Searching for Extra Dimensions in the Early Universe

We investigate extra spatial dimensions ($D = 3+\epsilon$) in the early universe using very high resolution molecular rotational spectroscopic data derived from a large molecular cloud containing moderately cold carbon monoxide gas at Z $\approx 6.42$. It turns out that the $\epsilon$-dependent quantum mechanical wavelength transitions are solvable for a linear molecule and we present the solution here. The CO microwave data allows a very precise determination of $= -0.00000657 \pm .10003032$. The probability that $\neq 0$ is one in 7794, only 850 million years (using the standard cosmology) after the Big Bang.Comment: 17 pages, 2 figure

Do children use different forms of verbal rehearsal in serial picture recall tasks? A multi-method study

Use of verbal rehearsal is a key issue in memory development. However, we still lack detailed and triangulated information about the early development and the circumstances in which different forms of rehearsal are used. To further understand significant factors that affect children’s use of various forms of rehearsal, the present study involving 108 primary school children adopted a multi-method approach. It combined a carefully chosen word length effect method with a self-paced presentation time method to obtain behavioural indicators of verbal rehearsal. In addition, subsequent trial-by-trial self-reports were gathered. Word length effects in recall suggested that phonological recoding (converting images to names - a necessary precursor for rehearsal) took place, with evidence of more rehearsal among children with higher performance levels. According to self-paced presentation times, cumulative rehearsal was the dominant form of rehearsal only for children with higher spans on difficult trials. The combined results of self-paced times and word length effects in recall suggest that ‘naming’ as simple form of rehearsal was dominant for most children. Self-reports were in line with these conclusions. Additionally, children used a mixture of strategies with considerable intra-individual variability, yet strategy use was nevertheless linked to age as well as performance levels

A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Models

Generative models, like large language models, are becoming increasingly relevant in our daily lives, yet a theoretical framework to assess their generalization behavior and uncertainty does not exist. Particularly, the problem of uncertainty estimation is commonly solved in an ad-hoc manner and task dependent. For example, natural language approaches cannot be transferred to image generation. In this paper we introduce the first bias-variance-covariance decomposition for kernel scores and their associated entropy. We propose unbiased and consistent estimators for each quantity which only require generated samples but not the underlying model itself. As an application, we offer a generalization evaluation of diffusion models and discover how mode collapse of minority groups is a contrary phenomenon to overfitting. Further, we demonstrate that variance and predictive kernel entropy are viable measures of uncertainty for image, audio, and language generation. Specifically, our approach for uncertainty estimation is more predictive of performance on CoQA and TriviaQA question answering datasets than existing baselines and can also be applied to closed-source models.Comment: Preprin

Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition

Reliably estimating the uncertainty of a prediction throughout the model lifecycle is crucial in many safety-critical applications. The most common way to measure this uncertainty is via the predicted confidence. While this tends to work well for in-domain samples, these estimates are unreliable under domain drift and restricted to classification. Alternatively, proper scores can be used for most predictive tasks but a bias-variance decomposition for model uncertainty does not exist in the current literature. In this work we introduce a general bias-variance decomposition for proper scores, giving rise to the Bregman Information as the variance term. We discover how exponential families and the classification log-likelihood are special cases and provide novel formulations. Surprisingly, we can express the classification case purely in the logit space. We showcase the practical relevance of this decomposition on several downstream tasks, including model ensembles and confidence regions. Further, we demonstrate how different approximations of the instance-level Bregman Information allow reliable out-of-distribution detection for all degrees of domain drift.Comment: Accepted at AISTATS 202

Sun-Protective Clothing Worn Regularly during Early Childhood Reduces the Number of New Melanocytic Nevi: The North Queensland Sun-Safe Clothing Cluster Randomized Controlled Trial

Numerous pigmented moles are associated with sun exposure and melanoma risk. This cluster randomized controlled trial aimed to determine if sun-protective clothing could prevent a significant proportion of the moles developing in young children (ACTRN12617000621314; Australian New Zealand Clinical Trials Registry). Twenty-five childcare centers in Townsville (19.25◦S), Australia, were matched on shade provision and socioeconomic status. One center from each pair was randomized to the intervention arm and the other to the control arm. Children at 13 intervention centers wore study garments and legionnaire hats at childcare and received sun-protective swimwear and hats for home use, while children at the 12 control centers did not. The 1–35-month-old children (334 intervention; 210 control) were examined for moles at baseline (1999–2002) and were re-examined annually for up to 4 years. Both groups were similar at baseline. Children at intervention centers acquired fewer new moles overall (median 12.5 versus 16, p = 0.02; 0.46 versus 0.68 moles/month, p = 0.001) and fewer new moles on clothing-protected skin (6 vs. 8; p = 0.021 adjusted for confounding and cluster sampling) than controls. Intervention children had 24.3% fewer new moles overall (26.5 versus 35) and 31.6% (13 versus 19) fewer moles on clothing-protected skin than controls after 3.5 years. Sunlight’s influence on nevogenesis is mitigated when children regularly wear UPF 30-50+ clothing covering half their body, implying that increased clothing cover reduces melanoma risk. Sun-protective clothing standards should mandate reporting of the percentage of garment coverage for childrenswear

