十八世紀歐洲文學裡的趙氏孤兒

我們在本文裡想要研究的對象是歐洲十八世紀裡對於趙氏孤兒的批評和擬作或改作。趙氏孤兒，我們都曉得，是元曲的一種，原名趙氏孤兒大報警，一作趙氏孤兒冤報冤。牠的著者是元朝第一期劇曲家紀君祥(一作紀天祥)。牠的故事內容，是根據史記趙世家裡晉大夫屠岸賈誅趙氏和晉景公和韓厥謀立趙孤兒的一段記載。紀君祥的藝術，在他當時本不算尋常，李調元在雨村曲話裡曾引涵虛曲論的話，以為紀氏之作，如「雪裡梅花」；可是趙氏孤兒譯進法文的時候——十八世紀——歐洲戲劇，久已發達，紀氏之作，與之相較，恐怕不無遜色。而且當時譯本，也很尋常，不能掩蓋原作的短處。然而中國戲劇有歐文譯本者，袛此一篇，所以歐洲戲家之矜意地尋找中國題材者，都不能不依靠趙氏孤兒的譯本

好逑傳之最早的歐譯

十八世紀歐洲的華化興味，以法國為中心：始而路易十四特派教士；中而教士屢刊專書，如李明（Louis Lecemte）之那新印象記（Nouveaux memories de la Chine），特赫爾特（Du Halde）之支那志（Deseription……de la Chine），以及多人結集的教士通訊（Letters edifiantes et curieuses），和支那雜記（Memories……concernant les ch nois），風行一時；終而引起一般文人的興趣，如孟特斯鳩，盧騷，第迪羅，服爾德等，對於中國，都有所論列。英國的華化興味，在那時雖非淡薄，卻是大半間接地從法國轉販過去的

魯濱孫的中國文化觀

魯濱遜飄流記以一七一九年四月二十五日出版於倫敦，牠的原文的標題是The Life and Strange Surprising Adventures of Robinson Crusoe, of York, Mariuer, 牠的著者是英國人但以理·第福（Daniel Defoe）。第福那時將近六十歲了，曾度過好幾十年的多方面的和繁複的生活，曾經嘗試過多少幸運厄運。就最有趣味而似乎極端的而說，牠曾在他父親在倫敦所開的屠店嬉戲；他曾預備做脫離國家宗派的基督教傳道人；曾在Cornhill開過一所賣襪的舖子，虧了萬多磅的本；曾為威廉第三的擁護者，作了很多本政治運動的小書，獲了不小的時譽；曾為女皇安尼（Queen Anne）捧場，寫過好幾首不大高明的詩；曾因言論迕時而負枷，下獄；曾替首相哈爾來Harley做過政治的間諜；曾經屢次改換他的政治的主張和立場；藐是流離，至於暮齒

十八世紀歐洲之中國園林

十七，八世紀之交，歐洲園林藝術，一以整齊為主；誠然當時荷蘭派與法蘭西派的造園法畧有差異，而大體趨於整齊，大體惟尚工緻。此種傾向，入十八世紀後而勢力漸微；，至十八世紀中葉而愈為衰息，於是所謂“中國式的園林”興味者，乃代之而興，風靡一時。我們今日看來，這“中國式的園林”是中，歐近代接觸中所產生的一件最為興味雋永的文化交流的現象；雖然這現象的生命不長

Characterization of Francisella species isolated from the cooling water of an air conditioning system.

Strains of Francisella spp. were isolated from cooling water from an air conditioning system in Guangzhou, China. These strains are Gram negative, coccobacilli, non-motile, oxidase negative, catalase negative, esterase and lipid esterase positive. In addition, these bacteria grow on cysteine-supplemented media at 20 °C to 40 °C with an optimal growth temperature of 30 °C. Analysis of 16S rRNA gene sequences revealed that these strains belong to the genus Francisella. Biochemical tests and phylogenetic and BLAST analyses of 16S rRNA, rpoB and sdhA genes indicated that one strain was very similar to Francisella philomiragia and that the other strains were identical or highly similar to the Francisella guangzhouensis sp. nov. strain 08HL01032 we previously described. Biochemical and molecular characteristics of these strains demonstrated that multiple Francisella species exist in air conditioning systems

Catch-Up Distillation: You Only Need to Train Once for Accelerating Sampling

Diffusion Probability Models (DPMs) have made impressive advancements in various machine learning domains. However, achieving high-quality synthetic samples typically involves performing a large number of sampling steps, which impedes the possibility of real-time sample synthesis. Traditional accelerated sampling algorithms via knowledge distillation rely on pre-trained model weights and discrete time step scenarios, necessitating additional training sessions to achieve their goals. To address these issues, we propose the Catch-Up Distillation (CUD), which encourages the current moment output of the velocity estimation model ``catch up'' with its previous moment output. Specifically, CUD adjusts the original Ordinary Differential Equation (ODE) training objective to align the current moment output with both the ground truth label and the previous moment output, utilizing Runge-Kutta-based multi-step alignment distillation for precise ODE estimation while preventing asynchronous updates. Furthermore, we investigate the design space for CUDs under continuous time-step scenarios and analyze how to determine the suitable strategies. To demonstrate CUD's effectiveness, we conduct thorough ablation and comparison experiments on CIFAR-10, MNIST, and ImageNet-64. On CIFAR-10, we obtain a FID of 2.80 by sampling in 15 steps under one-session training and the new state-of-the-art FID of 3.37 by sampling in one step with additional training. This latter result necessitated only 620k iterations with a batch size of 128, in contrast to Consistency Distillation, which demanded 2100k iterations with a larger batch size of 256. Our code is released at https://anonymous.4open.science/r/Catch-Up-Distillation-E31F

Functional analysis of RF2a, a rice transcription factor

RF2a is a bZIP transcription factor that regulates expression of the promoter of rice tungro bacilliform bad-navirus. RF2a is predicted to include three domains that contribute to its function. The results of transient assays with mutants of RF2a from which one or more domains were removed demonstrated that the acidic domain was essential for the activation of gene expression, although the proline-rich and glutamine-rich domains each played a role in this function. Studies using fusion proteins of different functional domains of RF2a with the 2C7 synthetic zinc finger DNA-binding domain showed that the acidic region is a relatively strong activation domain, the function of which is dependent on the context in which the domain is placed. Data from transgenic plants further supported the conclusion that the acidic domain was important for maintaining the biological function of RF2a. RF2a and TBP (TATA-binding protein) synergistically activate transcription in vitro (Zhu, Q., Ordiz, M. I., Dabi, T., Beachy, R. N., and Lamb, C. (2002) Plant Cell 14, 795-803). In vitro and in vivo assays showed that RF2a interacts with TBP through the glutamine-rich domain but not the acidic domain. Functional analysis of such interactions indicates that the acidic domain activates transcription through mechanisms other than via the direct recruitment of TBP.Fil: Dai, Shunhong. Chinese Academy of Sciences; República de China. Donald Danforth Plant Science Center; Estados UnidosFil: Petruccelli, Silvana. Donald Danforth Plant Science Center; Estados Unidos. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigación y Desarrollo en Criotecnología de Alimentos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Criotecnología de Alimentos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Criotecnología de Alimentos; ArgentinaFil: Ordiz, Maria Isabel. Donald Danforth Plant Science Center; Estados UnidosFil: Zhang, Zhihong. Donald Danforth Plant Science Center; Estados UnidosFil: Chen, Shouyi. Chinese Academy of Sciences; República de ChinaFil: Beachy, Roger N.. Donald Danforth Plant Science Center; Estados Unido