Automatic inference of causal reasoning chains from student essays

While there has been an increasing focus on higher-level thinking skills arising from the Common Core Standards, many high-school and middle-school students struggle to combine and integrate information from multiple sources when writing essays. Writing is an important learning skill, and there is increasing evidence that writing about a topic develops a deeper understanding in the student. However, grading essays is time consuming for teachers, resulting in an increasing focus on shallower forms of assessment that are easier to automate, such as multiple-choice tests. Existing essay grading software has attempted to ease this burden but relies on shallow lexico-syntactic features and is unable to understand the structure or validity of a student’s arguments or explanations. Without the ability to understand a student’s reasoning processes, it is impossible to write automated formative assessment systems to assist students with improving their thinking skills through essay writing. In order to understand the arguments put forth in an explanatory essay in the science domain, we need a method of representing the causal structure of a piece of explanatory text. Psychologists use a representation called a causal model to represent a student\u27s understanding of an explanatory text. This consists of a number of core concepts, and a set of causal relations linking them into one or more causal chains, forming a causal model. In this thesis I present a novel system for automatically constructing causal models from student scientific essays using Natural Language Processing (NLP) techniques. The problem was decomposed into 4 sub-problems - assigning essay concepts to words, detecting causal-relations between these concepts, resolving coreferences within each essay, and using the structure of the whole essay to reconstruct a causal model. Solutions to each of these sub-problems build upon the predictions from the solutions to earlier problems, forming a sequential pipeline of models. Designing a system in this way allows later models to correct for false positive predictions from downstream models. However, this also has the disadvantage that errors made in earlier models can propagate through the system, negatively impacting the upstream models, and limiting their accuracy. Producing robust solutions for the initial 2 sub problems, detecting concepts, and parsing causal relations between them, was critical in building a robust system. A number of sequence labeling models were trained to classify the concepts associated with each word, with the most effective approach being a bidirectional recurrent neural network (RNN), a deep learning model commonly applied to word labeling problems. This is because the RNN used pre-trained word embeddings to better generalize to rarer words, and was able to use information from both ends of each sentence to infer a word\u27s concept. The concepts predicted by this model were then used to develop causal relation parsing models for detecting causal connections between these concepts. A shift-reduce dependency parsing model was trained using the SEARN algorithm and out-performed a number of other approaches by better utilizing the structure of the problem and directly optimizing the error metric used. Two pre-trained coreference resolution systems were used to resolve coreferences within the essays. However a word tagging model trained to predict anaphors combined with a heuristic for determining the antecedent out-performed these two systems. Finally, a model was developed for parsing a causal model from an entire essay, utilizing the solutions to the three previous problems. A beam search algorithm was used to produce multiple parses for each sentence, which in turn were combined to generate multiple candidate causal models for each student essay. A reranking algorithm was then used to select the optimal causal model from all of the generated candidates. An important contribution of this work is that it represents a system for parsing a complete causal model of a scientific essay from a student\u27s written answer. Existing systems have been developed to parse individual causal relations, but no existing system attempts to parse a sequence of linked causal relations forming a causal model from an explanatory scientific essay. It is hoped that this work can lead to the development of more robust essay grading software and formative assessment tools, and can be extended to build solutions for extracting causality from text in other domains. In addition, I also present 2 novel approaches for optimizing the micro-F1 score within the design of two of the algorithms studied: the dependency parser and the reranking algorithm. The dependency parser uses a custom cost function to estimate the impact of parsing mistakes on the overall micro-F1 score, while the reranking algorithm allows the micro-F1 score to be optimized by tuning the beam search parameter to balance recall and precision

Cash flows: the gap between reported and estimated operating cash flow elements

The FASB and the IASB recently released a joint Discussion Paper “Preliminary Views on FinancialStatement Presentation” (International Accounting Standards Board 2008), which contains a major proposalrequiring companies to report operating cash flows using the direct method and it also requires that theindirect method of calculating operating cash flows be disclosed in the notes. This is a departure from currentrules and has generated considerable debate among respondents’ comment letters on the Discussion Paper.This paper adds to this debate by providing some evidence as to the size of the gap users confront when usingthe indirect method to estimate the major operating cash flow elements, such as cash collected fromcustomers and cash paid to suppliers. Using a sample of Australian companies which reported operating cashflows using the direct method, and presented the indirect method in the notes, we find significant differencesbetween reported and estimated figures for both cash collected from customers and cash paid to suppliers.These findings support the discussion paper’s proposal that companies be required to report cash flows usingboth the direct and indirect methods

