Miracle-dz/UniFAR-FTUs huggingface dataset:
The measurement of school students' understanding of statistical variation

The paper presents a questionnaire devised to assess school students' understanding of statistical variation. The questionnaire is based on earlier research into students' understanding of the chance and data curriculum and recent work, more specifically related to variation. It was devised, piloted, revised, and administered to 746 students in grades 3, 5, 7, and 9 in ten Tasmanian schools. The analysis of outcomes was carried out in three stages: a hierarchical coding scheme was developed based on a structural model of cognitive development; a Rasch analysis was carried out to produce a variable map of student performance and item difficulty on a single scale; and a holistic model of development was suggested for the questionnaire. Outcomes for individual items are presented to illustrate the range of student responses, and possible rubrics for use by teachers. For some items, comparisons are made with results of other researchers.
ESGO/ESHRE/ESGE Guidelines for the fertility-sparing treatment of patients with endometrial carcinoma

The standard surgical treatment of endometrial carcinoma, consisting of total hysterectomy with bilateral salpingo-oophorectomy, drastically affects the quality of life of patients and creates a challenge for clinicians. Recent evidence-based guidelines of the European Society of Gynaecological Oncology (ESGO), the European SocieTy for Radiotherapy and Oncology (ESTRO), and the European Society of Pathology (ESP) provide comprehensive information on all relevant issues of diagnosis and treatment in endometrial carcinoma in a multidisciplinary setting. While addressing also work-up for fertility preservation treatments and the management and follow-up for fertility preservation, it was considered relevant to further extend the guidance on fertility-sparing treatment. A collaboration was set up between the ESGO, the European Society of Human Reproduction and Embryology (ESHRE), and the European Society for Gynaecological Endoscopy (ESGE), aiming to develop clinically relevant and evidence-based guidelines focusing on key aspects of fertility-sparing treatment (patient selection, tumor clinicopathological characteristics, treatment, special issues) in order to improve the quality of care for women with endometrial carcinoma across Europe and worldwide. ESGO/ESHRE/ESGE nominated an international multidisciplinary development group consisting of practicing clinicians and researchers who have demonstrated leadership and expertise in the care and research of endometrial carcinoma (11 experts from across Europe). To ensure that the guidelines are evidence-based, the literature published since 2016, identified by a systematic search, was reviewed and critically appraised. In the absence of any clear scientific evidence, judgment was based on the professional experience and consensus of the development group. The guidelines are thus based on the best available evidence and expert agreement. Prior to publication, the guidelines were reviewed by 95 independent international practitioners in cancer care delivery and patient representatives.
Hypoglycemia in People with Type 2 Diabetes and CKD.

BACKGROUND AND OBJECTIVES Among people with diabetes mellitus, CKD may promote hypoglycemia through altered clearance of glucose-lowering medications, decreased kidney gluconeogenesis, and blunted counter-regulatory response. We conducted a prospective observational study of hypoglycemia among 105 individuals with type 2 diabetes treated with insulin or a sulfonylurea using continuous glucose monitors. DESIGN, SETTING, PARTICIPANTS & MEASUREMENTS We enrolled 81 participants with CKD, defined as eGFR<60 ml/min per 1.73 m2, and 24 control participants with eGFR≥60 ml/min per 1.73 m2 frequency-matched on age, duration of diabetes, hemoglobin A1c, and glucose-lowering medications. Each participant wore a continuous glucose monitor for two 6-day periods. We examined rates of sustained level 1 hypoglycemia (<70 mg/dl) and level 2 hypoglycemia (<54 mg/dl) among participants with CKD. We then tested differences compared with control participants as well as a second control population (n=73) using Poisson and linear regression, adjusting for age, sex, and race. RESULTS Over 890 total days of continuous glucose monitoring, participants with CKD were observed to have 255 episodes of level 1 hypoglycemia, of which 68 episodes reached level 2 hypoglycemia. Median rate of hypoglycemic episodes was 5.3 (interquartile range, 0.0-11.7) per 30 days and mean time spent in hypoglycemia was 28 (SD 37) minutes per day. Hemoglobin A1c and the glucose management indicator were the main clinical correlates of time in hypoglycemia (adjusted differences 6 [95% confidence interval, 2 to 10] and 13 [95% confidence interval, 7 to 20] fewer minutes per day per 1% higher hemoglobin A1c or glucose management indicator, respectively). Compared with control populations, participants with CKD were not observed to have significant differences in time in hypoglycemia (adjusted differences 4 [95% confidence interval, -12 to 20] and -12 [95% confidence interval, -29 to 5] minutes per day). CONCLUSIONS Among people with type 2 diabetes and moderate to severe CKD, hypoglycemia was common, particularly with tighter glycemic control, but not significantly different from groups with similar clinical characteristics and preserved eGFR.
μ‐opioid receptor agonist facilitates circulating tumor cell formation in bladder cancer via the MOR/AKT/Slug pathway: a comprehensive study including randomized controlled trial

