pages tagged academics

农历词表

Sat, 06 Jun 2020 00:16:50 +0000

仲秋：秋季的第二个月，即农历八月。因处秋季之中，故称。

酉月：以十二地支命名，正月寅月，二月卯月，三月辰月，四月巳月，五月午月，六月未月，七月申月，八月酉月。

壮月：指农历八月。《尔雅·释天》：“八月为壮。” 郝懿行义疏：“壮者，大也。八月阴大盛。”

南吕：阴历八月的异名。古人以十二律配十二月，南吕配在八月，故以之代八月。

桂秋：农历八月，桂花飘香，故名。唐韦琮《月明星稀赋》：“的的悠悠，蟾孤桂秋。”清程麟《此中人语·曾睹瑶池仙客》：“ 咸丰八年戊午桂秋，吴君方十五岁，在红杏山庄别墅小住。”

桂月：指农历八月。此时月桂花盛开，故称。厉荃《事物异名录·岁时》：“《提要录》：‘八月为桂月。’”

秋半：秋季过半之时；中秋。唐韩愈《独钓》诗之四：“秋半百物变，溪鱼去不来。”唐元稹《酬乐天八月十五夜禁中独直玩月见寄》诗：“一年秋半月偏深，况就烟霄极赏心。”唐姚合《酬李廓精舍南台望月见寄》诗：“远色当秋半，清光胜夜初。”

仲商：是指仲秋。古人以五季与五音相配，秋属商，故仲商即仲秋。《初学记》卷三引南朝梁元帝《纂要》：“八月仲秋，亦曰仲商。”

竹小春：在中国古代，一般认为各种作物初生的时候为春天，成熟的时候为秋天。但是竹笋二三月份从土里钻出来之后，就表示它已经长成了。因此农历二月又被称为“竹秋”。二月是“竹秋”，那么“竹春”呢？“竹春”就在秋天了，所以农历八月被叫作“竹小春”。

历史学中的时代划分

Mon, 01 Jun 2020 21:04:31 +0000

阶级史观

远古：史前：原始社会
上古：（原始社会）、奴隶社会
中古：封建社会
- 欧洲：称为中世纪，476—1453。即西罗马帝国灭亡—拜占庭（东罗马）帝国灭亡
- 中国：周—晚清
近代：资本主义产生到定型
- 欧洲：约16世纪—20世纪初。有英国1640资产阶级革命开端说、尼德兰资产阶级革命开端说、1500年开端说等；下至1917俄国社会主义革命
- 中国：1840—1919，开始沦为半殖民地半封建社会（至1949），称旧民主主义革命时期
现代：共产主义国家确立
- 欧洲：1917俄国革命—1945二战结束
- 中国：1919—1949，称新民主主义革命时期
当代：二战后至今

参考资料

胡不归（zhihu.com 用户）. https://www.zhihu.com/question/19847689/answer/19221356.

禮器

Sat, 23 May 2020 20:42:50 +0000

炊器
鼎 · 鬲 · 甑 · 甗食器
簋 · 簠 · 盨 · 敦 · 豆酒器
爵 · 角 · 斝 · 觚 · 觶 · 觥 · 尊 · 卣 · 鈃 · 壺 · 罍 · 瓿水器
盤 · 盉 · 匜 · 鑒玉器
璧 · 琮 · 圭 · 璋 · 琥 · 璜 · 環

日语参考书目

Thu, 14 May 2020 05:51:13 +0000

日语的语言学

翟东娜.《日语语言学》.
近藤安月子.《日本語語学入門》.
陈访泽. 《日语句法研究》.
朱春跃.《语音详解》.
- 朱春跃也是《日语语言学》中语音部分的作者。
小泉保.《言外的语言学》.
柴谷方良. The Languages of Japan.
柴谷方良. 言語の構造〈意味・統語篇〉―理論と分析
柴谷方良. 言語の構造〈教授資料〉―理論と分析

书写系统

馬渕和夫. 五十音図の話 (日本語) 単行本. 1993.

