Large Scale
Recently Published Documents


TOTAL DOCUMENTS

131199
(FIVE YEARS 55209)

H-INDEX

385
(FIVE YEARS 120)

2022 ◽  
Vol 16 (1) ◽  
pp. 0-0

The Domain Name System - DNS is regarded as one of the critical infrastructure component of the global Internet because a large-scale DNS outage would effectively take a typical user offline. Therefore, the Internet community should ensure that critical components of the DNS ecosystem - that is, root name servers, top-level domain registrars and registries, authoritative name servers, and recursive resolvers - function smoothly. To this end, the community should monitor them periodically and provide public alerts about abnormal behavior. The authors propose a novel quantitative approach for evaluating the health of authoritative name servers – a critical, core, and a large component of the DNS ecosystem. The performance is typically measured in terms of response time, reliability, and throughput for most of the Internet components. This research work proposes a novel list of parameters specifically for determining the health of authoritative name servers: DNS attack permeability, latency comparison, and DNSSEC validation.


2021 ◽  

Private associations abounded in the ancient Greek world and beyond, and this volume provides the first large-scale study of the strategies of governance which they employed. Emphasis is placed on the values fostered by the regulations of associations, the complexities of the private-public divide (and that divide's impact on polis institutions) and the dynamics of regional and global networks and group identity. The attested links between rules and religious sanctions also illuminate the relationship between legal history and religion. Moreover, possible links between ancient associations and the early Christian churches will prove particularly valuable for scholars of the New Testament. The book concludes by using the regulations of associations to explore a novel and revealing aspect of the interaction between the Mediterranean world, India and China.


BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Dexter Canoy ◽  
Jenny Tran ◽  
Mariagrazia Zottoli ◽  
Rema Ramakrishnan ◽  
Abdelaali Hassaine ◽  
...  

Abstract Background Myocardial infarction (MI), stroke and diabetes share underlying risk factors and commonalities in clinical management. We examined if their combined impact on mortality is proportional, amplified or less than the expected risk separately of each disease and whether the excess risk is explained by their associated comorbidities. Methods Using large-scale electronic health records, we identified 2,007,731 eligible patients (51% women) and registered with general practices in the UK and extracted clinical information including diagnosis of myocardial infarction (MI), stroke, diabetes and 53 other long-term conditions before 2005 (study baseline). We used Cox regression to determine the risk of all-cause mortality with age as the underlying time variable and tested for excess risk due to interaction between cardiometabolic conditions. Results At baseline, the mean age was 51 years, and 7% (N = 145,910) have had a cardiometabolic condition. After a 7-year mean follow-up, 146,994 died. The sex-adjusted hazard ratios (HR) (95% confidence interval [CI]) of all-cause mortality by baseline disease status, compared to those without cardiometabolic disease, were MI = 1.51 (1.49–1.52), diabetes = 1.52 (1.51–1.53), stroke = 1.84 (1.82–1.86), MI and diabetes = 2.14 (2.11–2.17), MI and stroke = 2.35 (2.30–2.39), diabetes and stroke = 2.53 (2.50–2.57) and all three = 3.22 (3.15–3.30). Adjusting for other concurrent comorbidities attenuated these estimates, including the risk associated with having all three conditions (HR = 1.81 [95% CI 1.74–1.89]). Excess risks due to interaction between cardiometabolic conditions, particularly when all three conditions were present, were not significantly greater than expected from the individual disease effects. Conclusion Myocardial infarction, stroke and diabetes were associated with excess mortality, without evidence of any amplification of risk in people with all three diseases. The presence of other comorbidities substantially contributed to the excess mortality risks associated with cardiometabolic disease multimorbidity.


Author(s):  
Bingzhi Chen ◽  
Yishu Liu ◽  
Zheng Zhang ◽  
Yingjian Li ◽  
Zhao Zhang ◽  
...  

Many studies on automated COVID-19 diagnosis have advanced rapidly with the increasing availability of large-scale CT annotated datasets. Inevitably, there are still a large number of unlabeled CT slices in the existing data sources since it requires considerable consuming labor efforts. Notably, cinical experience indicates that the neighboring CT slices may present similar symptoms and signs. Inspired by such wisdom, we propose DACE, a novel CNN-based deep active context estimation framework, which leverages the unlabeled neighbors to progressively learn more robust feature representations and generate a well-performed classifier for COVID-19 diagnosis. Specifically, the backbone of the proposed DACE framework is constructed by a well-designed Long-Short Hierarchical Attention Network (LSHAN), which effectively incorporates two complementary attention mechanisms, i.e., short-range channel interactions (SCI) module and long-range spatial dependencies (LSD) module, to learn the most discriminative features from CT slices. To make full use of such available data, we design an efficient context estimation criterion to carefully assign the additional labels to these neighbors. Benefiting from two complementary types of informative annotations from -nearest neighbors, i.e., the majority of high-confidence samples with pseudo labels and the minority of low-confidence samples with hand-annotated labels, the proposed LSHAN can be fine-tuned and optimized in an incremental learning manner. Extensive experiments on the Clean-CC-CCII dataset demonstrate the superior performance of our method compared with the state-of-the-art baselines.


2021 ◽  
Vol 14 (3) ◽  
pp. 354-391
Author(s):  
Richard Huyghe ◽  
Marine Wauquier

The formation of French agent nouns (ANs) involves a large variety of morphological constructions, and particularly of suffixes. In this study, we focus on the semantic counterpart of agentive suffix diversity and investigate whether the morphological variety of ANs correlates with different agentive subtypes. We adopt a distributional semantics approach and combine manual, computational and statistical analyses applied to French ANs ending in -aire, -ant, -eur, -ien, -ier and -iste. Our methodology allows for a large-scale study of ANs and involves both top-down and bottom-up procedures. We first characterize agentive suffixes with respect to their morphosemantic and distributional properties, outlining their specificities and similarities. Then we automatically cluster ANs into distributionally relevant subsets and examine their properties. Based on quantitative analysis, our study provides a new perspective on agentive suffix rivalry in French that both confirms existing claims and sheds light on previously unseen phenomena.


Author(s):  
Marlene Jensen ◽  
Juliane Wippler ◽  
Manuel Kleiner

Metaproteomics, the large-scale identification and quantification of proteins from microbial communities, provide direct insights into the phenotypes of microorganisms on the molecular level. To ensure the integrity of the metaproteomic data, samples need to be preserved immediately after sampling to avoid changes in protein abundance patterns.


Sign in / Sign up

Export Citation Format

Share Document