Decrements in neuromuscular performance and increases in creatine kinase impact training outputs in elite soccer players

The aim of the current investigation was to understand the impact of pretraining neuromuscular performance and creatine kinase (CK) status on subsequent training performance in elite soccer players. Thirty soccer players (age: 25.3 ± 3.1 years; height: 183 ± 7 cm; mass: 72 ± 7 kg) were involved in this observational study. Each morning before training, players completed assessments for neuromuscular performance (countermovement jump; CMJ) and CK levels. Global positioning technology provided external load: total distance, high-speed distance, sprint distance, accelerations, decelerations, average metabolic power, explosive distance, and high metabolic power distance (\u3e25.5 W·kg). Mixed-effect linear models revealed significant effects for CK and CMJ Z-score on total high-speed distance, very high-speed distance, accelerations, decelerations, explosive distance, and maximal velocity. Effects are reported with 90% confidence limits. A CK Z-score of +1 corresponded to a -5.5 ± 1.1, -3.9 ± 0.5, -4.3 ± 2.9%, -4.1 ± 2.9%, -3.1 ± 2.9%, and -4.6 ± 1.9%, reduction in total high-speed distance, very high-speed distance, accelerations, decelerations, explosive distance, and maximal velocity, respectively. Countermovement jump Z-score of -1 corresponded to a -3.5 ± 1.1, -2.9 ± 0.5, -2.1 ± 1.4, -5.3 ± 2.9%, -3.8 ± 2.9%, -1.1 ± 2.9%, and -5.6 ± 1.2% reduction in these external load measures. Magnitude-based analysis revealed that the practical size of the effect of a pretraining CMJ Z-score of -1 and CK Z-score of +1 would have on total high-speed distance, very high-speed distance, high metabolic power distance (\u3e25.5 W·kg), accelerations, decelerations, explosive distance, and maximal velocity was likely negative. The results of this study suggest that systematic pretraining monitoring of neuromuscular and muscle stress within soccer cohorts can provide coaches with information about the training output that can be expected from individual players during a training session

Gambling as a public health issue in Wales

No abstract available

ALMA 1.3 Millimeter Map of the HD 95086 System

Planets and minor bodies such as asteroids, Kuiper-belt objects and comets are integral components of a planetary system. Interactions among them leave clues about the formation process of a planetary system. The signature of such interactions is most prominent through observations of its debris disk at millimeter wavelengths where emission is dominated by the population of large grains that stay close to their parent bodies. Here we present ALMA 1.3 mm observations of HD 95086, a young early-type star that hosts a directly imaged giant planet b and a massive debris disk with both asteroid- and Kuiper-belt analogs. The location of the Kuiper-belt analog is resolved for the first time. The system can be depicted as a broad ($\Delta R/R \sim$0.84), inclined (30\arcdeg$\pm$3\arcdeg) ring with millimeter emission peaked at 200$\pm$6 au from the star. The 1.3 mm disk emission is consistent with a broad disk with sharp boundaries from 106$\pm$6 to 320$\pm$20 au with a surface density distribution described by a power law with an index of --0.5$\pm$0.2. Our deep ALMA map also reveals a bright source located near the edge of the ring, whose brightness at 1.3 mm and potential spectral energy distribution are consistent with it being a luminous star-forming galaxy at high redshift. We set constraints on the orbital properties of planet b assuming co-planarity with the observed disk.Comment: accepted for publication in A

Confirming the Primarily Smooth Structure of the Vega Debris Disk at Millimeter Wavelengths

Clumpy structure in the debris disk around Vega has been previously reported at millimeter wavelengths and attributed to concentrations of dust grains trapped in resonances with an unseen planet. However, recent imaging at similar wavelengths with higher sensitivity has disputed the observed structure. We present three new millimeter wavelength observations that help to resolve the puzzling and contradictory observations. We have observed the Vega system with the Submillimeter Array (SMA) at a wavelength of 880 μm and an angular resolution of 5"; with the Combined Array for Research in Millimeter-wave Astronomy (CARMA) at a wavelength of 1.3 mm and an angular resolution of 5"; and with the Green Bank Telescope (GBT) at a wavelength of 3.3 mm and angular resolution of 10". Despite high sensitivity and short baselines, we do not detect the Vega debris disk in either of the interferometric data sets (SMA and CARMA), which should be sensitive at high significance to clumpy structure based on previously reported observations. We obtain a marginal (3σ) detection of disk emission in the GBT data; the spatial distribution of the emission is not well constrained.We analyze the observations in the context of several different models, demonstrating that the observations are consistent with a smooth, broad, axisymmetric disk with inner radius 20–100 AU and width ≾50 AU. The interferometric data require that at least half of the 860 μm emission detected by previous single-dish observations with the James Clerk Maxwell Telescope be distributed axisymmetrically, ruling out strong contributions from flux concentrations on spatial scales of ≾100 AU. These observations support recent results from the Plateau de Bure Interferometer indicating that previous detections of clumpy structure in the Vega debris disk were spurious

