西语学习 A1 发音入门
Aula de español 西语课堂 西班牙语使用人数：约 4.5 亿人，20 个国家。 西班牙语是拼音文字，采用拉丁字母书写。 西语书写与发音密切相关，所见即所得。正字法优秀，赞一个👍 西语字母表 27 个字母，5 个元音，22 个辅音 -ch -ll 不在字母表中，看作双辅音 Aa a Bb be Cc ce Dd de Ee e Ff efe Gg ge Hh hache Ii i Jj jota Kk ca Ll ele Mm eme Nn ene Ññ eñe Oo o Pp pe Qq cu Rr ere Ss ese Tt te Uu u Vv uve Ww doble uve Xx equis Yy igriega Zz zeta 元音 单元音 A a [a] “啊” Asia, casa, mapa, patata E e [e] “鹅◡诶” eje, mes, jefe I i [i] “依” hijo, Italia, hoy, hay O o [o] “喔/哦” ojo, solo, cómo, poco U u [u] “呜” uso, único ...
法语学习 A1 Unité 3 Ça se trouve où?
Leçon 9 Appartement à louer Vocabulaire 序数词 premier/première 第一 deuxième 第二 troisième 第三 quatrième 第四 cinquième 第五 sixième 第六 septième 第七 huitième 第八 neuvième 第九 dixième 第十 onzième 第十一 douzième 第十二 treizième 第十三 quatorzième 第十四 quinzième 第十五 seizième 第十六 dix-septième 第十七 dix-huitième 第十八 dix-neuvième 第十九 vingtième 第二十 楼层有关的词 étage n.m. 楼层 le rez-de-chaussée 底层，一楼 le premier étage 二楼 le deuxième étage 三楼 名词 annonce n.f. 启示，告示，公告 bout n.m. 末端，尽头 au bout de … 在……的尽头，末端 immeuble n.m. 大楼，房屋 appa ...
丹麦语 DU 3.2 3 Bolig
Fælles feedback aflevering 2 wrong correct Kan du bedre lide te eller kaffe? Kan du bedst lide … medbringe (lidt formelt til en ven) … tage et par øl med jeg skal holde fest på lørdag jeg holder fest på lørdag hvad musik hvad (hvilken) slags musik Kan du bedre lide kaffe eller te? Kan du bedst lide kaffe eller te? eller Kan du bedre lide kaffe end te? OBS: Komparativ-formen elsker “end” SU: Svar udbedes eller bare “Kommer du?” eller “Skriv lige om du kan komme”. Eller: Skriv t ...
数据挖掘 4 Hierarchical and Subspace Clustering
When data has high dimensions or clusters have different densities, methods like DBSCAN and kkk-means might fail. Hierarchical clustering partitions the space in progressively smaller clusters. Hierarchical agglomerative clustering 层次凝聚聚类:single-link, complete-link and average-link strategies to merge clusters and form larger clusters. OPTICS clustering that order density reachable points to find cluster structures BRICH that summarizes the data to understand similarities Subspace finds differ ...
随机算法 4 Karger's Min Cut Algorithm
Lecturer this time: Ioannis Caragiannis Lecture notes is based on Karger’s Min Cut Algorithm by Sanjeev Arora, Sanjoy Dasgupta Karger’s min cut algorithm and its extension. It’s simple for finding the minimum cut in a graph. Clustering via Graph Cuts The minimum cut of an undirected graph G=(V,E)G=(V,E)G=(V,E) is a partition of the nodes into two groups V1,V2V_1,V_2V1,V2 (s.t. V=V1∪V2V=V_1\cup V_2V=V1∪V2 and V1∩V2=∅V_1\cap V_2=\emptyV1∩V2=∅) so that the number of edges between V1V_1V1 an ...
Fair Division 2 Relations of Fairness Properties
Objective: Investigate relations between several fairness properties and their approximations. Activities: Read and present the paper Multiple birds with one stone (2020). Abstract Algorithm proposed, achieves (ϕ−1)(\phi-1)(ϕ−1)-EFX and 2ϕ+2\frac{2}{\phi+2}ϕ+22-GMMS. (ϕ=5+12\phi={\sqrt5+1\over2}ϕ=25+1) Previous work: 121\over221-EFX. 121\over221-GMMS. EF1 + 232\over332-PMMS. GMMS PMMS EFX allocation always exist when # of goods ≤2×\le2\times≤2×# of agents. Introduction Relaxation of fa ...
数据挖掘 3 Densiy-based Clustering
Density-based departs from the rigid geometrical structure of representative-based clustering and defines cluster as high-density regions separated by low density regions. Density is number of points in a certain region in proportion to the size of the region. Different definition of density, different density-based approaches. DBSCAN A density-based clustering algorithm grounded into the intuition that a point in a cluster should be density reachable from any other point in that cluster, i.e., ...
随机算法 3 Hash Functions
Lecturer: Kasper Lecture notes is based on High Speed Hashing for Integers and Strings by Mikkel Thorup Hash Functions Universe UUU. Mapping randomly to a range [m]={0,⋯ ,m−1}[m]=\{0,\cdots,m-1\}[m]={0,⋯,m−1}. Truly random hash function h:U→[m]h:U\rarr[m]h:U→[m]. hhh is idealized, can not be implemented. To represent a truly random hash functions, we need to store at least ∣U∣log2m|U|\log_2m∣U∣log2m bits. (too large, impossible!) Idea: hash functions contain only a small element or seed of ra ...
丹麦语 DU 3.2 2 Invitationer (2)
Gennemgang af modul 1 复习 modul 1 的内容：数字、简单的问题、日期等。 Feedback aflevering 太正式/用法错误 更合适 … tilbringe tid sammen Skal vi ikke + infinitiv/Det kunne være hyggeligt at ses/Skal vi hænge lidt ud? i min bolig hjemme hos mig pas på! pas på dig selv Kære Peter, hvordan går det? Kære Peter Hvordan går det? （信件开头没有逗号，首字母大写） lad mig vide fortæl mig… På besøg lukker op 开门 gamle naboer 以前的邻居 smukke 华丽的 Om præferencer Godt godt 的比较级 bedre、最高级 bedst positiv: Jeg kan godt lide kager. 我喜欢蛋糕。 k ...
数据挖掘 2 Representation-based Clustering
What is Clustering Grouping a set of data objects into clusters Cluster: a collection of data objects Similar to one another within the same cluster Dissimilar to the objects in other clusters Cluster = unsupervised classification no predefined classes Usage Get insight into data distribution Preprocessing step for other algorithms Application of Clustering Pattern Recognition and Image Processing Spatial Data Analysis WWW Biology Information retrieval Marketing City-planning Social net ...
