1,355,131 research outputs found
Vard Coombs Talk - February 7, 2011
No full textIn a talk delivered on February 7, 2011, Vard Coombs reflects on his childhood growing up on the Coombs Site in Boulder, Utah, the location of an Anasazi archeological park that his father sold to the state. He vividly describes daily life without electricity, using hand-cranked tools, coal oil lamps, and bathing in a number three tub, and shares anecdotes about local landmarks like the old post office and the challenges of siphoning gasoline. Coombs recounts his experiences working on the archeological excavation of the Coombs Site in 1959, including the discovery of artifacts and burials, and touches on theories about why the Anasazi left the area. He concludes by discussing the ongoing preservation efforts at the site and the potential for future excavations
Vard Coombs Oral History - February 7, 2011
No full textIn an oral history interview conducted on February 7, 2011, Vard Coombs discusses his childhood in Boulder, Utah, emphasizing life before electricity and his memories of various household artifacts like a hand churn, coal oil lamps, and a number three tub. He recounts being the 13th of 14 children and being born on what is now known as the Coombs Site, which his father later sold to the State for a museum. A significant portion of the interview details his experiences working with archaeologists, including Dr. Jennings and Dr. Robert Lister, on excavations at the Coombs Site in 1959. He describes the process of digging, screening for artifacts like arrowheads and pottery, and discoveries such as ancient structures and burials, including the "Indian Princess" with turquoise jewelry. Coombs also shares anecdotes about local life, finding arrowheads, and the challenges of preventing looting at the archaeological site. He reflects on theories about why the Anasazi people left the area and discusses the early settlers\u27 potential use of Indigenous irrigation ditches. The interview concludes with a discussion about future archaeological work at the site, much of which remains uncovered
VARD 2.5 Released
No full textAnother short project note: Last week, Alistair Baron released a new version (2.5) of VARD. VARD is one of the most popular programs for normalizing or modernizing historical texts prior to linguistic analysis. The VARD Web site has more information. Finally! VARD 2.5 is now available. Loads of new features, and generally much nicer to use (IMHO). Details here: https://t.co/D2kFKrKM6t — Alistair Baron (@al586) July 8, 201
VARD2 : a tool for dealing with spelling variation in historical corpora
No full textWhen applying corpus linguistic techniques to historical corpora, the corpus researcher should be cautious about the results obtained. Corpus annotation techniques such as part of speech tagging, trained for modern languages, are particularly vulnerable to inaccuracy due to vocabulary and grammatical shifts in language over time. Basic corpus retrieval techniques such as frequency profiling and concordancing will also be affected, in addition to the more sophisticated techniques such as keywords, n-grams, clusters and lexical bundles which rely on word frequencies for their calculations. In this paper, we highlight these problems with particular focus on Early Modern English corpora. We also present an overview of the VARD tool, our proposed solution to this problem, which facilitates pre-processing of historical corpus data by inserting modern equivalents alongside historical spelling variants. Recent improvements to the VARD tool include the incorporation of techniques used in modern spell checking software
Necrosectomía retroperitoneal vídeo-asistida (VARD)
No full textEver since PANTER’s study publication in 2010, the superiority of the "step-up approach" in the management of infected necrotizing pancreatitis, in terms of reduction of the morbi-mortality compared with primary open surgery, was established. In fact, morbidity dramatically dropped from a 69% to a 40% compared to the classic management, and up to 35% of the patients treated with the step-up approach didn’t have the need for surgery (being the situation solved only with percutaneous drainage). In those cases where the percutaneous drainage failed, a retroperitoneal debridement was performed. This is a technique that has been developed until the present day. Nowadays, we prefer to use a minimal invasive surgery: a video-assisted retroperitoneal debridement (VARD), that requires experience in both videoscopic surgery and accessing the retroperitoneal space. We have developed a technical modification in the performance of VARD, applying a system often used in TAMIS, that we consider to be advantageous in this kind of surgery.Desde la publicación en 2010 del estudio PANTER, se estableció la superioridad en términos de disminución de la morbi-mortalidad del abordaje escalonado de la necrosis pancreática sobreinfectada ("step-up approach") respecto al abordaje quirúrgico tradicional por vía anterior. Tanto es así, que se objetivó una caída de la morbilidad del 69% al 40% respecto al manejo clásico, y hasta el 35% de los pacientes tratados con la estrategia escalonada no precisó cirugía (resolviéndose la situación únicamente con drenajes percutáneos). En aquellos casos donde fracasó el drenaje percutáneo, se realizó una necrosectomía retroperitoneal. Esta es una técnica que se ha ido desarrollando hasta el día de hoy. En la actualidad se opta por una técnica mínimamente invasiva: la necrosectomía retroperitoneal video-asistida (VARD), que requiere experiencia tanto en cirugía video-asistida como en el abordaje del retroperitoneo. Nosotros hemos desarrollado una modificación técnica en la realización de la VARD, aplicando un sistema generalmente utilizado en el TAMIS, que consideramos ventajoso para este tipo de cirugía
Tratamiento de la necrosis pancreática por VARD
No full textLa pancreatitis necrotizante constituye un cuadro clínico con alta morbimortalidad. El tratamiento inicial miniinvasivo con drenajes percutáneos ha permitido contemporizar la evolución de la misma de manera favorable. En un 30% de los casos es insuficiente, por lo que la necrosectomía retroperitoneal video-asistida (VARD) con stent constituye un eslabón clave en el ''step-up approach'' con mejores resultados en la morbi-mortalidad en relación a la cirugía convencional. Objetivo: Presentar nuestra experiencia y resultados del tratamiento de la necrosis pancreática por VARD con stent metálico.Facultad de Ciencias Médica
