Using a probabilistic approach for exploring latent patterns in high-dimensional co-occurrence data, topic models offer researchers a flexible and open framework for soft-clustering large data sets. In recent years, there has been a growing interest among marketing scholars and practitioners to adopt topic models in various marketing application domains. However, to this date, there is no comprehensive overview of this rapidly evolving field. By analyzing a set of 61 published papers along with conceptual contributions, we systematically review this highly heterogeneous area of research. In doing so, we characterize extant contributions employing topic models in marketing along the dimensions data structures and retrieval of input data, implementation and extensions of basic topic models, and model performance evaluation. Our findings confirm that there is considerable progress done in various marketing sub-areas. However, there is still scope for promising future research, in particular with respect to integrating multiple, dynamic data sources, including time-varying covariates and the combination of exploratory topic models with powerful predictive marketing models.

Currently, there are two main methodologies for the calculation of the electronic structure and properties of crystalline solids. Known as the Hartree-Fock Method (HF) and the Density Functional Theory (DFT) methods, they are based on two different theories for the numerical solution of the many electron Schrödinger equation. Unfortunately, in highly correlated electron systems like transition metal complexes, both the HF and DFT methods have severe shortcomings. In some cases they fail to provide the correct description of the electronic structure. <p>In general, the HF method overestimates the energy band gap due to the neglect of electron correlation effects and the incorrect description of electron interactions in the unoccupied orbitals. In contrast, even though electron correlation effects are implicitly included in the density functional, DFT often underestimates the band gap due to the improper treatment of the electron self-interaction. <p> To amend these problems, two approaches have been proposed. The deficiency in the HF scheme can be corrected using a hybrid method which adds exchange correlation energy borrowed from DFT to help reduce the band gap energy and bring the predictions in better agreement with experiment. To improve DFT, the LDA+U approach, which uses a model Hubbard-like Hamiltonian including an on-site repulsion parameter U, can be employed. This method is a convenient semi-quantitative way to efficiently calculate the band gap of insulators and semiconductors.<p> In this thesis, the electronic structure of YTiO₃ under pressure is investigated using the aforementioned approaches. The performance and reliability of these methods will be examined, compared and discussed.

Currently, there are two main methodologies for the calculation of the electronic structure and properties of crystalline solids. Known as the Hartree-Fock Method (HF) and the Density Functional Theory (DFT) methods, they are based on two different theories for the numerical solution of the many electron Schrödinger equation. Unfortunately, in highly correlated electron systems like transition metal complexes, both the HF and DFT methods have severe shortcomings. In some cases they fail to provide the correct description of the electronic structure. <p>In general, the HF method overestimates the energy band gap due to the neglect of electron correlation effects and the incorrect description of electron interactions in the unoccupied orbitals. In contrast, even though electron correlation effects are implicitly included in the density functional, DFT often underestimates the band gap due to the improper treatment of the electron self-interaction. <p> To amend these problems, two approaches have been proposed. The deficiency in the HF scheme can be corrected using a hybrid method which adds exchange correlation energy borrowed from DFT to help reduce the band gap energy and bring the predictions in better agreement with experiment. To improve DFT, the LDA+U approach, which uses a model Hubbard-like Hamiltonian including an on-site repulsion parameter U, can be employed. This method is a convenient semi-quantitative way to efficiently calculate the band gap of insulators and semiconductors.<p> In this thesis, the electronic structure of YTiO₃ under pressure is investigated using the aforementioned approaches. The performance and reliability of these methods will be examined, compared and discussed.

With the growth of digital content industry, publishers start to provide online services for ebook search, reading and downloading. Users can access to online resources from anywhere, any place with laptop or mobile devices at any time. Nowadays more and more libraries have purchased ebooks as an important part of the library collection. To access the online resources users can link directly to publisher's ebook portal or via the OPAC system. Compared to the library circulation process, ebooks are more convenient to patrons and improve the utilization of library online resources. There are various kinds of ebooks available in the market, so libraries have to focus their investment on the most valuable online resources. Usage statistics report plays an important role in providing valuable information to libraries. It is usually based on the standard of COUNTER to generate the statistic reports, although it provides when and where users access to specific ebooks, it fails show the general topics and how they change. In this study, we introduce a post process method to weighting the LDA topic model via the usage statistic report to emphasize the changes of topic and compare it to the classification method and subject heading method in the bibliographic, namely LCC and LCSH respectively. The result show that weighted topic model significantly affect the ranking of topics, and the topic model are independent from the classification method and the subject heading method in the bibliographic record.

