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scotianostra · 4 years ago

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25th December 1665 saw the birth near Greenlaw of songwriter and poet Lady Grisell Baillie.

Lady Grissel, also spelled Grizel at times, was born at Mellerstain House in the Scottish Borders, the daughter of Sir Patrick Hume of Polwarth. Religious fanaticism is a terrible thing when it results in the death of others, but it’s equally terrible when it results in the death of the fanatic. Three or four hundred years ago in Scotland, there was fanaticism on both sides of a religious divide caused by the ‘Reformation’. There were many Scottish men and women of that period, from 1517 to 1746, who endured torture, imprisonment and death, in an almost fanatical devotion to either the Catholic or the Presbyterian religion. The 1638 ‘National Covenant’ was an expression of faith on the Presbyterian side and its adherents were known as ‘Covenanters’. Lady Grizel Baillie was the daughter of a ‘Covenanter’, Sir Patrick Hume of Polwarth, who later became Lord Polwarth and the 1st Earl of Marchmont, after the ‘Glorious Revolution’ of the Protestant William and Mary. Grizel Hume became a bit of a heroine during the ‘Reformation’, long before she became Lady Grizel. In fact, she was a very brave wee girl, who served as a go-between for her father and her future, posthumous, father-in-law, Robert Baillie of Jerviswood, during the latter’s imprisonment. Baillie of Jerviswood wasn’t quite a fanatic, but he was a covenanting conspirator who was implicated in the ‘Rye House Plot’ against King Charles II. He was executed for treason on the 23rd of December, 1684, at the Mercat Cross in Edinburgh and became a cause célèbre for Jacobites in the years that followed. After Baillie was hanged, the Hume family, whose estates were then forfeited, fled to Holland, where they settled in Utrecht, with Grizel’s father, Sir Patrick, posing as a Dr. Wallace. After the ‘Glorious Revolution’, Mary of Orange offered Grizel the post of Maid of Honour. However, Grizel refused, preferring instead to return to Scotland, where, on the 17th of September, 1692, she married George Baillie, son of the ‘Covenanter’, and became Lady Grizel. The story of Grizel’s heroism is in two parts. Part one: when she was still just twelve years old, her father sent Grizel to Edinburgh with letters for his imprisoned mate, Baillie of Jerviswood. That was undoubtedly a perilous task for a girl not yet in her teens, however, the idea presented in some versions of the story that she had to make her own way to Edinburgh, a journey of between thirty and forty miles, is surely preposterous. Anyway, Sir Patrick dared not attempt to visit his ‘patriot’ friend personally, but as the story goes on, “wee Grizel”, attracting less suspicion than an adult, was able to gain admittance to the prison. She was tasked with more than delivering letters as it was also her mission to bring back any information she could. She contrived to deliver the letter and carried back useful messages, such as “It’s nae very comfy in here” and the gratitude of her father’s chum. Later, at Baillie’s trial, Sir Patrick, as described in contemporary broadsheets, did dare to go to court. Whereas he didn’t dare visit the prison, he was bold enough to intercede in defence of his great buddy, “sometimes blunting with rare skill the edge of manufactured ‘false witness’, to the rage of the prosecutors”. Hume’s friendship for Baillie meant the authorities were looking for an excuse to implicate him in the ‘Rye House Plot’ and so he took to hiding in the vaults of his ancestors, in Polwarth Kirk; his whereabouts known only to his wife and daughter and the proverbial ‘faithful retainer’, one Jamie Winter. Part two: brave wee Grizel, despite being scared of the usual ‘terrors’, which kirkyards were held to contain, was able to overcome her fears, stumble over graves, and night after wintry night, deliver a midnight feast to her father. She always managed to get home before daybreak and evade the soldiers searching for her fugitive. As ‘The Legend of Lady Grizelda Baillie’ from Joanna Baillie’s ‘Metrical Legends of Exalted Character’ has it: “Sad was his hiding−place, I ween, A fearful place, where sights had been, Full oft, by the benighted rustic seen; Aye, elrich forms in sheeted white, Which, in the waning moonlight blast, Pass by, nor shadow onward cast, Like any earthly wight;…” From earliest youth, Grizel was wont to write in verse and prose and her daughter had at one time in her possession, a manuscript volume with several of Grizel’s compositions, “many of [which were] interrupted, half writ, [and with] some broken off in the middle of a sentence”. Although Lady Grizel wrote a number of simple and sorrowful Scots songs, sadly only two are extant. One is the mournfully beautiful fragment ‘The ewe-buchtin’s bonnie’, which may have been inspired by her father’s peril. The other is ‘Were ne my Hearts light I wad Dye’, which originally appeared in ‘Orpheus Caledonius or a Collection of the best Scotch Songs set to Musick by W. Thomson’, in 1725. Some of her songs were printed in Allan Ramsay’s ‘Tea-Table Miscellany’, including the latter, which has been described as “of outstanding excellence and entirely Scottish in sentiment and style” by Tytler in Tytler and Watson’s ‘Songstresses of Scotland’. Tytler went on to state effusively, “Its sudden inspiration has fused and cast into one perfect line, the protest of thousands of stricken hearts in every generation”. Here’s a wee taste in a couple of verses: “When bonny young Johnny o’er ye sea, He said he saw nothing as bonny as me, He haight me baith Rings and mony bra things, And were ne my Hearts light I wad dye. His Kin was for ane of a higher degree, Said what had he do with the likes of me, Appose I was bonny I was ne for Johnny, And were ne my Hearts light I wad dye.” In addition to her songs, Lady Grizel is known for her ‘Household Book’, which was reprinted by the ‘Scottish History Society’ in 1911. That presents a unique and minutely detailed account of her expenditure, menus, and instructions to her housekeeper and others. It also presents the historian with an interesting insight to the running of a large, Scots country house at that time. Grizel Hume, was born at Redbraes Castle in Berwickshire on the 25th of December, 1665, and Lady Grizel Baillie died in London on the 6th of December, 1746. She was buried on her birthdate in the family burial place at Mellerstain.