A Bright Submillimeter Source in the Bullet Cluster (1E0657--56) Field Detected with BLAST

We present the 250, 350, and 500 micron detection of bright submillimeter emission in the direction of the Bullet Cluster measured by the Balloon-borne Large Aperture Submillimeter Telescope (BLAST). The 500 micron centroid is coincident with an AzTEC 1.1 mm point-source detection at a position close to the peak lensing magnification produced by the cluster. However, the 250 micron and 350 micron centroids are elongated and shifted toward the south with a differential shift between bands that cannot be explained by pointing uncertainties. We therefore conclude that the BLAST detection is likely contaminated by emission from foreground galaxies associated with the Bullet Cluster. The submillimeter redshift estimate based on 250-1100 micron photometry at the position of the AzTEC source is z_phot = 2.9 (+0.6 -0.3), consistent with the infrared color redshift estimation of the most likely IRAC counterpart. These flux densities indicate an apparent far-infrared luminosity of L_FIR = 2E13 Lsun. When the amplification due to the gravitational lensing of the cluster is removed, the intrinsic far-infrared luminosity of the source is found to be L_FIR <= 10^12 Lsun, consistent with typical luminous infrared galaxies.Comment: Accepted for publication in the Astrophysical Journal. Maps are available at http://blastexperiment.info

GAA repeat expansion mutation mouse models of Friedreich ataxia exhibit oxidative stress leading to progressive neuronal and cardiac pathology

Friedreich ataxia (FRDA) is a neurodegenerative disorder caused by an unstable GAA repeat expansion mutation within intron 1 of the FXN gene. However, the origins of the GAA repeat expansion, its unstable dynamics within different cells and tissues, and its effects on frataxin expression are not yet completely understood. Therefore, we have chosen to generate representative FRDA mouse models by using the human FXN GAA repeat expansion itself as the genetically modified mutation. We have previously reported the establishment of two lines of human FXN YAC transgenic mice that contain unstable GAA repeat expansions within the appropriate genomic context. We now describe the generation of FRDA mouse models by crossbreeding of both lines of human FXN YAC transgenic mice with heterozygous Fxn knockout mice. The resultant FRDA mice that express only human-derived frataxin show comparatively reduced levels of frataxin mRNA and protein expression, decreased aconitase activity, and oxidative stress, leading to progressive neurodegenerative and cardiac pathological phenotypes. Coordination deficits are present, as measured by accelerating rotarod analysis, together with a progressive decrease in locomotor activity and increase in weight. Large vacuoles are detected within neurons of the dorsal root ganglia (DRG), predominantly within the lumbar regions in 6-month-old mice, but spreading to the cervical regions after 1 year of age. Secondary demyelination of large axons is also detected within the lumbar roots of older mice. Lipofuscin deposition is increased in both DRG neurons and cardiomyocytes, and iron deposition is detected in cardiomyocytes after 1 year of age. These mice represent the first GAA repeat expansion-based FRDA mouse models that exhibit progressive FRDA-like pathology and thus will be of use in testing potential therapeutic strategies, particularly GAA repeat-based strategies. © 2006 Elsevier Inc. All rights reserved

An app-, web- and social support-based weight loss intervention for adults with obesity: the HelpMeDoIt! feasibility RCT

