162 research outputs found
Route Restoration Method for Sparse Taxi GPS trajectory based on Bayesian Network
In order to improve the availability of taxi GPS big data, we restore the chosen route for the sparse taxi GPS trajectory in this work. A trajectory restoration method based on Bayesian network is proposed. Compared with the traditional research solely based on time-spatial variables, this method additionally considers the characteristics of empty/heavy taxi status, weather conditions, drivers, vehicle running and other factors to carry out route restoration. A field case of grid network in Ningbo is taken to verify the applicability of the method, using the taxi GPS trajectory data from Ningbo Taxi Information Management Platform. The case results show that the accuracy of Bayesian network method based on multiple factors reaches 91.4%. Its performance is superior to the Multivariate logistic regression model. In addition, the proposed method is especially suitable for scenarios with a high missing rate of track data, such as a scene with timespan of about 5 min between neighbour trajectories
LLaSM: Large Language and Speech Model
Multi-modal large language models have garnered significant interest
recently. Though, most of the works focus on vision-language multi-modal models
providing strong capabilities in following vision-and-language instructions.
However, we claim that speech is also an important modality through which
humans interact with the world. Hence, it is crucial for a general-purpose
assistant to be able to follow multi-modal speech-and-language instructions. In
this work, we propose Large Language and Speech Model (LLaSM). LLaSM is an
end-to-end trained large multi-modal speech-language model with cross-modal
conversational abilities, capable of following speech-and-language
instructions. Our early experiments show that LLaSM demonstrates a more
convenient and natural way for humans to interact with artificial intelligence.
Specifically, we also release a large Speech Instruction Following dataset
LLaSM-Audio-Instructions. Code and demo are available at
https://github.com/LinkSoul-AI/LLaSM and
https://huggingface.co/spaces/LinkSoul/LLaSM. The LLaSM-Audio-Instructions
dataset is available at
https://huggingface.co/datasets/LinkSoul/LLaSM-Audio-Instructions
Conférences chinoises de la rue d’Ulm
La rĂ©fĂ©rence Ă la Chine devient de plus en plus fondamentale dans le paysage mondial des sciences humaines et sociales. La construction de ponts reliant les cherÂcheurs français Ă une sociĂ©tĂ© et un espace intellectuels trop longtemps rĂ©servĂ©s Ă une poignĂ©e de sinologues est devenue une exiÂgence du moment Ă laquelle l’universitĂ© Fudan et le labex TransferS ont souhaitĂ© rĂ©pondre en organisant une sĂ©rie de confĂ©Ârences, la première rue d’Ulm, qui prĂ©sente des regards de chercheurs chinois sur l’histoire, la philosophie, les Ă©tudes religieuses, la littĂ©rature, etc. Les professeurs Dai Yan, Ge Zhaoguang, Huang Bei, Huang Yang, Jin Guangyao, Li Hongtu, Li Tiangang, Mo Weimin, Sun Xiangchen, BenoĂ®t Vermander, Yu Xin ont acceptĂ© de dĂ©livrer aux auditeurs de l’Ecole normale supĂ©rieure (qui accueille maintenant chercheurs et Ă©tudiants chinois) un enseignement conçu non comme une inÂtroduction Ă la culture chinoise, mais plutĂ´t comme l’ouverture Ă un indispensable dĂ©bat sur des questions communes, un dialogue dont ces confĂ©rences se sont voulues le point de dĂ©part
Cell transcriptomic atlas of the non-human primate Macaca fascicularis.
Studying tissue composition and function in non-human primates (NHPs) is crucial to understand the nature of our own species. Here we present a large-scale cell transcriptomic atlas that encompasses over 1 million cells from 45 tissues of the adult NHP Macaca fascicularis. This dataset provides a vast annotated resource to study a species phylogenetically close to humans. To demonstrate the utility of the atlas, we have reconstructed the cell-cell interaction networks that drive Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases, and intersected our data with human genetic disease orthologues to establish potential clinical associations. Our M. fascicularis cell atlas constitutes an essential reference for future studies in humans and NHPs.We thank W. Liu and L. Xu from the Huazhen Laboratory Animal Breeding
Centre for helping in the collection of monkey tissues, D. Zhu and H. Li from the Bioland
Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory) for
technical help, G. Guo and H. Sun from Zhejiang University for providing HCL and MCA gene
expression data matrices, G. Dong and C. Liu from BGI Research, and X. Zhang, P. Li and C. Qi
from the Guangzhou Institutes of Biomedicine and Health for experimental advice or providing
reagents. This work was supported by the Shenzhen Basic Research Project for Excellent
Young Scholars (RCYX20200714114644191), Shenzhen Key Laboratory of Single-Cell Omics
(ZDSYS20190902093613831), Shenzhen Bay Laboratory (SZBL2019062801012) and Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011). In
addition, L.L. was supported by the National Natural Science Foundation of China (31900466),
Y. Hou was supported by the Natural Science Foundation of Guangdong Province
(2018A030313379) and M.A.E. was supported by a Changbai Mountain Scholar award
(419020201252), the Strategic Priority Research Program of the Chinese Academy of Sciences
(XDA16030502), a Chinese Academy of Sciences–Japan Society for the Promotion of Science
joint research project (GJHZ2093), the National Natural Science Foundation of China
(92068106, U20A2015) and the Guangdong Basic and Applied Basic Research Foundation
(2021B1515120075). M.L. was supported by the National Key Research and Development
Program of China (2021YFC2600200).S
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