Zobrazeno 1 - 10
of 4 393
pro vyhledávání: '"P. Vasan"'
Autor:
Dimopoulos, George, Parra, Sabrina De Los Angeles Reverol, Mondal, Parmita, Udin, Michael, Williams, Kyle, Naghdi, Parisa, Rahmatpour, Ahmad, Nagesh, Swetadri Vasan Setlur, Bhurwani, Mohammad Mahdi Shiraz, Davies, Jason, Ionita, Ciprian N.
Subarachnoid hemorrhage (SAH), typically due to intracranial aneurysms, demands precise imaging for effective treatment. Digital Subtraction Angiography (DSA), despite being the gold standard, broadly visualizes cerebral blood flow, potentially maski
Externí odkaz:
http://arxiv.org/abs/2411.16637
Autor:
Vasan, Gautham, Elsayed, Mohamed, Azimi, Alireza, He, Jiamin, Shariar, Fahim, Bellinger, Colin, White, Martha, Mahmood, A. Rupam
Modern deep policy gradient methods achieve effective performance on simulated robotic tasks, but they all require large replay buffers or expensive batch updates, or both, making them incompatible for real systems with resource-limited computers. We
Externí odkaz:
http://arxiv.org/abs/2411.15370
We propose a diffusion model-based approach, FloAtControlNet to generate cinemagraphs composed of animations of human clothing. We focus on human clothing like dresses, skirts and pants. The input to our model is a text prompt depicting the type of c
Externí odkaz:
http://arxiv.org/abs/2411.15028
Autor:
Goyal, Sahil, Mahajan, Abhinav, Mishra, Swasti, Udhayanan, Prateksha, Shukla, Tripti, Joseph, K J, Srinivasan, Balaji Vasan
Graphic designs are an effective medium for visual communication. They range from greeting cards to corporate flyers and beyond. Off-late, machine learning techniques are able to generate such designs, which accelerates the rate of content production
Externí odkaz:
http://arxiv.org/abs/2411.14959
Autor:
Naghdi, Parisa, Bhurwani, Mohammad Mahdi Shiraz, Rahmatpour, Ahmad, Mondal, Parmita, Udin, Michael, Williams, Kyle A, Nagesh, Swetadri Vasan Setlur, Ionita, Ciprian N
This study evaluates a multimodal machine learning framework for predicting treatment outcomes in intracranial aneurysms (IAs). Combining angiographic parametric imaging (API), patient biomarkers, and disease morphology, the framework aims to enhance
Externí odkaz:
http://arxiv.org/abs/2411.14407
Autor:
Zimmermann, Yoel, Bazgir, Adib, Afzal, Zartashia, Agbere, Fariha, Ai, Qianxiang, Alampara, Nawaf, Al-Feghali, Alexander, Ansari, Mehrad, Antypov, Dmytro, Aswad, Amro, Bai, Jiaru, Baibakova, Viktoriia, Biswajeet, Devi Dutta, Bitzek, Erik, Bocarsly, Joshua D., Borisova, Anna, Bran, Andres M, Brinson, L. Catherine, Calderon, Marcel Moran, Canalicchio, Alessandro, Chen, Victor, Chiang, Yuan, Circi, Defne, Charmes, Benjamin, Chaudhary, Vikrant, Chen, Zizhang, Chiu, Min-Hsueh, Clymo, Judith, Dabhadkar, Kedar, Daelman, Nathan, Datar, Archit, Evans, Matthew L., Fard, Maryam Ghazizade, Fisicaro, Giuseppe, Gangan, Abhijeet Sadashiv, George, Janine, Gonzalez, Jose D. Cojal, Götte, Michael, Gupta, Ankur K., Harb, Hassan, Hong, Pengyu, Ibrahim, Abdelrahman, Ilyas, Ahmed, Imran, Alishba, Ishimwe, Kevin, Issa, Ramsey, Jablonka, Kevin Maik, Jones, Colin, Josephson, Tyler R., Juhasz, Greg, Kapoor, Sarthak, Kang, Rongda, Khalighinejad, Ghazal, Khan, Sartaaj, Klawohn, Sascha, Kuman, Suneel, Ladines, Alvin