sundaram, Suresh, Besai, Suresh Kumar, Gopi, Mahesh
Underwater mine detection with deep learning suffers from limitations due to the scarcity of real-world data. This scarcity leads to overfitting, where models perform well on training data but poorly on unseen data. This paper proposes a Syn2Real (Sy
Externí odkaz:
http://arxiv.org/abs/2410.12953
Synthetic tabular data generation has gained significant attention for its potential in data augmentation, software testing and privacy-preserving data sharing. However, most research has primarily focused on larger datasets and evaluating their qual
Externí odkaz:
http://arxiv.org/abs/2410.01933
The activation functions are fundamental to neural networks as they introduce non-linearity into data relationships, thereby enabling deep networks to approximate complex data relations. Existing efforts to enhance neural network performance have pre
Externí odkaz:
http://arxiv.org/abs/2409.17021
Autor:
Sikdar, Satyaki, Venturini, Sara, Charpignon, Marie-Laure, Kumar, Sagar, Rinaldi, Francesco, Tudisco, Francesco, Fortunato, Santo, Majumder, Maimuna S.
Publikováno v:
Nat. Hum. Behav. 8 (2024) 1631-1634
Authors of COVID-19 papers produced during the pandemic were overwhelmingly not subject matter experts. Such a massive inflow of scholars from different expertise areas is both an asset and a potential problem. Domain-informed scientific collaboratio
Externí odkaz:
http://arxiv.org/abs/2410.01838
Autor:
Humnabadkar, Aditya, Sikdar, Arindam, Cave, Benjamin, Zhang, Huaizhong, Bakaki, Paul, Behera, Ardhendu
We present an innovative framework for traffic dynamics analysis using High-Order Evolving Graphs, designed to improve spatio-temporal representations in autonomous driving contexts. Our approach constructs temporal bidirectional bipartite graphs tha
Externí odkaz:
http://arxiv.org/abs/2409.11206
In the current artificial intelligence (AI) era, the scale and quality of the dataset play a crucial role in training a high-quality AI model. However, often original data cannot be shared due to privacy concerns and regulations. A potential solution
Externí odkaz:
http://arxiv.org/abs/2409.03612
Autor:
Cheng, Qiushuo, Morgan, Catherine, Sikdar, Arindam, Masullo, Alessandro, Whone, Alan, Mirmehdi, Majid
People with Parkinson's Disease (PD) often experience progressively worsening gait, including changes in how they turn around, as the disease progresses. Existing clinical rating tools are not capable of capturing hour-by-hour variations of PD sympto
Externí odkaz:
http://arxiv.org/abs/2408.08182
The Internet of Things (IoT) is a network of billions of interconnected, primarily low-end embedded devices. Despite large-scale deployment, studies have highlighted critical security concerns in IoT networks, many of which stem from firmware-related
Externí odkaz:
http://arxiv.org/abs/2408.05680
This study aims to learn a translation from visible to infrared imagery, bridging the domain gap between the two modalities so as to improve accuracy on downstream tasks including object detection. Previous approaches attempt to perform bi-domain fea
Externí odkaz:
http://arxiv.org/abs/2408.01843