#Scotland #scottish #poet #writer #history

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marylandparanormal · 7 years ago

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Exploring Phantasms of the Living (1886) through Machine Learning: Presentiment, Crisis Apparitions and Thought Transference

NOTE: Click to open graphics for an expanded and clearer view of the findings they contain

Phantasms of the Living, published in 1886 by the Society for Psychical Research (SPR), was a landmark ESP study that presented the case for “telepathy" or thought transference from mind to mind. The study consisted of 702 cases spanning over 1400 pages that considered several varieties of telepathic experiences collectively referred to as “phantasms of the living”

The case collection examined non-sensory and internalized impressions, many of which were presentiment experiences involving dreams, clairvoyance, visions, feelings or an awareness in connection with the deaths of family members or friends. These experiences often coincided with the approximate time of death

Cases also considered sensory and externalized impressions, in particular apparitional representations of living persons, who were perceived to be in moments of crisis or danger. These situations appeared evidential of shock-induced forms of thought transference from a distressed agent to a percipient in the form of telepathic hallucinations

As a follow-on to the earlier wordcloud project, we wondered whether unsupervised machine learning could discover main topics within Phantasms of the Living. For the project, two varieties of generative topic models were used: Latent Dirichlet Allocation (LDA) and probabilistic Latent Semantic Analysis (pLSA)

Both models view documents as having a latent semantic structure of topics that can be inferred from co-occurrences of words in documents. The mathematics underlying both models are beyond the scope of this post, but on an intuitive level there are key differences between the two methods

pLSA views topics as probability distributions over words. Topics are seen as conditionally independent across the documents that produced them. Non-Negative Matrix Factorization (NMF) is a method for finding topic clusters that equates to pLSA

LDA by contrast views documents as probability distributions over topics and topics as probability distributions over words. All documents share the same collection of topics, but each document contains those topics in different proportions. The LDA algorithm samples words across topics until it arrives at topics and word selections that most likely generated the documents

The project used various packages and libraries for natural language processing within the Python programming platform to include: the Natural Language ToolKit (NLTK) for processing the data set; scikit-learn to prepare and fit the LDA and NMF models; pyLDAvis was used to display the results and t-Distributed Stochastic Neighbor Embedding (t-SNE) to map topic distances

The end-to-end project pipeline involved: data set processing; conversion of words and documents into matrix and vector space; fitting the LDA and NMF models; and then displaying the results

Processing. The book was decomposed into several documents from its constituent sections, chapters and volumes for the data set. Stopwords were removed such as common prepositions and conjunctions using the wordcloud application

