Zobrazeno 1 - 10
of 3 532
pro vyhledávání: '"WANG, Ziyu"'
Autor:
Wang, Ziyu, Christov, Ivan C.
We propose an analytical approach to solving nonlocal generalizations of the Euler--Bernoulli beam. Specifically, we consider a version of the governing equation recently derived under the theory of peridynamics. We focus on the clamped--clamped case
Externí odkaz:
http://arxiv.org/abs/2412.09702
Lattice thermal conductivity, being integral to thermal transport properties, is indispensable to advancements in areas such as thermoelectric materials and thermal management. Traditional methods, such as Density Functional Theory and Molecular Dyna
Externí odkaz:
http://arxiv.org/abs/2412.05948
Offline reinforcement learning can enable policy learning from pre-collected, sub-optimal datasets without online interactions. This makes it ideal for real-world robots and safety-critical scenarios, where collecting online data or expert demonstrat
Externí odkaz:
http://arxiv.org/abs/2411.05273
Autor:
Yang, Lin, Zheng, Riyi, Zhang, Sheng, Zhang, Wenshuai, Du, Qiujiao, Peng, Pai, Wang, Ziyu, Ke, Manzhu, Huang, Xueqin, Liu, Fengming
We demonstrate that bound states in the continuum (BICs) form continuous lines along high-symmetry directions of momentum space in a simple phononic crystal slab. Contrary to common sense, these BICs are symmetry-protected (SP) BICs not only at the c
Externí odkaz:
http://arxiv.org/abs/2410.16674
A prior represents a set of beliefs or assumptions about a system, aiding inference and decision-making. In this work, we introduce the challenge of unsupervised prior learning in pose estimation, where AI models learn pose priors of animate objects
Externí odkaz:
http://arxiv.org/abs/2410.03858
In digital healthcare, large language models (LLMs) have primarily been utilized to enhance question-answering capabilities and improve patient interactions. However, effective patient care necessitates LLM chains that can actively gather information
Externí odkaz:
http://arxiv.org/abs/2409.19487
Unlocking Potential in Pre-Trained Music Language Models for Versatile Multi-Track Music Arrangement
Large language models have shown significant capabilities across various domains, including symbolic music generation. However, leveraging these pre-trained models for controllable music arrangement tasks, each requiring different forms of musical in
Externí odkaz:
http://arxiv.org/abs/2408.15176
Autor:
Ma, Yinghao, Øland, Anders, Ragni, Anton, Del Sette, Bleiz MacSen, Saitis, Charalampos, Donahue, Chris, Lin, Chenghua, Plachouras, Christos, Benetos, Emmanouil, Shatri, Elona, Morreale, Fabio, Zhang, Ge, Fazekas, György, Xia, Gus, Zhang, Huan, Manco, Ilaria, Huang, Jiawen, Guinot, Julien, Lin, Liwei, Marinelli, Luca, Lam, Max W. Y., Sharma, Megha, Kong, Qiuqiang, Dannenberg, Roger B., Yuan, Ruibin, Wu, Shangda, Wu, Shih-Lun, Dai, Shuqi, Lei, Shun, Kang, Shiyin, Dixon, Simon, Chen, Wenhu, Huang, Wenhao, Du, Xingjian, Qu, Xingwei, Tan, Xu, Li, Yizhi, Tian, Zeyue, Wu, Zhiyong, Wu, Zhizheng, Ma, Ziyang, Wang, Ziyu
In recent years, foundation models (FMs) such as large language models (LLMs) and latent diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This comprehensive review examines state-of-the-art (SOTA) pre-trained models
Externí odkaz:
http://arxiv.org/abs/2408.14340
Efficient data transmission and reasonable task allocation are important to improve multi-robot exploration efficiency. However, most communication data types typically contain redundant information and thus require massive communication volume. More
Externí odkaz:
http://arxiv.org/abs/2408.05808
Autor:
Wang, Ziyu, Kanduri, Anil, Aqajari, Seyed Amir Hossein, Jafarlou, Salar, Mousavi, Sanaz R., Liljeberg, Pasi, Malik, Shaista, Rahmani, Amir M.
While ECG data is crucial for diagnosing and monitoring heart conditions, it also contains unique biometric information that poses significant privacy risks. Existing ECG re-identification studies rely on exhaustive analysis of numerous deep learning
Externí odkaz:
http://arxiv.org/abs/2408.10228