Miracle-dz/UniFAR-FTUs huggingface dataset:
…Where did I lose you? Accessing the literacy demands of assessment

In this study we sought to And out how teachers could make assessment fairer for Indigenous students in learning mathematics, given the context of the high stakes of the National Assessment Program Literacy and Numeracy (NAPLAN). Today, teachers are experiencing the full range of demands from their own students who require individual attention, through to system level expectations of improved performances for all students. Many staff experience reform fatigue with limited time for critical reflection and a reduction in support for the use and the analysis of the overwhelming amount of data that has become available in recent years. Over the past three years we worked with teachers in seven schools to gradually refine our research focus to centre on how we might best support teachers in this demanding context with the important outcome of improved teaching and learning of mathematics with particular consideration of how to respond to the cultural needs of Indigenous (Aboriginal and Torres Strait Islander) students. The methodological approach of a design experiment, coupled with a socio-cultural theoretical lens for data analysis, challenged our thinking and our observations of teachers' assessment practice and pedagogy. Most recently, we worked closely with the teachers of the four schools in the final phase of the study to support them to modify their practice to engage their Indigenous students in effortful learning. The main actions we employed included: modelling problem-solving activities; strategic thinking; collaborative learning and employing Indigenous contexts to explicate mathematical terminology; and to decode written assessment tasks in order to facilitate Indigenous students' access to the literacy demands of these tasks. In this article we focus on the role of the teacher in promoting effortful learning in mathematics with Indigenous students. We explore how teachers can promote student mastery and work to improve results in high stakes testing through actively diagnosing and scaffolding student learning. Our research aim required us to critically examine the role of the teacher in effortful teaching to enhance the knowledge and understanding of the Indigenous students when learning mathematics in the specific concept area of problem-solving.
Successful removal of a guidewire from the jugular vein of a mature horse

Summary This case report describes the removal of a metal guidewire that was lost accidentally in the jugular vein during the placement of an indwelling catheter. The proximal end of the guidewire was identified in the distal third of the cervical region by radiography and ultrasonography. The retained wire was removed successfully by percutaneous fluoroscopically assisted endovascular retrieval with a 4 pronged retrieval catheter.
Healthcare Students' Perceptions of Electronic Feedback through GradeMark®

This paper reports on the findings from a study undertaken to explore students’ perceptions of the timeliness, accessibility, consistency, and quality of feedback and grading received electronically. The system used was GradeMark®, an electronic tool available through the plagiarism software provider, Turnitin®. 296 students from the Schools of Nursing and Midwifery, Medicine, and Dentistry at Cardiff University were included in the study. Data collection included an online survey and a focus group for each discipline. Findings revealed that the use of GradeMark® improved the timeliness and accessibility of feedback due to its immediate availability via any personal computer with internet access. The use of annotation was proven to be valuable; however consistency and quality of feedback were affected by markers’ individual comments, issues which GradeMark® may not necessarily address. Findings provide insight into what quality feedback could look like from the students’ perspective, which can help improve academic practice. Overall the study outcomes suggest there are benefits to using innovative technology such as GradeMark® to enhance learning. The paper provides valuable lessons that could assist others in adopting a pragmatic and planned approach to the introduction of electronic feedback using a system such as GradeMark®.
Classroom assistants : their impact in Scottish primary schools

This research project aimed to explore the impact of paid additional adults in classrooms on pupils and teachers in their day to day lives in primary schools. The project was devised and conducted against the backdrop of the class size discourse and in the context of Scottish primary school education system. In recent years the composition of the workforce in primary schools in Scotland has changed. This research project focused on the introduction of classroom assistants in primary schools in Scotland. There was little in the research literature that focused on pupil/adult ratios in primary school classes. There was a gap in the research and literature on the perceptions and experiences of pupils in this changing school and classroom environment. Data on the tasks and activities of classroom assistants were collected. This project investigated these three themes. In order to explore the complex real life setting of the primary school classroom the research design chosen allowed the researcher to collect both qualitative and quantitative data. The participants in the case study were drawn from three primary schools in the city of Aberdeen. In each of these three schools one middle stages class (primary four or five) its teacher and classroom assistant formed the participants in the case study. The researcher undertook direct observation of teachers and classroom assistants in their work place setting using an observation schedule. The data collected during this phase of the project was enhanced and supported by qualitative data from the participants from semi-structured interviews and focus group sessions. In addition the researcher’s in depth knowledge of the primary school class setting, her awareness and understanding of relationships and roles of the participants added strength to the validity of the data collected. This multiple small-scale multi-method study allowed the researcher to create a detailed description of the impact of classroom assistants on the day to day experiences of teachers and pupils in primary schools. The influence of classroom assistant support was seen in teacher behaviour, workload and the activities they undertook. The researcher also found evidence to support the positive influence of classroom assistants on pupil behaviour.
Playing Doctor: Television, Storytelling, and Medical Power