Noe, Leang, Sarom, Lederbauer, Magdalena, Liao, Sheng-Lun Mark, Liu, Hao, Liu, Xuefeng, Lo, Stanley, Madireddy, Sandeep, Maharana, Piyush Ranjan, Maheshwari, Shagun, Mahjoubi, Soroush, Márquez, José A., Mills, Rob, Mohanty, Trupti, Mohr, Bernadette, Moosavi, Seyed Mohamad, Moßhammer, Alexander, Naghdi, Amirhossein D., Naik, Aakash, Narykov, Oleksandr, Näsström, Hampus, Nguyen, Xuan Vu, Ni, Xinyi, O'Connor, Dana, Olayiwola, Teslim, Ottomano, Federico, Ozhan, Aleyna Beste, Pagel, Sebastian, Parida, Chiku, Park, Jaehee, Patel, Vraj, Patyukova, Elena, Petersen, Martin Hoffmann, Pinto, Luis, Pizarro, José M., Plessers, Dieter, Pradhan, Tapashree, Pratiush, Utkarsh, Puli, Charishma, Qin, Andrew, Rajabi, Mahyar, Ricci, Francesco, Risch, Elliot, Ríos-García, Martiño, Roy, Aritra, Rug, Tehseen, Sayeed, Hasan M, Scheidgen, Markus, Schilling-Wilhelmi, Mara, Schloz, Marcel, Schöppach, Fabian, Schumann, Julia, Schwaller, Philippe, Schwarting, Marcus, Sharlin, Samiha, Shen, Kevin, Shi, Jiale, Si, Pradip, D'Souza, Jennifer, Sparks, Taylor, Sudhakar, Suraj, Talirz, Leopold, Tang, Dandan, Taran, Olga, Terboven, Carla, Tropin, Mark, Tsymbal, Anastasiia, Ueltzen, Katharina, Unzueta, Pablo Andres, Vasan, Archit, Vinchurkar, Tirtha, Vo, Trung, Vogel, Gabriel, Völker, Christoph, Weinreich, Jan, Yang, Faradawn, Zaki, Mohd, Zhang, Chi, Zhang, Sylvester, Zhang, Weijie, Zhu, Ruijie, Zhu, Shang, Janssen, Jan, Foster, Ian, Blaiszik, Ben
Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spann
Externí odkaz:
http://arxiv.org/abs/2411.15221
Autor:
Mondal, Parmita, Williams, Kyle A, Naghdi, Parisa, Rahmatpour, Ahmad, Bhurwani, Mohammad Mahdi Shiraz, Nagesh, Swetadri Vasan Setlur, Ionita, Ciprian N
In intracranial aneurysm (IA) treatment, digital subtraction angiography (DSA) monitors device-induced hemodynamic changes. Quantitative angiography (QA) provides more precise assessments but is limited by hand-injection variability. This study evalu
Externí odkaz:
http://arxiv.org/abs/2411.14475
We consider the problem of conditional text-to-image synthesis with diffusion models. Most recent works need to either finetune specific parts of the base diffusion model or introduce new trainable parameters, leading to deployment inflexibility due
Externí odkaz:
http://arxiv.org/abs/2411.10800
Autor:
Rahmatpour, Ahmad, Shields, Allison, Mondal, Parmita, Naghdi, Parisa, Udin, Michael, Williams, Kyle A, Bhurwani, Mohammad Mahdi Shiraz, Nagesh, Swetadri Vasan Setlur, Ionita, Ciprian N
This study leverages convolutional neural networks to enhance the temporal resolution of 3D angiography in intracranial aneurysms focusing on the reconstruction of volumetric contrast data from sparse and limited projections. Three patient-specific I
Externí odkaz:
http://arxiv.org/abs/2411.09632
Autor:
Mondal, Parmita, Shields, Allison, Bhurwani, Mohammad Mahdi Shiraz, Williams, Kyle A, Nagesh, Swetadri Vasan Setlur, Siddiqui, Adnan H, Ionita, Ciprian N
This study aims to mitigate these biases and enhance QA analysis by applying a path-length correction (PLC) correction, followed by singular value decomposition (SVD)-based deconvolution, to angiograms obtained through both in-silico and in-vitro met
Externí odkaz:
http://arxiv.org/abs/2411.08185