XinzeZhang / HUST-PhD-Thesis-Latex

华中科技大学博士毕业论文Latex模板
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会议的引用格式缺少会议时间 #20

Open narutozxp opened 2 months ago

narutozxp commented 2 months ago

根据理工科博士论文模板

3)会议论文集:最多列出6个作者,作者之间用逗号分隔. 文章名. 见(英文用“in”):会议名称(或论文集). 会议城市, 国家, 会议时间, 出版者, 出版年: 起页-止页

会议论文需要会议时间(通常是一个时间范围),因此需要在bib中新添加一个字段才能实现,不知道或者有什么好的建议吗?

除此之外,当bib中存在address字段时,结果不正确 示例如下

S. Yamaki, M. Abet, M. Kawamata, M. Yoshizawa. Performance Evaluation of Phase-Only Correlation Functions from the Viewpoint of Correlation Filters. in: 2018 Asia-PacificSignalandInformationProcessingAssociationAnnualSummitandConference(APSIPA ASC). Honolulu, HI, USA: IEEE, 2018. 1361–1364

正确的结果应该是

S. Yamaki, M. Abet, M. Kawamata, M. Yoshizawa. Performance Evaluation of Phase-Only Correlation Functions from the Viewpoint of Correlation Filters. in: 2018 Asia-PacificSignalandInformationProcessingAssociationAnnualSummitandConference(APSIPA ASC). Honolulu, HI, USA, 12-15 Nov. 2018, IEEE, 2018: 1361–1364

相较而言,输出的结果缺少了会议时间。且会议地点、会议时间、出版商之间应该用,隔开。

除此之外出版时间与页数之间应该用:隔开,而不是. https://github.com/XinzeZhang/HUST-PhD-Thesis-Latex/blob/9be4048278a2bb95ce93e7127113ed78f8922d1a/HUSTThesis.bst#L772-L783

XinzeZhang commented 2 months ago

@narutozxp 对于会议时间,可以在booktitle里面添加时间等信息作为补充。

当前版本,出版时间与页数之间已是用:隔开,见下图。

CB12E9F9-8958-47F1-925D-70C33F9A4EF8

你的这个问题(用.隔开year与pages字段),能否提供对应的bib样例,以便复现?

narutozxp commented 2 months ago

可以在booktitle里面添加时间等信息作为补充

比如下面两个bibtex

@inproceedings{10.1145/3626202.3637578,
author = {Klaisoongnoen, Mark and Brown, Nick and Dykes, Tim and Jones, Jessica R. and Haus, Utz-Uwe},
title = {Evaluating Versal AI Engines for Option Price Discovery in Market Risk Analysis},
year = {2024},
isbn = {9798400704185},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3626202.3637578},
doi = {10.1145/3626202.3637578},
abstract = {Whilst Field-Programmable Gate Arrays (FPGAs) have been popular in accelerating high-frequency financial workload for many years, their application in quantitative finance, the utilisation of mathematical models to analyse financial markets and securities, is less mature. Nevertheless, recent work has demonstrated the benefits that FPGAs can deliver to quantitative workloads, and in this paper, we study whether the Versal ACAP and its AI Engines (AIEs) can also deliver improved performance. We focus specifically on the industry standard Strategic Technology Analysis Center's (STAC) derivatives risk analysis benchmark STAC-A2. Porting a purely FPGA-based accelerator STAC-A2 inspired market risk (SIMR) benchmark to the Versal ACAP device by combining Programmable Logic (PL) and AIEs, we explore the development approach and techniques, before comparing performance across PL and AIEs. Ultimately, we found that our AIE approach is slower than a highly optimised existing PL-only version due to limits on both the AIE and PL that we explore and describe.},
booktitle = {Proceedings of the 2024 ACM/SIGDA International Symposium on Field Programmable Gate Arrays},
pages = {176–182},
numpages = {7},
keywords = {ai engines, cgras, fpgas, option price discovery, reconfigurable architectures, simr},
location = {<conf-loc>, <city>Monterey</city>, <state>CA</state>, <country>USA</country>, </conf-loc>},
series = {FPGA '24}
}

@inproceedings{yamaki_performance_2018,
    address = {Honolulu, HI, USA},
    title = {Performance {Evaluation} of {Phase}-{Only} {Correlation} {Functions} from the {Viewpoint} of {Correlation} {Filters}},
    isbn = {978-988-14768-5-2},
    url = {https://ieeexplore.ieee.org/document/8659705/},
    doi = {10.23919/APSIPA.2018.8659705},
    abstract = {This paper proposes performance evaluation of phase-only correlation (POC) functions using signal-to-noise ratio (SNR) and peak-to correlation energy (PCE) from the viewpoint of correlation filters. Correlation functions can be thought as the output from the correlation filters. Maximizing SNR leads to matched filters, whereas maximizing PCE results in the inverse filters. We also derive the general expressions of SNR and PCE of the POC functions based on directional statistics. SNR is expressed by simple fractional function of circular variance. PCE is simply given by squared peak value of the POC functions, and its expectation can be expressed in terms of circular variance.},
    language = {en},
    urldate = {2024-06-03},
    booktitle = {2018 {Asia}-{Pacific} {Signal} and {Information} {Processing} {Association} {Annual} {Summit} and {Conference} ({APSIPA} {ASC})},
    publisher = {IEEE},
    author = {Yamaki, Shunsuke and Abet, Masahide and Kawamata, Masayuki and Yoshizawa, Makoto},
    month = nov,
    year = {2018},
    pages = {1361--1364},
    file = {Yamaki 等 - 2018 - Performance Evaluation of Phase-Only Correlation Functions from the Viewpoint of Correlation Filters.pdf:files/1538/Yamaki 等 - 2018 - Performance Evaluation of Phase-Only Correlation Functions from the Viewpoint of Correlation Filters.pdf:application/pdf},
}