Background: Finding solutions to rising levels of obesity continues to be a major public health focus. Social support has an important role in successful weight loss, and digital interventions can reach a large proportion of the population at low cost. Objective: To develop and assess the feasibility and acceptability of an application (app), web- and social support-based intervention in supporting adults with obesity to achieve weight loss goals. Design: Stage 1 – intervention development phase involved three focus groups (n = 10) with users, and think-aloud interviews and field testing with another group (n = 28). Stage 2 – the intervention and evaluation methods were explored in a feasibility randomised controlled trial with economic and process evaluation. Setting: Greater Glasgow and Clyde, UK. Participants: Adults with a body mass index of ≥ 30kg/m2 who owned a smartphone and were interested in losing weight were randomised 2 : 1 (intervention : control) and followed up at 12 months. Recruitment took place in April–October 2016. Interventions: The intervention group had access to HelpMeDoIt! for 12 months. This encouraged them to (1) set goals, (2) monitor progress and (3) harness social support by inviting ‘helpers’ from their existing social network. The control group received a healthy lifestyle leaflet. Main outcome measures: Data from stage 1 informed the intervention design. Key measures in stage 2 assessed the feasibility and acceptability of the intervention and trial methods against prespecified progression criteria. Three primary outcomes were explored: body mass index, diet and physical activity. Secondary outcomes included weight, waist and hip circumference, social support, self-efficacy, motivation, mental health, health-related quality of life, NHS resource use, participant-borne costs and intervention costs. Qualitative interviews with participants (n = 26) and helpers (n = 9) explored the feasibility and acceptability of the trial methods and intervention. Results: Stage 1 produced (1) a website that provided evidence-based information for lifestyle change and harnessing social support, and (2) an app that facilitated goal-setting, self-monitoring and supportive interaction between participants and their helper(s). Progression criteria were met, demonstrating that the intervention and trial methods were feasible and acceptable. A total of 109 participants (intervention, n = 73; control, n = 36) were recruited, with 84 participants (77%: intervention, 71%; control, 89%) followed up at 12 months. Data were successfully collected for most outcome measures (≥ 82% completion). Participants and helpers were generally positive, although helper engagement with the app was low. Of the 54 (74%) participants who downloaded the app, 48 (89%) used it twice or more, 28 helpers enrolled via the app, and 19 (36%) participants interacted with their helper(s) via the app. Interview data indicated that HelpMeDoIt! prompted support from helpers that often occurred without the helpers using the app. Limitations: Early technical problems meant that some participants and helpers had difficulty accessing the app. Ethical constraints meant that we were unable to contact helpers directly for interview. Conclusions: The HelpMeDoIt! study demonstrated that a weight loss intervention delivered via an app and a website is feasible and acceptable. Progression criteria were met, supporting further evaluation of the intervention. Future work: To further explore (1) the motivation and engagement of helpers, (2) the programme theory and (3) the effectiveness and cost-effectiveness of the intervention. Trial registration: Current Controlled Trials ISRCTN85615983. Funding: This project was funded by the National Institute for Health Research (NIHR) Public Health Research programme and will be published in full in Public Health Research; Vol. 8, No. 3. See the NIHR Journals Library website for further project information

The effectiveness and cost-effectiveness of erythropoiesis-stimulating agents (epoetin and darbepoetin) for treating cancer-treatment induced anaemia (including review of TA142): a systematic review and economic model

Background: Anaemia is a common side-effect of cancer treatments and can lead to a reduction in quality of life. Erythropoiesis-stimulating agents (ESAs) are licensed for use in conjunction with red blood cell transfusions (RBCTs) to improve cancer treatment-induced anaemia (CIA). Methods: The clinical effectiveness review followed principles published by NHS CRD. Randomised controlled trials (RCTs), or systematic reviews of RCTs, of ESAs (epoetin or darbepoetin) for treating people with CIA were eligible for inclusion in the review. Comparators were best supportive care (BSC), placebo, or other ESA. Anaemia- and malignancy-related outcomes, health-related quality of life (HRQoL), and adverse events (AEs) were evaluated. Where appropriate, data were pooled using meta-analysis. An empirical health economic model was developed comparing ESA treatment to no ESA treatment. The model has two components: one evaluating short-term costs and QALYs (while patients are anaemic); and one evaluating long-term QALYs. Costs and benefits were discounted at 3.5% pa. Probabilistic and univariate deterministic sensitivity analyses were performed. Results: Twenty-three studies assessing ESAs within their licensed indication (based on start dose administered) were included. None of the RCTs were completely aligned with current EU licenses. Results suggest that there is clinical benefit from ESAs for anaemia-related outcomes. Data suggest improvement in HRQoL scores. The impact of ESAs on AEs and survival remains highly uncertain; although point estimates are lower confidence intervals are wide and not statistically significant. Base case incremental cost-effectiveness ratios (ICERs) for ESA treatment versus no ESA treatment ranged from £19,429–£35,018 per quality-adjusted life year (QALY) gained, but sensitivity and scenario analyses demonstrate considerable uncertainty in these ICERs, including the possibility of overall health disbenefit. All ICERs were sensitive to survival and cost. Conclusions: ESAs could be cost-effective when used closer to licence but there is considerable uncertainty mainly due to unknown impacts on overall survival