Prime-time medical dramas have stamped the American mind with an indelible gallery of physicians. Dr. Kildare, Ben Casey, Hawkeye Pierce, Trapper John, and Marcus Welby are more widely recognized than any living physician, and in many cases are more trusted as well. In Playing Doctor, a colorful and highly perceptive history of TV medical series, Joseph Turow offers an entertaining inside look at how these shows were created. He provides a detailed history of the programs, an analysis of changing public perceptions of doctors and medicine, and an insightful commentary on how medical dramas have exploited and even shaped these perceptions. The pressures brought to bear on the creators of prime-time television are various and powerful and, as Turow demonstrates, in no area of TV is this more evident than on medical dramas. Turow excells at depicting the wheeling and dealing among network executives, powerful advertisers, interest groups such as the Catholic Church, egotistical actors, contentious writers and, most notably, the medical establishment. He reveals that, from the very first show--Medic, which premiered in 1954--these programs relied on the medical establishment for authentic locations, expert advice, and official blessing, to convince viewers that what they saw was authentic. But organized medicine's help came at a price--to society as well as to TV storytellers. It gave the AMA and other medical organizations considerable power over scripts. And it encouraged a wide gap between the view of medicine that policy makers (including medical administrators) now hold and the view that TV fiction presents. For instance, television presents medical care as an unlimited resource, but administrators (from the heads of small hospitals to high-ranking government officials) all see medical care as a limited commodity. The gap continues because of TV producers' adherence to outmoded assumptions about the medical world as well as the medical community's reluctance to encourage the public to think about changes in health care. Based on interviews with numerous actors, producers, writers, and television executives--including Larry Gelbart, Vince Edwards, Elliot Gould, Sterling Silliphant, Howie Mandell, and Brandon Tartikoff--Playing Doctor reveals what happens when a powerful American institution tries to guide TV's fictional representation of its members and itself. It is sure to ignite discussion and controversy among people who care about television's role in American society.
Prevalence of Rhesus Negative Gene and Awareness of its Implications

To determine the prevalence of the Rhesus negative gene in pregnant women attending antenatal clinics at Abia State University Teaching Hospital, Aba and to assess the awareness of women to any implications of their blood group, a retrospective study of 1200 women was carried out and a prospective study of 100 women randomly selected from same population at follow up visits to ascertain their awareness of the implication of their Rhesus status. The prevalence of the Rhesus negative gene was 7.5%. Only 5% were aware of the implications of Rhesus status. The prevalence is comparable with previous reports, the use of anti-D immunoglobulin is advised when appropriate. Key Words: Rhesus negative, genotype anti-D immunoglobulin. Jnl of Medical Investigation and Practice Vol.2 2001: 11-13
Analysis of 113 hospitalized patients with confirmed 2009 influenza A (H1N1) virus infection

To evaluate the demographic characteristics, clinical and laboratory features, underlying medical conditions, treatment, and outcome of hospitalized patients with laboratory-confirmed infection due to the 2009 influenza A(H1N1) virus. Materials and methods: This prospective study was performed between October and December 2009. The hospitalized patients with laboratory-confirmed diagnosis of 2009 influenza A(H1N1) virus infection were included in the study. The diagnosis was confirmed with detection of the virus in nasal and nasopharyngeal swabs by RT-PCR assay. Demographic and epidemiological characteristics, clinical features, treatment, and outcome of the patients were recorded on the individual forms. A comparison was made between patients with and without pneumonia. Results: From October to December 2009, 113 confirmed patients with 2009 influenza A(H1N1) virus infection were hospitalized in our clinic. The ages of the patients were between 17 and 77 years (mean age 35.7 ± 15.8 years) and 73 (64.6%) were female. Cough (92.9%), fever (85.8%), malaise (85.8%), headache (80.5%), and muscle aches (85.0%) were the most common presenting symptoms. The time from symptom onset to hospital admission was 1-15 days (mean time 3.8 ± 3 days). On physical examination, fever (74.3%), pharyngeal hyperemia (66.4%), rales (43.4%), dyspnea (29.2%), and cyanosis (4.4%) were the presenting clinical findings. Of the patients, 64 (56.6%) had at least 1 underlying medical condition. The most common underlying disease was chronic pulmonary disease (14.5%). A total of 25 patients were pregnant. On admission to hospital, 25 patients (22.1%) had leukopenia, 12 patients (10.6%) had leukocytosis, and 29 patients (25.7%) had thrombocytopenia. Pulmonary infiltrates consistent with pneumonia were detected on chest radiography in 47 (41.6%) patients.Of these patients, 98 (86.7%) received oseltamivir treatment on admission. Six patients (5.3%) were admitted to the ICU because of acute respiratory failure and 4 of them (3.5%) required mechanical ventilation. Among these patients, 3 (2.7%) died. A comparison between the patients with and without pneumonia was performed statistically. Age older than 40 years old, time from onset of symptoms to admission longer than 3 days, leukocytosis, ALT/AST elevation, and hyperglycemia were significantly higher in patients with pneumonia. Conclusion: Although the 2009 influenza A(H1N1) virus causes severe illness including pneumonia and death, the mortality rate is not very high. The characteristics of the disease are similar to those of seasonal influenza. Young adults were the most affected persons and most of the patients had underlying medical conditions.
Principled Estimation of Regression Discontinuity Designs

