OPTION: OPTImization Algorithm Benchmarking ONtology

Many optimization algorithm benchmarking platforms allow users to share their experimental data to promote reproducible and reusable research. However, different platforms use different data models and formats, which drastically complicates the identification of relevant datasets, their interpretation, and their interoperability. Therefore, a semantically rich, ontology-based, machine-readable data model that can be used by different platforms is highly desirable. In this paper, we report on the development of such an ontology, which we call OPTION (OPTImization algorithm benchmarking ONtology). Our ontology provides the vocabulary needed for semantic annotation of the core entities involved in the benchmarking process, such as algorithms, problems, and evaluation measures. It also provides means for automatic data integration, improved interoperability, and powerful querying capabilities, thereby increasing the value of the benchmarking data. We demonstrate the utility of OPTION, by annotating and querying a corpus of benchmark performance data from the BBOB collection of the COCO framework and from the Yet Another Black-Box Optimization Benchmark (YABBOB) family of the Nevergrad environment. In addition, we integrate features of the BBOB functional performance landscape into the OPTION knowledge base using publicly available datasets with exploratory landscape analysis. Finally, we integrate the OPTION knowledge base into the IOHprofiler environment and provide users with the ability to perform meta-analysis of performance data

Explainable Model-specific Algorithm Selection for Multi-Label Classification

Multi-label classification (MLC) is an ML task of predictive modeling in which a data instance can simultaneously belong to multiple classes. MLC is increasingly gaining interest in different application domains such as text mining, computer vision, and bioinformatics. Several MLC algorithms have been proposed in the literature, resulting in a meta-optimization problem that the user needs to address: which MLC approach to select for a given dataset? To address this algorithm selection problem, we investigate in this work the quality of an automated approach that uses characteristics of the datasets - so-called features - and a trained algorithm selector to choose which algorithm to apply for a given task. For our empirical evaluation, we use a portfolio of 38 datasets. We consider eight MLC algorithms, whose quality we evaluate using six different performance metrics. We show that our automated algorithm selector outperforms any of the single MLC algorithms, and this is for all evaluated performance measures. Our selection approach is explainable, a characteristic that we exploit to investigate which meta-features have the largest influence on the decisions made by the algorithm selector. Finally, we also quantify the importance of the most significant meta-features for various domains

Per-run Algorithm Selection with Warm-starting using Trajectory-based Features

Per-instance algorithm selection seeks to recommend, for a given problem instance and a given performance criterion, one or several suitable algorithms that are expected to perform well for the particular setting. The selection is classically done offline, using openly available information about the problem instance or features that are extracted from the instance during a dedicated feature extraction step. This ignores valuable information that the algorithms accumulate during the optimization process. In this work, we propose an alternative, online algorithm selection scheme which we coin per-run algorithm selection. In our approach, we start the optimization with a default algorithm, and, after a certain number of iterations, extract instance features from the observed trajectory of this initial optimizer to determine whether to switch to another optimizer. We test this approach using the CMA-ES as the default solver, and a portfolio of six different optimizers as potential algorithms to switch to. In contrast to other recent work on online per-run algorithm selection, we warm-start the second optimizer using information accumulated during the first optimization phase. We show that our approach outperforms static per-instance algorithm selection. We also compare two different feature extraction principles, based on exploratory landscape analysis and time series analysis of the internal state variables of the CMA-ES, respectively. We show that a combination of both feature sets provides the most accurate recommendations for our test cases, taken from the BBOB function suite from the COCO platform and the YABBOB suite from the Nevergrad platform

Association of Methylenetetrahydrofolate Reductase (MTHFR-677 and MTHFR-1298) Genetic Polymorphisms with Occlusive Artery Disease and Deep Venous Thrombosis in Macedonians