The aim of this thesis is to investigate the effects of JPEG2000 compression on texture feature extraction from digitized mammograms. A partially automated computer aided diagnosis system is designed, implemented, and tested for this analysis. The system is tested on a database of 60 digital mammograms obtained from the Digital Database for Screening Mammography at the University of South Florida. Using JPEG2000, the mammograms are compressed at 20 different compression ratios ranging from 17:1 to 10,000:1. Two approaches to texture feature extraction are investigated: (i) region of interest (ROI), which is a bounding box around the segmented mass and (ii) rubber band straightening transform (RBST), which is a band of pixels around the segmented mass transformed to a rectangular strip. The gray tone spatial dependent matrices are computed from the ROI and the RBST for the original uncompressed mammograms as well as each group of compressed images. Feature selection and optimization is achieved via stepwise linear discriminant analysis. The efficacy of the features is measured using receiver operator characteristic (ROC) curves. The efficacy of the texture features obtained from the original mammograms is compared to those of the compressed mammograms. Overall, the texture feature efficacy was preserved even for relatively high compression ratios. For example, the area under the ROC curve was greater than 0.99 for compression ratios as high as 5000:1, when the RBST method was utilized. Overall, the JPEG2000 compression distorted the RBST texture features lesser than the ROI texture features.

隨著Web 2.0興起，社群網站在資訊傳遞與獲取所占比重相當高。以美食領域來看，人們在進餐廳前先行閱覽食記評論之情形越來越常見，而部落格文章因圖文並茂，常被消費者列入參考比較之來源。儘管這一類食記內容相對短篇食評來說較為完整，但評論分散於文章中，且多半沒有評分可供參考，讀者很難在第一時間獲悉評論樣貌，得花上一番心力進行閱覽，才能對餐廳整體有所評鑑。 本研究提出一套基於意見探勘與主題模型的食記剖析方法，由部落格中各餐廳貼文情緒量來反映正負面評價，將提及評論歸納為「食物」、「服務」及「環境」三個評分面向，進而提供該家餐廳的整體推薦分數，供讀者快速參閱之。實驗語料自痞客邦美食類貼文中選定添好運台灣－台北站前店、京星港式飲茶PART2、金泰日式料理－內湖店以及喀佈貍（一店）大眾和風串燒居酒洋食堂，合計4家餐廳與200篇語料。 透過LDA主題模型對食記敘述進行主題式分群，使擁有相近主題概念的句子分為一群，並歸類至各面向，例如喀佈貍（一店）之語料可分為10群主題語句，食物面向上有6群，服務與環境面向各為2群。另一方面，為了更有效辨別食記中含有的正負向情緒，本研究透過語意導向方法(SO-PMI)來計算食記中常出現情緒詞彙之極性，以建置該領域的意見詞詞庫。 實驗結果方面，以線上餐廳評論網站－iPeen愛評網作為驗證對象，顯示其語料的平均情緒量相近，於大眾觀感與評價上傾向一致，且相較一般評論網站，本研究能從較細微的面向來切入，並以情緒量反映真實的餐廳評價。最後提出未來欲探討與改善之處，供後續研究參考之。 / As the time of Web 2.0 rise, social media platform plays a crucial role in transferring and receiving information. More and more people get used to reading the related posts before having meal. Because of its richness in content and referring photographs, blog posts are most frequently used for reference. Although the blog posts are more complete regarding their content than other short reviews, the actual reviews are scattered among words that are simply descriptions, and there are no grading scale to take as reference. These all together gives the reader a hard time to efficiently organize the overview of the review, and for them to, therefore, make the decision if they should go to the restaurant. Our study offers a method of analyzing food diaries based on opinion mining and topic model. The scale of emotion in a blog post about a restaurant is used as the reflection of its review's positive or negative. The comments are categorized into food, service and environment. And the restaurant will be graded based on these three aspects to further provide the user an overall score of recommendation. We collected total of 200 articles written on 4 restaurants in PIXNET, then categorized the contents using LDA (Latent Dirichlet Allocation) model base on their theme. The sentences with similar theme with be put into a group, then be further categorized to the three aspects that was mentioned earlier. On the other hand, to better distinguish if the emotion in certain food diary is positive or negative, our study calculated the polarity of common opinion-based words in food diaries using semantic orientation (SO-PMI), and built an opinion corpus specifically for food diaries. In terms of the result, using iPeen, a restaurant rating website, as test reference, it shows that the average scales of opinion of the restaurants we got using our method are close to iPeen, which in this case we can say are close to the public opinion and review. Furthermore, compare to common rating website, our study touches on even the minute aspect, and use the cumulative opinion to reflect the true blog authors' evaluation of the restaurant. Lastly, we would like to bring up what we intend to discuss and improve in the future for upcoming research's reference.