The regression discontinuity design (RDD) has become the "gold standard" for causal inference with observational data. Local average treatment effects (LATE) for RDDs are often estimated using local linear regressions with pre-treatment covariates typically added to increase the efficiency of treatment effect estimates, but their inclusion can have large impacts on LATE point estimates and standard errors, particularly in small samples. In this paper, I propose a principled, efficiency-maximizing approach for covariate adjustment of LATE in RDDs. This approach allows researchers to combine context-specific, substantive insights with automated model selection via a novel adaptive lasso algorithm. When combined with currently existing robust estimation methods, this approach improves the efficiency of LATE RDD with pre-treatment covariates. The approach will be implemented in a forthcoming R package, AdaptiveRDD which can be used to estimate and compare treatment effects generated by this approach with extant approaches.
Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data

Abstract This article proposes an imputation procedure that uses the factors estimated from a tall block along with the re-rotated loadings estimated from a wide block to impute missing values in a panel of data. Assuming that a strong factor structure holds for the full panel of data and its sub-blocks, it is shown that the common component can be consistently estimated at four different rates of convergence without requiring regularization or iteration. An asymptotic analysis of the estimation error is obtained. An application of our analysis is estimation of counterfactuals when potential outcomes have a factor structure. We study the estimation of average and individual treatment effects on the treated and establish a normal distribution theory that can be useful for hypothesis testing.
Challenges in Bayesian inference via Markov chain Monte Carlo for neural networks

Markov chain Monte Carlo (MCMC) methods and neural networks are instrumental in tackling inferential and prediction problems. However, Bayesian inference based on joint use of MCMC methods and of neural networks is limited. This paper reviews the main challenges posed by neural networks to MCMC developments, including lack of parameter identifiability due to weight symmetries, prior specification effects, and consequently high computational cost and convergence failure. Population and manifold MCMC algorithms are combined to demonstrate these challenges via multilayer perceptron (MLP) examples and to develop case studies for assessing the capacity of approximate inference methods to uncover the posterior covariance of neural network parameters. Some of these challenges, such as high computational cost arising from the application of neural networks to big data and parameter identifiability arising from weight symmetries, stimulate research towards more scalable approximate MCMC methods or towards MCMC methods in reduced parameter spaces.
Automated Ransomware Behavior Analysis: Pattern Extraction and Early Detection

Security operation centers (SOCs) typically use a variety of tools to collect large volumes of host logs for detection and forensic of intrusions. Our experience, supported by recent user studies on SOC operators, indicates that operators spend ample time (e.g., hundreds of man-hours) on investigations into logs seeking adversarial actions. Similarly, reconfiguration of tools to adapt detectors for future similar attacks is commonplace upon gaining novel insights (e.g., through internal investigation or shared indicators). This paper presents an automated malware pattern-extraction and early detection tool, testing three machine learning approaches: TF-IDF (term frequency-inverse document frequency), Fisher's LDA (linear discriminant analysis) and ET (extra trees/extremely randomized trees) that can (1) analyze freshly discovered malware samples in sandboxes and generate dynamic analysis reports (host logs); (2) automatically extract the sequence of events induced by malware given a large volume of ambient (un-attacked) host logs, and the relatively few logs from hosts that are infected with potentially polymorphic malware; (3) rank the most discriminating features (unique patterns) of malware and from the learned behavior detect malicious activity; and (4) allows operators to visualize the discriminating features and their correlations to facilitate malware forensic efforts. To validate the accuracy and efficiency of our tool, we design three experiments and test seven ransomware attacks (i.e., WannaCry, DBGer, Cerber, Defray, GandCrab, Locky, and nRansom). The experimental results show that TF-IDF is the best of the three methods to identify discriminating features, and ET is the most time-efficient and robust approach.
Development of Deep Learning Methods for Inflow Turbulence Generation.