μ‐opioid receptor agonists (MORAs) are indispensable for analgesia in bladder cancer (BC) patients, both during surgery and for chronic pain treatment. Whether MORAs affect BC progression and metastasis remains largely unknown. This study focused on the effects of MORAs on the formation of circulating tumor cells (CTCs) in BC and aimed to provide potential therapeutic targets, which would retain the pain‐relieving effects of MORAs in BC patients without sacrificing their long‐term prognosis.
Reconsidering osteoconduction in the era of additive manufacturing.

Bone regeneration procedures in clinics and bone tissue-engineering stand on three pillars: osteoconduction, osteoinduction, and stem cells. In the last two decades, the focus in this field has been on osteoinduction, realized by the use of bone morphogenetic proteins (BMPs) and the application of mesenchymal stem cells to treat bone defects. Osteoconduction, however, was reduced to a surface phenomenon, since the supposedly ideal pore size of osteoconductive scaffolds was identified in the 1990s, and with 0.3 to 0.5 mm in diameter, forcing bone formation to occur predominantly on the surface. Meanwhile additive manufacturing has evolved as new tool to realize designed microarchitectures in bone substitutes, enabling us to study osteoconduction as true 3D-phenomenon. Moreover, by additive manufacturing wide-open porous scaffolds can be produced, where bone formation occurs distant to the surface at a superior bony defect-bridging rate enabled by highly osteoconductive pores of 1.2 mm in diameter. This review provides a historical overview and an updated definition of osteoconduction and related terms. Additionally, it shows how additive manufacturing can be instrumental to study and optimize osteoconduction of bone substitutes, and to provide novel optimized features and boundaries of osteoconductive microarchitectures.
Model theory of probability spaces

This expository paper treats the model theory of probability spaces using the framework of continuous $[0,1]$-valued first order logic. The metric structures discussed, which we call probability algebras, are obtained from probability spaces by identifying two measurable sets if they differ by a set of measure zero. The class of probability algebras is axiomatizable in continuous first order logic; we denote its theory by $Pr$. We show that the existentially closed structures in this class are exactly the ones in which the underlying probability space is atomless. This subclass is also axiomatizable; its theory $APA$ is the model companion of $Pr$. We show that $APA$ is separably categorical (hence complete), has quantifier elimination, is $\omega$-stable, and has built-in canonical bases, and we give a natural characterization of its independence relation. For general probability algebras, we prove that the set of atoms (enlarged by adding $0$) is a definable set, uniformly in models of $Pr$. We use this fact as a basis for giving a complete treatment of the model theory of arbitrary probability spaces. The core of this paper is an extensive presentation of the main model theoretic properties of $APA$. We discuss Maharam's structure theorem for probability algebras, and indicate the close connections between the ideas behind it and model theory. We show how probabilistic entropy provides a rank connected to model theoretic forking in probability algebras. In the final section we mention some open problems.
GLADIS: A General and Large Acronym Disambiguation Benchmark

Acronym Disambiguation (AD) is crucial for natural language understanding on various sources, including biomedical reports, scientific papers, and search engine queries. However, existing acronym disambiguationbenchmarks and tools are limited to specific domains, and the size of prior benchmarks is rather small. To accelerate the research on acronym disambiguation, we construct a new benchmark with three components: (1) a much larger acronym dictionary with 1.5M acronyms and 6.4M long forms; (2) a pre-training corpus with 160 million sentences;(3) three datasets that cover thegeneral, scientific, and biomedical domains.We then pre-train a language model, {emph{AcroBERT}, on our constructed corpus for general acronym disambiguation, and show the challenges and values of our new benchmark.
Effect of Lake Ontario on Precipitation

Abstract Radar and raingage data collected during the International Field Year for the Great Lakes were used to determine the effect of Lake Ontario on precipitation patterns. Objective analysis techniques were used to combine the radar and gage data. During the warm season the relatively cold lake frequently suppressed afternoon shower activity, particularly when the showers were not associated with large-scale well-organized weather systems. When the showers were scattered, the land portion of the watershed received 402% more rain than the lake compared to 14% more for widespread rain. During the cold season, the lake frequently stimulated precipitation when the 850 mb temperature was more than 7°C colder than the lake. While the lake influenced the precipitation patterns for about half the days, the total effect on precipitation amounts was small. The lake-effect days were generally those with small-area average amounts. The total warm season rainfall for land areas within 30 km of the lake was 10% mor.
An Insurance Paradigm for Improving Power System Resilience via Distributed Investment

