Data on the Web in HTML tables is mostly structured, but we usually do not know the structure in advance. Thus, we cannot directly query for data of interest. We propose a solution to this problem for the case of mostly structured data in the form of HTML tables, based on document-independent extraction ontologies. The solution entails elements of table location and table understanding, data integration, and wrapper creation. Table location and understanding allows us to locate the table of interest, recognize attributes and values, pair attributes with values, and form records. Data-integration techniques allow us to match source records with a target schema. Ontologically specified wrappers allow us to extract data from source records into a target schema. Experimental results show that we can successfully map data of interest from source HTML tables with unknown structure to a given target database schema. We can thus "directly" query source data with unknown structure through a known target schema.

Non-invasive techniques to explore intracranial compliance and pressure have been extensively explored in recent years. Previous techniques have used expensive technologies to make these measurements, often with difficulty. We present a novel, inexpensive provocative test to observe trends in intracranial compliance measurement targeted towards the treatment and management of hydrocephalus. Two techniques are proposed which derive data from the digital and supraorbital arteries as well as tympanic membrane displacement. This requires the use of two photo-plethysmographic sensors and a TMD sensor. A common tilt table apparatus is used to methodically and artificially increase intracranial pressure to stress the cranial system during the test. The results from this test are computed using a digital signal processing algorithm to determine phase difference between the waveforms. Further research is also proposed.

The current World Wide Web is a web of pages. Users have to guess possible keywords that might lead through search engines to the pages that contain information of interest and browse hundreds or even thousands of the returned pages in order to obtain what they want. This frustrating problem motivates an approach to turn the web of pages into a web of knowledge, so that web users can query the information of interest directly. This dissertation provides a step in this direction and a way to partially overcome the challenges. Specifically, this dissertation shows how to turn machine-generated web pages like those on the hidden web into semantic web pages for the web of knowledge. We design and develop three systems to address the challenge of turning the web pages into web-of-knowledge pages: TISP (Table Interpretation for Sibling Pages), TISP++, and FOCIH (Form-based Ontology Creation and Information Harvesting). TISP can automatically interpret hidden-web tables. Given interpreted tables, TISP++ can generate ontologies and semantically annotate the information present in the interpreted tables automatically. This way, we can offer a way to make the hidden information publicly accessible. We also provide users with a way where they can generate personalized ontologies. FOCIH provides users with an interface with which they can provide their own view by creating a form that specifies the information they want. Based on the form, FOCIH can generate user-specific ontologies, and based on patterns in machine-generated pages, FOCIH can harvest information and annotate these pages with respect to the generated ontology. Users can directly query on the annotated information. With these contributions, this dissertation serves as a foundational pillar for turning the current web of pages into a web of knowledge.

We show that any two split metacyclic groups with the same character tables are isomorphic. We then use this to show that among metacyclic groups that are either 2-groups or are of odd order divisible by at most two primes, that the dihedral and generalized quaternion groups of order 2^n, n = 3, are the only pairs that have the same character tables.

