Deep learning with knowledge graphs for fine-grained emotion classification in text

This PhD thesis investigates two key challenges in the area of fine-grained emotion detection in textual data. More specifically, this work focuses on (i) the accurate classification of emotion in tweets and (ii) improving the learning of representations from knowledge graphs using graph convolutional neural networks.The first part of this work outlines the task of emotion keyword detection in tweets and introduces a new resource called the EEK dataset. Tweets have previously been categorised as short sequences or sentence-level sentiment analysis, and it could be argued that this should no longer be the case, especially since Twitter increased its allowed character limit. Recurrent Neural Networks have become a well-established method to classify tweets over recent years, but have struggled with accurately classifying longer sequences due to the vanishing and exploding gradient descent problem. A common technique to overcome this problem has been to prune tweets to a shorter sequence length. However, this also meant that often potentially important emotion carrying information, which is often found towards the end of a tweet, was lost (e.g., emojis and hashtags). As such, tweets mostly face also problems with classifying long sequences, similar to other natural language processing tasks. To overcome these challenges, a multi-scale hierarchical recurrent neural network is proposed and benchmarked against other existing methods. The proposed learning model outperforms existing methods on the same task by up to 10.52%. Another key component for the accurate classification of tweets has been the use of language models, where more recent techniques such as BERT and ELMO have achieved great success in a range of different tasks. However, in Sentiment Analysis, a key challenge has always been to use language models that do not only take advantage of the context a word is used in but also the sentiment it carries. Therefore the second part of this work looks at improving representation learning for emotion classification by introducing both linguistic and emotion knowledge to language models. A new linguistically inspired knowledge graph called RELATE is introduced. Then a new language model is trained on a Graph Convolutional Neural Network and compared against several other existing language models, where it is found that the proposed embedding representations achieve competitive results to other LMs, whilst requiring less pre-training time and data. Finally, it is investigated how the proposed methods can be applied to document-level classification tasks. More specifically, this work focuses on the accurate classification of suicide notes and analyses whether sentiment and linguistic features are important for accurate classification

Hierarchical Multiscale Recurrent Neural Networks for Detecting Suicide Notes

Recent statistics in suicide prevention show that people are increasingly posting their last words online and with the unprecedented availability of textual data from social media platforms researchers have the opportunity to analyse such data. Furthermore, psychological studies have shown that our state of mind can manifest itself in the linguistic features we use to communicate. In this paper, we investigate whether it is possible to automatically identify suicide notes from other types of social media blogs in two document-level classification tasks. The first task aims to identify suicide notes from depressed and blog posts in a balanced dataset, whilst the second experiment looks at how well suicide notes can be classified when there is a vast amount of neutral text data, which makes the task more applicable to real-world scenarios. Furthermore we perform a linguistic analysis using LIWC (Linguistic Inquiry and Word Count). We present a learning model for modelling long sequences in two experiment series. We achieve an f1-score of 88.26% over the baselines of 0.60 in experiment 1 and 96.1% over the baseline in experiment 2. Finally, we show through visualisations which features the learning model identifies, these include emotions such as love and personal pronouns

The AGeS2 (Awards for Geochronology Student research 2) Program: Supporting Community Geochronology Needs and Interdisciplinary Science

Geochronology is essential in the geosciences. It is used to resolve the durations and rates of earth processes, as well as test causative relationships among events. Such data are increasingly required to conduct cutting-edge, transformative, earth-science research. The growing need for geochronology is accompanied by strong demand to enhance the ability of labs to meet this pressure and to increase community awareness of how these data are produced and interpreted. For example, a 2015 National Science Foundation (NSF) report on opportunities and challenges for U.S. geochronology research noted: While there has never been a time when users have had greater access to geo-chronologic data, they remain, by and large, dissatisfied with the available style/ quantity/cost/efficiency (Harrison et al., 2015, p. 1). And the 2012 National Research Council NROES (New Research Opportunities in the Earth Sciences) report (Lay et al., 2012, p. 82) recommended: [NSF] EAR should explore new mechanisms for geochronology laboratories that will service the geochronology requirements of the broad suite of research opportunities while sustaining technical advances in methodologies. The AGeS (Awards for Geochronology Student research) program is one way that these calls are being answered

Small-molecule lysophosphatidic acid receptor 5 (LPAR5) antagonists: versatile pharmacological tools to regulate inflammatory signaling in BV-2 microglia cells