Extreme events, exacerbated by climate change, pose significant risks to the energy system and its consumers. However there are natural limits to the degree of protection that can be delivered from a centralised market architecture. Distributed energy resources provide resilience to the energy system, but their value remains inadequately recognized by regulatory frameworks. We propose an insurance framework to align residual outage risk exposure with locational incentives for distributed investment. We demonstrate that leveraging this framework in large-scale electricity systems could improve consumer welfare outcomes in the face of growing risks from extreme events via investment in distributed energy.
Topological data analysis reveals differences between simulated galaxies and dark matter haloes

We use topological summaries based on Betti curves to characterize the large-scale spatial distribution of simulated dark matter haloes and galaxies. Using the IllustrisTNG and CAMELS-SAM simulations, we show that the topology of the galaxy distribution is significantly different from the topology of the dark matter halo distribution. Further, there are significant differences between the distributions of star-forming and quiescent galaxies. These topological differences are broadly consistent across all simulations, while at the same time there are noticeable differences when comparing between different models. Finally, using the CAMELS-SAM simulations, we show that the topology of the quiescent galaxies, in particular, depends strongly on the amount of supernova feedback. These results suggest that topological summary statistics could be used to help better understand the processes of galaxy formation and evolution.
Contextuality and inductive bias in quantum machine learning

Generalisation in machine learning often relies on the ability to encode structures present in data into an inductive bias of the model class. To understand the power of quantum machine learning, it is therefore crucial to identify the types of data structures that lend themselves naturally to quantum models. In this work we look to quantum contextuality -- a form of nonclassicality with links to computational advantage -- for answers to this question. We introduce a framework for studying contextuality in machine learning, which leads us to a definition of what it means for a learning model to be contextual. From this, we connect a central concept of contextuality, called operational equivalence, to the ability of a model to encode a linearly conserved quantity in its label space. A consequence of this connection is that contextuality is tied to expressivity: contextual model classes that encode the inductive bias are generally more expressive than their noncontextual counterparts. To demonstrate this, we construct an explicit toy learning problem -- based on learning the payoff behaviour of a zero-sum game -- for which this is the case. By leveraging tools from geometric quantum machine learning, we then describe how to construct quantum learning models with the associated inductive bias, and show through our toy problem that they outperform their corresponding classical surrogate models. This suggests that understanding learning problems of this form may lead to useful insights about the power of quantum machine learning.
Towards Inclusive Avatars: Disability Representation in Avatar Platforms

Digital avatars are an important part of identity representation, but there is little work on understanding how to represent disability. We interviewed 18 people with disabilities and related identities about their experiences and preferences in representing their identities with avatars. Participants generally preferred to represent their disability identity if the context felt safe and platforms supported their expression, as it was important for feeling authentically represented. They also utilized avatars in strategic ways: as a means to signal and disclose current abilities, access needs, and to raise awareness. Some participants even found avatars to be a more accessible way to communicate than alternatives. We discuss how avatars can support disability identity representation because of their easily customizable format that is not strictly tied to reality. We conclude with design recommendations for creating platforms that better support people in representing their disability and other minoritized identities.
Wave Statistics and Energy Dissipation of Breaking Waves Generated by a Wave Plate

The present study focuses on direct numerical simulations of breaking waves generated by a wave plate at constant water depths. The aim is to quantify the dynamics and kinematics in the breaking process, together with the air entrainment and energy dissipation due to breaking. Good agreement is achieved between numerical and experimental results in terms of free-surface profiles, energy budget, and bubble statistics during wave breaking. A parametric study was conducted to examine the effects of wave properties and initial conditions on breaking features. According to research on the Bond number ($Bo$, the ratio of gravitational to surface tension forces), a larger surface tension produces more significant parasitic capillaries at the forward face of the wave profile and a thicker plunging jet, which causes a delayed breaking time and is tightly correlated with the main cavity size. A close relationship between wave statistics and the initial conditions of the wave plate is discovered, showing that breaker types can be classified according to the ratio of wave height to water depth, $H/d$. Moreover, the energy dissipation rate due to breaking can be related to the initial conditions of the wave plate by applying the conventional dissipation scaling of turbulence theory, and further correlated with the local breaking crest geometry using inertial-scaling arguments. The proposed scaling of the dissipation rate during the active breaking period is found to be in good agreement with our numerical results.
Exact and Approximate Inferences for Nonlinear Mixed-Effects Models With Missing Covariates