Bakgrund: Inom slutenvården i Sverige varje år utförs cirka 800 000 operationer där patienter skall positioneras inför operationen på ett säkert sätt för att förebygga vårdskador. Den perioperativa vården utgör därmed en central del. Ett operationsteam som arbetar tillsammans och använder sina unika kunskaper ihop stärker patientsäkerheten. Delaktighet är centralt i allt vårdande men det saknas kunskap om hur detta synliggörs i patientens positionering på operationsbordet. Syfte: Syftet med denna studie var att undersöka hur operationsteamet gör patienten delaktig i sin positionering på operationsbordet. Metod: En kvalitativ icke-deltagande observationsstudie genomfördes på två sjukhus i sydvästra Sverige. Insamlad data analyserades med hjälp av Graneheim och Lundmans beskrivning av en kvalitativ innehållsanalys. Resultat: Operationsteamet gör patienten delaktig i sin positionering på operationsbordet på olika sätt; genom att guida patienten genom processen, inge trygghet och lugn på operationssalen, ge information, ge bekräftelse och inkludera patienten i teamet. I några fall var patienten inte i fokus och gjordes inte delaktig, därmed exkluderades patienten ur teamet. Slutsats: Operationsteamet är oftast väl rutinerade i hur de skall delaktiggöra patienten i sin positionering på operationsbordet. Att teamet är välfungerande och kan lyssna på och bekräfta patienten samt förmedla lugn och trygghet, bidrar till ökad delaktighet. / Background: Within Swedish inpatient care around 800000 surgeries are performed each year and where patients are positioned on the operating table in a way that prevents injuries. An operating team that works and uses their unique skills together can prevent lifethreatening complications. Patient participation is central to all care but there is a lack of knowledge about how this is made visible in the patient's positioning on the operating table.  Aim: Investigate the work of the surgical team to involve the patient in their positioning on the operating table. Method: A qualitative non-participation observational study was conducted in two hospitals in south-western Sweden. Collected data was analyzed using Graneheim and Lundmans description of a qualitative content analysis. Results: The surgical team usually involves the patient in their positioning and this in several different ways; by guiding the patient through the process, offering safety in the operating room, providing information, confirmation and including the patient. In some cases, the patient was not involved and excluded from the team. Conclusion: The operating team is usually very familiar with how they're going to involve the patient in their positioning. A team that is well-functioning listens to and confirm the patient and offer security, contributes to increased participation.

Montane meadows provide essential habitat for a variety of unique species and important ecosystem services in the western United States. Although important, meadows have experienced increased rates of conifer encroachment due to climate change, fire suppression and grazing. To combat meadow degradation from conifer encroachment, land managers have employed various restoration strategies one of which is conifer removal. Multiple studies have investigated the relationship between meadow hydrology and vegetation; however, few have assessed the effect of conifer removal on meadow groundwater. The goal of this study is to determine if the removal of conifers from an encroached meadow has an effect on depth to the groundwater table (WTD) and soil moisture content (SMC), and to investigate the accuracy and potential usefulness of evapotranspiration (ET) calculation methodologies for montane meadows. This goal will be accomplished by the subsequent objectives: 1) perform an analysis of WTD and SMC in an encroached meadow preceding and following conifer removal and upland thinning; 2) calculate and compare daily ET estimates in a previously restored meadow using diurnal groundwater table fluctuation, diurnal groundwater fluctuation modelling, and SMC. Miranda Cabin Meadow (MC) is located within the Upper American River Watershed, southeast of French Meadows Reservoir, at an elevation of 6,200 feet. MC received conifer removal, upland thinning and road decommissioning in the fall of 2018 as part of the American River Conservancy’s American River Headwaters Restoration Project. This study found the average WTD in MC during the growing season decreased from 4.91 feet prior to restoration, to 3.39 feet after restoration. In addition, the number of days the WTD was within 0.98 feet and 3 feet increased from 12 days and 34 days, to 31 and 49 days. Analysis of SMC in MC was limited due to gaps in data, however this study found that after restoration the average weekly SMC decreased at a slower rate than prior to restoration, possibly indicating decreased hydrologic output from ET. Based upon WTD during the growing season and the limited SMC data it appears that removal of conifers and upland thinning at MM promotes SMC and WTD conditions conducive to meadow vegetation communities. Marian Meadow (MM), located in Plumas County, CA at an elevation of 4,900 feet, received conifer removal as part of a timber harvest plan carried out by Collins Pine Company in July 2015. The soil moisture sensors used in this study were installed in MM in September 2013 for previous graduate thesis research. Groundwater table data was collected using 10-foot wells installed in July of 2018. Daily ET was calculated during August 2018 using three methodologies, and during September 2018 using two methodologies. Daily ET estimates calculated using diurnal groundwater table fluctuation and the White method averaged 11.8 mm per day in August and 9.1 mm in September. Using diurnal groundwater table fluctuation modelling this study calculated an average daily ET of 4.2 mm in August and 3 mm in September. Daily ET estimates based on SMC were calculated for August 2018 using two methods which produced estimates of 0.9 mm and 1.2 mm per day. All three methods for calculating ET produced some daily estimates that compare well to previous research of Et in Sierra Nevada meadows, however the White method generally overestimated daily Et while SMC methods underestimated ET. Groundwater table fluctuation modelling produced the best estimates of daily ET for both August and September. ET results in this study support previous research on the applicability of the White method; and they also suggest that the applicability of groundwater fluctuation modelling to estimate meadow daily ET in Sierra Nevada montane meadows be investigated further.

