Learning and Generalizing Polynomials in Simulation Metamodeling

The ability to learn polynomials and generalize out-of-distribution is essential for simulation metamodels in many disciplines of engineering, where the time step updates are described by polynomials. While feed forward neural networks can fit any function, they cannot generalize out-of-distribution for higher-order polynomials. Therefore, this paper collects and proposes multiplicative neural network (MNN) architectures that are used as recursive building blocks for approximating higher-order polynomials. Our experiments show that MNNs are better than baseline models at generalizing, and their performance in validation is true to their performance in out-of-distribution tests. In addition to MNN architectures, a simulation metamodeling approach is proposed for simulations with polynomial time step updates. For these simulations, simulating a time interval can be performed in fewer steps by increasing the step size, which entails approximating higher-order polynomials. While our approach is compatible with any simulation with polynomial time step updates, a demonstration is shown for an epidemiology simulation model, which also shows the inductive bias in MNNs for learning and generalizing higher-order polynomials

On the Limits to Multi-Modal Popularity Prediction on Instagram -- A New Robust, Efficient and Explainable Baseline

Our global population contributes visual content on platforms like Instagram, attempting to express themselves and engage their audiences, at an unprecedented and increasing rate. In this paper, we revisit the popularity prediction on Instagram. We present a robust, efficient, and explainable baseline for population-based popularity prediction, achieving strong ranking performance. We employ the latest methods in computer vision to maximize the information extracted from the visual modality. We use transfer learning to extract visual semantics such as concepts, scenes, and objects, allowing a new level of scrutiny in an extensive, explainable ablation study. We inform feature selection towards a robust and scalable model, but also illustrate feature interactions, offering new directions for further inquiry in computational social science. Our strongest models inform a lower limit to population-based predictability of popularity on Instagram. The models are immediately applicable to social media monitoring and influencer identification.Comment: Presented at ICAART 202

Automated detection of e-scooter helmet use with deep learning

E-scooter riders have an increased crash risk compared to cyclists [1 ]. Hospital data finds increasing numbers of injured e-scooter riders, with head injuries as one of the most common injury types [2]. To decrease this high prevalence of head injuries, the use of e-scooter helmets could present a potential countermeasure [3]. Despite this, studies show a generally low rate of helmet use rates in countries without mandatory helmet use laws [4][5][6]. In countries with mandatory helmet use laws for e-scooter riders, helmet use rates are higher, but generally remain lower than bicycle use rates [7]. As the helmet use rate is a central factor for the safety of e-scooter riders in case of a crash and a key performance indicator in the European Commission's Road Safety Policy Framework 2021-2030 [8], efficient e-Scooter helmet use data collection methods are needed. However, currently, human observers are used to register e-scooter helmet use either in direct roadside observations or in indirect video-based observation, which is time-consuming and costly. In this study, a deep learning-based method for the automated detection of e-scooter helmet use in video data was developed and tested, with the aim to provide an efficient data collection tool for road safety researchers and practitioners

COX-2-PGE2 Signaling Impairs Intestinal Epithelial Regeneration and Associates with TNF Inhibitor Responsiveness in Ulcerative Colitis

Background: Inhibition of tumor necrosis factor-α (TNF) signaling is beneficial in the management of ulcerative colitis (UC), but up to one-third of patients do not have a clinical response of relevance to TNF inhibitors during induction therapy (i.e. primary non-responders [PNRs]). Through production of prostaglandins (PGs) and thromboxanes, cyclooxygenase-2 (COX-2) affects inflammation and epithelial regeneration and may in this way be implicated in treatment resistance to TNF inhibitors. Methods: In this study, COX-2 expression was analyzed in human intestinal biopsies and patient-derived monocytes, and the downstream consequences of COX-2 activity was evaluated by assessing the influence of the down-stream effector, PGE2, on intestinal epithelial stem cell self-renewal and differentiation using primary human intestinal organoids (“mini-guts”). Findings: We found that TNF stimulation induced COX-2 expression in monocytes isolated from responders (Rs), whereas COX-2 expression was constitutively high and non-inducible in monocytes from PNRs. Additionally, PGE2 in combination with proliferative signals transformed human intestinal epithelial cells to a proinflammatory state akin to flaring UC, whereas PGE2 in combination with differentiation signals supported robust mucin induction. Interpretation: Our work indicates that COX-2-PGE2 signaling could be a novel target for the management of PNRs to TNF inhibitors. We additionally demonstrate that COX-2–PGE2 signaling has dual functions during tissue repair and normal lineage differentiation, explaining in part the lack of response to TNF inhibitors among PNRs. Fund: This work was funded by grants from the Novo Nordisk Foundation, the Lundbeck Foundation, the Vanderbilt Digestive Disease Research Center, NIH Grants, Aase and Ejnar Danielsen's Foundation and the A.P. Møller Foundation. Keywords: COX-2, Intestinal epithelial cells, Monocytes, Prostaglandin E2, Ulcerative coliti