本研究目的在於結合關聯規則新詞發掘演算法來擴增詞庫，並藉此提高結斷詞句的精確度以及透過非監督式情感分析方法，從中央通訊社中抓取國民黨以及民進黨的相關新聞文本，建立主題模型與情緒傾向的標注。再藉由監督式學習方法建立分類模型並驗證其成果。 　　本研究藉由n-gram with a-priori algorithm來進行斷詞斷句的詞庫擴增。共有32007組詞被發掘，於這些詞中具有真正意義的詞共有28838筆，成功率可達88%。 　　本研究比較兩種分群方法建立主題模型，分別為TFIDF-Kmeans以及LDA。在TFIDF-Kmeans分群結果中，因為文本數量遠大於議題詞數量，造成TFIDF矩陣過於稀疏，造成分群效果不佳。在LDA的分群結果底下，因為LDA模型其多文章多主題共享的特性，主題分類的精準度更高達八成以上。故本研究認為在分析具有多主題特性之文本，採用LDA模型來進行議題詞分群會有較佳的表現。 　　本研究透過結合不同的資料時間區間，呈現出中央通訊社的新聞文本在我國近五次總統大選前後三個月間的新聞情緒傾向。同時探討各主題模型中各類別於大選前後三個月之情緒傾向變化。可以觀察到大致上文本的情感指數高峰值會出現於投票日的時候，而近三次總統大選的結果顯示，相關的政黨新聞情感值會於選舉過後趨於平緩。而從新聞文本的正負向情感統計以及以及整體情緒傾向分析可以看出，不論執政黨為何，中央通訊社的新聞對於國民黨以及民進黨皆呈現了正向且平穩的內容，大抵不會特別偏向單一政黨 / The purpose of this research is to combine association rules and new word mining algorithms to expand the lexicons so as to improve the accuracy of word segmentations, and by capturing the KMT and DPP news from the Central News Agency, it establishes the theme model and sentiment orientation through the unsupervised sentiment analysis method. Finally, by means of supervised learning methods, this research establishes classifications models and verifies its results. 　　This research uses n-gram with a-priori algorithm to segment words and sentences to expand the lexicons. A total of 32007 word are found, and among them, there have 28838 words with real meaning. The success rate is up to 88%. 　　In this research, we compare two different clustering methods to form the theme model, which are the TFIDF-Kmeans, and the LDA. From the results of TFIDF-Kmeans, the TFIDF matrix is too sparse, resulting in poor clustering because the number of texts is a lot larger than that of the issues. Unlike TFIDF-Kmeans, because of LDA model with more features of multi-topic sharing, the accuracy of topic classification is more than 80%. Therefore, this research suggests that it will have a better performance to analyze the multi-subjective texts with LDA model to classify the word clustering. 　　Through the combination of different data time interval, this research presents the sentimental tendencies of Central News Agency’s news in three months before and after the last five presidential elections in Taiwan. At the same time, it also explores the changes of the sentimental tendencies in the various theme models in the three months before and after the election. It can be observed the sentimental peak of the text will be appeared on the polling day, and nearly three times of the presidential election results show that the sentimental value of the relevant party’s news will become smooth after the election. From the positive and negative sentimental statistics of the news text and the analysis of the overall sentimental tendencies, no matter which the ruling party is, the news of the Central News Agency for the KMT and the DPP presents a positive and stable content, not particularly toward any political party.

Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2018-02-21T21:24:26Z No. of bitstreams: 1 AlineDeSousaMarques_TESE.pdf: 5792072 bytes, checksum: 2628df662db724e0c0d729e9a2af99c5 (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2018-02-22T21:38:32Z (GMT) No. of bitstreams: 1 AlineDeSousaMarques_TESE.pdf: 5792072 bytes, checksum: 2628df662db724e0c0d729e9a2af99c5 (MD5) / Made available in DSpace on 2018-02-22T21:38:32Z (GMT). No. of bitstreams: 1 AlineDeSousaMarques_TESE.pdf: 5792072 bytes, checksum: 2628df662db724e0c0d729e9a2af99c5 (MD5) Previous issue date: 2017-08-18 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES) / Esta tese de doutorado ? um aporte te?rico-pr?tica para o desenvolvimento de estudos que utilizem a bioanal?tica, particulamente materiais biol?gicos provenientes de bact?rias, podendo estes ser isolados, DNA, entre outros, em conjunto com ferramentas quimiom?ticas de an?lise. Para isso, buscou-se identificar diferen?as bacterianas quando submetidas a uma fonte de estresse a partir de diferentes t?cnicas anal?ticas. A primeira abordagem foi realizada partindo da bioespectroscopia, utilizando-se de dados espectrosc?picos obtidos na regi?o do infravermelho. A bioespectroscopia na regi?o do infravermelho ? descrita como uma t?cnica n?o invasiva, de alto rendimento, baixo custo (quando comparado com t?cnica padr?es de an?lise) e objetivas, e que possui um enorme potencial na an?lise de bact?rias, complementando ou mesmo substituindo m?todos de diagn?stico de doen?as convencionalmente conduzidos por especialistas atrav?s de m?todos padr?es de an?lises de alto custo e que necessitam de reagentes espec?ficos. Os dados obtidos a partir da bioespectroscopia em amostras bacterianas s?o complexos e apresentam muitas bandas de sobreposi??o sendo necess?ria a aplica??o de ferramentas matem?ticas para superar estas dificuldades. Para isso, algumas ferramentas matem?ticas, como os m?todos de sele??o de vari?veis, que utilizam a an?lise discriminante linear com Algoritmo de Proje??o Sucessiva (SPA-LDA) e Algoritmo Gen?tico (GA-LDA), geralmente s?o utilizadas com a finalidade de facilitando a extra??o de informa??es relevantes. A espectroscopia na regi?o do infravermelho, em espec?fico infravermelho pr?ximo (NIR) e infravermelho com trasformata de Fourier e reflect?ncia total atenuada (ATR-FTIR), em conjunto com m?todos de sele??o de vari?veis (SPA-LDA e GA-LDA) foram utilizadas na discrimina??o de amostras de bact?rias (Sthaphylococcus aureus, Klebsiella pneumoneae e Pseudomonas aeruginosa). Foram identificados prov?veis biomarcadores como lip?deos e prote?nas em ~1550 cm-1 e 1400 cm-1 e vibra??es de DNA em ~1080 cm-1. Valores de sensibilidade de 75% e 95% para modelos de SPA-LDA e 100% e 93% para modelos GA-LDA foram encontrados. Com base nesses resultados, pode-se concluir que o SPA-LDA e GA-LDA em conjunto com a espectroscopia na regi?o do infravermelho mostraram-se ferramentas eficientes melhorando o tempo e custo de diagn?stico possibilitando o tratamento mais r?pido em rela??o aos m?todos padr?es de diagn?stico e, consequentemente, sendo poss?vel evitar a evolu??o de uma poss?vel infec??o. A segunda abordagem foi avaliar poss?veis mudan?as no perfil lipid?mico de bact?rias resultante de sua exposi??o a uma fonte de estresse externa (Ars?nio (III)), utilizando as cianobact?rias Anabaena sp. e Planktothrix agardhii. Os dados foram obtidos a partir a Cromatografia L?quida- Espectrometria de Massas (LC-MS) que por gerar uma matriz de dados muito extensa foi necess?ria a utiliza??o de uma estrat?gia de sele??o proposta recentemente, definida como ROI (do ingl?s regions of interests) que diminui significativamente o tamanho da matriz de