Lysophosphatidic acid (LPA) species in the extracellular environment induce downstream signaling via six different G protein-coupled receptors (LPAR1-6). These signaling cascades are essential for normal brain development and function of the nervous system. However, in response to acute or chronic central nervous system (CNS) damage, LPA levels increase and aberrant signaling events can counteract brain function. Under neuro-inflammatory conditions signaling along the LPA/LPAR5 axis induces a potentially neurotoxic microglia phenotype indicating the need for new pharmacological intervention strategies. Therefore, we compared the effects of two novel small-molecule LPAR5 antagonists on LPA-induced polarization parameters of the BV-2 microglia cell line. AS2717638 is a selective piperidine-based LPAR5 antagonist (IC(50) 0.038 μM) while compound 3 is a diphenylpyrazole derivative with an IC(50) concentration of 0.7 μM in BV-2 cells. Both antagonists compromised cell viability, however, at concentrations above their IC(50) concentrations. Both inhibitors blunted LPA-induced phosphorylation of STAT1 and STAT3, p65, and c-Jun and consequently reduced the secretion of pro-inflammatory cyto-/chemokines (IL-6, TNFα, IL-1β, CXCL10, CXCL2, and CCL5) at non-toxic concentrations. Both compounds modulated the expression of intracellular (COX-2 and Arg1) and plasma membrane-located (CD40, CD86, and CD206) polarization markers yet only AS2717638 attenuated the neurotoxic potential of LPA-activated BV-2 cell-conditioned medium towards CATH.a neurons. Our findings from the present in vitro study suggest that the two LPAR5 antagonists represent valuable pharmacological tools to interfere with LPA-induced pro-inflammatory signaling cascades in microglia

volumetric characterisation and correlation to established classification systems

Objective and sensitive assessment of cartilage repair outcomes lacks suitable methods. This study investigated the feasibility of 3D ultrasound biomicroscopy (UBM) to quantify cartilage repair outcomes volumetrically and their correlation with established classification systems. 32 sheep underwent bilateral treatment of a focal cartilage defect. One or two years post- operatively the repair outcomes were assessed and scored macroscopically (Outerbridge, ICRS-CRA), by magnetic resonance imaging (MRI, MOCART), and histopathology (O'Driscoll, ICRS-I and ICRS-II). The UBM data were acquired after MRI and used to reconstruct the shape of the initial cartilage layer, enabling the estimation of the initial cartilage thickness and defect volume as well as volumetric parameters for defect filling, repair tissue, bone loss and bone overgrowth. The quantification of the repair outcomes revealed high variations in the initial thickness of the cartilage layer, indicating the need for cartilage thickness estimation before creating a defect. Furthermore, highly significant correlations were found for the defect filling estimated from UBM to the established classification systems. 3D visualisation of the repair regions showed highly variable morphology within single samples. This raises the question as to whether macroscopic, MRI and histopathological scoring provide sufficient reliability. The biases of the individual methods will be discussed within this context. UBM was shown to be a feasible tool to evaluate cartilage repair outcomes, whereby the most important objective parameter is the defect filling. Translation of UBM into arthroscopic or transcutaneous ultrasound examinations would allow non-destructive and objective follow-up of individual patients and better comparison between the results of clinical trials

Dislocation imaging of an InAlGaAs opto-electronic modulator using IBICC

This paper presents ion beam induced charge collection (IBICC) contrast images showing regions of differing charge collection efficiency within optoelectronic modulator devices. The experiments were carried out at the Sandia nuclear microprobe using 18 MeV carbon and 2 MeV helium ions. Lines of varying densities are observed to run along the different {l_brace}110{r_brace} directions which correlate with misfit dislocations within the 392nm thick strained layer superlattice quantum well of the modulator structure. Independent cross-sectional TEM studies and the electrical properties of the devices under investigation suggest the presence of threading dislocations in the active device region at a density of {approximately} 10{sup 6} cm{sup {minus}2}. However, no clear evidence of threading dislocations was observed in the IBICC images as they are possibly masked by the strong contrast of the misfit dislocations. Charge carrier transport within the modulator is used to explain the observed contrast. The different signal to noise levels and rates of damage of the incident ions are assessed

Evaluating uncertainties in the calibration of isotopic reference materials and multi-element isotopic tracers (EARTHTIME Tracer Calibration Part II)

