Zobrazeno 1 - 10
of 212 956
pro vyhledávání: '"Osman, A"'
Autor:
Bruintjes, Robert-Jan, Lengyel, Attila, Rios, Marcos Baptista, Kayhan, Osman Semih, Zambrano, Davide, Tomen, Nergis, van Gemert, Jan
The fourth edition of the "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" workshop features two data-impaired challenges. These challenges address the problem of training deep learning models for computer vision tasks with limite
Externí odkaz:
http://arxiv.org/abs/2406.18176
Existing perception methods for autonomous driving fall short of recognizing unknown entities not covered in the training data. Open-vocabulary methods offer promising capabilities in detecting any object but are limited by user-specified queries rep
Externí odkaz:
http://arxiv.org/abs/2406.09126
Autor:
Fendor, Zuzanna, van der Velden, Bas H. M., Wang, Xinxin, Carnoli, Andrea Jr., Mutlu, Osman, Hürriyetoğlu, Ali
Research in the food domain is at times limited due to data sharing obstacles, such as data ownership, privacy requirements, and regulations. While important, these obstacles can restrict data-driven methods such as machine learning. Federated learni
Externí odkaz:
http://arxiv.org/abs/2406.06202
Autor:
Lorenc, Dusan, Volosniev, Artem G., Zhumekenov, Ayan A., Lee, Seungho, Ibáñez, Maria, Bakr, Osman M., Lemeshko, Mikhail, Alpichshev, Zhanybek
We observe strong photoluminescence of a lead-halide perovskite driven by deep sub-gap irradiation. Using the quasi-adiabatic Landau-Dykhne approach, we interpret this observation in terms of the dynamical Schwinger effect -- tunneling ionization in
Externí odkaz:
http://arxiv.org/abs/2406.05032
Tensor Train~(TT) decomposition is widely used in the machine learning and quantum physics communities as a popular tool to efficiently compress high-dimensional tensor data. In this paper, we propose an efficient algorithm to accelerate computing th
Externí odkaz:
http://arxiv.org/abs/2406.02749
Autor:
Palit, Jewel Rana, Osman, Osama A
Traffic flow forecasting is a crucial first step in intelligent and proactive traffic management. Traffic flow parameters are volatile and uncertain, making traffic flow forecasting a difficult task if the appropriate forecasting model is not used. A
Externí odkaz:
http://arxiv.org/abs/2406.00619
Autor:
Shakir, Hafiz Muhammad, Suleiman, Abdulsalam Aji, Kalkan, Kübra Nur, Parsi, Amir, Başçı, Uğur, Durmuş, Mehmet Atıf, Ölçer, Ahmet Osman, Korkut, Hilal, Sevik, Cem, Sarpkaya, İbrahim, Kasırga, Talip Serkan
Excitons in monolayer transition metal dichalcogenides (TMDCs) offer intriguing new possibilities for optoelectronics with no analogues in bulk semiconductors. Yet, intrinsic defects in TMDCs limit the radiative exciton recombination pathways. As a r
Externí odkaz:
http://arxiv.org/abs/2405.20636
Autor:
Moured, Omar, Alzalabny, Sara, Osman, Anas, Schwarz, Thorsten, Muller, Karin, Stiefelhagen, Rainer
Visualizations, such as charts, are crucial for interpreting complex data. However, they are often provided as raster images, which are not compatible with assistive technologies for people with blindness and visual impairments, such as embossed pape
Externí odkaz:
http://arxiv.org/abs/2405.19117
Autor:
Osman, Islam, Shehata, Mohamed S.
Current state-of-the-art video object segmentation models have achieved great success using supervised learning with massive labeled training datasets. However, these models are trained using a single source domain and evaluated using videos sampled
Externí odkaz:
http://arxiv.org/abs/2405.19525
To efficiently utilize the scarce wireless resource, the random access scheme has been attaining renewed interest primarily in supporting the sporadic traffic of a large number of devices encountered in the Internet of Things (IoT). In this paper we
Externí odkaz:
http://arxiv.org/abs/2405.17979