dados obtidas por LC-MS. Resolu??o Multivariada de Curvas com M?nimos Quadrados Alternantes (MCR-ALS) foi utilizado como m?todo de resolu??o das fontes de varia??o, recuperando as informa??es de seus componentes puros que se encontravam misturadas. As massas majorit?rias encontradas, sendo algumas delas 766.54, 565.40 e 871.56 (m/z), determinam que as cianobact?rias estudadas, ao serem submetidas a As(III), sofrem mudan?as relacionadas a estruturas que comp?em os processos fotossint?ticos das mesmas. / This doctoral thesis is a theoretical-practical contribution for the development of studies that use bioanalytical, particularly biological materials from bacteria, which can be isolated, DNA, among others, in conjunction with chemistry analysis tools. For this, it was sought to identify bacterial differences when submitted to a source of stress from different analytical techniques. The first approach was based on biospectroscopy, using spectroscopic data obtained in the infrared region. Biospectroscopy in the infrared region is described as a non-invasive, high-throughput, low-cost (when compared with standard analytical techniques) and objective techniques, and has a huge potential in the analysis of bacteria, complementing or even replacing diagnostic methods of diseases conventionally conducted by skilled persons by standard methods of expensive analyzes and requiring specific reagents. The data obtained from biospectroscopy in bacterial samples are complex and have many overlapping bands and it is necessary to apply mathematical tools to overcome these difficulties. For this, some mathematical tools, such as variable selection methods, using Linear Discriminant Analysis with Successive Projection Algorithm (SPA-LDA) and Genetic Algorithm (GA-LDA), are generally used for the purpose of solving these data, facilitating the extraction of information. Infrared spectroscopy, in specific Near Infrared (NIR) and infrared spectroscopy with Fourier transform and Attenuated Total Reflectance (ATR- FTIR), in conjunction with variable selection methods (SPA-LDA and GA-LDA) was used in the discrimination of bacterial samples (Sthaphylococcus aureus, Klebsiella pneumoneae and Pseudomonas aeruginosa). Possible biomarkers such as lipids and proteins were identified at ~ 1550 cm -1 and 1400 cm -1 and DNA vibrations at ~ 1080 cm -1. Sensitivity values of 75% and 95% for SPA-LDA models and 100% and 93% for GA-LDA models were found. Based on these results, it can be concluded that the SPA-LDA and GA- LDA in conjunction with the infrared spectroscopy showed efficient tools improving the time and cost of diagnosis allowing the treatment faster than the standard methods of diagnosis, and consequently, it is possible to avoid the evolution of a possible infection. The second approach was to evaluate possible changes in the lipid profile of bacteria resulting from its exposure to an external stress source (Arsenic (III)), using the cyanobacteria Anabaena sp. and Planktothrix agardhii. The data were obtained from Liquid Chromatography-Mass Spectrometry (LC-MS), which, in order to generate a very extensive data matrix, required the use of a recent selection strategy, defined as ROI (regions of interest), which significantly decreased the Size of the data matrix obtained by LC-MS. Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS) was used as a method to solve variation sources, retrieving the information of its pure components that were mixed. The majority masses found, such as 766.54, 565.40 and 871.56 (m/z), determine that the studied cyanobacteria, when subjected to As (III), undergo changes related to structures that make up the photosynthetic processes of the same.

