Zobrazeno 1 - 10
of 17 730
pro vyhledávání: '"A, Elahi"'
Autor:
Biswas, Amrijit, Hossain, Md. Ismail, Elahi, M M Lutfe, Cheraghian, Ali, Rahman, Fuad, Mohammed, Nabeel, Rahman, Shafin
A point cloud is a crucial geometric data structure utilized in numerous applications. The adoption of deep neural networks referred to as Point Cloud Neural Networks (PC- NNs), for processing 3D point clouds, has significantly advanced fields that r
Externí odkaz:
http://arxiv.org/abs/2408.14601
Additive noise models (ANMs) are an important setting studied in causal inference. Most of the existing works on ANMs assume causal sufficiency, i.e., there are no unobserved confounders. This paper focuses on confounded ANMs, where a set of treatmen
Externí odkaz:
http://arxiv.org/abs/2407.10014
Identifying causal effects is a key problem of interest across many disciplines. The two long-standing approaches to estimate causal effects are observational and experimental (randomized) studies. Observational studies can suffer from unmeasured con
Externí odkaz:
http://arxiv.org/abs/2407.05330
Autor:
Li, Haozhi, Elahi, Tariq
High latency is a critical limitation within the Tor network. A key factor exacerbating Tor latency is the creation of lengthy circuits that span across geographically distant regions, causing significant transmission delays. To address this issue, a
Externí odkaz:
http://arxiv.org/abs/2406.15055
Autor:
Elahi, Reza, Nazari, Mahdis
Current imaging methods for diagnosing BC are associated with limited sensitivity and specificity and modest positive predictive power. The recent progress in image analysis using artificial intelligence (AI) has created great promise to improve brea
Externí odkaz:
http://arxiv.org/abs/2406.14735
Autor:
Karami, Parsa, Elahi, Reza
With a high rate of morbidity and mortality, colorectal cancer (CRC) ranks third in mortality among cancers. By analyzing the texture properties of images and quantifying the heterogeneity of tumors, radiomics and radiogenomics are non-invasive metho
Externí odkaz:
http://arxiv.org/abs/2406.12467
Causal discovery aims to uncover cause-and-effect relationships encoded in causal graphs by leveraging observational, interventional data, or their combination. The majority of existing causal discovery methods are developed assuming infinite interve
Externí odkaz:
http://arxiv.org/abs/2405.11548
Autor:
Chatterjee, Suman, Elahi, Khandakar Md Asif, Bharadwaj, Somnath, Sarkar, Shouvik, Choudhuri, Samir, Sethi, Shiv, Patwa, Akash Kumar
Drift scan observations provide the broad sky coverage and instrumental stability needed to measure the Epoch of Reionization (EoR) 21-cm signal. In such observations, the telescope's pointing center (PC) moves continuously on the sky. The Tracking T
Externí odkaz:
http://arxiv.org/abs/2405.10080
Autor:
Mirza, Adrian, Alampara, Nawaf, Kunchapu, Sreekanth, Emoekabu, Benedict, Krishnan, Aswanth, Wilhelmi, Mara, Okereke, Macjonathan, Eberhardt, Juliane, Elahi, Amir Mohammad, Greiner, Maximilian, Holick, Caroline T., Gupta, Tanya, Asgari, Mehrdad, Glaubitz, Christina, Klepsch, Lea C., Köster, Yannik, Meyer, Jakob, Miret, Santiago, Hoffmann, Tim, Kreth, Fabian Alexander, Ringleb, Michael, Roesner, Nicole, Schubert, Ulrich S., Stafast, Leanne M., Wonanke, Dinga, Pieler, Michael, Schwaller, Philippe, Jablonka, Kevin Maik
Large language models (LLMs) have gained widespread interest due to their ability to process human language and perform tasks on which they have not been explicitly trained. This is relevant for the chemical sciences, which face the problem of small
Externí odkaz:
http://arxiv.org/abs/2404.01475
Causal discovery, i.e., learning the causal graph from data, is often the first step toward the identification and estimation of causal effects, a key requirement in numerous scientific domains. Causal discovery is hampered by two main challenges: li
Externí odkaz:
http://arxiv.org/abs/2403.09300