语音学

柴谷方良. 言語の構造〈音声・音韻篇〉―理論と分析
学日语必读丛书. 上海：外语教学与研究出版社. https://book.douban.com/series/6545.

文法

于康.《语法学》.
沈宇澄.《现代日语词汇学》.
池上嘉彦. 《意味論》.
寺村秀夫.《日本語のシンタクスと意味》.
寺村秀夫. 《日本語文法ハンドブック》.
三上章.《象は鼻が長い》.
三上章.《日本語の論理》.
三上章.《現代語法序説》.
三上章.《現代語法序説》.
三上章.《続・現代語法序説》.
三上章.《現代語法新説》.
金谷武洋.《日本語に主語はいらない》
金谷武洋.《英語にも主語はなかった》

文字学方面

武部良明.《漢字の用法》.
武部良明.《日本語の表記》.

社会语言学

東照二. 《社会言語学入門》.
真田信治等.《社会语言学概论》.

语言史

山口仲美.《写给大家的日语史》.
潘钧. XXX.
沖森卓也.《日本語史概説》.

语言规划与语言政策

真田信治.《事典日本の多言語社会》.

参考文献

虚牝

Wed, 13 May 2020 02:23:02 +0000

指溪谷。《大戴礼．易本命》：“丘陵为牡，溪谷为牝。”殷仲文《南州桓公九井作》：“爽簌惊幽律，哀壑叩虚牝。”

牝 pìn

〈名〉

(1) (形声。从牛，匕(bǐ)声。依甲骨文，“匕”为雌性动物的标志。本义:雌性的禽兽)
(2) 同本义 [female bird or animal]

牝，畜母也。——《说文》

利牝马之贞。——《易·坤》

畜牝牛，吉。——《易·离》

牝鸡之晨，惟家之索。——《书·牧誓》

(3) 又如:牝朝(唐人称武后当政为牝朝)；牝牡(雌雄两性)

(4) 泛指阴性的事物 [negative]

谿谷为牝。——《大戴礼记·本命》

肾者，牝藏也。——《素问·水热穴论》

牝常以静胜牡。——《老子》

(5) 锁孔 [lock hole]