Cilj Ispitati moguću povezanost genetičkog polimorfizma metilen-tetrahidrofolatne reduktaze (MTHFR-677, MTHFR-1298) s okluzivnom arterijskom bolešću i dubokom venskom trombozom u Makedonaca. Postupci Radili smo s 83 zdrave osobe, 76 bolesnika s okluzivnom arterijskom bolešću i 67 bolesnika s dubokom venskom trombozom. Od njih su prikupljeni su uzorci krvi i iz leukocita je izolirana DNA. Mutacije gena za MTHFR identificirane su testom CVD StripAssay (ViennaLab, Labordiagnostika GmbH, Beč, Austrija), a za analizu je uporabljen sustav za genetičku analizu PyPop. Potom su izračunani Pearsonove P vrijednosti, grubi omjer izgleda (odds ratio, OR) i Waldovi 95% intervali pouzdanosti (confidence intervals, CI). Rezultati Frekvencija alela C lokusa za MTHFR-677 bila je 0,575 u bolesnika s dubokom venskom trombozom, 0,612 u onih a okluzivnom arterijskom bolešću i 0,645 u zdravih osoba. Frekvencija alela T lokusa za MTHFR-677 bila je niža u zdravih osoba (0,355) nego u bolesnika s okluzivnom arterijskom bolešću (0,388) i dubokom venskom trombozom (0,425). Frekvencija alela A u lokusu MTHFR-1298 bila je 0,729 u zdravih osoba, 0,770 u bolesnika s okluzivnom arterijskom bolešću i 0,746 u bolesnika s dubokom venskom trombozom. Frekvencija alela C lokusa za MTHFR-1298 bila je 0,271 u zdravih osoba, 0,230 u bolesnika s okluzivnom arterijskom bolešću i 0,425 u bolesnika s dubokom venskom trombozom. Nije opažena povezanost polimorfizma MTHFR-677 i MTHFR-1289 s okluzivnom arterijskom bolešću ili dubokom venskom trombozom, nego se samo pokazao protektivni učinak diplotipa MTHFR/CA:CC za okluzivnu arterijsku bolest. Zaključak Osim protektivnoga učinka diplotipa MTHFR/CA:CC za okluzivnu arterijsku bolest, nismo našli značajnu povezanost polimorfizma lokusa MTHFR-677 i MTHFR-1289 s okluzivnom arterijskom bolešću i dubokom venskom trombozom.Aim To analyze the association of methylenetetrahydrofolate reductase polymorphisms (MTHFR-677 and MTHFR-1298) with occlusive artery disease and deep venous thrombosis in Macedonians. Methods We examined 83 healthy respondents, 76 patients with occlusive artery disease, and 67 patients with deep venous thrombosis. Blood samples were collected and DNA was isolated from peripheral blood leukocytes. Identification of MTHFR mutations was done with CVD StripAssay (ViennaLab, Labordiagnostika GmbH, Vienna, Austria) and the population genetics analysis package, PyPop, was used for the analysis. Pearson P values, crude odds ratio, and Wald’s 95% confidence intervals were calculated. Results The frequency of C alleles of MTHFR-677 was 0.575 in patients with deep venous thrombosis, 0.612 in patients with occlusive artery disease, and 0.645 in healthy participants. The frequency of T allele of MTHFR-677 was lower in healthy participants (0.355) than in patients with occlusive artery disease (0.388) and deep venous thrombosis (0.425). The frequency of A allele for MTHFR-1298 was 0.729 in healthy participants, 0.770 in patients with occlusive artery disease, and 0.746 in patients with deep venous thrombosis. The frequency of C allele of MTHFR-1298 was 0.271 in healthy participants, 0.230 in patients with occlusive artery disease, and 0.425 in patients with deep venous thrombosis. No association of MTHFR-677 and MTHFR- 1289 polymorphisms with occlusive artery disease and deep venous thrombosis was found, except for the protective effect of MTHFR/CA: CC diplotype for occlusive artery disease. Conclusion We could not confirm a significant association of MTHFR- 677 and MTHFR-1289 polymorphisms with occlusive artery disease or deep venous thrombosis in Macedonians, except for the protective effect of MTHFR/CA:CC diplotype against occlusive artery disease

