The mechanics of fibre-reinforced sand

Fibres can be an effective means of reinforcing soils. This paper presents data from laboratory triaxial tests on quartzitic sand reinforced with polypropylene fibres. By keeping the studied composite consistent throughout the study (host sand and fibre characteristics kept constant), it has been possible to develop a framework of behaviour for the sand-fibre material, which provides a solid base for future research on fibre-reinforced soils. Data from previous work and from new tests have been analysed within the Critical State framework, that is in terms of normal compression line, critical state line and state boundary surface.published_or_final_versio

OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text

There is growing evidence that pretraining on high quality, carefully thought-out tokens such as code or mathematics plays an important role in improving the reasoning abilities of large language models. For example, Minerva, a PaLM model finetuned on billions of tokens of mathematical documents from arXiv and the web, reported dramatically improved performance on problems that require quantitative reasoning. However, because all known open source web datasets employ preprocessing that does not faithfully preserve mathematical notation, the benefits of large scale training on quantitive web documents are unavailable to the research community. We introduce OpenWebMath, an open dataset inspired by these works containing 14.7B tokens of mathematical webpages from Common Crawl. We describe in detail our method for extracting text and LaTeX content and removing boilerplate from HTML documents, as well as our methods for quality filtering and deduplication. Additionally, we run small-scale experiments by training 1.4B parameter language models on OpenWebMath, showing that models trained on 14.7B tokens of our dataset surpass the performance of models trained on over 20x the amount of general language data. We hope that our dataset, openly released on the Hugging Face Hub, will help spur advances in the reasoning abilities of large language models

Mesoscopic structure conditions the emergence of cooperation on social networks

We study the evolutionary Prisoner's Dilemma on two social networks obtained from actual relational data. We find very different cooperation levels on each of them that can not be easily understood in terms of global statistical properties of both networks. We claim that the result can be understood at the mesoscopic scale, by studying the community structure of the networks. We explain the dependence of the cooperation level on the temptation parameter in terms of the internal structure of the communities and their interconnections. We then test our results on community-structured, specifically designed artificial networks, finding perfect agreement with the observations in the real networks. Our results support the conclusion that studies of evolutionary games on model networks and their interpretation in terms of global properties may not be sufficient to study specific, real social systems. In addition, the community perspective may be helpful to interpret the origin and behavior of existing networks as well as to design structures that show resilient cooperative behavior.Comment: Largely improved version, includes an artificial network model that fully confirms the explanation of the results in terms of inter- and intra-community structur

Evolution of Cooperation and Coordination in a Dynamically Networked Society

Situations of conflict giving rise to social dilemmas are widespread in society and game theory is one major way in which they can be investigated. Starting from the observation that individuals in society interact through networks of acquaintances, we model the co-evolution of the agents' strategies and of the social network itself using two prototypical games, the Prisoner's Dilemma and the Stag Hunt. Allowing agents to dismiss ties and establish new ones, we find that cooperation and coordination can be achieved through the self-organization of the social network, a result that is non-trivial, especially in the Prisoner's Dilemma case. The evolution and stability of cooperation implies the condensation of agents exploiting particular game strategies into strong and stable clusters which are more densely connected, even in the more difficult case of the Prisoner's Dilemma.Comment: 18 pages, 14 figures. to appea

Prostate Cancer Disparities in Risk Group at Presentation and Access to Treatment for Asian Americans, Native Hawaiians, and Pacific Islanders: A Study With Disaggregated Ethnic Groups

PURPOSE: We identified (1) differences in localized prostate cancer (PCa) risk group at presentation and (2) disparities in access to initial treatment for Asian American, Native Hawaiian, and Pacific Islander (AANHPI) men with PCa after controlling for sociodemographic factors. METHODS: We assessed all patients in the National Cancer Database with localized PCa with low-, intermediate-, and high-risk disease who identified as Thai, White, Asian Indian, Chinese, Vietnamese, Korean, Japanese, Filipino, Hawaiian, Pacific Islander, Laotian, Pakistani, Kampuchean, and Hmong. Multivariable logistic regression defined adjusted odds ratios (AORs) with 95% CI of (1) presenting at progressively higher risk group and (2) receiving treatment or active surveillance with intermediate- or high-risk disease, adjusting for sociodemographic and clinical factors. RESULTS: Among 980,889 men (median age 66 years), all AANHPI subgroups with the exception of Thai (AOR = 0.84 [95% CI, 0.58 to 1.21], P > .05), Asian Indian (AOR = 1.12 [95% CI, 1.00 to 1.25], P > .05), and Pakistani (AOR = 1.34 [95% CI, 0.98 to 1.83], P > .05) men had greater odds of presenting at a progressively higher PCa risk group compared with White patients (Chinese AOR = 1.18 [95% CI, 1.11 to 1.25], P < .001; Japanese AOR = 1.36 [95% CI, 1.26 to 1.47], P < .001; Filipino AOR = 1.37 [95% CI, 1.29 to 1.46], P < .001; Korean AOR = 1.32 [95% CI, 1.18 to 1.48], P < .001; Vietnamese AOR = 1.20 [95% CI, 1.07 to 1.35], P = .002; Laotian AOR = 1.60 [95% CI, 1.08 to 2.36], P = .018; Hmong AOR = 4.07 [95% CI, 1.54 to 10.81], P = .005; Kampuchean AOR = 1.55 [95% CI, 1.03 to 2.34], P = .036; Asian Indian or Pakistani AOR = 1.15 [95% CI, 1.07 to 1.24], P < .001; Native Hawaiians AOR = 1.58 [95% CI, 1.38 to 1.80], P < .001; and Pacific Islanders AOR = 1.58 [95% CI, 1.37 to 1.82], P < .001). Additionally, Japanese Americans (AOR = 1.46 [95% CI, 1.09 to 1.97], P = .013) were more likely to receive treatment compared with White patients. CONCLUSION: Our findings suggest that there are differences in PCa risk group at presentation by race or ethnicity among Asian American, Native Hawaiian, and Pacific Islander subgroups and that there exist disparities in treatment patterns. Although AANHPI are often studied as a homogenous group, heterogeneity upon subgroup disaggregation underscores the importance of further study to assess and address barriers to PCa care