近年來ETF規模快速成長，亞洲區域經濟成長與穩步發展更是帶動國際ETF市場動力來源，而元大台灣50指數型證券投資信託基金因規模大，受到投資人的青睞。根據過去的研究指出，網路上的文本訊息會對群眾情緒造成影響，進而影響股價波動，對投資者而言，若能從大量網路財金快速分析投資者大眾情緒進而預測股價波動走勢，勢必可提高報酬率。然而，每日有上百篇的財金文本產生，人工分析耗時耗力，本研究採用文字探勘技術，提出一套情感分析的價格預測模型。 過去文本情感分析的研究中已證實監督式學習方法可以透過簡單量化的方式達到良好的分類效果，然而，為解決監督式學習無法預期未知的限制，本研究透過非監督式學習將2016整年度的財金文本進行文章主題判別，計算情緒指數並標記文本情緒傾向，再來使用監督式學習結合台股資訊指標、國際指標、總體經濟指標、技術指標等，建立分類模型以預測元大台灣50ETF的價格趨勢。 實驗結果中，主題標注方面，本研究發現因文本數量遠大於議題詞數量造成TF-IDF矩陣過於稀疏，使得TF-IDF結合K-means主題模型分類效果不佳。LDA主題模型基於所有主題被所有文章共享的特性，使得在字詞分群優於TF-IDF結合K-means。情緒傾向標注方面，證實本研究擴充後的情感詞集比起NTUSD有更好的字詞極性判斷效果。 本研究透過比較情緒指數結合技術指標之分類模型與單純技術指標分類模型的準確率發現，前者較後者高出7%的準確率。進一步結合間接情緒指標的分類模型更有71%準確率，故證實財金文本的情感分析確實能有效提升元大台灣50的價格趨勢預測。 / Rapid and stable economic growth in Asia motivated the asset scale of ETF in the globe growing rapidly in the recent years. Yuanta Taiwan Top 50 ETF gains the investors’ favor because of the advantages of large market scale. Past research have shown that the text documents on the internet, e.g. news and tweets, would make great effect on public emotion, and the public emotion could even affect the stock price. For investors, it is important to know how to analyze the potential emotion in text documents to predict the stock trend. However, the traditional way to analyze text documents by human cannot afford the large volume of financial text documents on the internet. In past sentimental analysis research, supervised method is proven as a method with high accuracy, but there are limits about predicting unknown future trend. This research combined supervised and unsupervised methods to deal with these large financial text documents. By using unsupervised method to find out the topic of documents, and then calculate the sentimental index of each documents to differentiate the sentiment polarity. Afterwards, using supervised method to build a prediction model with the sentimental index. According to the result, we found that the performance of LDA model is better than the TF-IDF with K-means model. Moreover, the prediction model which include the sentiment index has higher accuracy than the one include the technical indicators only.

La présente thèse traite de la description de systèmes complexes, notamment des polymères et des cuprates, par la théorie de la fonctionnelle de la densité. En premier lieu, la théorie de la fonctionnelle de la densité ainsi que différentes fonctionnelles utilisées pour simuler les matériaux à l’étude sont présentées. Plus spécifiquement, les fonctionnelles LDA et GGA sont décrites et leurs limites sont exposées. De plus, le modèle de Hubbard ainsi que la fonctionnelle LDA+U qui en découle sont abordés dans ce chapitre afin de permettre la simulation des propriétés de matériaux à forte corrélation électronique. Par la suite, les résultats obtenus sur les polymères sont résumés par deux articles. Le premier traite de la variation de la bande interdite entre les polymères pontés et leurs homologues non pontés. Le second se penche sur l’étude de polymères à faible largeur de bande interdite. Dans ce dernier, il sera démontré qu’une fonctionnelle hybride, contenant de l’échange exact, est nécessaire afin de décrire les propriétés électroniques des systèmes à l’étude. Finalement, le dernier chapitre est consacré à l’étude des cuprates supraconducteurs. La LDA+U pouvant rendre compte de la forte localisation dans les orbitales 3d des atomes de cuivre, une étude de l’impact de cette fonctionnelle sur les propriétés électroniques est effectuée. Un dernier article investiguant différents ordres magnétiques dans le La2CuO4 dopé termine le dernier chapitre. On trouve aussi, en annexe, un complément d’information pour le second article et une description de la théorie de la supraconductivité de Bardeen, Cooper et Schrieffer. / Description of complex systems by Density functional theory is treated in this thesis. First, the Density functional theory and a few functionals used to simulate cristals are presented. Specifically, the LDA and GGA functionnals are described and their limits are exposed. Furthermore, the Hubbard model as well as the LDA+U functionnal are addressed in this chapter. These methods enable the study of highly correlated materials. Then, results obtained on polymers are summarized in two articles. The first one treats the band gap variation of ladder-type polymers compared to non ladder type ones. The second article considers small band gap polymers. In this case, it will be shown that an hybrid functional, which contains exact exchange, is required to describe the electronic properties of the polymers under study. Finally, the last chapter address the study of cuprates superconductors. The LDA+U can account for the localization of electrons in copper orbitals. Consequently, a study of the impact of this functionnal on electronic properties of cuprates is conducted. The chapter is ended by an article treating magnetic orders in doped La2CuO4. Supplementary materials of the second article and a description of the theory of superconductivity of Bardeen, Cooper and Schrieffer are put in annex.