Nonlinear mixed-effects (NLME) models are popular in many longitudinal studies, including human immunodeficiency virus (HIV) viral dynamics, pharmacokinetic analyses, and studies of growth and decay. In practice, covariates in these studies often contain missing data, and so standard complete-data methods are not directly applicable. In this article we propose Monte Carlo parameter-expanded (PX)-EM algorithms for exact and approximate likelihood inferences for NLME models with missing covariates when the missing-data mechanism is ignorable. We allow arbitrary missing-data patterns and allow the covariates to be categorical, continuous, and mixed. The PX-EM algorithm maintains the simplicity and stability of the standard EM algorithm and may converge much faster than EM. The approximate method is computationally more efficient and may be preferable to the exact method when the exact method exhibits convergence problems, such as slow convergence or nonconvergence. It becomes an exact method for linear mixed-effects models and certain NLME models with missing covariates. We also discuss several sampling methods and convergence of the Monte Carlo (PX) EM algorithms. We illustrate the methods using a real data example from the study of HIV viral dynamics and compare the methods via a simulation study.
Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies

Recently, the impressive empirical success of policy gradient (PG) methods has catalyzed the development of their theoretical foundations. Despite the huge efforts directed at the design of efficient stochastic PG-type algorithms, the understanding of their convergence to a globally optimal policy is still limited. In this work, we develop improved global convergence guarantees for a general class of Fisher-non-degenerate parameterized policies which allows to address the case of continuous state action spaces. First, we propose a Normalized Policy Gradient method with Implicit Gradient Transport (N-PG-IGT) and derive a $\tilde{\mathcal{O}}(\varepsilon^{-2.5})$ sample complexity of this method for finding a global $\varepsilon$-optimal policy. Improving over the previously known $\tilde{\mathcal{O}}(\varepsilon^{-3})$ complexity, this algorithm does not require the use of importance sampling or second-order information and samples only one trajectory per iteration. Second, we further improve this complexity to $\tilde{ \mathcal{\mathcal{O}} }(\varepsilon^{-2})$ by considering a Hessian-Aided Recursive Policy Gradient ((N)-HARPG) algorithm enhanced with a correction based on a Hessian-vector product. Interestingly, both algorithms are $(i)$ simple and easy to implement: single-loop, do not require large batches of trajectories and sample at most two trajectories per iteration; $(ii)$ computationally and memory efficient: they do not require expensive subroutines at each iteration and can be implemented with memory linear in the dimension of parameters.
Lateralization in Agents' Decision Making: Evidence of Benefits/Costs from Artificial Intelligence

Lateralization is ubiquitous in vertebrate brains which, as well as its role in locomotion, is considered an important factor in biological intelligence. Lateralization has been associated with both poor and good performance. It has been hypothesized that lateralization has benefits that may counterbalance its costs. Given that lateralization is ubiquitous, it likely has advantages that can benefit artificial intelligence. In turn, lateralized artificial intelligent systems can be used as tools to advance the understanding of lateralization in biological intelligence. Recently lateralization has been incorporated into artificially intelligent systems to solve complex problems in computer vision and navigation domains. Here we describe and test two novel lateralized artificial intelligent systems that simultaneously represent and address given problems at constituent and holistic levels. The experimental results demonstrate that the lateralized systems outperformed state-of-the-art non-lateralized systems in resolving complex problems. The advantages arise from the abilities, (i) to represent an input signal at both the constituent level and holistic level simultaneously, such that the most appropriate viewpoint controls the system; (ii) to avoid extraneous computations by generating excite and inhibit signals. The computational costs associated with the lateralized AI systems are either less than the conventional AI systems or countered by providing better solutions.
Serum miR-21 expression correlates with radiographic progression but also low bone mineral density in patients with ankylosing spondylitis: a cross-sectional study

Increased expressions of miR-21 have been detected in ankylosing spondylitis (AS) patients. The current study was performed to examine the serum miR-21 expression with radiographic severity in AS patients, which was determined based on the modified New York (NY) criteria for sacroiliac joints assessment and modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS) system for spine involvement. Bone mineral density at lumbar 1–4 and femoral neck were examined by dual-energy absorptiometry (DXA). Serum miR-21 expressions were determined by quantitative real-time PCR, and receiver operating characteristic curve analysis was performed to identify the diagnostic value of miR-21 expression levels regarding the NY criteria. Elevated levels of serum miR-21 expressions were detected in AS patients compared with healthy controls. AS patients with modified NY grade 4 showed significantly higher miR-21 expression than grade 3 and grade 2. AS patients with spinal syndesmophytes had significantly higher serum miR-21 expressions than non-syndesmophyte patients. Increased miR-21 expressions were significantly related to the disease radiographic severity. In addition, serum miR-21 expressions were negatively associated with lumbar 1–4 and femoral neck bone mineral density. In summary, serum miR-21 expressions were related to structural damage and radiological progression in AS, indicating that miR-21 may act as a switch between inflammation and new bone information and regulate different signal ways between lesioned enthesis and trabecular bone.