223 research outputs found
Wen yi sui bi
No full textChapter 第一輯幾本古典名著關於'伊索寓言' ---p.3褒頓與'天方夜譚' ---p.13'十日談','七日談'和'五日談' ---p.20喬叟的'坎特伯雷故事集' ---p.30Chapter 第二輯作家與作品巴爾札克和他的'人間喜劇' ---p.35左拉和他的'盧貢馬加爾家傳' ---p.40史諦芬遜和他的'金銀島' ---p.44霍桑和動人的'紅字'故事 ---p.49莫泊桑的短篇傑作 ---p.53可愛的童話作家安徒生 ---p.58蘇格蘭農民詩人彭斯 ---p.64詩人小說家愛倫坡 ---p.70Chapter 第三輯讀書偶記巴爾札克的'詼諧故事集' ---p.77拉封歹的寓言 ---p.79喬治吉辛和他的散文集 ---p.82淮德的'塞爾彭自然史' ---p.85品托的'遠東旅行記' ---p.88'猴爪'和三個願望的故事 ---p.91意大利的'笑林廣記' ---p.96紀德關於王爾德的回憶 ---p.101'贗幣犯'和'贗幣犯日記' ---p.104潘的性格和故事 ---p.107歌德和席勒的友情 ---p.109艾克曼的'歌德談話錄' ---p.114達爾文和赫胥黎 ---p.117托爾斯泰夫妻失和的內幕 ---p.122Chapter 第四輯幾本書的故事迦撒諾伐和他的'回憶錄' ---p.127王爾德'獄中記'的全文 ---p.131'循環舞'的風波 ---p.137小仲馬和他的'茶花女' ---p.145'茶花女'和茶花女型的故事 ---p.150比亞斯萊,王爾德與'黃面誌' ---p.156'魯濱遜飄流記'的作者 ---p.163'查泰萊夫人之情人'的遭遇 ---p.166'查泰萊夫人之情人'解禁經過 ---p.175後記 ---p.183葉靈鳳著Copy 4 printed in 1979.Ye Lingfeng zh
Corrigendum to 'A phosphorus/silicon-based, hyperbranched polymer for high-performance, fire-safe, transparent epoxy resins' [Polymer Degradation and Stability, 203 (2022) 110065]
No full textThe authors regret that the affiliation of Prof. Zhitian Liu in current manuscript is incorrect. The affiliation of Prof. Zhitian Liu is not ‘Center for Future Materials, University of Southern Queensland, Toowoomba 4350, Australia’, and his affilication is ‘Hubei Engineering Technology Research Center of Optoelectronic and New Energy Materials, School of Materials Science & Engineering, Wuhan Institute of Technology, Wuhan 430205, PR China’. Hence, the author and affilication list should be as follows. Qiu Shia, Siqi Huob, Cheng Wanga, Guofeng Yea, Lingfeng Yua, Zhengping Fangb, Hao Wangc, Zhitian Liua a Hubei Engineering Technology Research Center of Optoelectronic and New Energy Materials, School of Materials Science & Engineering, Wuhan Institute of Technology, Wuhan 430205, PR China b Laboratory of Polymer Materials and Engineering, NingboTech University, Ningbo 315100, PR China c Center for Future Materials, University of Southern Queensland, Toowoomba 4350, Australia The authors would like to apologise for any inconvenience caused
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No full textAxon specification is a critical step in neuronal development, and the function of glial cells in this process is not fully understood. Here, we show that C. elegans GLR glial cells regulate axon specification of their nearby GABAergic RME neurons through GLR-RME gap junctions. Disruption of GLR-RME gap junctions causes misaccumulation of axonal markers in non-axonal neurites of RME neurons and converts microtubules in those neurites to form an axon-like assembly. We further uncover that GLR-RME gap junctions regulate RME axon specification through activation of the CDK-5 pathway in a calcium-dependent manner, involving a calpain clp-4. Therefore, our study reveals the function of glia-neuron gap junctions in neuronal axon specification and shows that calcium originated from glial cells can regulate neuronal intracellular pathways through gap junctions
Trajectory Modeling by Distributed Gaussian Processes in Multiagent Systems
No full textThis paper considers trajectory a modeling problem for a multi-agent system by using the Gaussian processes. The Gaussian process, as the typical data-driven method, is well suited to characterize the model uncertainties and perturbations in a complex environment. To address model uncertainties and noises disturbances, a distributed Gaussian process is proposed to characterize the system model by using local information exchange among neighboring agents, in which a number of agents cooperate without central coordination to estimate a common Gaussian process function based on local measurements and datum received from neighbors. In addition, both the continuous-time system model and the discrete-time system model are considered, in which we design a control Lyapunov function to learn the continuous-time model, and a distributed model predictive control-based approach is used to learn the discrete-time model. Furthermore, we apply a Kullback–Leibler average consensus fusion algorithm to fuse the local prediction results (mean and variance) of the desired Gaussian process. The performance of the proposed distributed Gaussian process is analyzed and is verified by two trajectory tracking examples
A new training method for sequence data
No full textAbstractUnder the framework of max margin method, this work proposes a model for training sequence data, which can be solved as a binary classification. However, there are too many samples in the auxiliary classification problem to make the model efficient enough for median to large scale data sets in practice. Therefore, under the additive assumption for the feature mapping and loss function, a simplified model is introduced in order to speed up training. The major advantage of our method is that the new model does not share slack variable for a sequence. This provides the ability to utilize the discriminate information within the sequence and select the discriminative patterns more precisely. Experiment on the task of named entity recognition validates the effectiveness of the new method
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