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628,929 results on '"Jensen AN"'

207. Relationship between reasons for intermittent missing patient-reported outcomes data and missing data mechanisms

Author

Nielsen, Lene Kongsgaard, Mercieca-Bebber, Rebecca, Möller, Sören, Redder, Louise, Jarden, Mary, Andersen, Christen Lykkegaard, Frederiksen, Henrik, Svirskaite, Asta, Silkjær, Trine, Steffensen, Morten Saaby, Pedersen, Per Trøllund, Hinge, Maja, Frederiksen, Mikael, Jensen, Bo Amdi, Helleberg, Carsten, Mylin, Anne Kærsgaard, Abildgaard, Niels, and King, Madeleine T.

Published
2024
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225. Untersuchung von Schockraumalarmierungskriterien basierend auf dem Unfallhergang – Ergebnisse einer Multizenterstudie

Author

Laue, Fabian, Matthes, Gerrit, Ahnert, Tobias, Baacke, Markus, Bieler, Dan, Caspers, Michael, Clemens, Valentin, Fröhlich, Matthias, Großner, Tobias, Hackenberg, Lisa, Hagebusch, Paul, Hartensuer, René, Jensen, Kai Oliver, Keß, Annette, Kleber, Christian, Nohl, André, Özkurtul, Orkun, Paffrath, Thomas, Pedersen, Vera, Pfläging, Tristan, Schweigkofler, Uwe, Sprengel, Kai, Störmann, Philipp, Trentzsch, Heiko, Waydhas, Christian, and Lefering, Rolf

Published
2024
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228. Genome-wide association meta-analysis identifies five loci associated with postpartum hemorrhage

Author

Westergaard, David, Steinthorsdottir, Valgerdur, Stefansdottir, Lilja, Rohde, Palle Duun, Wu, Xiaoping, Geller, Frank, Tyrmi, Jaakko, Havulinna, Aki S., Solé-Navais, Pol, Flatley, Christopher, Ostrowski, Sisse Rye, Pedersen, Ole Birger, Erikstrup, Christian, Sørensen, Erik, Mikkelsen, Christina, Bruun, Mie Topholm, Aagaard Jensen, Bitten, Brodersen, Thorsten, Ullum, Henrik, Magnus, Per, Andreassen, Ole A., Njolstad, Pål R., Kolte, Astrid Marie, Krebs, Lone, Nyegaard, Mette, Hansen, Thomas Folkmann, Feenstra, Bjarke, Daly, Mark, Lindgren, Cecilia M., Thorleifsson, Gudmar, Stefansson, Olafur A., Sveinbjornsson, Gardar, Gudbjartsson, Daniel F., Thorsteinsdottir, Unnur, Banasik, Karina, Jacobsson, Bo, Laisk, Triin, Laivuori, Hannele, Stefansson, Kari, Brunak, Søren, and Nielsen, Henriette Svarre

Published
2024
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245. CGAAL: Distributed On-The-Fly ATL Model Checker with Heuristics

Author

Carlsen, Falke B. Ø., Frydenskov, Lars Bo P., Jensen, Nicolaj Ø., Rasmussen, Jener, Sørensen, Mathias M., Weirsøe, Asger G., Jensen, Mathias C., and Larsen, Kim G.

Subjects
Computer Science - Logic in Computer Science, D.2.1, D.2.4
Abstract

We present CGAAL, our efficient on-the-fly model checker for alternating-time temporal logic (ATL) on concurrent game structures (CGS). We present how our tool encodes ATL as extended dependency graphs with negation edges and employs the distributed on-the-fly algorithm by Dalsgaard et al. Our tool offers multiple novel search strategies for the algorithm, including DHS which is inspired by PageRank and uses the in-degree of configurations as a heuristic, IHS which estimates instability of assignment values, and LPS which estimates the distance to a state satisfying the constituent property using linear programming. CGS are input using our modelling language LCGS, where composition and synchronisation are easily described. We prove the correctness of our encoding, and our experiments show that our tool CGAAL is often one to three orders of magnitude faster than the popular tool PRISM-games on case studies from PRISM's documentation and among case studies we have developed. In our evaluation, we also compare and evaluate our search strategies, and find that our custom search strategies are often significantly faster than the usual breadth-first and depth-first search strategies., Comment: In Proceedings GandALF 2023, arXiv:2309.17318

Published
2023
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246. Dipole superfluid hydrodynamics II

Author

Jain, Akash, Jensen, Kristan, Liu, Ruochuan, and Mefford, Eric

Subjects
High Energy Physics - Theory, Condensed Matter - Strongly Correlated Electrons
Abstract

We present a dissipative hydrodynamic theory of "s-wave dipole superfluids" that arise in phases of translation-invariant and dipole-symmetric models in which the U(1) symmetry is spontaneously broken. The hydrodynamic description is subtle on account of an analogue of dangerously irrelevant operators, which requires us to formalize an entirely new derivative counting scheme suitable for these fluids. We use our hydrodynamic model to investigate the linearized response of such a fluid, characterized by sound modes $\omega \sim \pm k - ik^2$, shear modes $\omega\sim-ik^2$, and magnon-like propagating modes $\omega \sim \pm k^2 - ik^4$ that are the dipole-invariant version of superfluid "second sound" modes. We find that these fluids can also admit equilibrium states with "dipole superflow" that resemble a polarized medium. Finally, we couple our theory to slowly varying background fields, which allows us to compute response functions of hydrodynamic operators and Kubo formulas for hydrodynamic transport coefficients., Comment: 54 pages; we have included a Mathematica notebook to the arXiv submission which computes dispersion relations and response functions

Published
2024

247. Disentangling Imperfect: A Wavelet-Infused Multilevel Heterogeneous Network for Human Activity Recognition in Flawed Wearable Sensor Data

Author

Liu, Mengna, Xiang, Dong, Cheng, Xu, Liu, Xiufeng, Zhang, Dalin, Chen, Shengyong, and Jensen, Christian S.