European Society of Cardiology: Cardiovascular Disease Statistics 2019

Aims The 2019 report from the European Society of Cardiology (ESC) Atlas provides a contemporary analysis of cardiovascular disease (CVD) statistics across 56 member countries, with particular emphasis on international inequalities in disease burden and healthcare delivery together with estimates of progress towards meeting 2025 World Health Organization (WHO) non-communicable disease targets. Methods and results In this report, contemporary CVD statistics are presented for member countries of the ESC. The statistics are drawn from the ESC Atlas which is a repository of CVD data from a variety of sources including the WHO, the Institute for Health Metrics and Evaluation, and the World Bank. The Atlas also includes novel ESC sponsored data on human and capital infrastructure and cardiovascular healthcare delivery obtained by annual survey of the national societies of ESC member countries. Across ESC member countries, the prevalence of obesity (body mass index ≥30 kg/m2) and diabetes has increased two- to three-fold during the last 30 years making the WHO 2025 target to halt rises in these risk factors unlikely to be achieved. More encouraging have been variable declines in hypertension, smoking, and alcohol consumption but on current trends only the reduction in smoking from 28% to 21% during the last 20 years appears sufficient for the WHO target to be achieved. The median age-standardized prevalence of major risk factors was higher in middle-income compared with high-income ESC member countries for hypertension {23.8% [interquartile range (IQR) 22.5–23.1%] vs. 15.7% (IQR 14.5–21.1%)}, diabetes [7.7% (IQR 7.1–10.1%) vs. 5.6% (IQR 4.8–7.0%)], and among males smoking [43.8% (IQR 37.4–48.0%) vs. 26.0% (IQR 20.9–31.7%)] although among females smoking was less common in middle-income countries [8.7% (IQR 3.0–10.8) vs. 16.7% (IQR 13.9–19.7%)]. There were associated inequalities in disease burden with disability-adjusted life years per 100 000 people due to CVD over three times as high in middle-income [7160 (IQR 5655–8115)] compared with high-income [2235 (IQR 1896–3602)] countries. Cardiovascular disease mortality was also higher in middle-income countries where it accounted for a greater proportion of potential years of life lost compared with high-income countries in both females (43% vs. 28%) and males (39% vs. 28%). Despite the inequalities in disease burden across ESC member countries, survey data from the National Cardiac Societies of the ESC showed that middle-income member countries remain severely under-resourced compared with high-income countries in terms of cardiological person-power and technological infrastructure. Under-resourcing in middle-income countries is associated with a severe procedural deficit compared with high-income countries in terms of coronary intervention, device implantation and cardiac surgical procedures. Conclusion A seemingly inexorable rise in the prevalence of obesity and diabetes currently provides the greatest challenge to achieving further reductions in CVD burden across ESC member countries. Additional challenges are provided by inequalities in disease burden that now require intensification of policy initiatives in order to reduce population risk and prioritize cardiovascular healthcare delivery, particularly in the middle-income countries of the ESC where need is greatest

AiTLAS: Artificial Intelligence Toolbox for Earth Observation

We propose AiTLAS—an open-source, state-of-the-art toolbox for exploratory and predictive analysis of satellite imagery. It implements a range of deep-learning architectures and models tailored for the EO tasks illustrated in this case. The versatility and applicability of the toolbox are showcased in a variety of EO tasks, including image scene classification, semantic image segmentation, object detection, and crop type prediction. These use cases demonstrate the potential of the toolbox to support the complete data analysis pipeline starting from data preparation and understanding, through learning novel models or fine-tuning existing ones, using models for making predictions on unseen images, and up to analysis and understanding of the predictions and the predictive performance yielded by the models. AiTLAS brings the AI and EO communities together by facilitating the use of EO data in the AI community and accelerating the uptake of (advanced) machine-learning methods and approaches by EO experts. It achieves this by providing: (1) user-friendly, accessible, and interoperable resources for data analysis through easily configurable and readily usable pipelines; (2) standardized, verifiable, and reusable data handling, wrangling, and pre-processing approaches for constructing AI-ready data; (3) modular and configurable modeling approaches and (pre-trained) models; and (4) standardized and reproducible benchmark protocols including data and models

