Zobrazeno 1 - 10
of 10 138
pro vyhledávání: '"Hijazi, A."'
The paper studies a large-scale order fulfillment problem for a leading e-commerce company in the United States. The challenge involves selecting fulfillment centers and shipping carriers with observational data only to efficiently process orders fro
Externí odkaz:
http://arxiv.org/abs/2409.06918
Autor:
Hijazi, Faris, AlHarbi, Somayah, AlHussein, Abdulaziz, Shairah, Harethah Abu, AlZahrani, Reem, AlShamlan, Hebah, Knio, Omar, Turkiyyah, George
The rapid advancements in Large Language Models (LLMs) have led to significant improvements in various natural language processing tasks. However, the evaluation of LLMs' legal knowledge, particularly in non-English languages such as Arabic, remains
Externí odkaz:
http://arxiv.org/abs/2408.07983
Solid-State Reactions at Niobium-Germanium Interfaces in Hybrid Superconductor-Semiconductor Devices
Autor:
Langa Jr., Bernardo, Sapkota, Deepak, Lainez, Ivan, Haight, Richard, Srijanto, Bernadeta, Feldman, Leonard, Hijazi, Hussein, Zhu, Xiangyu, Hu, Lifang, Kim, Moon, Sardashti, Kasra
Hybrid Superconductor-Semiconductor (S-Sm) materials systems are promising candidates for quantum computing applications. Their integration into superconducting electronics has enabled on-demand voltage tunability at millikelvin temperatures. Ge quan
Externí odkaz:
http://arxiv.org/abs/2405.20517
Autor:
Hijazi, Maeshal, Dehghanian, Payman
Maintaining the privacy of power system data is essential for protecting sensitive information and ensuring the operation security of critical infrastructure. Therefore, the adoption of centralized deep learning (DL) transient stability assessment (T
Externí odkaz:
http://arxiv.org/abs/2403.03126
As more speech technologies rely on a supervised deep learning approach with clean speech as the ground truth, a methodology to onboard said speech at scale is needed. However, this approach needs to minimize the dependency on human listening and ann
Externí odkaz:
http://arxiv.org/abs/2402.12482
Autor:
Wang, Runhui, Kong, Luyang, Tao, Yefan, Borthwick, Andrew, Golac, Davor, Johnson, Henrik, Hijazi, Shadie, Deng, Dong, Zhang, Yongfeng
Locality-sensitive hashing (LSH) is a fundamental algorithmic technique widely employed in large-scale data processing applications, such as nearest-neighbor search, entity resolution, and clustering. However, its applicability in some real-world sce
Externí odkaz:
http://arxiv.org/abs/2401.18064
Autor:
Zhao, Haoruo, Hijazi, Hassan, Jones, Haydn, Moore, Juston, Tanneau, Mathieu, Van Hentenryck, Pascal
Neural network verification aims at providing formal guarantees on the output of trained neural networks, to ensure their robustness against adversarial examples and enable their deployment in safety-critical applications. This paper introduces a new
Externí odkaz:
http://arxiv.org/abs/2401.05280
Transformer-based Named Entity Recognition in Construction Supply Chain Risk Management in Australia
Autor:
Shishehgarkhaneh, Milad Baghalzadeh, Moehler, Robert C., Fang, Yihai, Hijazi, Amer A., Aboutorab, Hamed
The construction industry in Australia is characterized by its intricate supply chains and vulnerability to myriad risks. As such, effective supply chain risk management (SCRM) becomes imperative. This paper employs different transformer models, and
Externí odkaz:
http://arxiv.org/abs/2311.13755
Autor:
van Thiel, T. C., Weaver, M. J., Berto, F., Duivestein, P., Lemang, M., Schuurman, K. L., Žemlička, M., Hijazi, F., Bernasconi, A. C., Ferrer, C., Lachman, E., Field, M., Mohan, Y., de Vries, F. K., Bultink, C. C., van Oven, J., Mutus, J. Y., Stockill, R., Gröblacher, S.
Superconducting quantum processors have made significant progress in size and computing potential. As a result, the practical cryogenic limitations of operating large numbers of superconducting qubits are becoming a bottleneck for further scaling. Du
Externí odkaz:
http://arxiv.org/abs/2310.06026
Autor:
Adrian F. Daly, Leslie A. Dunnington, David F. Rodriguez-Buritica, Erica Spiegel, Francesco Brancati, Giovanna Mantovani, Vandana M. Rawal, Fabio Rueda Faucz, Hadia Hijazi, Jean-Hubert Caberg, Anna Maria Nardone, Mario Bengala, Paola Fortugno, Giulia Del Sindaco, Marta Ragonese, Helen Gould, Salvatore Cannavò, Patrick Pétrossians, Andrea Lania, James R. Lupski, Albert Beckers, Constantine A. Stratakis, Brynn Levy, Giampaolo Trivellin, Martin Franke
Publikováno v:
Genome Medicine, Vol 16, Iss 1, Pp 1-11 (2024)
Abstract Background X-linked acrogigantism (X-LAG; MIM: 300942) is a severe form of pituitary gigantism caused by chromosome Xq26.3 duplications involving GPR101. X-LAG-associated duplications disrupt the integrity of the topologically associating do
Externí odkaz:
https://doaj.org/article/3554c83b75ce4cf485ca6c1f7161c979