Zobrazeno 1 - 10
of 75 609
pro vyhledávání: '"Halil, A"'
Autor:
Mutuk, Halil
This study reexamines the spectroscopic parameters of light-flavor diquarks within the framework of quantum chromodynamics sum rules (QCDSR) using the inverse matrix method. Conventional QCDSR analyses are based on assumptions such as quark-hadron du
Externí odkaz:
http://arxiv.org/abs/2412.08620
Autor:
Gevgilili, Halil, Kalyon, Dilhan M.
This study explores the impact of wall slip on parameter estimation for integral-type non-linear viscoelastic models with time-strain separable memory functions, specifically examining high-density polyethylene (HDPE) and thermoplastic elastomer (TPE
Externí odkaz:
http://arxiv.org/abs/2412.03779
Autor:
Mutuk, Halil
The magnetic moment of a hadron is an important spectroscopic parameter as its mass and encodes valuable information about its internal structure. In this present study, we systematically study magnetic moments of the $P_{c}(4457)$ and its related hi
Externí odkaz:
http://arxiv.org/abs/2411.16486
Autor:
Kurt, Halil ibrahim
This paper deals with the long-term behavior of positive solutions for the following parabolic-elliptic chemotaxis competition system with weak singular sensitivity and logistic source \begin{equation} \label{abstract-eq} \begin{cases} u_t=\Delta u-\
Externí odkaz:
http://arxiv.org/abs/2411.15852
Sequential sentence classification (SSC) in scientific publications is crucial for supporting downstream tasks such as fine-grained information retrieval and extractive summarization. However, current SSC methods are constrained by model size, sequen
Externí odkaz:
http://arxiv.org/abs/2411.15623
Autor:
Pan, Hongyi, Hong, Ziliang, Durak, Gorkem, Keles, Elif, Aktas, Halil Ertugrul, Taktak, Yavuz, Medetalibeyoglu, Alpay, Zhang, Zheyuan, Velichko, Yury, Spampinato, Concetto, Schoots, Ivo, Bruno, Marco J., Tiwari, Pallavi, Bolan, Candice, Gonda, Tamas, Miller, Frank, Keswani, Rajesh N., Wallace, Michael B., Xu, Ziyue, Bagci, Ulas
Accurate classification of Intraductal Papillary Mucinous Neoplasms (IPMN) is essential for identifying high-risk cases that require timely intervention. In this study, we develop a federated learning framework for multi-center IPMN classification ut
Externí odkaz:
http://arxiv.org/abs/2411.05697
Recent methods in quantile regression have adopted a classification perspective to handle challenges posed by heteroscedastic, multimodal, or skewed data by quantizing outputs into fixed bins. Although these regression-as-classification frameworks ca
Externí odkaz:
http://arxiv.org/abs/2411.01266
We introduce an innovative approach to advancing semantic understanding in zero-shot object goal navigation (ZS-OGN), enhancing the autonomy of robots in unfamiliar environments. Traditional reliance on labeled data has been a limitation for robotic
Externí odkaz:
http://arxiv.org/abs/2410.21926
Autor:
Pan, Hongyi, Durak, Gorkem, Zhang, Zheyuan, Taktak, Yavuz, Keles, Elif, Aktas, Halil Ertugrul, Medetalibeyoglu, Alpay, Velichko, Yury, Spampinato, Concetto, Schoots, Ivo, Bruno, Marco J., Keswani, Rajesh N., Tiwari, Pallavi, Bolan, Candice, Gonda, Tamas, Goggins, Michael G., Wallace, Michael B., Xu, Ziyue, Bagci, Ulas
Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data, making it an attractive solution for medical imaging tasks. However, traditional FL methods, such as Federated Averaging (FedAvg), face d
Externí odkaz:
http://arxiv.org/abs/2410.22530
In this paper, we present a novel method for reliable frontier selection in Zero-Shot Object Goal Navigation (ZS-OGN), enhancing robotic navigation systems with foundation models to improve commonsense reasoning in indoor environments. Our approach i
Externí odkaz:
http://arxiv.org/abs/2410.21037