Trajectory-based Algorithm Selection with Warm-starting

International audienceLandscape-aware algorithm selection approaches have so far mostly been relying on landscape feature extraction as a preprocessing step, independent of the execution of optimization algorithms in the portfolio. This introduces a significant overhead in computational cost for many practical applications, as features are extracted and computed via sampling and evaluating the problem instance at hand, similarly to what the optimization algorithm would perform anyway within its search trajectory. As suggested in [Jankovic et al., EvoAPP 2021], trajectory-based algorithm selection circumvents the problem of costly feature extraction by computing landscape features from points that a solver sampled and evaluated during the optimization process. Features computed in this manner are used to train algorithm performance regression models, upon which a per-run algorithm selector is then built. In this work, we apply the trajectory-based approach onto a portfolio of five algorithms. We study the quality and accuracy of performance regression and algorithm selection models in the scenario of predicting different algorithm performances after a fixed budget of function evaluations. We rely on landscape features of the problem instance computed using one portion of the aforementioned budget of the same function evaluations. Moreover, we consider the possibility of switching between the solvers once, which requires them to be warm-started, i.e. when we switch, the second solver continues the optimization process already being initialized appropriately by making use of the information collected by the first solver. In this new context, we show promising performance of the trajectory-based per-run algorithm selection with warm-starting

Trajectory-based Algorithm Selection with Warm-starting

Landscape-aware algorithm selection approaches have so far mostly been relying on landscape feature extraction as a preprocessing step, independent of the execution of optimization algorithms in the portfolio. This introduces a significant overhead in computational cost for many practical applications, as features are extracted and computed via sampling and evaluating the problem instance at hand, similarly to what the optimization algorithm would perform anyway within its search trajectory. As suggested in Jankovic et al. (EvoAPPs 2021), trajectory-based algorithm selection circumvents the problem of costly feature extraction by computing landscape features from points that a solver sampled and evaluated during the optimization process. Features computed in this manner are used to train algorithm performance regression models, upon which a per-run algorithm selector is then built. In this work, we apply the trajectory-based approach onto a portfolio of five algorithms. We study the quality and accuracy of performance regression and algorithm selection models in the scenario of predicting different algorithm performances after a fixed budget of function evaluations. We rely on landscape features of the problem instance computed using one portion of the aforementioned budget of the same function evaluations. Moreover, we consider the possibility of switching between the solvers once, which requires them to be warm-started, i.e. when we switch, the second solver continues the optimization process already being initialized appropriately by making use of the information collected by the first solver. In this new context, we show promising performance of the trajectory-based per-run algorithm selection with warm-starting

Methylenetetrahydrofolate reductase (MTHFR-677 and MTHFR-1298) genotypes and haplotypes and plasma homocysteine levels in patients with occlusive artery disease and deep venous thrombosis

The aim was to investigate different genotypes and haplotypes of methylenetetrahydrofolate reductase (MTHFR-677, -1298) and plasma concentration of total homocysteine (tHcy) in Macedonian patients with occlusive artery disease (OAD) and deep venous thrombosis (DVT). Investigated groups consists of 80 healthy, 74 patients with OAD, and 63 patients with DVT. Plasma tHcy was measured with Microplate Enzyme Immunoassay. Identification of MTHFR genotypes and haplotypes was done with CVD StripAssay. The probability level (P-value) was evaluated by the Student's t-test. Plasma concentration of tHcy in CC and CT genotypes of MTHFR C677T was significantly increased in patients with OAD and in patients with DVT. Plasma concentration of tHcy in AC genotype of MTHFR A1298C was increased in patients with OAD and in patients with DVT. Plasma concentration of tHcy was significantly increased in AA genotype of patients with OAD, but not in patients with DVT. We found a significant increase of plasma tHcy in patients with OAD in comparison with healthy respondents for normal:heterozygote (CC:AC), heterozygote:normal (CT:AA), and heterozygote:heterozygote (CT:AC) haplotypes. Plasma concentration of tHcy in patients with DVT in comparison with healthy respondents was significantly increased for normal:normal (CC:AA), normal heterozygote (CC:AC), and heterozygote:heterozygote (CT:AC) haplotypes. We conclude that MTHFR C677T and MTHFR A1289C genotypes and haplotypes are connected with tHcy plasma levels in Macedonian patients with OAD and DVT

Association of Methylenetetrahydrofolate Reductase (MTHFR-677 and MTHFR-1298) Genetic Polymorphisms with Occlusive Artery Disease and Deep Venous Thrombosis in Macedonians

