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Climate change: What is computing’s responsibility? (Dagstuhl Perspectives Workshop 25122)
No full textThis Manifesto was produced from the Perspectives Workshop 25122 entitled "Climate Change: What is Computing’s Responsibility?" held March 16-19, 2025 at Schloss Dagstuhl, Germany. The Workshop provided a forum for world-leading computer scientists and expert consultants on environmental policy and sustainable transition to engage in a critical and urgent conversation about computing’s responsibilities in addressing climate change - or more aptly, climate crisis. The resulting Manifesto outlines commitments and directions for future action which, if adopted as a basis for more responsible computing practices, will help ensure that these technologies do not threaten the long-term habitability of the planet.
We preface our Manifesto with a recognition that humanity is on a path that is not in agreement with international global warming targets and explore how computing technologies are currently hastening the overshoot of these boundaries. We critically assess the vaunted potential for harnessing computing technologies for the mitigation of global warming, agreeing that, under current circumstances, computing is contributing to negative environmental impacts in other sectors. Computing primarily improves efficiency and reduces costs which leads to more consumption and more negative environmental impact. Relying solely on efficiency gains in computing has thus far proven to be insufficient to curb global greenhouse gas emissions. Therefore, computing’s purpose within a strategy for tackling climate change must be reimagined.
Our recommendations cover changes that need to be urgently made to the design priorities of computing technologies, but also speak to the more systemic shift in mindset, with sustainability and human rights providing a necessary moral foundation for developing the kinds of computing technologies most needed by society. We also stress the importance of digital policy that accounts for both the direct material impacts of computing and the detrimental indirect impacts arising from computing-enabled efficiencies, and the role of computing professionals in informing policy making
flexi-framework / flexi-extensions
No full textFLEXI is a high-order numerical framework for solving PDEs, with a special focus on Computational Fluid Dynamic
Communication strategies driving online health community patient awareness and engagement investigated within atrial fibrillation context
No full textOnline health communities (OHCs) enhance personalized care by connecting patients, professionals and leveraging patient-reported outcomes. This study investigates how communication elements (emotions, patient-generated topics, appeals, and linguistic style) affect patient awareness (click-through-rate) and engagement (average engagement time and community subscriptions) in an atrial fibrillation (AF) OHC. A quasi-randomized online field experiment targeted Dutch adults via Facebook and Instagram with 12 communication concepts, directing them to the AF OHC. From May to June 2023, 795,812 users were reached, generating 18,426 visits, 478 subscriptions, and an average engagement time of 35 s. Communication elements significantly influenced awareness and engagement, with emotions and topics as strongest predictors. Fear was most effective for self-protection topics, love for affiliation and kin care. Expert appeals increased awareness, while testimonials boosted engagement. These findings offer novel insights into the role of communication strategies across patient journey stages, aiding health managers in optimizing patient engagement and OHC impact
Scalable Bayesian structure learning for Gaussian graphical models using marginal pseudo-likelihood
No full textBayesian methods for learning Gaussian graphical models offer a principled framework for quantifying model uncertainty and incorporating prior knowledge. However, their scalability is constrained by the computational cost of jointly exploring graph structures and precision matrices. To address this challenge, we perform inference directly on the graph by integrating out the precision matrix. We adopt a marginal pseudo-likelihood approach, eliminating the need to compute intractable normalizing constants and perform computationally intensive precision matrix sampling. Building on this framework, we develop continuous-time (birth–death) and discrete-time (reversible jump) Markov chain Monte Carlo (MCMC) algorithms that efficiently explore the posterior over graph space. We establish theoretical guarantees for posterior contraction, convergence, and graph selection consistency. The algorithms scale to large graph spaces, enabling parallel exploration for graphs with over 1,000 nodes, while providing uncertainty quantification and supporting flexible prior specification over the graph space. Extensive simulations show substantial computational gains over state-of-the-art Bayesian approaches without sacrificing graph recovery accuracy. Applications to human and mouse gene expression datasets demonstrate the ability of our approach to recover biologically meaningful structures and quantify uncertainty in complex networks. An implementation is available in the R package BDgraph
On the challenges of studying bias in Recommender Systems: The effect of data characteristics and algorithm configuration
Get PDFStatements on the propagation of bias by recommender systems are often hard to verify or falsify. Research on bias tends to draw from a small pool of publicly available datasets and is therefore bound by their specific properties. Additionally, implementation choices are often not explicitly described or motivated in research, while they may have an effect on bias propagation. In this paper, we explore the challenges of measuring and reporting popularity bias. We showcase the impact of data properties and algorithm configurations on popularity bias by combining real and synthetic data with well known recommender systems frameworks. First, we identify data characteristics that might impact popularity bias, and explore their presence in a set of available online datasets. Accordingly, we generate various datasets that combine these characteristics. Second, we locate algorithm configurations that vary across implementations in literature. We evaluate popularity bias for a number of datasets, three real and five synthetic, and configurations, and offer insights on their joint effect. We find that, depending on the data characteristics, various configurations of the algorithms examined can lead to different conclusions regarding the propagation of popularity bias. These results motivate the need for explicitly addressing algorithmic configuration and data properties when reporting and interpreting bias in recommender systems
PointPCA+: A full-reference Point Cloud Quality Assessment metric with PCA-based features
Get PDFThis paper introduces an enhanced Point Cloud Quality Assessment (PCQA) metric, termed PointPCA+, as an extension of PointPCA, with a focus on computational simplicity and feature richness. PointPCA+ refines the original PCA-based descriptors by employing Principal Component Analysis (PCA) solely on geometry data; additionally, the texture descriptors are refined through a direct application of the function on YCbCr values, enhancing the efficiency of computation. The metric combines geometry and texture features, capturing local shape and appearance properties, through a learning-based fusion to generate a total quality score. Prior to fusion, a feature selection module is incorporated to identify the most effective features from a proposed super-set. Experimental results demonstrate the high predictive performance of PointPCA+ against subjective ground truth scores obtained from four publicly available datasets. The metric consistently outperforms state-of-the-art solutions, offering valuable insights into the design of similarity measurements and the effectiveness of handcrafted features across various distortion types. The code of the proposed metric is available at https://github.com/cwi-dis/pointpca_suite/
Computation of the confluent hypergeometric function M(a, b, x)
Get PDFThe computation of the Kummer function M(a, b, x) is considered in this paper.An efficient and accurate algorithm combining asymptotic expansions for large values of the parameters and power series is presented. Numerical tests show that a Matlab implementation of the algorithm allows the computation of the function with relative accuracy in the parameter region in double precision floating point arithmetic