键，牡；闭，牝也。——《礼记》。

常用词组

牝鸡司晨
牝牡骊黄

Basic Japanese vocabulary

Tue, 12 May 2020 17:47:54 +0000

これ / この (Kore / Kono) – “This” or “This _ (thing/person)”
それ / その (Sore / Sono) – “That / It” or “That _ (thing/person)”
あれ / あの (Are / Ano) – “That over there” or “That _ (thing/person) over there”
私 / 僕 (Watashi / Boku) – “I” (私 is gender neutral, while 僕 is masculine.)
彼 (Kare) – “He”
彼女 (Kanojo) – “She”
私たち (Watashitachi) – “We”
彼ら (Karera) – “They”
年 (Toshi or Nen) – “Year”
月 (Getsu or Tsuki) – “Month” and “Moon”
日 (Nichi or Hi) – “Day” and “Sun”
週 (Shuu) – “Week”
今日 (Kyou) – “Today”
明日 (Ashita) – “Tomorrow”
昨日 (Kinou) – “Yesterday”
時間 (Jikan) – “Time” (As in, a time frame.)
分 (Fun or Bun) – “Minute”
時 (Ji or Toki) – “Hour” or “Time”
こと (Koto) – “About (this thing)”
日本 (Nihon) – “Japan”
ため (Tame) – “For” or “In regards to”
人 (Hito or Nin) – “Person”
物 (Mono) – “Thing”
国 (Kuni or Koku) – “Country”
大学 (Daigaku) – “College”
今 (Ima) – “Now”
前 (Mae) – “Before”
後 (Ato) – “After”
駅 (Eki) – “(Train) Station”
線 (Sen) – “Line”
電車 (Densha) – “Train”
車 (Kuruma) – “Car”
部屋 (Heya) – “Room”
名前 (Namae) – “Name”
所 / 場所 (Tokoro / Basho) – “Place”
地下鉄 (Chikatetsu) – “Subway”
中 (Naka or Chuu) – “Middle,” “Inside,” or “During”
外 (Soto or Gai) – “Outside”
学校 (Gakkou) – “School”
語 (Go) – “Language” (Combine it with other words like: 言語 (gengo, “language”), 単語 (tango, “words”), 日本語 (Nihongo, “Japanese”), 英語 (Eigo, “English”), スペイン語 (Supeingo, “Spanish”).)
水 (Mizu) – “Water”
映画 (Eiga) – “Movie”
テレビ (Terebi) – “TV”
家族 (Kazoku) – “Family”
町 (Machi) – “Town”
他の (Hoka no) – “Other”
出身 (Shusshin) – “Hometown”
トイレ / お手洗い (Toire / Otearai) – “Bathroom”
家 (Uchi or Ie) – “Home” or “House”
店 (Mise or Ya) – “Shop”
する (Suru) – “To do”
です (Desu) – “To be” or “it is”
なる (Naru) – “To become”
ある (Aru) – “There is” for inanimate objects and plants.
いる (Iru) – “There is” for living things, like humans and animals.
言う (Iu) – “To say”
行く (Iku) – “To go”
出来る (Dekiru) – “To be able to do” or “can do”
見る (Miru) – “To see”
送る (Okuru) – “To send”
持つ (Motsu) – “To have” or “to hold”
待つ (Matsu) – “To wait”
会う (Au) – “To meet”
呼ぶ (Yobu) – “To call”
置く (Oku) – “To put”
受ける (Ukeru) – “To receive”
作る (Tsukuru) – “To make”
着く (Tsuku) – “To arrive”
使う (Tsukau) – “To use”
学ぶ (Manabu) – “To learn”
食べる (Taberu) – “To eat”
飲む (Nomu) – “To drink”
帰る (Kaeru) – “To return home”
多い (Ooi) – “Many”
たくさん (Takusan) – “Lots of”
少し (Sukoshi) – “Few”
遠い (Tooi) – “Far”
近い (Chikai) – “Near”
小さい (Chiisai) – “Small”
大きい (Ookii) – “Big”
良い (Yoi) – “Good”
悪い (Warui) – “Bad”
きれいな (Kirei na) – “Clean” and “Pretty”
醜い (Minikui) – “Ugly”
難しい (Muzukashii) – “Difficult”
簡単な (Kantan na) – “Easy”
うまい (Umai) – “Nice”
美味しい (Oishii) – “Delicious”
まずい (Mazui) – “Disgusting”
大丈夫 (Daijoubu) – “All right”
すごい (Sugoi) – “Amazing”
楽しい (Tanoshii) – “Enjoyable” or “Pleasant”
とても (Totemo) – “Very”
しかし (Shikashi) – “However”
また (Mata) – “Also”
その後 (Sono ato) – “After that”
その時 (Sono toki) – “At that time”
場合は (Baai wa) – “If you” or “If this happens”
例えば (Tatoeba) – “For example”
それから (Sorekara) – “Then”
だから (Dakara) – “So”

(END)

中国古代书籍分类

Fri, 08 May 2020 20:51:23 +0000

中国古代书籍分类

中国古代书籍可以定义为1912年之前出版印行及1912年之后影印等形式的同类电子图书。
经部
- 易、书、诗、三礼礼制、乐、春秋、四书、孝经、小学、群经总义、谶纬、丛编、石经等
史部
- 正史別史、编年、纪事本末、杂史、载记地理、史表、史评史抄、传记、政书职官、诏令奏议、金石、目录、丛编等
子部
- 儒家、道家、兵家、法家、农家、医家、杂家、小说家、天文历算、术数、艺术工艺、丛编等
集部
- 楚辞、别集、总集、诗文评、词、曲、小说、丛编等
丛部
- 类书通类、类书专类、杂纂丛书、辑佚丛书、郡邑丛书、氏族丛书、独撰丛书、丛书补缺等

