Supersolid behavior in confined geometry

We have carried out torsional oscillator (TO) and heat capacity (HC) measurements on solid 4He samples grown within a geometry which restricts the helium to thin (150 um) cylindrical discs. In contrast to previously reported values from Rittner and Reppy of 20% non-classical rotational inertia (NCRI) for similar confining dimensions, 0.9% NCRI (consistent with that found in bulk samples and samples imbedded in porous media) was observed in our TO cell. In this confined geometry the heat capacity peak is consistent with that found in bulk solid samples of high crystalline quality

Unified Scaling Law for flux pinning in practical superconductors: II. Parameter testing, scaling constants, and the Extrapolative Scaling Expression

A scaling study of several thousand Nb$_{3}$Sn critical-current $(I_c)$ measurements is used to derive the Extrapolative Scaling Expression (ESE), a relation that can quickly and accurately extrapolate limited datasets to obtain full three-dimensional dependences of I c on magnetic field (B), temperature (T), and mechanical strain (ε). The relation has the advantage of being easy to implement, and offers significant savings in sample characterization time and a useful tool for magnet design. Thorough data-based analysis of the general parameterization of the Unified Scaling Law (USL) shows the existence of three universal scaling constants for practical Nb$_{3}$Sn conductors. The study also identifies the scaling parameters that are conductor specific and need to be fitted to each conductor. This investigation includes two new, rare, and very large I c(B,T,ε) datasets (each with nearly a thousand I c measurements spanning magnetic fields from 1 to 16 T, temperatures from ~2.26 to 14 K, and intrinsic strains from –1.1% to +0.3%). The results are summarized in terms of the general USL parameters given in table 3 of Part 1 (Ekin J W 2010 Supercond. Sci. Technol. 23 083001) of this series of articles. The scaling constants determined for practical Nb$_{3}$Sn conductors are: the upper-critical-field temperature parameter v = 1.50 ± 0.04; the cross-link parameter w = 3.0 ± 0.3; and the strain curvature parameter u = 1.7 ± 0.1 (from equation (29) for b c2(ε) in Part 1). These constants and required fitting parameters result in the ESE relation, given by $I_c(B,T,ε)$B = $C[b_{c2}(ε)]^S (1−t^{1.5}) η−μ (1-t^2)^{μ} b ^{p}(1−b)^{q}$ with reduced magnetic field $b ≡ B/B^{c2}$*(T,ε) and reduced temperature $t ≡ T/T^{c*}$(ε), where: $B_{c2} *(T,ε) = B_{c2} *(0,0)(1−t^{1.5})b_{c2} (ε) T_c *(ε)=T_c *(0)[b_{c2}(ε)]^{1/3}$ and fitting parameters: $C, B_{c2}*(0,0), T_c*(0), s$, either η or μ (but not both), plus the parameters in the strain function b c2(ε). The pinning-force shape parameters p and q are also preferably fitted (simultaneously with the other parameters), but default values p = 0.5 and q = 2.0 also give high fitting accuracy when the range of relative magnetic fields is not extensive. Default values are also essential when the magnetic field data range is insufficient to determine p and q. The scaling constants are remarkably stable (changes less than ~1%) with respect to different values of p and q, Nb$_{3}$Sn conductor configurations, magnetic self-field corrections, and pinning-force trim values. The results demonstrate that the scaling of transport critical current holds down to the lowest temperatures measured ~2.2 K, for both magnetic self-field corrected and uncorrected data. An initial comparison is also made between transport and magnetization scaling data in matched Nb$_{3}$Sn samples and significant differences are found, especially for the upper critical field $B_{c2}*(T,ε)$, which may be a result of inhomogeneous shielding currents. In Part 3 of this topical review series (Ekin J W 2017 Supercond. Sci. Technol. at press), the smallest practical minimum dataset for extrapolating full I c(B,T,ε) datasets is derived. Application of the ESE relation is illustrated in several new areas, including full characterization of Nb$_{3}$Sn conductors from as little as a single $I_c(B)$ curve when a few core parameters have been determined for similar conductors

Transverse stress and fatigue effects in Y-Ba-Cu-O coated IBAD tapes

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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-benc

Global variation in postoperative mortality and complications after cancer surgery: a multicentre, prospective cohort study in 82 countries

© 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 licenseBackground: 80% of individuals with cancer will require a surgical procedure, yet little comparative data exist on early outcomes in low-income and middle-income countries (LMICs). We compared postoperative outcomes in breast, colorectal, and gastric cancer surgery in hospitals worldwide, focusing on the effect of disease stage and complications on postoperative mortality. Methods: This was a multicentre, international prospective cohort study of consecutive adult patients undergoing surgery for primary breast, colorectal, or gastric cancer requiring a skin incision done under general or neuraxial anaesthesia. The primary outcome was death or major complication within 30 days of surgery. Multilevel logistic regression determined relationships within three-level nested models of patients within hospitals and countries. Hospital-level infrastructure effects were explored with three-way mediation analyses. This study was registered with ClinicalTrials.gov, NCT03471494. Findings: Between April 1, 2018, and Jan 31, 2019, we enrolled 15 958 patients from 428 hospitals in 82 countries (high income 9106 patients, 31 countries; upper-middle income 2721 patients, 23 countries; or lower-middle income 4131 patients, 28 countries). Patients in LMICs presented with more advanced disease compared with patients in high-income countries. 30-day mortality was higher for gastric cancer in low-income or lower-middle-income countries (adjusted odds ratio 3·72, 95% CI 1·70–8·16) and for colorectal cancer in low-income or lower-middle-income countries (4·59, 2·39–8·80) and upper-middle-income countries (2·06, 1·11–3·83). No difference in 30-day mortality was seen in breast cancer. The proportion of patients who died after a major complication was greatest in low-income or lower-middle-income countries (6·15, 3·26–11·59) and upper-middle-income countries (3·89, 2·08–7·29). Postoperative death after complications was partly explained by patient factors (60%) and partly by hospital or country (40%). The absence of consistently available postoperative care facilities was associated with seven to 10 more deaths per 100 major complications in LMICs. Cancer stage alone explained little of the early variation in mortality or postoperative complications. Interpretation: Higher levels of mortality after cancer surgery in LMICs was not fully explained by later presentation of disease. The capacity to rescue patients from surgical complications is a tangible opportunity for meaningful intervention. Early death after cancer surgery might be reduced by policies focusing on strengthening perioperative care systems to detect and intervene in common complications. Funding: National Institute for Health Research Global Health Research Unit

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

© 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licenseBackground: Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide. Methods: A multimethods analysis was performed as part of the GlobalSurg 3 study—a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital. Findings: Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3·85 [95% CI 2·58–5·75]; p<0·0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63·0% vs 82·7%; OR 0·35 [0·23–0·53]; p<0·0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer. Interpretation: Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised. Funding: National Institute for Health and Care Research