Zobrazeno 1 - 10
of 5 466
pro vyhledávání: '"Zhang, Xinyue"'
Autor:
Do, Hue T. B., Zhao, Meng, Li, Pengfei, Soh, Yu Wei, Rangaraj, Jagadesh, Liu, Bingyan, Jiang, Tianyu, Zhang, Xinyue, Lu, Jiong, Song, Peng, Teng, Jinghua, Bosman, Michel
Extreme light confinement down to the atomic scale has been theoretically predicted for ultrathin, Ta-based transition metal dichalcogenides (TMDs). In this work, we demonstrate in free-hanging 2H-TaS2 monolayers and bilayers slow light behaviour wit
Externí odkaz:
http://arxiv.org/abs/2411.07572
While automatically generated polynomial elimination templates have sparked great progress in the field of 3D computer vision, there remain many problems for which the degree of the constraints or the number of unknowns leads to intractability. In re
Externí odkaz:
http://arxiv.org/abs/2411.03745
Polarization-entangled photon pairs are essential sources for photonic quantum information processing. However, generating entangled photon pairs with large detuning via spontaneous parametric down-conversion (SPDC) often requires complex configurati
Externí odkaz:
http://arxiv.org/abs/2410.22789
Autor:
Gong, Letian, Lin, Yan, Zhang, Xinyue, Lu, Yiwen, Han, Xuedi, Liu, Yichen, Guo, Shengnan, Lin, Youfang, Wan, Huaiyu
Location-based services (LBS) have accumulated extensive human mobility data on diverse behaviors through check-in sequences. These sequences offer valuable insights into users' intentions and preferences. Yet, existing models analyzing check-in sequ
Externí odkaz:
http://arxiv.org/abs/2411.00823
The impact of initial connectivity on learning has been extensively studied in the context of backpropagation-based gradient descent, but it remains largely underexplored in biologically plausible learning settings. Focusing on recurrent neural netwo
Externí odkaz:
http://arxiv.org/abs/2410.11164
In the realm of computer vision, the perception and reconstruction of the 3D world through vision signals heavily rely on camera intrinsic parameters, which have long been a subject of intense research within the community. In practical applications,
Externí odkaz:
http://arxiv.org/abs/2409.19641
Autor:
Li, Yizhi, Zhang, Ge, Ma, Yinghao, Yuan, Ruibin, Zhu, Kang, Guo, Hangyu, Liang, Yiming, Liu, Jiaheng, Wang, Zekun, Yang, Jian, Wu, Siwei, Qu, Xingwei, Shi, Jinjie, Zhang, Xinyue, Yang, Zhenzhu, Wang, Xiangzhou, Zhang, Zhaoxiang, Liu, Zachary, Benetos, Emmanouil, Huang, Wenhao, Lin, Chenghua
Recent advancements in multimodal large language models (MLLMs) have aimed to integrate and interpret data across diverse modalities. However, the capacity of these models to concurrently process and reason about multiple modalities remains inadequat
Externí odkaz:
http://arxiv.org/abs/2409.15272
Autor:
Zhou, Ziya, Wu, Yuhang, Wu, Zhiyue, Zhang, Xinyue, Yuan, Ruibin, Ma, Yinghao, Wang, Lu, Benetos, Emmanouil, Xue, Wei, Guo, Yike
Symbolic Music, akin to language, can be encoded in discrete symbols. Recent research has extended the application of large language models (LLMs) such as GPT-4 and Llama2 to the symbolic music domain including understanding and generation. Yet scant
Externí odkaz:
http://arxiv.org/abs/2407.21531
Autor:
Xu, Zhiqiang, Zhang, Xinyue
This paper investigates the stability of the least squares approximation $P_m^n$ within the univariate polynomial space of degree $m$, denoted by ${\mathbb P}_m$. The approximation $P_m^n$ entails identifying a polynomial in ${\mathbb P}_m$ that appr
Externí odkaz:
http://arxiv.org/abs/2407.10221
Autor:
Zhang, Pan, Dong, Xiaoyi, Zang, Yuhang, Cao, Yuhang, Qian, Rui, Chen, Lin, Guo, Qipeng, Duan, Haodong, Wang, Bin, Ouyang, Linke, Zhang, Songyang, Zhang, Wenwei, Li, Yining, Gao, Yang, Sun, Peng, Zhang, Xinyue, Li, Wei, Li, Jingwen, Wang, Wenhai, Yan, Hang, He, Conghui, Zhang, Xingcheng, Chen, Kai, Dai, Jifeng, Qiao, Yu, Lin, Dahua, Wang, Jiaqi
We present InternLM-XComposer-2.5 (IXC-2.5), a versatile large-vision language model that supports long-contextual input and output. IXC-2.5 excels in various text-image comprehension and composition applications, achieving GPT-4V level capabilities
Externí odkaz:
http://arxiv.org/abs/2407.03320