Zobrazeno 1 - 10
of 1 020
pro vyhledávání: '"MUKHOPADHYAY, ANIRBAN"'
Autor:
Mukhopadhyay, Anirban, Luther, Kurt
Small businesses need vulnerability assessments to identify and mitigate cyber risks. Cybersecurity clinics provide a solution by offering students hands-on experience while delivering free vulnerability assessments to local organizations. To scale t
Externí odkaz:
http://arxiv.org/abs/2409.11672
We give an asymptotic formula for the number of $\mathbb{F}_{q}$-rational points over a fixed determinant moduli space of stable vector bundles of rank $r$ and degree $d$ over a smooth, projective curve $X$ of genus $g \geq 2$ defined over $\mathbb{F
Externí odkaz:
http://arxiv.org/abs/2409.10558
Autor:
Sanner, Antoine P., Grauhan, Nils F., Brockmann, Marc A., Othman, Ahmed E., Mukhopadhyay, Anirban
Whole-body CT is used for multi-trauma patients in the search of any and all injuries. Since an initial assessment needs to be rapid and the search for lesions is done for the whole body, very little time can be allocated for the inspection of a spec
Externí odkaz:
http://arxiv.org/abs/2408.10768
Autor:
Sanner, Antoine P., Grauhan, Nils F., Brockmann, Marc A., Othman, Ahmed E., Mukhopadhyay, Anirban
Patients with Intracranial Hemorrhage (ICH) face a potentially life-threatening condition, and patient-centered individualized treatment remains challenging due to possible clinical complications. Deep-Learning-based methods can efficiently analyze t
Externí odkaz:
http://arxiv.org/abs/2407.21580
Medical image distributions shift constantly due to changes in patient population and discrepancies in image acquisition. These distribution changes result in performance deterioration; deterioration that continual learning aims to alleviate. However
Externí odkaz:
http://arxiv.org/abs/2407.21216
The disparity in access to machine learning tools for medical imaging across different regions significantly limits the potential for universal healthcare innovation, particularly in remote areas. Our research addresses this issue by implementing Neu
Externí odkaz:
http://arxiv.org/abs/2407.18114
Despite considerable success, large Denoising Diffusion Models (DDMs) with UNet backbone pose practical challenges, particularly on limited hardware and in processing gigapixel images. To address these limitations, we introduce two Neural Cellular Au
Externí odkaz:
http://arxiv.org/abs/2401.06291
Open Source Intelligence (OSINT) investigations, which rely entirely on publicly available data such as social media, play an increasingly important role in solving crimes and holding governments accountable. The growing volume of data and complex na
Externí odkaz:
http://arxiv.org/abs/2401.00928
Autor:
Ranem, Amin, González, Camila, Santos, Daniel Pinto dos, Bucher, Andreas M., Othman, Ahmed E., Mukhopadhyay, Anirban
Continual learning (CL) methods designed for natural image classification often fail to reach basic quality standards for medical image segmentation. Atlas-based segmentation, a well-established approach in medical imaging, incorporates domain knowle
Externí odkaz:
http://arxiv.org/abs/2311.00548