High-dimensional Bayesian optimization with projections using quantile Gaussian processes

Key challenges of Bayesian optimization in high dimensions are both learning the response surface and optimizing an acquisition function. The acquisition function selects a new point to evaluate the black-box function. Both challenges can be addressed by making simplifying assumptions, such as additivity or intrinsic lower dimensionality of the expensive objective. In this article, we exploit the effective lower dimensionality with axis-aligned projections and optimize on a partitioning of the input space. Axis-aligned projections introduce a multiplicity of outputs for a single input that we refer to as inconsistency. We model inconsistencies with a Gaussian process (GP) derived from quantile regression. We show that the quantile GP and the partitioning of the input space increases data-efficiency. In particular, by modeling only a quantile function, we overcome issues of GP hyper-parameter learning in the presence of inconsistencies

Effects of fibre content and textile structure on dynamic-mechanical and shape-memory properties of ELO/flax biocomposites

Biocomposites were prepared using epoxidized linseed oil (ELO) and flax fibre reinforcements in different assemblies. ELO was cured by two different anhydrides to check how its thermomechanical properties can be influenced. As reinforcements nonwoven mat, twill weave and quasi-unidirectional textile fabrics with two different yarn finenesses were used. Their reinforcing effect was determined in dynamic mechanical analysis (DMA) in flexure. DMA served also to determine the glass transition temperature (Tg). Shape memory properties were derived from quasiunconstrained flexural tests performed near to the Tg of the ELO and its biocomposites. Flax reinforcement reduced the Tg that was attributed to off-stoichiometry owing to chemical reaction between the hydroxyl groups of flax and anhydride hardener. The shape memory parameters were moderate or low. They were affected by both textile content and type

The global, regional, and national burden of oesophageal cancer and its attributable risk factors in 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

Background Oesophageal cancer is a common and often fatal cancer that has two main histological subtypes: oesophageal squamous cell carcinoma and oesophageal adenocarcinoma. Updated statistics on the incidence and mortality of oesophageal cancer, and on the disability-adjusted life-years (DALYs) caused by the disease, can assist policy makers in allocating resources for prevention, treatment, and care of oesophageal cancer. We report the latest estimates of these statistics for 195 countries and territories between 1990 and 2017, by age, sex, and Socio-demographic Index (SDI), using data from the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD). Methods We used data from vital registration systems, vital registration-samples, verbal autopsy records, and cancer registries, combined with relevant modelling, to estimate the mortality, incidence, and burden of oesophageal cancer from 1990 to 2017. Mortality-to-incidence ratios (MIRs) were estimated and fed into a Cause of Death Ensemble model (CODEm) including risk factors. MIRs were used for mortality and non-fatal modelling. Estimates of DALYs attributable to the main risk factors of oesophageal cancer available in GBD were also calculated. The proportion of oesophageal squamous cell carcinoma to all oesophageal cancers was extracted by use of publicly available data, and its variation was examined against SDI, the Healthcare Access and Quality (HAQ) Index, and available risk factors in GBD that are specific for oesophageal squamous cell carcinoma (eg, unimproved water source and indoor air pollution) and for oesophageal adenocarcinoma (gastro-oesophageal reflux disease). Findings There were 473 000 (95% uncertainty interval [95% UI] 459 000–485 000) new cases of oesophageal cancer and 436 000 (425 000–448 000) deaths due to oesophageal cancer in 2017. Age-standardised incidence was 5·9 (5·7–6·1) per 100 000 population and age-standardised mortality was 5·5 (5·3–5·6) per 100 000. Oesophageal cancer caused 9·78 million (9·53–10·03) DALYs, with an age-standardised rate of 120 (117–123) per 100 000 population. Between 1990 and 2017, age-standardised incidence decreased by 22·0% (18·6–25·2), mortality decreased by 29·0% (25·8–32·0), and DALYs decreased by 33·4% (30·4–36·1) globally. However, as a result of population growth and ageing, the total number of new cases increased by 52·3% (45·9–58·9), from 310 000 (300 000–322 000) to 473 000 (459 000–485 000); the number of deaths increased by 40·0% (34·1–46·3), from 311 000 (301 000–323 000) to 436 000 (425 000–448 000); and total DALYs increased by 27·4% (22·1–33·1), from 7·68 million (7·42–7·97) to 9·78 million (9·53–10·03). At the national level, China had the highest number of incident cases (235 000 [223 000–246 000]), deaths (213 000 [203 000–223 000]), and DALYs (4·46 million [4·25–4·69]) in 2017. The highest national-level age-standardised incidence rates in 2017 were observed in Malawi (23·0 [19·4–26·5] per 100 000 population) and Mongolia (18·5 [16·4–20·8] per 100 000). In 2017, age-standardised incidence was 2·7 times higher, mortality 2·9 times higher, and DALYs 3·0 times higher in males than in females. In 2017, a substantial proportion of oesophageal cancer DALYs were attributable to known risk factors: tobacco smoking (39·0% [35·5–42·2]), alcohol consumption (33·8% [27·3–39·9]), high BMI (19·5% [6·3–36·0]), a diet low in fruits (19·1% [4·2–34·6]), and use of chewing tobacco (7·5% [5·2–9·6]). Countries with a low SDI and HAQ Index and high levels of indoor air pollution had a higher proportion of oesophageal squamous cell carcinoma to all oesophageal cancer cases than did countries with a high SDI and HAQ Index and with low levels of indoor air pollution. Interpretation Despite reductions in age-standardised incidence and mortality rates, oesophageal cancer remains a major cause of cancer mortality and burden across the world. Oesophageal cancer is a highly fatal disease, requiring increased primary prevention efforts and, possibly, screening in some high-risk areas. Substantial variation exists in age-standardised incidence rates across regions and countries, for reasons that are unclear. Funding Bill & Melinda Gates Foundation

