The Curse of Wealth – Middle Eastern Countries Need to Address the Rapidly Rising Burden of Diabetes

The energy boom of the last decade has led to rapidly increasing wealth in the Middle East, particularly in the oil and gas-rich Gulf Cooperation Council (GCC) countries. This exceptional growth in prosperity has brought with it rapid changes in lifestyles that have resulted in a significant rise in chronic disease. In particular the number of people diagnosed with diabetes has increased dramatically and health system capacity has not kept pace. In this article, we summarize the current literature to illustrate the magnitude of the problem, its causes and its impact on health and point to options how to address it

Leveraging Reinforcement Learning for Task Resource Allocation in Scientific Workflows

Scientific workflows are designed as directed acyclic graphs (DAGs) and consist of multiple dependent task definitions. They are executed over a large amount of data, often resulting in thousands of tasks with heterogeneous compute requirements and long runtimes, even on cluster infrastructures. In order to optimize the workflow performance, enough resources, e.g., CPU and memory, need to be provisioned for the respective tasks. Typically, workflow systems rely on user resource estimates which are known to be highly error-prone and can result in over- or underprovisioning. While resource overprovisioning leads to high resource wastage, underprovisioning can result in long runtimes or even failed tasks. In this paper, we propose two different reinforcement learning approaches based on gradient bandits and Q-learning, respectively, in order to minimize resource wastage by selecting suitable CPU and memory allocations. We provide a prototypical implementation in the well-known scientific workflow management system Nextflow, evaluate our approaches with five workflows, and compare them against the default resource configurations and a state-of-the-art feedback loop baseline. The evaluation yields that our reinforcement learning approaches significantly reduce resource wastage compared to the default configuration. Further, our approaches also reduce the allocated CPU hours compared to the state-of-the-art feedback loop by 6.79% and 24.53%.Comment: Paper accepted in 2022 IEEE International Conference on Big Data Workshop BPOD 202

Towards a Cognitive Compute Continuum: An Architecture for Ad-Hoc Self-Managed Swarms

In this paper we introduce our vision of a Cognitive Computing Continuum to address the changing IT service provisioning towards a distributed, opportunistic, self-managed collaboration between heterogeneous devices outside the traditional data center boundaries. The focal point of this continuum are cognitive devices, which have to make decisions autonomously using their on-board computation and storage capacity based on information sensed from their environment. Such devices are moving and cannot rely on fixed infrastructure elements, but instead realise on-the-fly networking and thus frequently join and leave temporal swarms. All this creates novel demands for the underlying architecture and resource management, which must bridge the gap from edge to cloud environments, while keeping the QoS parameters within required boundaries. The paper presents an initial architecture and a resource management framework for the implementation of this type of IT service provisioning.Comment: 8 pages, CCGrid 2021 Cloud2Things Worksho

Macaw: The Machine Learning Magnetometer Calibration Workflow

In Earth Systems Science, many complex data pipelines combine different data sources and apply data filtering and analysis steps. Typically, such data analysis processes are historically grown and implemented with many sequentially executed scripts. Scientific workflow management systems (SWMS) allow scientists to use their existing scripts and provide support for parallelization, reusability, monitoring, or failure handling. However, many scientists still rely on their sequentially called scripts and do not profit from the out-of-the-box advantages a SWMS can provide. In this work, we transform the data analysis processes of a Machine Learning-based approach to calibrate the platform magnetometers of non-dedicated satellites utilizing neural networks into a workflow called Macaw (MAgnetometer CAlibration Workflow). We provide details on the workflow and the steps needed to port these scripts to a scientific workflow. Our experimental evaluation compares the original sequential script executions on the original HPC cluster with our workflow implementation on a commodity cluster. Our results show that through porting, our implementation decreased the allocated CPU hours by 50.2% and the memory hours by 59.5%, leading to significantly less resource wastage. Further, through parallelizing single tasks, we reduced the runtime by 17.5%.Comment: Paper accepted in 2022 IEEE International Conference on Data Mining Workshops (ICDMW

Probabilistic Time Series Forecasting for Adaptive Monitoring in Edge Computing Environments

With increasingly more computation being shifted to the edge of the network, monitoring of critical infrastructures, such as intermediate processing nodes in autonomous driving, is further complicated due to the typically resource-constrained environments. In order to reduce the resource overhead on the network link imposed by monitoring, various methods have been discussed that either follow a filtering approach for data-emitting devices or conduct dynamic sampling based on employed prediction models. Still, existing methods are mainly requiring adaptive monitoring on edge devices, which demands device reconfigurations, utilizes additional resources, and limits the sophistication of employed models. In this paper, we propose a sampling-based and cloud-located approach that internally utilizes probabilistic forecasts and hence provides means of quantifying model uncertainties, which can be used for contextualized adaptations of sampling frequencies and consequently relieves constrained network resources. We evaluate our prototype implementation for the monitoring pipeline on a publicly available streaming dataset and demonstrate its positive impact on resource efficiency in a method comparison.Comment: 6 pages, 5 figures, 2 table

The curse of wealth – Middle Eastern countries need to address the rapidly rising burden of diabetes

The energy boom of the last decade has led to rapidly increasing wealth in the Middle East, particularly in the oil and gas-rich Gulf Cooperation Council (GCC) countries. This exceptional growth in prosperity has brought with it rapid changes in lifestyles that have resulted in a significant rise in chronic disease. In particular the number of people diagnosed with diabetes has increased dramatically and health system capacity has not kept pace. In this article, we summarize the current literature to illustrate the magnitude of the problem, its causes and its impact on health and point to options how to address it

Towards Advanced Monitoring for Scientific Workflows

Scientific workflows consist of thousands of highly parallelized tasks executed in a distributed environment involving many components. Automatic tracing and investigation of the components' and tasks' performance metrics, traces, and behavior are necessary to support the end user with a level of abstraction since the large amount of data cannot be analyzed manually. The execution and monitoring of scientific workflows involves many components, the cluster infrastructure, its resource manager, the workflow, and the workflow tasks. All components in such an execution environment access different monitoring metrics and provide metrics on different abstraction levels. The combination and analysis of observed metrics from different components and their interdependencies are still widely unregarded. We specify four different monitoring layers that can serve as an architectural blueprint for the monitoring responsibilities and the interactions of components in the scientific workflow execution context. We describe the different monitoring metrics subject to the four layers and how the layers interact. Finally, we examine five state-of-the-art scientific workflow management systems (SWMS) in order to assess which steps are needed to enable our four-layer-based approach.Comment: Paper accepted in 2022 IEEE International Conference on Big Data Workshop SCDM 202

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders