1,720,959 research outputs found
Optimal transport-based displacement interpolation with data augmentation for reduced order modeling of nonlinear dynamical systems
Full text linkWe present a novel reduced-order Model (ROM) that leverages optimal transport (OT) theory and displacement interpolation to enhance the representation of nonlinear dynamics in complex systems. While traditional ROM techniques face challenges in this scenario, especially when data (i.e., observational snapshots) are limited, our method addresses these issues by introducing a data augmentation strategy based on OT principles. The proposed framework generates interpolated solutions tracing geodesic paths in the space of probability distributions, enriching the training dataset for the ROM. A key feature of our approach is its ability to provide a continuous representation of the solution's dynamics by exploiting a virtual-to-real time mapping. This enables the reconstruction of solutions at finer temporal scales than those provided by the original data. To further improve prediction accuracy, we employ Gaussian Process Regression to learn the residual and correct the representation between the interpolated snapshots and the physical solution.
We demonstrate the effectiveness of our methodology with atmospheric mesoscale benchmarks characterized by highly nonlinear, advection-dominated dynamics. Our results show improved accuracy and efficiency in predicting complex system behaviors, indicating the potential of this approach for a wide range of applications in computational physics and engineering
EMoCy: Towards Physiological Signals-Based Stress Detection
No full textThe prompt detection of stress can prevent long-term consequences on people's health, economy and society. In this work, we present EMoCy, a reproducible methodology and analysis pipeline for automatic and continuous stress detection based on physiological signals. By providing reproducible experiments and exploring more constrained settings with state-of-the-art accuracy, we set new benchmarks for future works on stress detection. Our study includes signal selection and preprocessing, feature engineering, and classification using machine learning algorithms. We have tested our approach on WESAD dataset using blood volume pulse, electrodermal activity, and respiration signals. We identify a set of features that allow us to reach effective detection using low sampling frequency and short time windows. This reduces the computational power and the detection delay, going towards real-time applications on wearable devices. Using 60s windows, we reach an accuracy of 0.972 and an F1 score of 0.979 on the stress/baseline discrimination task. Moreover, our machine learning models achieve an accuracy greater than 0.93 on windows of 25s sampled at 64Hz. We have also observed that it is possible to obtain a good estimate of the models' performance by training them with a small window overlap
Optimal Transport-inspired Deep Learning Framework for Slow-Decaying Problems: Exploiting Sinkhorn Loss and Wasserstein Kernel
No full textReduced order models (ROMs) are widely used in scientific computing to tackle
high-dimensional systems. However, traditional ROM methods may only partially
capture the intrinsic geometric characteristics of the data. These
characteristics encompass the underlying structure, relationships, and
essential features crucial for accurate modeling.
To overcome this limitation, we propose a novel ROM framework that integrates
optimal transport (OT) theory and neural network-based methods. Specifically,
we investigate the Kernel Proper Orthogonal Decomposition (kPOD) method
exploiting the Wasserstein distance as the custom kernel, and we efficiently
train the resulting neural network (NN) employing the Sinkhorn algorithm. By
leveraging an OT-based nonlinear reduction, the presented framework can capture
the geometric structure of the data, which is crucial for accurate learning of
the reduced solution manifold. When compared with traditional metrics such as
mean squared error or cross-entropy, exploiting the Sinkhorn divergence as the
loss function enhances stability during training, robustness against
overfitting and noise, and accelerates convergence.
To showcase the approach's effectiveness, we conduct experiments on a set of
challenging test cases exhibiting a slow decay of the Kolmogorov n-width. The
results show that our framework outperforms traditional ROM methods in terms of
accuracy and computational efficiency
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
OPTIMAL TRANSPORT-INSPIRED DEEP LEARNING FRAMEWORK FOR SLOW-DECAYING KOLMOGOROV \bfitn-WIDTH PROBLEMS: EXPLOITING SINKHORN LOSS AND WASSERSTEIN KERNEL
No full textReduced-order models (ROMs) are widely used in scientific computing to tackle high-dimensional systems. However, traditional ROM methods may only partially capture the intrinsic geometric characteristics of the data. These characteristics encompass the underlying structure, relationships, and essential features crucial for accurate modeling. To overcome this limitation, we propose a novel ROM framework that integrates optimal transport (OT) theory and neural network-based methods. Specifically, we investigate the kernel proper orthogonal decomposition method exploiting the Wasserstein distance as the custom kernel, and we efficiently train the resulting neural network employing the Sinkhorn algorithm. By leveraging an OT-based nonlinear reduction, the presented framework can capture the geometric structure of the data, which is crucial for accurate learning of the reduced solution manifold. When compared with traditional metrics such as mean squared error or cross-entropy, exploiting the Sinkhorn divergence as the loss function enhances stability during training, robustness against overfitting and noise, and accelerates convergence. To showcase the approach's effectiveness, we conduct experiments on a set of challenging test cases exhibiting a slow decay of the Kolmogorov n-width. The results show that our framework outperforms traditional ROM methods in terms of accuracy and computational efficiency.EPF
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
- …