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning
Abstract

The popularity and diffusion of wearable devices provides new opportunities for sensor-based human activity recognition that leverages deep learning-based algorithms. Although impressive advances have been made, two major challenges remain. First, sensor data is often incomplete or noisy due to sensor placement and other issues as well as data transmission failure, calling for imputation of missing values, which also introduces noise. Second, human activity has multi-scale characteristics. Thus, different groups of people and even the same person may behave differently under different circumstances. To address these challenges, we propose a multilevel heterogeneous neural network, called MHNN, for sensor data analysis. We utilize multilevel discrete wavelet decomposition to extract multi-resolution features from sensor data. This enables distinguishing signals with different frequencies, thereby suppressing noise. As the components resulting from the decomposition are heterogeneous, we equip the proposed model with heterogeneous feature extractors that enable the learning of multi-scale features. Due to the complementarity of these features, we also include a cross aggregation module for enhancing their interactions. An experimental study using seven publicly available datasets offers evidence that MHNN can outperform other cutting-edge models and offers evidence of robustness to missing values and noise. An ablation study confirms the importance of each module., Comment: 14 pages, 7 figures

Published
2024

248. Hades Again and Again: A Study on Frustration Tolerance, Physiology and Player Experience

Author

Jensen, Maj Frost, Dixen, Laurits, and Burelli, Paolo

Subjects
Computer Science - Human-Computer Interaction
Abstract

Accurately quantifying player experience is challenging for many reasons: identifying a ground truth and building validated and reliable scales are both challenging tasks; on top of that, empirical results are often moderated by individual factors. In this article, we present a study on the rogue-like game Hades designed to investigate the impact of individual differences in the operationalisation of player experience by cross-referencing multiple modalities (i.e., questionnaires, gameplay, and heart rate) and identifying the interplay between their scales.

Published
2024

249. Data-dependent density estimation for the Fokker-Planck equation in higher dimensions

Author

Jensen, Max, Merle, Fabian, and Prohl, Andreas

Subjects
Mathematics - Numerical Analysis, 65M75, 65M12, 65C30
Abstract

We present a new strategy to approximate the global solution of the Fokker-Planck equation efficiently in higher dimensions and show its convergence. The main ingredients are the Euler scheme to solve the associated stochastic differential equation and a histogram method for tree-structured density estimation on a data-dependent partitioning of the state space R^d.

Published
2024

250. Fast Partition-Based Cross-Validation With Centering and Scaling for $\mathbf{X}^\mathbf{T}\mathbf{X}$ and $\mathbf{X}^\mathbf{T}\mathbf{Y}$

Author

Engstrøm, Ole-Christian Galbo and Jensen, Martin Holm

Subjects
Computer Science - Machine Learning, Computer Science - Data Structures and Algorithms
Abstract

We present algorithms that substantially accelerate partition-based cross-validation for machine learning models that require matrix products $\mathbf{X}^\mathbf{T}\mathbf{X}$ and $\mathbf{X}^\mathbf{T}\mathbf{Y}$. Our algorithms have applications in model selection for, e.g., principal component analysis (PCA), principal component regression (PCR), ridge regression (RR), ordinary least squares (OLS), and partial least squares (PLS). Our algorithms support all combinations of column-wise centering and scaling of $\mathbf{X}$ and $\mathbf{Y}$, and we demonstrate in our accompanying implementation that this adds only a manageable, practical constant over efficient variants without preprocessing. We prove the correctness of our algorithms under a fold-based partitioning scheme and show that the running time is independent of the number of folds; that is, they have the same time complexity as that of computing $\mathbf{X}^\mathbf{T}\mathbf{X}$ and $\mathbf{X}^\mathbf{T}\mathbf{Y}$ and space complexity equivalent to storing $\mathbf{X}$, $\mathbf{Y}$, $\mathbf{X}^\mathbf{T}\mathbf{X}$, and $\mathbf{X}^\mathbf{T}\mathbf{Y}$. Importantly, unlike alternatives found in the literature, we avoid data leakage due to preprocessing. We achieve these results by eliminating redundant computations in the overlap between training partitions. Concretely, we show how to manipulate $\mathbf{X}^\mathbf{T}\mathbf{X}$ and $\mathbf{X}^\mathbf{T}\mathbf{Y}$ using only samples from the validation partition to obtain the preprocessed training partition-wise $\mathbf{X}^\mathbf{T}\mathbf{X}$ and $\mathbf{X}^\mathbf{T}\mathbf{Y}$. To our knowledge, we are the first to derive correct and efficient cross-validation algorithms for any of the $16$ combinations of column-wise centering and scaling, for which we also prove only $12$ give distinct matrix products., Comment: 31 pages, 2 tables, 1 figure, 7 algorithms

Published
2024

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