The present work proposes an inflow turbulence generation strategy using deep learning methods. This is achieved with the help of an autoencoder architecture with two different types of operational layers in the latent-space: a fully connected multi-layer perceptron and convolutional long short-term memory layers. A wall-resolved large eddy simulation of a turbulent channel flow at a high Reynolds number is performed to create a large database of instantaneous snapshots of turbulent flow-fields used to train neural networks. The training is performed with sequences of instantaneous snapshots so as to learn a snapshot at the next time instant. For the present work, velocity and pressure fluctuations are used for training to produce the same parameters at the next time instant. Within the autoencoders, different types of methods like average pooling and strided convolutions are tested for the reduction of spatial dimensions. For convolutional neural networks, the physical boundary conditions are implemented in the form of symmetric as well as periodic paddings. A priori simulations are performed with the trained deep learning models to check the accuracy of turbulence statistics produced. It is found that the use of convolutional long short-term memory layers in the latent space improved the quality of statistics, although issues related to stability for longer times are observed. Though instantaneous snapshots of the target flow are required for training, these a priori simulations suggest that the deep learning methods for generating inflow turbulence are one of the possible alternatives to existing methods.
Congruences involving central trinomial coefficients

In this paper, we confirm some congruences conjectured by the second author. For example, we prove that for any prime $p>3$ $$ \sum_{k=0}^{p-1}\frac{\binom{2k}{k}}{12^k}T_k\equiv\left(\frac{p}{3}\right)\frac{3^{p-1}+3}{4}\pmod{p^2} $$ and $$ \sum_{k=0}^{p-1}\frac{T_kH_k}{3^k}\equiv\frac{3+\left(\frac{p}{3}\right)}{2}-p\left(1+\left(\frac{p}{3}\right)\right)\pmod{p^2}, $$ where $T_k$ is the coefficient of $x^k$ in the expansion of $(1+x+x^2)^k$, $(-)$ denotes the Legendre symbol and $H_k:=\sum_{0
To Recurse or not to Recurse,a Low Dose CT Study

Restoring high-quality CT images from low dose CT counterparts is an ill-posed, nonlinear problem to which Deep Learning approaches have been giving superior solutions compared to classical model-based approaches. In this article, a framework is presented wherein a Recurrent Neural Network (RNN) is utilized to remove the streaking artefacts from low projection number CT imaging. The results indicate similar image restoration performance for the RNN compared to the feedforward network in low noise cases while in high noise levels the RNN returns better results. The computational costs are also compared between RNN and feedforward networks.
Quantum isomorphism is equivalent to equality of homomorphism counts from planar graphs

Over 50 years ago, Lovász proved that two graphs are isomorphic if and only if they admit the same number of homomorphisms from any graph. Other equivalence relations on graphs, such as cospectrality or fractional isomorphism, can be characterized by equality of homomorphism counts from an appropriately chosen class of graphs. Dvořák [J. Graph Theory 2010] showed that taking this class to be the graphs of treewidth at most $k$ yields a tractable relaxation of graph isomorphism known as $k$-dimensional Weisfeiler-Leman equivalence. Together with a famous result of Cai, Fürer, and Immerman [FOCS 1989], this shows that homomorphism counts from graphs of bounded treewidth do not determine a graph up to isomorphism. Dell, Grohe, and Rattan [ICALP 2018] raised the questions of whether homomorphism counts from planar graphs determine a graph up to isomorphism, and what is the complexity of the resulting relation. We answer the former in the negative by showing that the resulting relation is equivalent to the so-called quantum isomorphism [Mančinska et al, ICALP 2017]. Using this equivalence, we further resolve the latter question, showing that testing whether two graphs have the same number of homomorphisms from any planar graph is, surprisingly, an undecidable problem, and moreover is complete for the class coRE (the complement of recursively enumerable problems). Quantum isomorphism is defined in terms of a one-round, two-prover interactive proof system in which quantum provers, who are allowed to share entanglement, attempt to convince the verifier that the graphs are isomorphic. Our combinatorial proof leverages the quantum automorphism group of a graph, a notion from noncommutative mathematics.
BoTorch: Programmable Bayesian Optimization in PyTorch

Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental design. We introduce BoTorch, a modern programming framework for Bayesian optimization that combines Monte-Carlo (MC) acquisition functions, a novel sample average approximation optimization approach, auto-differentiation, and variance reduction techniques. BoTorch's modular design facilitates flexible specification and optimization of probabilistic models written in PyTorch, simplifying implementation of new acquisition functions. Our approach is backed by novel theoretical convergence results and made practical by a distinctive algorithmic foundation that leverages fast predictive distributions, hardware acceleration, and deterministic optimization. In experiments, we demonstrate the improved sample efficiency of BoTorch relative to other popular libraries.