Cilj Ispitati moguću povezanost genetičkog polimorfizma metilen-tetrahidrofolatne reduktaze (MTHFR-677, MTHFR-1298) s okluzivnom arterijskom bolešću i dubokom venskom trombozom u Makedonaca. Postupci Radili smo s 83 zdrave osobe, 76 bolesnika s okluzivnom arterijskom bolešću i 67 bolesnika s dubokom venskom trombozom. Od njih su prikupljeni su uzorci krvi i iz leukocita je izolirana DNA. Mutacije gena za MTHFR identificirane su testom CVD StripAssay (ViennaLab, Labordiagnostika GmbH, Beč, Austrija), a za analizu je uporabljen sustav za genetičku analizu PyPop. Potom su izračunani Pearsonove P vrijednosti, grubi omjer izgleda (odds ratio, OR) i Waldovi 95% intervali pouzdanosti (confidence intervals, CI). Rezultati Frekvencija alela C lokusa za MTHFR-677 bila je 0,575 u bolesnika s dubokom venskom trombozom, 0,612 u onih a okluzivnom arterijskom bolešću i 0,645 u zdravih osoba. Frekvencija alela T lokusa za MTHFR-677 bila je niža u zdravih osoba (0,355) nego u bolesnika s okluzivnom arterijskom bolešću (0,388) i dubokom venskom trombozom (0,425). Frekvencija alela A u lokusu MTHFR-1298 bila je 0,729 u zdravih osoba, 0,770 u bolesnika s okluzivnom arterijskom bolešću i 0,746 u bolesnika s dubokom venskom trombozom. Frekvencija alela C lokusa za MTHFR-1298 bila je 0,271 u zdravih osoba, 0,230 u bolesnika s okluzivnom arterijskom bolešću i 0,425 u bolesnika s dubokom venskom trombozom. Nije opažena povezanost polimorfizma MTHFR-677 i MTHFR-1289 s okluzivnom arterijskom bolešću ili dubokom venskom trombozom, nego se samo pokazao protektivni učinak diplotipa MTHFR/CA:CC za okluzivnu arterijsku bolest. Zaključak Osim protektivnoga učinka diplotipa MTHFR/CA:CC za okluzivnu arterijsku bolest, nismo našli značajnu povezanost polimorfizma lokusa MTHFR-677 i MTHFR-1289 s okluzivnom arterijskom bolešću i dubokom venskom trombozom.Aim To analyze the association of methylenetetrahydrofolate reductase polymorphisms (MTHFR-677 and MTHFR-1298) with occlusive artery disease and deep venous thrombosis in Macedonians. Methods We examined 83 healthy respondents, 76 patients with occlusive artery disease, and 67 patients with deep venous thrombosis. Blood samples were collected and DNA was isolated from peripheral blood leukocytes. Identification of MTHFR mutations was done with CVD StripAssay (ViennaLab, Labordiagnostika GmbH, Vienna, Austria) and the population genetics analysis package, PyPop, was used for the analysis. Pearson P values, crude odds ratio, and Wald’s 95% confidence intervals were calculated. Results The frequency of C alleles of MTHFR-677 was 0.575 in patients with deep venous thrombosis, 0.612 in patients with occlusive artery disease, and 0.645 in healthy participants. The frequency of T allele of MTHFR-677 was lower in healthy participants (0.355) than in patients with occlusive artery disease (0.388) and deep venous thrombosis (0.425). The frequency of A allele for MTHFR-1298 was 0.729 in healthy participants, 0.770 in patients with occlusive artery disease, and 0.746 in patients with deep venous thrombosis. The frequency of C allele of MTHFR-1298 was 0.271 in healthy participants, 0.230 in patients with occlusive artery disease, and 0.425 in patients with deep venous thrombosis. No association of MTHFR-677 and MTHFR- 1289 polymorphisms with occlusive artery disease and deep venous thrombosis was found, except for the protective effect of MTHFR/CA: CC diplotype for occlusive artery disease. Conclusion We could not confirm a significant association of MTHFR- 677 and MTHFR-1289 polymorphisms with occlusive artery disease or deep venous thrombosis in Macedonians, except for the protective effect of MTHFR/CA:CC diplotype against occlusive artery disease