TODO

补充各类书籍实例。

一蟹不如一蟹

Fri, 08 May 2020 20:48:54 +0000

宋‧蘇軾《艾子雜說》記載：艾子來到海邊，看見一隻體形扁圓的多足動物。他不認識，詢問後，才知是螃蟹。後來又看到好幾種螃蟹，但是一種比一種小，不禁嘆了口氣說：「怎麼一蟹不如一蟹呢？」後用「一蟹不如一蟹」比喻一個比一個差。

例：

「相較先祖的勤儉樸實，子孫的揮霍無度，令人深有一蟹不如一蟹之嘆」。
金．王若虛《文辨二》：「晏殊以為柳勝韓，李淑又謂劉勝柳，所謂一蟹不如一蟹。」
《通俗編．禽魚》引《聖宋掇遺》：「陶穀奉使吳越，忠懿王宴之，以其嗜蟹，自蝤蛑至蟛蜞，凡羅列十餘種。穀笑曰：『真所謂一蟹不如一蟹也。』」

Functional programming principles in Scala

Tue, 16 May 2017 23:59:39 +0000

Lectures

Academics-Course-2012FunctionalProgrammingPrinciplesInScala-Lecture1-Functions_and_evaluations

Notes

Scala development environment

Study material

Related courses

MIT, 6.001 - Structure and Interpretation of Computer Programs
- Spring 2007
- Sprint 2005

Software

Scala development environment

References

Scala language official Web site

Bookmarks

Database, Encyclopedia, Computing, Database, Academics, ComputerScienceAndEngineering, Programming, Scala, ProgrammingLanguage,FunctionalProgramming, Course, 2012FunctionalProgrammingPrincipleInScala

Relearning p-value

Tue, 16 May 2017 23:59:39 +0000

After reading "The ASA's statement on p-values: context, process, and purpose", and some other related references, here are some excerpts and notes I took on p-value and null-hypothesis significance testing.

American Statistical Association (ASA) has stated the following five principles about p-values and null hypothesis significance testing:
1. "P-values can indicate how incompatible the data are with a specified statistical model."
2. "P-values do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone."
3. " … It is a statement about data in relation to a specified hypothetical explanation, and is not a statement about the explanation itself."
4. "Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold."
5. "… Practices that reduce data analysis or scientific inference to mechanical “bright-line” rules (such as “p < 0.05”) for justifying scientific claims or conclusions can lead to erroneous beliefs and poor decision-making. …"
6. "Proper inference requires full reporting and transparency."
7. "A p-value, or statistical significance, does not measure the size of an effect or the importance of a result."
8. "… Smaller p-values do not necessarily imply the presence of larger or more important effects, and larger p-values do not imply a lack of importance or even lack of effect. Any effect, no matter how tiny, can produce a small p-value if the sample size or measurement precision is high enough, and large effects may produce unimpressive p-values if the sample size is small or measurements are imprecise. …"
Null hypothesis is usually a hypothesis that assumes that observed data and its distribution is a result of random chances rather than that of effects caused by some intrinsic mechanisms. It is usually what is to disapprove or to reject in order to establish evidence to or belief in that there is some real effect due to underlying intrinsic mechanism. In turn, the details of the statistical model used in this evaluation can be used to make quantitative estimations on properties of the underlying mechanism.
The p-value is the probability that one has falsely rejected the null hypothesis.
- The smaller is, the smaller the chance is that one has falsely rejected the null hypothesis.
- Being able to reject or not being able to reject the null hypothesis may tells one if the observed data suggests that there is an effect, however, it does not tell one how much an effect there is and if the effect is true. See effect size.
- "a p-value near 0.05 taken by itself offers only weak evidence against the null hypothesis".
- UK statistician and geneticist Sir Ronald Fisher introduced the p-value in the 1920s. "The p-value was never meant to be used the way it's used today."
As ASA p-value principle No. 3 states, the decision to reject the null hypothesis should not be based solely on if p-value passes a "bright-line" threshold. Rather, in order to reject the null hypothesis, one must make a subjective judgment involving the degree of risk acceptable for being wrong. The degree of risk of being wrong may be specified in terms of confidence levels which characterizes the sampling variability.
Alternative ways used for referring to data cherry-picking include data dredging, significance chasing, significance questing, selective inference, p-hacking, snooping, fishing, and double-dipping.
"The difference between statistically significant and statistically insignificant is not, itself, statistically significant."
"According to one widely used calculation [¹], a p-value of 0.01 corresponds to a false-alarm probability of at least 11%, depending on the underlying probability that there is a true effect; a p-value of 0.05 raises that chance to at least 29%." See the following figure:

