Novel Method for Incorporating Model Uncertainties into Gravitational Wave Parameter Estimates

Posterior distributions on parameters computed from experimental data using Bayesian techniques are only as accurate as the models used to construct them. In many applications these models are incomplete, which both reduces the prospects of detection and leads to a systematic error in the parameter estimates. In the analysis of data from gravitational wave detectors, for example, accurate waveform templates can be computed using numerical methods, but the prohibitive cost of these simulations means this can only be done for a small handful of parameters. In this work a novel method to fold model uncertainties into data analysis is proposed; the waveform uncertainty is analytically marginalised over using with a prior distribution constructed by using Gaussian process regression to interpolate the waveform difference from a small training set of accurate templates. The method is well motivated, easy to implement, and no more computationally expensive than standard techniques. The new method is shown to perform extremely well when applied to a toy problem. While we use the application to gravitational wave data analysis to motivate and illustrate the technique, it can be applied in any context where model uncertainties exist.Comment: 6 pages, 3 figures, accepted for publication in Physical Review Letter

Exploring the Boundaries of GPT-4 in Radiology

The recent success of general-domain large language models (LLMs) has significantly changed the natural language processing paradigm towards a unified foundation model across domains and applications. In this paper, we focus on assessing the performance of GPT-4, the most capable LLM so far, on the text-based applications for radiology reports, comparing against state-of-the-art (SOTA) radiology-specific models. Exploring various prompting strategies, we evaluated GPT-4 on a diverse range of common radiology tasks and we found GPT-4 either outperforms or is on par with current SOTA radiology models. With zero-shot prompting, GPT-4 already obtains substantial gains ($\approx$ 10% absolute improvement) over radiology models in temporal sentence similarity classification (accuracy) and natural language inference ($F_1$). For tasks that require learning dataset-specific style or schema (e.g. findings summarisation), GPT-4 improves with example-based prompting and matches supervised SOTA. Our extensive error analysis with a board-certified radiologist shows GPT-4 has a sufficient level of radiology knowledge with only occasional errors in complex context that require nuanced domain knowledge. For findings summarisation, GPT-4 outputs are found to be overall comparable with existing manually-written impressions.Comment: EMNLP 2023 mai

How to combine collaboration scripts and heuristic worked examples to foster mathematical argumentation – when working memory matters

Mathematical argumentation skills (MAS) are considered an important outcome of mathematics learning, particularly in secondary and tertiary education. As MAS are complex, an effective way of supporting their acquisition may require combining different scaffolds. However, how to combine different scaffolds is a delicate issue, as providing learners with more than one scaffold may be overwhelming, especially when these scaffolds are presented at the same time in the learning process and when learners’ individual learning prerequisites are suboptimal. The present study therefore investigated the effects of the presentation sequence of introducing two scaffolds (collaboration script first vs. heuristic worked examples first) and the fading of the primarily presented scaffold (fading vs. no fading) on the acquisition of dialogic and dialectic MAS of participants of a preparatory mathematics course at university. In addition, we explored how prior knowledge and working memory capacity moderated the effects. Overall, 108 university freshmen worked in dyads on mathematical proof tasks in four treatment sessions. Results showed no effects of the presentation sequence of the collaboration script and heuristic worked examples on dialogic and dialectic MAS. Yet, fading of the initially introduced scaffold had a positive main effect on dialogic MAS. Concerning dialectic MAS, fading the collaboration script when it was presented first was most effective for learners with low working memory capacity. The collaboration script might be appropriate to initially support dialectic MAS, but might be overwhelming for learners with lower working memory capacity when combined with heuristic worked examples later on

Non-verbal IQ Gains from Relational Operant Training Explain Variance in Educational Attainment: An Active-Controlled Feasibility Study

Research suggests that training relational operant patterns of behavior can lead to increases in general cognitive ability and educational outcomes. Most studies to date have been under-powered and included proxy measures of educational attainment. We attempted to extend previous findings with increased experimental control in younger children (aged 6.9–10.1 years). Participants (N = 49) were assigned to either a relational training or chess control group. Over 5 months, teachers assigned class time to complete either relational training or play chess. Those who were assigned relational training gained 8.9 non-verbal IQ (NVIQ) points, while those in the control condition recorded no gains (dppc2 = .99). Regression analyses revealed that post-training NVIQ predicted reading test scores (conducted approximately 1 month later) over and above baseline NVIQ in the experimental condition only, consistent with what we might expect in a full test of far transfer towards educational outcomes

Looking at Lp(a) and Related Cardiovascular Risk: from Scientific Evidence and Clinical Practice

Purpose of Review: A considerable body of data from genetic and epidemiological studies strongly support a causal relationship between high lipoprotein(a) [Lp(a)] levels, and the development of atherosclerosis and cardiovascular disease. This relationship is continuous, unrelated to Lp(a) threshold, and independent of low-density lipoprotein (LDL) and high-density lipoprotein (HDL) cholesterol levels. Unfortunately, the mechanism(s) through which Lp(a) promotes atherosclerosis are not clarified yet. Suggested hypotheses include: an increased Lp(a)-associated cholesterol entrapment in the arterial intima followed by inflammatory cell recruitment, abnormal upload of proinflammatory oxidized phospholipids, impaired fibrinolysis by inhibition of plasminogen activation, and enhanced coagulation, through inhibition of the tissue factor pathway inhibitor. This review is aimed at summarizing the available evidence on the topic. Recent Findings: There are two clinical forms, isolated hyperlipidemia(a) [HyperLp(a)] with acceptable LDL-C levels (< 70 mg/dL), and combined elevation of Lp(a) and LDL-C in plasma. To date, no drugs that selectively decrease Lp(a) are available. Some novel lipid-lowering drugs can lower Lp(a) levels, but to a limited extent, as their main effect is aimed at decreasing LDL-C levels. Significant Lp(a) lowering effects were obtained with nicotinic acid at high doses. However, adverse effects apart, nicotinic acid is no longer prescribed and available in Europe for clinical use, after European Agency of Medicines (EMA) ban. Summary: The only effective therapeutic option for now is Lipoprotein Apheresis (LA), albeit with some limitations. Lastly, it is to be acknowledged that the body of evidence confirming that reducing plasma isolated elevation of Lp(a) brings cardiovascular benefit is still insufficient. However, the growing bulk of clinical, genetic, mechanistic, and epidemiological available evidence strongly suggests that Lp(a) is likely to be the smoking gun