Ray tracing is a popular technique used in movies and video games to create compelling visuals. Ray traced computer images are increasingly becoming more realistic and almost indistinguishable from real-word images. Due to the complexity of scenes and the desire for high resolution images, ray tracing can become very expensive in terms of computation and memory. To address these concerns, researchers have examined data structures to efficiently store geometric and material information. Sparse voxel octrees (SVOs) and directed acyclic graphs (DAGs) have proven to be successful geometric data structures for reducing memory requirements. Moxel DAGs connect material properties to these geometric data structures, but experience limitations related to memory, build times, and render times. This thesis examines the efficacy of connecting an alternative material data structure to existing geometric representations. The contributions of this thesis include the creation of a new material representation using hashing to accompany DAGs, a method to calculate surface normals using neighboring voxel data, and a demonstration and validation that DAGs can be used to super sample based on proximity. This thesis also validates the visual acuity from these methods via a user survey comparing different output images. In comparison to the Moxel DAG implementation, this work increases render time, but reduces build times and memory, and improves the visual quality of output images.

To achieve multiple learning objectives, the ideal preschool activity center should promote development across all domains, from adaptive to social-communicative. Though early childhood practitioners describe the sensory table as capable of doing so, empirical accounts stand in stark contrast and suggest that it is a non-social functional activity. The intent of the present investigation was to reconcile this distinct dichotomy through the systematic manipulation of four sensory table substances (sand, soil, rocks, and water) and provision sets that differed in realism to determine their effect on preschoolers' free play behavior. Preschoolers' play forms and social participation were observed at the sensory table as they used a novel surface, which was introduced weekly without repetition, and either a set of minimally structured objects or realistic toys. Preschoolers' play and social participation were indeed influenced by the arrangement of the table. The sand, water, and provision sets yielded the most salient effects. Sand pulled for more sophisticated cognitive and social play forms while water pulled for more rudimentary ones. Regarding provision sets, the highly structured toys pulled for the most mature cognitive play form while the minimally structured toys pulled for the most sophisticated social context. The highly structured toys, with realism that lent to specific themes, appear to have functioned as a thematic anchor and cultivated a greater occurrence of dramatic play as compared to the minimally structured objects, which pulled for more functional play. Conversely, the minimally structured toy set, containing objects that loosely represented realistic objects and/or were capable of multiple functions, fostered a greater amount of socialization through parallel, social, and social-constructive play. Aside from its motoric and adaptive value, findings from this investigation suggest that under certain ecological conditions the sensory table fosters the development of cognitive and social skills. Recommendations for early childhood education practitioners are provided.

