We present a new high-order accurate computational fluid dynamics model based on the incompressible Navier-Stokes equations with a free surface for the accurate simulation of nonlinear and dispersive water waves in th...
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New algorithms for embedding graphs have reduced the asymptotic complexity of finding low-dimensional representations. One-Hot Graph Encoder Embedding (GEE) uses a single, linear pass over edges and produces an embedd...
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AI -systems that are based on large language models, such as ChatGPT, have quickly increased their prowess over the last year, and at the same time became readily available. As of now, many disciplines gain experience...
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ISBN:
(数字)9798350394023
ISBN:
(纸本)9798350394030
AI -systems that are based on large language models, such as ChatGPT, have quickly increased their prowess over the last year, and at the same time became readily available. As of now, many disciplines gain experience in using tools such as ChatGPT in a professional setting - and software engineering is no exception. Just as with any new kind of tooling, it is to be expected that in the era of ChatGPT, some traditional skills of the discipline will become rather obsolete, while at the same time new skill sets emerge that will be required from future professionals. Therefore, as educators we must reconsider the skill set we aim at fostering in our software engineering students, and adapt our intended learning outcomes accordingly. Furthermore, we need to adapt both assessment strategies and the teaching and learning methods we employ, in order to provide our students with a study experience that adheres to the principle of constructive alignment.
In this paper, we present a novel visual servoing (VS) approach based on latent Denoising Diffusion Probabilistic Models (DDPMs), that explores the application of generative models for vision-based navigation of UAVs ...
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ISBN:
(数字)9798331513283
ISBN:
(纸本)9798331513290
In this paper, we present a novel visual servoing (VS) approach based on latent Denoising Diffusion Probabilistic Models (DDPMs), that explores the application of generative models for vision-based navigation of UAVs (Uncrewed Aerial Vehicles). Opposite to classical VS methods, the proposed approach allows reaching the desired target view, even when the target is initially not visible. This is possible thanks to the learning of a latent representation that the DDPM uses for planning and a dataset of trajectories encompassing target-invisible initial views. A compact representation is learned from raw images using a Cross-Modal Variational Autoencoder. Given the current image, the DDPM generates trajectories in the latent space driving the robotic platform to the desired visual target. The approach has been validated in simulation using two generic multi-rotor UAVs (a quadrotor and a hexarotor). The results show that we can successfully reach the visual target, even if not visible in the initial view. A video summary with simulations can be found in: https://***/2Hb3nkkcszE.
This paper presents and validates a novel lung nodule classification algorithm that uses multifractal features found in X-ray images. The proposed method includes a pre-processing step where two enhancement techniques...
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Due to covid-19 pandemic, research in e-healthcare system is gaining popularity because in most of the cases e-healthcare system does not require to present patient physically at doctor’s door. The reason behind this...
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The equilibrium configuration of a plasma in an axially symmetric reactor is described mathematically by a free boundary problem associated with the celebrated Grad-Shafranov equation. The presence of uncertainty in t...
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Recommender systems, widely used in information filtering systems, demonstrate efficacy in suggesting novel items to users. In this study, we propose the application of a recommender system to predict new drug-target ...
Recommender systems, widely used in information filtering systems, demonstrate efficacy in suggesting novel items to users. In this study, we propose the application of a recommender system to predict new drug-target associations. Here, drugs are represented as users, while protein targets are treated as items within the recommender system. However, due to sparse data in a matrix of drugs and protein targets, matrix factorization (MF) techniques are employed to decompose the extensive matrix into smaller matrices. We identify non-negative matrix factorization (NMF) and singular value decomposition (SVD) as top-performing models for this task. Subsequently, we integrated them into the traditional recommender system, encompassing both drug-based collaborative filtering and target-based collaborative filtering, to identify new drug-target associations aligned with observed interactions between drugs and target proteins. Finally, we evaluated the performance of our developed recommender system with matrix factorization for drug-target associations and compared the results with those obtained from a recommender system without matrix factorization.
We study the local geometry of empirical risks in high dimensions via the spectral theory of their Hessian and information matrices. We focus on settings where the data, (Y)n=1 ∈ d, are i.i.d. draws of a k-component ...
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