Risk attribution of Campylobacter infection by age group using exposure modelling

Knowledge on the relative importance of alternative sources of human campylobacteriosis is important in order to implement effective disease prevention measures. The objective of this study was to assess the relative importance of three key exposure pathways (travelling abroad, poultry meat, pet contact) for different patient age groups in Switzerland. With a stochastic exposure model data on Campylobacter incidence for the years 2002-2007 were linked with data for the three exposure pathways and the results of a case-control study. Mean values for the population attributable fractions (PAF) over all age groups and years were 27% (95% CI 17-39) for poultry consumption, 27% (95% CI 22-32) for travelling abroad, 8% (95% CI 6-9) for pet contact and 39% (95% CI 25-50) for other risk factors. This model provided robust results when using data available for Switzerland, but the uncertainties remained high. The output of the model could be improved if more accurate input data are available to estimate the infection rate per exposure. In particular, the relatively high proportion of cases attributed to ‘other risk factors' requires further attentio

The Parallel Persistent Memory Model

We consider a parallel computational model that consists of $P$ processors, each with a fast local ephemeral memory of limited size, and sharing a large persistent memory. The model allows for each processor to fault with bounded probability, and possibly restart. On faulting all processor state and local ephemeral memory are lost, but the persistent memory remains. This model is motivated by upcoming non-volatile memories that are as fast as existing random access memory, are accessible at the granularity of cache lines, and have the capability of surviving power outages. It is further motivated by the observation that in large parallel systems, failure of processors and their caches is not unusual. Within the model we develop a framework for developing locality efficient parallel algorithms that are resilient to failures. There are several challenges, including the need to recover from failures, the desire to do this in an asynchronous setting (i.e., not blocking other processors when one fails), and the need for synchronization primitives that are robust to failures. We describe approaches to solve these challenges based on breaking computations into what we call capsules, which have certain properties, and developing a work-stealing scheduler that functions properly within the context of failures. The scheduler guarantees a time bound of $O(W/P_A + D(P/P_A) \lceil\log_{1/f} W\rceil)$ in expectation, where $W$ and $D$ are the work and depth of the computation (in the absence of failures), $P_A$ is the average number of processors available during the computation, and $f \le 1/2$ is the probability that a capsule fails. Within the model and using the proposed methods, we develop efficient algorithms for parallel sorting and other primitives.Comment: This paper is the full version of a paper at SPAA 2018 with the same nam

Australian women's prediagnostic values and influencing sociodemographic variables relating to treatment choices for early breast cancer treatment

Women are often asked by their doctors to choose their preferred treatment for early breast cancer. Evidence shows that many women are distressed and confused about how to make this treatment decision and frequently seek help from nurses. Very little is known about women's value-centred decision-making in relation to selecting treatment for breast cancer and for nurses it is difficult to know how to assist these women with this process. In this study, 377 women participated prior to undergoing routine mammography screening and the data were collected using the Pre-Decision Portfolio Questionnaire (PDPQ) by Pierce 1. The partipants identified that expected treatment outcomes were the most important factor in choosing early breast cancer treatment. The majority reported that it was very important that a treatment would reduce the chances the cancer would return (95.6%), increase the length of their life (82.1%) and lead them to being healthy (80.4%). In addition, the participants indicated that it was important, or very important, that the emotional consequences of the treatment did "not make you depressed" (88.6%) or "sad" (90.4%) and should "keep you from worrying" (97%) and "give you peace of mind" (98.6%). Other factors, such as treatment's side effects, were identified as less important. Age, employment, education and having a family history of breast cancer were found to be significant influencing variables on the values of the participants. It was concluded that assessing and understanding the treatment values of women can help nurses focus on areas of importance to the woman and lead to informed decision-making when they are choosing treatment for early breast cancer

Prognostic significance of the controlling nutritional status (CONUT) score in patients undergoing hepatectomy for hepatocellular carcinoma: a systematic review and meta-analysis

Background: The clinical value of the controlling nutritional status (CONUT) score in hepatocellular carcinoma (HCC) has increased. The aim of this meta-analysis was to systematically review the association between the CONUT score and outcomes in patients undergoing hepatectomy for HCC. Methods: Embase, Medline Ovid, Web of Science, Cochrane CENTRAL, and Google Scholar were systematically searched. Random effects meta-analyses were conducted to examine the prognostic value of the CONUT score in HCC patients. Results: A total of five studies including 4679 patients were found to be eligible and analyzed in the meta-analysis. The CONUT score was significantly associated with overall survival (HR 1.78, 95%CI = 1.20-2.64, P = 0.004, I-2 = 79%), recurrence-free survival (HR 1.34, 95%CI = 1.17-1.53, P Conclusions: The CONUT score is an independent prognostic indicator of the prognosis and is associated with postoperative major complications and hepatic functional reserve in HCC patients