A statistical approach to evaluating uncertainties in the calibration of multi-element isotopic tracers has been developed and applied to determining the isotopic composition of mixed U-Pb (202Pb-205Pb-233U-235U) tracers used for accurate isotope dilution U-Pb geochronology. Our experiment, part of the EARTHTIME initiative, directly links the tracer calibration to first-principles measurements of mass and purity that are all traceable to SI units, thereby quantifying the accuracy and precision of U-Pb dates in absolute time. The calibration incorporates new more accurate and precise purity measurements for a number of commonly used Pb and U reference materials, and requires inter-relating their isotopic compositions and uncertainties. Similar methods can be used for other isotope systems that utilize multiple isotopic standards for calibration purposes. We also detail the inter-calibration of three publicly available U-Pb gravimetric solutions, which can be used to bring the same first-principles traceability to in-house U-Pb tracers from other laboratories. Accounting for uncertainty correlations in the tracer isotope ratios yields a tracer calibration contribution to the relative uncertainty of a 206Pb/238U date that is only half of the relative uncertainty in the 235U/205Pb ratio of the tracer, which was historically used to approximate the tracer related uncertainty contribution to 206Pb/238U dates. The tracer uncertainty contribution to 206Pb/238U dates has in this way been reduced to <300 ppm when using the EARTHTIME and similarly calibrated tracers

Endovascular Treatment for Acute Isolated Internal Carotid Artery Occlusion : A Propensity Score Matched Multicenter Study.

The benefit of endovascular treatment (EVT) in patients with acute symptomatic isolated occlusion of the internal carotid artery (ICA) without involvement of the middle and anterior cerebral arteries is unclear. We aimed to compare clinical and safety outcomes of best medical treatment (BMT) versus EVT + BMT in patients with stroke due to isolated ICA occlusion. We conducted a retrospective multicenter study involving patients with isolated ICA occlusion between January 2016 and December 2020. We stratified patients by BMT versus EVT and matched the groups using propensity score matching (PSM). We assessed the effect of treatment strategy on favorable outcome (modified Rankin scale ≤ 2) 90 days after treatment and compared reduction in NIHSS score at discharge, rates of symptomatic intracranial hemorrhage (sICH) and 3‑month mortality. In total, we included 149 patients with isolated ICA occlusion. To address imbalances, we matched 45 patients from each group using PSM. The rate of favorable outcomes at 90 days was 56% for EVT and 38% for BMT (odds ratio, OR 1.89, 95% confidence interval, CI 0.84-4.24; p = 0.12). Patients treated with EVT showed a median reduction in NIHSS score at discharge of 6 points compared to 1 point for BMT patients (p = 0.02). Rates of symptomatic intracranial hemorrhage (7% vs. 4%; p = 0.66) and 3‑month mortality (11% vs. 13%; p = 0.74) did not differ between treatment groups. Periprocedural complications of EVT with early neurological deterioration occurred in 7% of cases. Although the benefit on functional outcome did not reach statistical significance, the results for NIHSS score improvement, and safety support the use of EVT in patients with stroke due to isolated ICA occlusion

Quantitative ultrasound biomicroscopy for the analysis of healthy and repair cartilage tissue

The increasing spectrum of different cartilage repair strategies requires the introduction of adequate non-destructive methods to analyse their outcome in-vivo, i.e. arthroscopically. The validity of non-destructive quantitative ultrasound biomicroscopy (UBM) was investigated in knee joints of five miniature pigs. After 12 weeks, six 5-mm defects, treated with different cartilage repair approaches, provided tissues with different structural qualities. Healthy articular cartilage from each contralateral unoperated knee joint served as a control. The reflected and backscattered ultrasound signals were processed to estimate the integrated reflection coefficient (IRC) and apparent integrated backscatter (AIB) parameters. The cartilage repair tissues were additionally assessed biomechanically by cyclic indentation, histomorphologically and immunohistochemically. UBM allowed high-resolution visualisation of the structure of the joint surface and subchondral bone plate, as well as determination of the cartilage thickness and demonstrated distinct differences between healthy cartilage and the different repair cartilage tissues with significant higher IRC values and a steeper negative slope of the depth-dependent backscatter amplitude AIBslope for healthy cartilage. Multimodal analyses revealed associations between IRC and the indentation stiffness. Furthermore, AIBslope and AIB at the cartilage-bone boundary (AIBdC) were associated with the quality of the repair matrices and the subchondral bone plate, respectively. This ex-vivo pilot study confirms that UBM can provide detailed imaging of articular cartilage and the subchondral bone interface also in repaired cartilage defects, and furthermore, contributes in certain aspects to a basal functional characterization of various forms of cartilage repair tissues. UBM could be further established to be applied arthroscopically in-vivo