NOSTROMO: Lessons learned, conclusions and way forward

This White Paper sets out to explain the value that metamodelling can bring to air traffic management (ATM) research. It will define metamodelling and explore what it can, and cannot, do. The reader is assumed to have basic knowledge of SESAR: the Single European Sky ATM Research project. An important element of SESAR, as the technological pillar of the Single European Sky initiative, is to bring about improvements, as measured through specific key performance indicators (KPIs), and as implemented by a series of so-called SESAR 'Solutions'. These 'Solutions' are new or improved operational procedures or technologies, designed to meet operational and performance improvements described in the European ATM Master Plan.Comment: White Paper of the NOSTROMO, an exploratory research project funded by the SESAR Joint Undertaking (SJU) under the European Union's Horizon 2020 research and innovation programm

Bayesian Machine Learning for Simulation Metamodeling

Policymaking is a complex task that demands skilled leaders who can navigate the intricate web of competing interests and priorities, a challenge exacerbated by recent crises such as environmental issues, pandemics, and economic instability. In transportation, these challenges invoke policy goals such as reducing congestion and achieving carbon neutrality, which introduce numerous variables and uncertainties, often overwhelming decision-makers. Policymakers typically simplify the problem by examining a limited set of scenarios, potentially overlooking the probabilistic nature of the issues and missing valuable insights. Decision-making in transportation often involves simulation models, but they cannot fully handle the computational demands of diverse scenarios. The central issues are the abundance of scenarios and the complexities of simulation models.These challenges boil down to two critical issues in simulation-based decision-making. First, the multitude of scenarios leads to cognitive overload, constraining decision-makers due to bounded rationality. Second, the substantial computational complexity of detailed models limits the number of options that can be analyzed in a reasonable time frame. Innovative approaches to policymaking and simulation-based studies are essential to tackle the complexities of our ever-changing world. An efficient approach to alleviate the computational challenges is to employ a metamodel (also known as a surrogate model). These simulation metamodels approximate the simulation model’s function with mathematical simplicity, speed, and interpretability. They offer a computationally economical solution to the burdens of simulation-based policy analyses, providing a higher level of insight into complex systems.This dissertation focuses on mitigating the two aforementioned issues in simulation-based decision-making by advancing the metamodeling techniques. Within the practical context, the dissertation emphasizes enhancing the interpretability and explainability of resultant metamodels to reduce cognitive overload. Methodologically, it underscores the use of Bayesian non-parametric models, with a particular emphasis on Gaussian processes and their integration with Bayesian active learning, encompassed within Bayesian machine learning. These methods are employed to tackle the complexities arising from computationaldemands. Therefore, the thesis consists of two parts. In the first part, the emphasis is on advancing techniques to enhance the explainability of metamodels and devising a framework for seamlessly integrating these improvements into existing active learning metamodeling approaches. The findings suggest that active learning metamodels can assist in decision-making by making the exploration of scenarios more manageable, particularly using the explainable sub-component in the form of SHAP values. In the second part, the thesis delves into the design of novel active learning strategies, followed by the creation ofa new model aimed at reducing both the number of required simulations and the training time for the metamodel itself. The results suggest that the extra Bayesian information from the fully Bayesian Gaussian processes and the proposed mixture of Gaussian processes are more data efficient and thus reduce the computational burden further. All these methodologies and frameworks are applied to an air traffic management (ATM) simulator, effectively demonstrating their tangible value in addressing real-world problems.This thesis concludes with a brief discussion on short- and long-term next steps toward the goal of computationally efficient and explainable data-driven decision-making for simulation-based policymaking