Zobrazeno 1 - 10
of 20 338
pro vyhledávání: '"Caglar SO"'
Publikováno v:
Applied Sciences, Vol 14, Iss 16, p 6984 (2024)
This study aimed to analyze both thoracic and lumbar erector spinae muscle activations during three different types of 3-dimensional elongation exercises in individuals with adolescent idiopathic scoliosis (AIS). Participants included 24 adolescents
Externí odkaz:
https://doaj.org/article/5d18af64487a434398b86a0d53405005
Large Language Models (LLMs) can transfer their reasoning skills to smaller models by teaching them to generate the intermediate reasoning process required to solve multistep reasoning tasks. While LLMs can accurately solve reasoning tasks through a
Externí odkaz:
http://arxiv.org/abs/2410.18574
In recent years, knowledge graph embedding models have been successfully applied in the transductive setting to tackle various challenging tasks including link prediction, and query answering. Yet, the transductive setting does not allow for reasonin
Externí odkaz:
http://arxiv.org/abs/2410.06742
Autor:
Fortman, Margaret A., Harrison, David C., Rodriguez, Ramiro H., Krebs, Zachary J., Han, Sangjun, Jang, Min Seok, McDermott, Robert, Girit, Caglar O., Brar, Victor W.
Josephson junction spectroscopy is a powerful local microwave spectroscopy technique that has promising potential as a diagnostic tool to probe the microscopic origins of noise in superconducting qubits. We present advancements toward realizing Josep
Externí odkaz:
http://arxiv.org/abs/2410.03009
Autor:
Tunc, Caglar
With the massive advancements in processing power, Digital Twins (DTs) have become powerful tools to monitor and analyze physical entities. However, due to the potentially very high number of Physical Systems (PSs) to be tracked and emulated, for ins
Externí odkaz:
http://arxiv.org/abs/2410.02487
We introduce a novel embedding method diverging from conventional approaches by operating within function spaces of finite dimension rather than finite vector space, thus departing significantly from standard knowledge graph embedding techniques. Ini
Externí odkaz:
http://arxiv.org/abs/2409.14857
Precipitation nowcasting is crucial for mitigating the impacts of severe weather events and supporting daily activities. Conventional models predominantly relying on radar data have limited performance in predicting cases with complex temporal featur
Externí odkaz:
http://arxiv.org/abs/2409.10367
Deep learning regularization techniques, such as dropout, layer normalization, or weight decay, are widely adopted in the construction of modern artificial neural networks, often resulting in more robust training processes and improved generalization
Externí odkaz:
http://arxiv.org/abs/2409.07606
The emergence of beyond 5G (B5G) and 6G networks underscores the critical role of advanced computer-aided tools, such as network digital twins (DTs), in fostering autonomous networks and ubiquitous intelligence. Existing solutions in the DT domain pr
Externí odkaz:
http://arxiv.org/abs/2409.01136
Autor:
Terekhov, Mikhail, Gulcehre, Caglar
Multi-objective reinforcement learning (MORL) is essential for addressing the intricacies of real-world RL problems, which often require trade-offs between multiple utility functions. However, MORL is challenging due to unstable learning dynamics wit
Externí odkaz:
http://arxiv.org/abs/2407.16807