Since telepathic experiences are spontaneous and can occur at any time or place, words conveying times and locations were removed as well as ordinal and cardinal types of numeric rankings

Nouns or titles representing persons were removed (e.g. man, woman, Mr., Mrs., etc.); however, interpersonal relationships were preserved (i.e. family, friends, acquaintances or strangers)

Conversion. Vector transformations converted the data set into a document-term matrix for mathematical processing

The rows of the matrix correspond to documents with columns corresponding to the frequency of a term. Count vectorizers count word frequencies. Term Frequency-Inverse Document Frequency (TF-IDF) vectorizers normalize (divide) word counts by their frequency in the documents

Both vectorizers converted words to lower case and removed non-word expressions. The vectorizers were also instructed to look for bigrams (or words that were often used together) such as "thought-transference" and "telepathic hallucination"

Model Fit/Display. The LDA and NMF models were fitted using ten topics. Words within topics were sorted and ranked with respect to their frequency in and relevance within a topic

The LDA model was fitted with using Count and TF-IDF vectorization and ran with a maximum of 10 iterations. LDA model results were displayed using pyLDAvis and t-SNE to map topic distances

The NMF model was fitted with TF-IDF vectorization only and ran with a maximum of 200 iterations. NMF model results were displayed via spreadsheet

Results. The topics produced from the models are unlabeled. However words within topics often can be woven into a coherent theme

The first two pyLDAvis graphs provide the top 30 words and bigrams in Topics 1 and 2 using Count vectorization

Words in Topic 1 include: “dreams”; “visions”; “impressions”; and “experiences” in connection with the “death”(s) of family members and friends. This can be considered a presentiment topic and it generated 67% of the content. This mirrors results from the prior wordcloud project

Words and bigrams in Topic 2 include: “thought-transference”, “hallucination(s)”, “phantasms”, “mind(s)”, “percipients”, “agent” and “telepathy.” This can be considered a telepathic hallucinations topic and it produced 27% of the content

The third pyLDAvis graph provides the top 30 words in Topic 1 using TF-IDF vectorization

Topic 1 combines all the aforementioned words into one topic. This can be considered a “presentiment and telepathic hallucinations ” topic and it accounts for 95% of the content, rendering all other topics practically insignificant in influence. The reason for this consolidation is that TF-IDF vectorization lowers the contribution weight of commonly used words

The spreadsheets compare LDA and NMF model runs using TF-IDF vectorizations with results limited to the top 10 words. Although topic weights and distances are not available, some topics appear more meaningful and cohesive, and are likely more impactful than others

There is overlap between topics 5 and 6 in the LDA model and together they form the presentiment and telepathic hallucinations topic. Topics 0 and 1 in the NMF model respectively appear to correspond to presentiment and crisis apparitions topics

The bigram “thought-transference” arises in both the LDA and NMF results and appears associated with the “Society” for “Psychical” Research and the late F.W.H. “Myers” who invented the term “telepathy”

This project had an extended preparation and production pipeline. The results indicate that unsupervised machine learning using LDA and NMF effectively and comprehensively summarized topical content in Phantasms of the Living. Moreover, key topics approximately corresponded to the types of internalized and externalized telepathic experiences described in the book

This project demonstrates the usefulness of topic generation models for finding meaningful patterns in masses of unlabeled or unstructured data. Moreover, visualization and graphing tools are essential for fully comprehending these patterns. Elsewhere in parapsychology LDA or NMF could also be applied to survey data, case collections, web or social media content of interest.

REFERENCES

Anaya, L. A. Comparing Latent Dirichlet Allocation and Latent Semantic Analysis as Classifiers. University of North Texas, 2011.

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993-1022.

Christou, D. (2016). Feature extraction using Latent Dirichlet Allocation and Neural Networks: A case study on movie synopses. arXiv preprint arXiv:1604.01272.

Deerwester, S. (1988). Improving information retrieval with latent semantic indexing.

Gurney, E., Myers, F. W., & Podmore, F. (1886). Phantasms of the Living (2 vols.). London: Trübner. Reprinted at the Esalen Center.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Vanderplas, J. (2011). Scikit-learn: Machine learning in Python. Journal of machine learning research, 12(Oct), 2825-2830.

Sievert, C., & Shirley, K. (2014). LDAvis: A method for visualizing and interpreting topics. In Proceedings of the workshop on interactive language learning, visualization, and interfaces (pp. 63-70).

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