Global organizations have adopted Industry 4.0 practices to stay viable through the information shared through billions of digital documents. The information in such documents is vital to the daily functioning of such organizations. Most critical information is laid out in tabular format in order to provide the information in a concise manner. Extracting this critical data and providing access to the latest information can help institutions to make evidence based and data driven decisions. Assembling such data for analysis can further enable organizations to automate certain processes such as manufacturing. A generalized solution for table text extraction would have to handle the variations in the page content and table layouts in order to accurately extract the text. We hypothesize that a table text extraction pipeline can extract this data in three stages. The first stage would involve identifying the images that contain tables and detecting the table region. The second stage would consider the detected table region and detect the rows and columns of the table. The last stage would involve extracting the text from the cell locations generated by the intersecting lines of the detected rows and columns. For first stage of the pipeline, we propose TableDet: a deep learning (artificial neural network) based methodology to solve table detection and table image classification in datasheet (document) images in a single inference. TableDet utilizes a Cascade R-CNN architecture with Complete IOU (CIOU) loss at each box head and a deformable convolution backbone to capture the variations of tables that appear at multiple scales and orientations. It also detects text and figures to enhance its table detection performance. We demonstrate the effectiveness of training TableDet with a dual-step transfer learning process and fine-tuning it with Table Aware Cutout (TAC) augmented images. TableDet achieves the highest F1 score for table detection against state-of-the-art solutions on ICDAR 2013 (complete set), ICDAR 2017 (test set) and ICDAR 2019 (test set) with 100%, 99.3% and 95.1% respectively. We show that the enhanced table detection performance can be utilized to address the table image classification task with the addition of a classification head which comprises of 3 conditions. For the table image classification task TableDet achieves 100% recall and above 92% precision on three test sets. These classification results indicate that all images with tables along with a significantly reduced number of images without tables would be promoted to the next stage of the table text extraction pipeline. For the second stage we propose TableStrDet, a deep learning (artificial neural network) based approach to recognize the structure of the detected tables regions from stage 1 by detecting and classifying rows and columns. TableStrDet comprises of two Cascade R-CNN architectures each with a deformable backbone and Complete IOU loss to improve their detection performance. One architecture detects and classifies columns as regular columns (column without a merged cell) and irregular columns (group of regular columns that share a merged cell). The second architecture detects and classifies rows as regular rows (row without a merged cell) and irregular rows (group of regular rows that share a merged cell). Both architectures work in parallel to provide the results in a single inference. We show that utilizing TableStrDet to detect four classes of objects enhances the quality of table structure detection by capturing table contents that may or may not have hierarchical layouts on two public test sets. Under the TabStructDB test set we achieve 72.7% and 78.5% weighted average F1 score for rows and columns respectively. On the ICDAR 2013 test set we achieve 90.5% and 89.6% weighted average F1 score for rows and columns respectively. Furthermore, we show that TableStrDet has a higher generalization potential on the available datasets.