p-value and probable cause.png

Some related concepts

The standard score, or z-score is the deviation from the mean in units of standard deviation. A small p-value corresponds to a large positive z-score.
68-95-99.7 rule
MAGIC criteria.
- Magnitude - How big is the effect? Large effects are more compelling than small ones.
- Articulation - How specific is it? Precise statements are more compelling than imprecise ones.
- Generality - How generally does it apply?
- Interestingness - interesting effects are those that "have the potential, through empirical analysis, to change what people believe about an important issue".
- Credibility - Credible claims are more compelling than incredible ones. The researcher must show that the claims made are credible.

References

"The problem with p-values: how significant are they, really?", phys.org Science News Wire, 2013, http://phys.org/wire-news/145707973/the-problem-with-p-values-how-significant-are-they-really.html
Regina Nuzzo, "Scientific method: statistical errors," 2014, http://folk.ntnu.no/slyderse/Nuzzo%20and%20Editorial%20-%20p-values.pdf
Tom Siegfried, "Odds Are, It's Wrong - Science fails to face the shortcomings of statistics," 2010, https://www.sciencenews.org/article/odds-are-its-wrong
Gelman, A., and Loken, E., "The Statistical Crisis in Science," American Scientist, 102., 2014, http://www.americanscientist.org/issues/feature/2014/6/thestatistical-crisis-in-science
"The vast majority of statistical analysis is not performed by statisticians," simplystatistics.org, 2013, http://simplystatistics.org/2013/06/14/the-vast-majority-of-statistical-analysis-is-not-performed-by-statisticians/
"On the scalability of statistical procedures: why the p-value bashers just don't get it," simplystatistics.org, 2014, http://simplystatistics.org/2014/02/14/on-the-scalability-of-statistical-procedures-why-the-p-value-bashers-just-dont-get-it/
Andrew Gelmana and Hal Sterna, The Difference Between “Significant” and “Not Significant” is not Itself Statistically Significant, The American Statistician, Volume 60, Issue 4, 2006, http://www.tandfonline.com/doi/abs/10.1198/000313006X152649

Goodman, "Of P-Values and Bayes: A Modest Proposal," S. N. Epidemiology 12, 295–297 (2001), http://journals.lww.com/epidem/fulltext/2001/05000/of_p_values_and_bayes__a_modest_proposal.6.aspx ↩

pages tagged academics

农历词表

历史学中的时代划分

阶级史观

参考资料

禮器

日语参考书目

日语的语言学

书写系统

语音学

文法

文字学方面

社会语言学

语言史

语言规划与语言政策

参考文献

虚牝

牝 pìn

Basic Japanese vocabulary

中国古代书籍分类

中国古代书籍分类

TODO

一蟹不如一蟹

Functional programming principles in Scala

Lectures

Notes

Study material

Course Web site

Tutorials

Books

Related courses

Software

References

Bookmarks

Relearning p-value

Some related concepts

References