Deep clustering analysis via variational autoencoder with Gamma mixture latent embeddings.

This article proposes a novel deep clustering model based on the variational autoencoder (VAE), named GamMM-VAE, which can learn latent representations of training data for clustering in an unsupervised manner. Most existing VAE-based deep clustering methods use the Gaussian mixture model (GMM) as a prior on the latent space. We employ a more flexible asymmetric Gamma mixture model to achieve higher quality embeddings of the data latent space.

Affinity-Driven Transfer Learning for Load Forecasting.

In this study, we introduce an innovative method for load forecasting that capitalizes on the concept of task affinity score to measure the similarity between various tasks. The task affinity score emerges as a superior technique for assessing task similarity within the realm of transfer learning. Through empirical evaluation on a synthetic dataset, we establish the superiority of the task affinity score over traditional metrics in task selection scenarios.

Isopropanol sensor based on sprayed InS thin film using linear discriminant analysis for real-time selectivity.

Metal sulfides have been studied for their high performance as new sensitive materials for gas detection. These material innovations contribute significantly to the development of more sensitive, stable and specific conductivity sensors, opening the way to new applications in detecting gases at low concentrations. Hence, this work reports on the sensing performance of InS for isopropanol detection.

Conductometric HS Sensors Based on TiO Nanoparticles.

High-performance hydrogen sulfide (HS) sensors are mandatory for many industrial applications. However, the development of HS sensors still remains a challenge for researchers. In this work, we report the study of a TiO-based conductometric sensor for HS monitoring at low concentrations.

FishSegSSL: A Semi-Supervised Semantic Segmentation Framework for Fish-Eye Images.

The application of large field-of-view (FoV) cameras equipped with fish-eye lenses brings notable advantages to various real-world computer vision applications, including autonomous driving. While deep learning has proven successful in conventional computer vision applications using regular perspective images, its potential in fish-eye camera contexts remains largely unexplored due to limited datasets for fully supervised learning. Semi-supervised learning comes as a potential solution to manage this challenge.