Association of Cystic Medial Necrosis of the Aorta and Undiagnosed Thyroiditis [Scripta Medica]

We have recently seen two patients with cystic medial necrosis of the aorta. The first patient died of a dissecting aneurysm of the thoracic aorta. At autopsy, classical Hashimoto’s thyroiditis was discovered. The second patient died of a rupture of the ascending aorta. At autopsy, chronic thyroiditis was seen with multiple large germinal center and diffuse fibrosis. Neither patient was clinically suspected of thyroid dysfunction although the second patient had had a partial thyroidectomy in the remote past

Letter from S. C. Hain to John Muir, 1903 Dec 11.

Cook. Cal. Dec. 11th 1903.John Muir Esq.Martinez. Cal.My Dear Sir:- We are very desirous of getting a portion of our Co. (San Benito) known as the Pinnacles, set apart permanently as a public park. Our congressman is ready to draft the bill and introduce it at once and we would like to have you visit the place and give a letter to the effect that it should be set apart, that is after seeing it such should be your opinion.We are in a position to meet your expenses and would like to have you come at once if possible.Vancouver mentions this spot as the most remarkable mountain he ever saw. in Vol. VI. pp 95 of his voyages and discoveries. He was not nearer than 15 or 20 miles. Many say it is a rival to Yosemite and say the Alps have nothing on the same area to compare with it.03314 If it is possible for you to come advise me at once and I will send full directions.If you can come at once come to Tres Pinos Cal and take the San Benito stage to Cook. 22 miles and we will look after you.Very truly yours.S. C. Hain.Cook.Cal.0331

Colloquialising modern standard Arabic text for improved speech recognition

Modern standard Arabic (MSA) is the official language of spoken and written Arabic media. Colloquial Arabic (CA) is the set of spoken variants of modern Arabic that exist in the form of regional dialects. CA is used in informal and everyday conversations while MSA is formal communication. An Arabic speaker switches between the two variants according to the situation. Developing an automatic speech recognition system always requires a large collection of transcribed speech or text, and for CA dialects this is an issue. CA has limited textual resources because it exists only as a spoken language, without a standardised written form unlike MSA. This paper focuses on the data sparsity issue in CA textual resources and proposes a strategy to emulate a native speaker in colloquialising MSA to be used in CA language models (LMs) by use of a machine translation (MT) framework. The empirical results in Levantine CA show that using LMs estimated from colloquialised MSA data outperformed MSA LMs with a perplexity reduction up to 68% relative. In addition, interpolating colloquialised MSA LMs with a CA LMs improved speech recognition performance by 4% relative

Student Perspectives on Mandatory Conversion to Online Classes: A Qualitative Study

This qualitative research study investigates students’ perspectives on the mandatory conversion to online classes due to COVID-19. In particular, this study explores (1) students’ struggles with conversion of class to online, (2) students’ likes of converted online class, (3) students’ dislikes of converted online class, 4) students’ happiness toward converted online classes, and (5) students’ recommendations on ways to improve online classes. The study was conducted at three universities in the southeastern region of the United States. The major findings of the study are (1) almost 80 percent of students reported struggles when class was converted to online, (2) 88 percent of students reported dislikes about class being converted to online, and (3) 86 percent of students were happier when class met on campus. The top three struggles for students in converted to online classes were learning course materials, time management, and adjusting to changes in the course. The top three dislikes from students in converted to online classes were lack of interaction with professor and classmates, not being able to ask questions, and the course material was harder to learn online. Students did report some likes about class being converted to online such as more convenient, more flexible, responsive instructors, more time, and savings on gas and transportation costs. Overall, the research study found both positive and negative reactions from students when their classes were converted to online

Social impacts reflected in CSR reports:Method of extraction and link to firms innovation capacity

Assessing and comprehending the social impact of firms at global and local level is a pressing concern for both researchers and policy-makers. To address this concern, our paper contributes to the stream of literature that studies the content of Corporate Social Responsibility (CSR) reports (which are also referred to as non-financial statements, sustainability reports or parts of annual reports) using text mining methods. We present a novel approach called Standard-based Impact Classification method (SBIC method), which employs natural language processing (NLP) and supervised machine learning techniques to identify the types of social impacts reflected in CSR reports. We deploy a Random Forest model which we train on reports adhering to Global Reporting Initiative (GRI) framework, enabling the identification of social impact in the majority of CSR reports that do not conform to this standard. Our proposed SBIC method serves as a valuable tool for comparing the social impacts generated by firms, industries, or countries. We showcase an application of our approach by examining the relationship between a company’s social impact and its innovation capacity. Our findings support the existing literature consensus that CSR activities generally exhibit a positive correlation with a firm’s ability to innovate. Furthermore, we reveal that specific types of social impacts have a more pronounced influence on innovation capacity

The abelianization of the Johnson kernel

We prove that the first complex homology of the Johnson subgroup of the Torelli group Tg is a non-trivial, unipotent Tg-module for all g ≥ 4 and give an explicit presentation of it as a Sym H 1(Tg,C)-module when g ≥ 6. We do this by proving that, for a finitely generated group G satisfying an assumption close to formality, the triviality of the restricted characteristic variety implies that the first homology of its Johnson kernel is a nilpotent module over the corresponding Laurent polynomial ring, isomorphic to the infinitesimal Alexander invariant of the associated graded Lie algebra of G. In this setup, we also obtain a precise nilpotence test. © European Mathematical Society 2014

PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT

This study provides an efficient approach for using text data to calculate patent-to-patent (p2p) technological similarity, and presents a hybrid framework for leveraging the resulting p2p similarity for applications such as semantic search and automated patent classification. We create embeddings using Sentence-BERT (SBERT) based on patent claims. We leverage SBERTs efficiency in creating embedding distance measures to map p2p similarity in large sets of patent data. We deploy our framework for classification with a simple Nearest Neighbors (KNN) model that predicts Cooperative Patent Classification (CPC) of a patent based on the class assignment of the K patents with the highest p2p similarity. We thereby validate that the p2p similarity captures their technological features in terms of CPC overlap, and at the same demonstrate the usefulness of this approach for automatic patent classification based on text data. Furthermore, the presented classification framework is simple and the results easy to interpret and evaluate by end-users. In the out-of-sample model validation, we are able to perform a multi-label prediction of all assigned CPC classes on the subclass (663) level on 1,492,294 patents with an accuracy of 54% and F1 score > 66%, which suggests that our model outperforms the current state-of-the-art in text-based multi-label and multi-class patent classification. We furthermore discuss the applicability of the presented framework for semantic IP search, patent landscaping, and technology intelligence. We finally point towards a future research agenda for leveraging multi-source patent embeddings, their appropriateness across applications, as well as to improve and validate patent embeddings by creating domain-expert curated Semantic Textual Similarity (STS) benchmark datasets.Comment: 18 pages, 7 figures and 4 Table