Prevalence and attributable health burden of chronic respiratory diseases, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

Background Previous attempts to characterise the burden of chronic respiratory diseases have focused only on specific disease conditions, such as chronic obstructive pulmonary disease (COPD) or asthma. In this study, we aimed to characterise the burden of chronic respiratory diseases globally, providing a comprehensive and up-to-date analysis on geographical and time trends from 1990 to 2017. Methods Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017, we estimated the prevalence, morbidity, and mortality attributable to chronic respiratory diseases through an analysis of deaths, disability-adjusted life-years (DALYs), and years of life lost (YLL) by GBD super-region, from 1990 to 2017, stratified by age and sex. Specific diseases analysed included asthma, COPD, interstitial lung disease and pulmonary sarcoidosis, pneumoconiosis, and other chronic respiratory diseases. We also assessed the contribution of risk factors (smoking, second-hand smoke, ambient particulate matter and ozone pollution, household air pollution from solid fuels, and occupational risks) to chronic respiratory disease-attributable DALYs. Findings In 2017, 544·9 million people (95% uncertainty interval [UI] 506·9–584·8) worldwide had a chronic respiratory disease, representing an increase of 39·8% compared with 1990. Chronic respiratory disease prevalence showed wide variability across GBD super-regions, with the highest prevalence among both males and females in high-income regions, and the lowest prevalence in sub-Saharan Africa and south Asia. The age-sex-specific prevalence of each chronic respiratory disease in 2017 was also highly variable geographically. Chronic respiratory diseases were the third leading cause of death in 2017 (7·0% [95% UI 6·8–7·2] of all deaths), behind cardiovascular diseases and neoplasms. Deaths due to chronic respiratory diseases numbered 3 914 196 (95% UI 3 790 578–4 044 819) in 2017, an increase of 18·0% since 1990, while total DALYs increased by 13·3%. However, when accounting for ageing and population growth, declines were observed in age-standardised prevalence (14·3% decrease), age-standardised death rates (42·6%), and age-standardised DALY rates (38·2%). In males and females, most chronic respiratory disease-attributable deaths and DALYs were due to COPD. In regional analyses, mortality rates from chronic respiratory diseases were greatest in south Asia and lowest in sub-Saharan Africa, also across both sexes. Notably, although absolute prevalence was lower in south Asia than in most other super-regions, YLLs due to chronic respiratory diseases across the subcontinent were the highest in the world. Death rates due to interstitial lung disease and pulmonary sarcoidosis were greater than those due to pneumoconiosis in all super-regions. Smoking was the leading risk factor for chronic respiratory disease-related disability across all regions for men. Among women, household air pollution from solid fuels was the predominant risk factor for chronic respiratory diseases in south Asia and sub-Saharan Africa, while ambient particulate matter represented the leading risk factor in southeast Asia, east Asia, and Oceania, and in the Middle East and north Africa super-region. Interpretation Our study shows that chronic respiratory diseases remain a leading cause of death and disability worldwide, with growth in absolute numbers but sharp declines in several age-standardised estimators since 1990. Premature mortality from chronic respiratory diseases seems to be highest in regions with less-resourced health systems on a per-capita basis. Funding Bill & Melinda Gates Foundation