Northern peatlands have acted as persistent sinks of CO2 throughout the Holocene largely owing to their ability to maintain shallow water table depths that limit decomposition rates and supports the growth of keystone vegetation including Sphagnum mosses. There is concern, however, that the future success and ecosystem function of these northern peat deposits may be at risk to climate change, where temperatures and evaporation rates are predicted to increase substantially in the next century. While numerous studies have examined the hydrology and carbon dynamics in large expansive peatland systems where a water table (WT) is ever-present, relatively little research has been done on small scale peat-accumulating systems where their vulnerability remains unknown. One region where a broad spectrum in the scale of peat accumulation is present is in the bedrock depressions of Canadian Shield rock barrens, which are of special importance as many peat deposits here provide habitat to species at risk including the Blanding’s Turtle and the Massassauga Rattlesnake. This study examines the controls that govern water storage dynamics and moss water availability in 18 different peat-accumulating depressions that vary in size, catchment area, and sediment composition. The magnitude of WT variability was often several times greater in shallower bedrock depressions (<50 cm deep) as compared to deeper ‘bogs’ (>60 cm deep). The magnitude of depression WT variability appeared to be closely linked to the WT depth (WTD), the relative proportions of different sediment types within the depression, and the depth dependant specific yield (Sy) of each sediment type. Sites which contained large fractions of Polytrichum moss or mineral soil – which were more common in shallow depressions ¬¬– had the greatest WT variability due to the lower porosity and Sy of this sediment as compared to Sphagnum peat. Sphagnum dominated ‘vernal pools’ (30-50 cm deep) had a WT variability two to three times greater than Sphagnum dominated bogs at WTDs > 20-25 cm, which may be related to exceptionally high ash concentrations near the base of vernal pools which reduced peat porosity and Sy as compared to more organic-rich peat. As compared to bogs, pits (<15 cm deep) and vernal pools had greater rates of WT decline during drying intervals, deeper average WTDs when a WT was present, and extended periods of WT absence during the summer months. As such, moss growing in pits and vernal pools generally had lower near-surface water availability as compared to bogs, though the importance of depression depth in determining the timing of moss stress is also dependant on the hydrophysical properties (Kunsat and moisture retention) of the moss species in question. WT dynamics and moss water availability were generally weakly correlated to depression catchment size, although during wetter periods of the year the rate of WT recession was moderated in pits and vernal pools which had an upslope depression that could provide sustained water inputs for multiple days after rainfall. The results of this study suggest that depression depth may be a first order control in determining peatland vulnerability to future regime shifts induced by external forcings or disturbances. Furthermore, this study suggests that systematic differences may exist between the hydrophysical properties of peat in shallow vs. large bedrock depressions, potentially resulting from contrasts in fire frequency/severity, and/or the degree of humification/compression among geological settings. / Thesis / Master of Science (MSc) / Canada is home to one of the largest reservoirs of organic carbon stored on land in the world, in unique ecosystems called peatlands. Peatlands are formed in wetland environments where a thick layer of organic matter has accumulated over time due to the average rate of vegetation growth on the surface of peatlands exceeding the rate of decomposition of the underlying organic matter. This net accumulation of organic matter over time has caused peatlands to act as a long term sink of carbon dioxide, which is a greenhouse gas that is a primary driver of global warming. The ability of peatlands to have slow decomposition rates and support the growth of key peatland vegetation, most notably various species of ‘peat moss’, is highly dependent upon their ability to keep their water table (i.e. the surface below which pore spaces in the organic matter are saturated with water) close to their growing surface. There is concern, however, that a warmer and dryer climate in the future could cause deeper water table positions in peatlands, thereby increasing decomposition rates, decreasing the growth rate of peat moss, and potentially turning peatlands into a net source of carbon dioxide. Most peatland studies to date, however, have focused on water storage/movement and carbon exchange in large, deep peatland systems, whereas relatively little research has been conducted on smaller peatlands. As such, the vulnerability of these smaller peatlands to future climate change remains uncertain. One region where peatlands exist over a wide range of different sizes and landscape positions is in bedrock depressions of the Canadian Shield, which are of special interest as they also provide habitat for species at risk including the Blanding’s Turtle and the Massassauga Rattlesnake. This study looked at how the water table positions and water availability to different species of peat moss compared over the growing season between 18 peatlands of different sizes and landscape position (i.e. peatlands with a relatively ‘small’ and ‘large’ area upslope of them). This study finds that deeper peatlands (with organic matter layers > 60 cm deep) usually had a shallower water table over the summer months than shallower peatlands (< 50 cm deep), primarily due to differences in the properties of the organic matter underlying their growing surfaces. Furthermore, each of the 12 studied peatlands < 50 cm deep lost their water table for a considerable amount of time during the summer (when their water table position dropped below the underlying bedrock of the depression), whereas each of the six peatlands > 60 cm deep had a water table present for the entire growing season. Surprisingly, a peatland’s position on the landscape seemed to have a relatively minor effect on determining the depth/presence of its water table. As deeper peatlands usually had a water table that was closer to the growing surface and was always present, more moisture was available to the peat moss growing at their surface than for peat moss in shallower depressions, though this moisture availability also depended upon the growth form of the different species of peat moss (some species of peat moss were better at accessing subsurface water than others). Through its impact on water table positions and moisture availability for peat moss, peatland depth is likely a primary control governing peatland vulnerability climate change, with shallower peatlands being more vulnerable to warmer and dryer conditions in the future.