VARD versus Word A comparison of the UCREL variant detector and modern spell checkers on
No full textAnalysis of English historical texts poses a number of obstacles for standard corpus analysis and annotation techniques. In addition to nonstandard spellings and contractions, there are difficulties at the morphological, phonetic and syntactic levels. Our response has been to develop a VARiant Detector (VARD). We trained VARD on 16th-19th century data, specifically, the Nameless Shakespeare and a selection of texts taken from Chadwyck-Healey’s Eighteenth and Nineteenth Century Fiction collection. We have chosen to explore data from these centuries as, even though variant usage remains an issue up to the present day (because of the use of dialectal forms/ongoing standardisation), it falls substantially in the 18th-19th centuries. This paper reports on experiments to test the utility of VARD. The experiments compared VARD’s performance on unseen data with that of spell checkers for modern English (MS-Word and Aspell). Our hypothesis is that, as these spell checkers are not intended to work on historical data, VARD will be superior at both recognising variants and suggesting modern forms. VARD includes modern equivalents via an XML <reg> tag rather than removing the original variants. 1
A new convolutional neural network based on combination of circlets and wavelets for macular OCT classification
No full textArtificial intelligence (AI) algorithms, encompassing machine learning and deep learning, can assist ophthalmologists in early detection of various ocular abnormalities through the analysis of retinal optical coherence tomography (OCT) images. Despite considerable progress in these algorithms, several limitations persist in medical imaging fields, where a lack of data is a common issue. Accordingly, specific image processing techniques, such as time–frequency transforms, can be employed in conjunction with AI algorithms to enhance diagnostic accuracy. This research investigates the influence of non-data-adaptive time–frequency transforms, specifically X-lets, on the classification of OCT B-scans. For this purpose, each B-scan was transformed using every considered X-let individually, and all the sub-bands were utilized as the input for a designed 2D Convolutional Neural Network (CNN) to extract optimal features, which were subsequently fed to the classifiers. Evaluating per-class accuracy shows that the use of the 2D Discrete Wavelet Transform (2D-DWT) yields superior outcomes for normal cases, whereas the circlet transform outperforms other X-lets for abnormal cases characterized by circles in their retinal structure (due to the accumulation of fluid). As a result, we propose a novel transform named CircWave by concatenating all sub-bands from the 2D-DWT and the circlet transform. The objective is to enhance the per-class accuracy of both normal and abnormal cases simultaneously. Our findings show that classification results based on the CircWave transform outperform those derived from original images or any individual transform. Furthermore, Grad-CAM class activation visualization for B-scans reconstructed from CircWave sub-bands highlights a greater emphasis on circular formations in abnormal cases and straight lines in normal cases, in contrast to the focus on irrelevant regions in original B-scans. To assess the generalizability of our method, we applied it to another dataset obtained from a different imaging system. We achieved promising accuracies of 94.5% and 90% for the first and second datasets, respectively, which are comparable with results from previous studies. The proposed CNN based on CircWave sub-bands (i.e. CircWaveNet) not only produces superior outcomes but also offers more interpretable results with a heightened focus on features crucial for ophthalmologists
Toni, Crystal, Vard and Dale Openshaw
No full textOpenshaw children pose together for a photo. From left are Toni, Crystal and Vard. They are the children of Linnus and Josephine Openshaw. The boy in front is a cousin, Dale Openshaw, who is the son of Glenn and Ada Openshaw
CircWaveDL: Modeling of optical coherence tomography images based on a new supervised tensor-based dictionary learning for classification of macular abnormalities
No full textModeling Optical Coherence Tomography (OCT) images is crucial for numerous image processing applications and aids ophthalmologists in the early detection of macular abnormalities. Sparse representation-based models, particularly dictionary learning (DL), play a pivotal role in image modeling. Traditional DL methods often transform higher-order tensors into vectors and then aggregate them into a matrix, which overlooks the inherent multi-dimensional structure of the data. To address this limitation, tensor-based DL approaches have been introduced. In this study, we present a novel tensor-based DL algorithm, CircWaveDL, for OCT classification, where both the training data and the dictionary are modeled as higher-order tensors. We named our approach CircWaveDL to reflect the use of CircWave atoms for dictionary initialization, rather than random initialization. CircWave has previously shown effectiveness in OCT classification, making it a fitting basis function for our DL method. The algorithm employs CANDECOMP/PARAFAC (CP) decomposition to factorize each tensor into lower dimensions. We then learn a sub-dictionary for each class using its respective training tensor. For testing, a test tensor is reconstructed with each sub-dictionary, and each test B-scan is assigned to the class that yields the minimal residual error. To evaluate the model's generalizability, we tested it across three distinct databases. Additionally, we introduce a new heatmap generation technique based on averaging the most significant atoms of the learned sub-dictionaries. This approach highlights that selecting an appropriate sub-dictionary for reconstructing test B-scans improves reconstructions, emphasizing the distinctive features of different classes. CircWaveDL demonstrated strong generalizability across external validation datasets, outperforming previous classification methods. It achieved accuracies of 92.5 %, 86.1 %, and 89.3 % on datasets 1, 2, and 3, respectively, showcasing its efficacy in OCT image classification
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