Differentially Private Empirical Risk Minimization with Sparsity-Inducing Norms

Differential privacy is concerned about the prediction quality while measuring the privacy impact on individuals whose information is contained in the data. We consider differentially private risk minimization problems with regularizers that induce structured sparsity. These regularizers are known to be convex but they are often non-differentiable. We analyze the standard differentially private algorithms, such as output perturbation, Frank-Wolfe and objective perturbation. Output perturbation is a differentially private algorithm that is known to perform well for minimizing risks that are strongly convex. Previous works have derived excess risk bounds that are independent of the dimensionality. In this paper, we assume a particular class of convex but non-smooth regularizers that induce structured sparsity and loss functions for generalized linear models. We also consider differentially private Frank-Wolfe algorithms to optimize the dual of the risk minimization problem. We derive excess risk bounds for both these algorithms. Both the bounds depend on the Gaussian width of the unit ball of the dual norm. We also show that objective perturbation of the risk minimization problems is equivalent to the output perturbation of a dual optimization problem. This is the first work that analyzes the dual optimization problems of risk minimization problems in the context of differential privacy

Vertical uplift capacity of horizontal anchors

The method of characteristics coupled with a log-spiral failure surface was used to develop a theory for vertical uplift capacity of shallow horizontal strip anchors in a general c-phi soil. Uplift-capacity factors F(c), F(q) and F(gamma), for the effects of cohesion, surcharge, and density, respectively, have been established as functions of embedment ratio lambda and angle of friction phi. The extent of the failure surface at the ground has also been determined. Comparisons made with existing test results support the predictive capability of the theory, and comparisons with the analysis proposed by Meyerhof and Adams show the proposed analysis provides slightly more conservative predictions of pullout capacity

High-dimensional Bayesian optimization using low-dimensional feature spaces

Bayesian optimization (BO) is a powerful approach for seeking the global optimum of expensive black-box functions and has proven successful for fine tuning hyper-parameters of machine learning models. However, in practice, BO is typically limited to optimizing 10--20 parameters. To scale BO to high dimensions, we normally make structural assumptions on the decomposition of the objective and/or exploit the intrinsic lower dimensionality of the problem, e.g. by using linear projections. The limitation of aforementioned approaches is the assumption of a linear subspace. We could achieve a higher compression rate with nonlinear projections, but learning these nonlinear embeddings typically requires much data. This contradicts the BO objective of a relatively small evaluation budget. To address this challenge, we propose to learn a low-dimensional feature space jointly with (a) the response surface and (b) a reconstruction mapping. Our approach allows for optimization of the acquisition function in the lower-dimensional subspace. We reconstruct the original parameter space from the lower-dimensional subspace for evaluating the black-box function. For meaningful exploration, we solve a constrained optimization problem

Electron-beam irradiation effects on poly(ethylene-co-vinyl acetate) polymer

Poly(ethylene-co-vinyl acetate) (EVA) films were irradiated with a 1.2MeV electron beam at varied doses over the range 0-270kGy in order to investigate the modifications induced in its optical, electrical and thermal properties. It was observed that optical band gap and activation energy of EVA films decreased upon electron irradiation, whereas the transition dipole moment, oscillator strength and number of carbon atoms per cluster were found to increase upon irradiation. Further, the dielectric constant, the dielectric loss, and the ac conductivity of EVA films were found to increase with an increase in the dose of electron radiation. The result further showed that the thermal stability of EVA film samples increased upon electron irradiation

Electron-Beam-Induced Modifications in High-Density Polyethylene

Post-irradiation studies have been carried out to elucidate the effects of electron beam irradiation on the structural, optical, dielectric, and thermal properties of high-density polyethylene (HDPE) films. The experimental results showed that both the optical band gap and activation energy of HDPE films decreases with an increase in the doses of electron radiation. The electrical measurements showed that dielectric constant and the ac conductivity of HDPE increases with an increase in the dose of electron radiation. The thermal analysis carried out using DSC and TGA revealed that the melting temperature, degree of crystallinity, and thermal stability of the HDPE films increased, obviously, due to the predominant cross-linking reaction following high doses of electron irradiation